REVIEW | doi:10.20944/preprints202101.0426.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: deep learning; machine learning; ischemic stroke; demyelinating disease; image processing; computer aided diagnostics; brain MRI; CNN; White Matter Hyperintensities; VOSViewer
Online: 21 January 2021 (14:55:05 CET)
Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.
ARTICLE | doi:10.20944/preprints202007.0688.v1
Subject: Keywords: Computer aided diagnosis (CAD), brain magnetic resonance imaging (MRI) scans, feature extraction, feature reduction, classifiers, classification rule.
Online: 29 July 2020 (10:14:43 CEST)
Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to identify the optimal pathway for brain MR images (normal vs neoplastic) binary classification task. External validation dataset was used to test the generalizability of the optimal predictive models implemented. Relevant and informative features were selected to construct cross-validated decision tree and eventually simple rule set was built based on the decision tree. The experimental results show that almost all pattern recognition paradigms achieve high accuracy with careful selection of number of attributes. LDA+ELM with 55 features are the optimal pipelines which achieve perfect classification when training and test data are of same source; and achieving (accuracy=97.5%, AUC=0.989, sensitivity=95% and specificity=100%) under balanced test dataset; (accuracy=99.5%, AUC=0.988, sensitivity=95% and specificity=100%). Cross-validated decision tree model also shows comparable performance: accuracy=98.8%, AUC=99.1%, sensitivity=99.6% and specificity=98.2%. Three highly relevant and robust attributes are visualized and selected for construction of decision tree models and finally a rule sets are read directly off the decision tree. This rule sets can potentially serve as fast and accurate classification algorithm.
ARTICLE | doi:10.20944/preprints201906.0166.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: MRI image; Texture Features; GLCM
Online: 18 June 2019 (05:36:29 CEST)
This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o and 135o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.
ARTICLE | doi:10.20944/preprints201612.0060.v1
Subject: Mathematics & Computer Science, Other Keywords: neonatal MRI; brain structure segmentation; volume extraction
Online: 10 December 2016 (08:44:55 CET)
1) Introduction: Brain parcellation is an important processing step in the analysis of structural brain MRI. Existing software implementations are optimized for fully developed adult brains, and provide inadequate results when applied to neonatal brain imaging. 2) Methods: We developed a semi-automated pipeline, NeBSS, for extracting 50 discrete brain structures from neonatal brain MRI, using an atlas registration method that leverages the existing ALBERT neonatal atlas 3) Results: We demonstrate a simple linear workflow for neonatal brain parcellation. NeBSS is robust to variation in imaging acquisition protocol and magnet field strength. 4) Conclusion: NeBSS is a robust pipeline capable of parcellating neonatal brain MRIs using a simple processing workflow. NeBSS fills a need in clinical translational research in neonatal imaging, where existing automated or semi-automated implementations are too rigid to be successfully applied to multi-center neuroprotection studies and clinically heterogeneous cohorts. The software is open source and freely available.
ARTICLE | doi:10.20944/preprints202208.0192.v1
Subject: Engineering, Automotive Engineering Keywords: Transfer Learning; Generative Adversarial Networks; MRI Brain Images
Online: 10 August 2022 (05:04:02 CEST)
Segmentation is an important step in medical imaging. In particular, machine learning, especially deep learning, has been widely used to efficiently improve and speed up the segmentation process in clinical practice. Despite the acceptable segmentation results of multi-stage models, little attention was paid to the use of deep learning algorithms for brain image segmentation, which could be due to the lack of training data. Therefore, in this paper, we propose a Generative Adversarial Network (GAN) model that performs transfer learning to segment MRI brain images.Our model enables the generation of more labeled brain images from existing labeled and unlabeled images. Our segmentation targets brain tissue images, including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). We evaluate the performance of our GAN model using a commonly used evaluation metric, which is Dice Coefficient (DC). Our experimental results reveal that our proposed model significantly improves segmentation results compared to the standard GAN model. We observe that our model is 2.1–10.83 minutes faster than stat-of-the-art-models.
ARTICLE | doi:10.20944/preprints202007.0196.v1
Subject: Medicine & Pharmacology, Behavioral Neuroscience Keywords: coma; unconsiousness; EEG; MRI; Freesurfer; TBI
Online: 9 July 2020 (12:58:28 CEST)
This study reports a correlation between EEG and structural brain changes in patients after severe traumatic brain injury in a coma. The novelty of our approach was based on the combination of structural visualization (MRI) and functional neuroimaging (EEG) during tactile stimulation. The structural morphometry indicated a decrease of whole-brain cortical thickness, the gray-matter volume of the cortex, and subcortical structures in comatose patients compared to healthy subjects. In resting-state EEG, coma patients had significantly higher power of the slow-wave activity of 2-6 Hz and significantly less power of the alpha and beta rhythm. Importantly, coma patients showed a significant decrease of theta-rhythm power in tactile stimulation compared to the resting state, and this EEG pattern was not found in the control group. The decrease of the theta-rhythm power significantly correlated with the better outcome from a coma. Spectral changes in EEG in response to tactile stimuli showed no association with brain morphometric measures in healthy controls. In patients, decreasing theta-rhythm power correlated positively with the volume of whole-brain gray matter, right putamen, and insula; and negatively with the volume of damaged brain tissue. Increasing beta-rhythm power, specific tactile EEG response for a healthy brain, correlated with the cortical thickness of the somatosensory Paracentral and Precentral area. The observed decrease of gray-matter volume indicates brain atrophy in coma patients, which could be associated with neurodegeneration induced by injury. Our results also demonstrate that slow-wave desynchronization, as a nonspecific response to tactile stimulation, can serve as a sensitive index of morphometric changes after brain injury and coma outcome.
Subject: Keywords: Ultrasound imaging; photoacoustic; functional MRI; calcium imaging; rodents; fluorescence imaging
Online: 24 May 2021 (16:12:34 CEST)
In the past decade, the idea that single populations of neurons support cognition and behavior has gradually given way to the realization that connectivity matters, and that complex behavior results from interactions between remote yet anatomically connected areas that form specialized networks. In parallel, innovation in brain imaging techniques has led to the availability of a broad set of imaging tools to characterize the functional organization of complex networks. However, each of these tools poses significant technical challenges and faces limitations, which require careful consideration of their underlying anatomical, physiological and physical specificity. In this review, we focus on emerging methods for measuring spontaneous or evoked activity in the brain. We discuss methods that can measure large-scale brain activity (directly or indirectly) with relatively high temporal resolution, from milliseconds to seconds. We further focus on methods designed for studying the mammalian brain in preclinical models, specifically in mice and rats. This field has seen a great deal of innovation in recent years, facilitated by concomitant innovation in gene editing techniques and the possibility of more invasive recordings. This review aims to give an overview of currently available preclinical imaging methods and an outlook on future developments. This information is suitable for educational purposes and for assisting scientists in choosing the appropriate method for their own research question.
ARTICLE | doi:10.20944/preprints202203.0136.v1
Subject: Biology, Other Keywords: structural MRI; MRS; maternal immune activation; altered trajectories
Online: 10 March 2022 (03:06:59 CET)
Serological human birth cohort studies have identified maternal infection during pregnancy as a risk factor for development of disorders such as Autism Spectrum Disorder and schizophrenia in offspring. Similarly, in experiments using animal models, maternal immune activation (MIA) has been shown to alter neuroanatomical and behavioral development in offspring. This study employs magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in conjunction with behavioral assays to refine our understanding of the impact of MIA on neurobiological development in exposed animals. On gestational day nine, pregnant dams were injected with either polyinosinic:polycytidylic acid (POL) to induce MIA or saline (SAL) as a control. Whole-brain MRI, localized proton MRS, and behavioral tests (open field, three chambered social approach, and prepulse inhibition) were acquired at two timepoints, during adolescence (postnatal day [PND] 35) and adulthood (PND 60). Whole-brain voxel-wise volumetric analyses revealed that MIA offspring exhibited altered volume in the hippocampus and caudate putamen (CPu) between adolescence and early adulthood. MRS data were assessed at each timepoint separately; MIA offspring during early adulthood but not adolescence exhibited trending reductions in γ-aminobutyrate (GABA) (p = 0.06) and myo-inositol (Ins) (p = 0.08) compared to saline controls. However, these metabolite differences did not reach levels of significance, even before multiple comparison corrections. Open field testing revealed that during adolescence, MIA offspring displayed a more anxious phenotype than controls wherein they spent less time in the anxiogenic center zone of the open field arena (p < 0.007), but this difference normalized by adulthood. There were no significant differences in sociability preference, novelty preference, or prepulse inhibition comparing the groups. Results suggest that early gestational exposure to MIA results in subtle neuroanatomical changes in the trajectories of development, trending behavioral changes in adolescent offspring, and slight neurochemical changes in young adult offspring. Maternal infection alone may not be enough; additional genetic or environmental risk factors may be required to elicit the more typical symptoms of neuropsychiatric disorders.
ARTICLE | doi:10.20944/preprints202209.0290.v1
Subject: Life Sciences, Molecular Biology Keywords: Sulfatide; cerebroside sulfotransferase; ventricular enlargement; Alzheimer’s disease; brain MRI; aquaporins
Online: 20 September 2022 (03:56:30 CEST)
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive memory loss and a decline in activities of daily life. Ventricular enlargement has been associated with worse performance on global cognitive tests and AD. Our previous studies demonstrated that brain sulfatides, myelin-enriched lipids, are dramatically reduced in subjects at the earliest clinically recognizable AD stages via an apolipoprotein E (APOE)-dependent and isoform-specific process. Herein, we provided pre-clinical evidence that sulfatide deficiency is causally associated with brain ventricular enlargement. Specifically, taking advantage of genetic mouse models of global and adult-onset sulfatide deficiency, we demonstrated that sulfatide losses cause ventricular enlargement without significantly affecting hippocampal or whole brain volumes using histological and magnetic resonance imaging approaches. Mild decreases in sulfatide content and mild increases in ventricular areas were also observed in human APOE4 compared to APOE2 knock-in mice. Finally, we provided Western blot and immunofluorescence evidence that aquaporin-4, the most prevalent aquaporin channel in the central nervous system (CNS) that provides fast water transportation and regulates cerebrospinal fluid in the ventricles, is significantly increased under sulfatide-deficient conditions, while other major brain aquaporins (e.g., aquaporin-1) are not altered. In short, we unraveled a novel molecular mechanism that may contribute to ventricular enlargement in AD.
ARTICLE | doi:10.20944/preprints202107.0548.v1
Subject: Engineering, Automotive Engineering Keywords: ANN; COVID-19; CT; mRNA; MRI; RT-PCR; SARS-CoV-2; XCR
Online: 23 July 2021 (15:02:40 CEST)
Accurate early diagnosis of COVID-19 viral pneumonia, primarily in asymptomatic people is essential to reduce the spread of the disease, the burden on healthcare capacity, and the overall death rate. It is essential to design affordable and accessible solutions to distinguish pneumonia caused by COVID-19 from other types of pneumonia. In this work, we propose a reliable approach based on deep transfer learning that requires few computations and converges faster. Experimental results demonstrate that our proposed framework for transfer learning is a potential and effective approach to detect and diagnose types of pneumonia from chest X-ray images with a test accuracy of 94.0%.
ARTICLE | doi:10.20944/preprints202010.0200.v1
Subject: Medicine & Pharmacology, Other Keywords: digital reference object; blood-brain barrier permeability; DCE-MRI; Spatio-temporal imaging artefacts; endothelial dysfunction; cerebral small vessel disease
Online: 9 October 2020 (12:44:33 CEST)
Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four-dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits a greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.
ARTICLE | doi:10.20944/preprints202002.0364.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: Glioblastoma Multiforme; rat model; NK-Cell Therapy; MRI Cell traking; Fouresecent cell tracking; blood brain barrier
Online: 25 February 2020 (06:51:39 CET)
Natural killer (NK) cell therapy is one of the most promising treatments for Glioblastoma Multiforme (GBM). However, this emerging technology is limited by the availability of sufficient numbers of fully functional cells. Here, we investigated the efficacy of NK cells that were expanded and treated by interleukin-2 (IL-2) and heat shock protein70 (HSP70), both in vitro and in vivo. Proliferation and cytotoxicity assays were used to assess the functionality of NK cells in vitro, after which treated and naïve NK cells were administrated intra-cranially and systemically to compare the potential antitumor activities in our in vivo rat GBM models. In vitro assays provided strong evidence of NK cell efficacy against C6 tumor cells. In vivo tracking of NK cells showed efficient homing around and within the tumor site. Furthermore, significant amelioration of the tumor in rats treated with HSP70/Il-2 treated NK cells as compared to those subjected to non-treated NK cells, as confirmed by MRI, proved the efficacy of adoptive NK cell therapy. Moreover, results obtained with systemic injection confirmed migration of activated NK cells over the blood brain barrier and subsequent targeting of GBM tumor cells. Our data suggest that administration of HSP70/Il-2 treated NK cells may be a promising therapeutic approach to be considered in the treatment of GBM.
REVIEW | doi:10.20944/preprints202111.0216.v1
Online: 12 November 2021 (11:37:40 CET)
MRI shows in-vivo the three archetypal patterns of CNS volume loss underlying progressive ataxias, namely spinal atrophy (SA), cortical cerebellar atrophy (CCA) and olivopontocerebellar atrophy (OPCA). The MRI-based CNS atrophy pattern was reviewed in 128 progressive ataxias. A CNS atrophy pattern was identified in 91 conditions: SA in Freidreich’s ataxia, CCA in 5 acquired and 72 (24 dominant, 47 recessive,1 X-linked) inherited ataxias, OPCA in Multi-System Atrophy and 12 (9 dominant, 2 recessive,1 X-linked) inherited ataxias. The MRI-based CNS atrophy pattern may be useful for genetic assessment, identification of shared cellular targets, and repurposing therapies or enlargement of drugs indications in progressive ataxias.
ARTICLE | doi:10.20944/preprints202109.0356.v1
Online: 21 September 2021 (11:14:09 CEST)
Olfactory system is a vital sensory system in mammals, giving them the ability to connect with their environment. Anosmia, or the complete loss of olfaction ability, which could be caused by injuries, is an interesting topic for inspectors with the aim of diagnosing patients. Sniffing test is currently utilized to examine if an individual is suffering from anosmia; however, functional Magnetic Resonance Imaging (fMRI) provides unique information about the structure and function of the different areas of the human brain, and therefore this noninvasive method could be used as a tool to locate the olfactory-related regions of the brain. In this study, by recruiting 31 healthy and anosmic individuals, we investigated the neural BOLD responses in the olfactory cortices following two odor stimuli, rose and eucalyptus, by using a 3T MR scanner. Comparing the two groups, we observed a network of brain areas being more active in the normal individuals when smelling the odors. In addition, a number of brain areas also showed an activation decline during the odor stimuli, which is hypothesized as a resource allocation deactivation. This study illustrated alterations in the brain activity between the normal individuals and anosmic patients when smelling odors, and could potentially help for a better anosmia diagnosis in the future.
ARTICLE | doi:10.20944/preprints201610.0042.v3
Online: 10 December 2016 (08:32:59 CET)
Based upon Maxwell's equations, it has long been established that oscillating electromagnetic (EM) fields incident upon a metal surface decay exponentially inside the conductor, leading to a virtual EM vacuum at sufficient depths. Magnetic resonance imaging (MRI) utilizes radiofrequency (r.f.) EM fields to produce images. Here we present the first visualization of a virtual EM vacuum inside a bulk metal strip by MRI, amongst several novel findings. We uncover unexpected MRI intensity patterns arising from two orthogonal pairs of faces of a metal strip, and derive formulae for their intensity ratios, revealing differing effective elemental volumes (voxels) underneath these faces. Further, we furnish chemical shift imaging (CSI) results that discriminate different faces (surfaces) of a metal block according to their distinct nuclear magnetic resonance (NMR) chemical shifts, which holds much promise for monitoring surface chemical reactions noninvasively. Bulk metals are ubiquitous, and MRI is a premier noninvasive diagnostic tool. Combining the two, the emerging field of bulk metal MRI can be expected to grow in importance. The fundamental nature of results presented here may impact bulk metal MRI and CSI across many fields.
REVIEW | doi:10.20944/preprints202208.0084.v1
Subject: Materials Science, Nanotechnology Keywords: Nanomaterial; optical imaging; MRI imaging; nanoparticles
Online: 3 August 2022 (10:43:49 CEST)
Visualization of deep biological structures in human and animal bodies is not possible through the naked eye due to the scattering of visible light by tissues in tolerable intensities. Different types of imaging modalities based on electromagnetic and pressure waves have been developed that help us image deep biological tissues with varying resolution and contrast. Some of the most widely used modalities are X-ray imaging, ultrasound imaging, MRI imaging, fluorescence imaging, and photoacoustic imaging. Although these techniques have significantly helped the advancement of our understanding of deep biological tissues and functions, they often require the use of exogenous contrast agents to improve their image quality for better investigation. Nanoparticle-based contrast agents have captivated scientists because of multiple advantages associated with them such as their excellent photophysical and chemical properties, ability to be precisely delivered at the target, and superlative tunability. This article is aimed to give a brief outlook on the recent state of art advances in the usage of nanoparticles for preclinical and clinical bioimaging through fluorescence, photoacoustic, and MRI imaging modalities.
ARTICLE | doi:10.20944/preprints202109.0450.v1
Online: 27 September 2021 (12:54:10 CEST)
(1) Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning on diverse task distributions. The proposed network learns the regularization function in a variational model and reconstructs MR images with various under-sampling ratios or patterns that may or may not be seen in the training data by leveraging a heterogeneous dataset. (2) Methods: We propose an unrolling network induced by learnable optimization algorithms (LOA) for solving our nonconvex nonsmooth variational model for MRI reconstruction. In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network. To train the network we split the training data into two parts: training and validation, and introduce a bilevel optimization algorithm. The lower-level optimization trains task-invariant parameters for the feature encoder with fixed parameters of the task-specific learner on the training dataset, and the upper-level optimizes the parameters of the task-specific learner on the validation dataset. (3) Results: The PSNR increases 1.5 dB on average compared to the network trained through conventional supervised learning on the seen CS ratios. We test the result of quick adaption on the unseen tasks after meta-training, the average PSNR arises 1.22 dB compared to the conventional learning procedure that is directly trained on the unseen CS ratios in the meanwhile saving half of the training time. The average PSNR arises 1.87 dB for unseen sampling patterns comparing to conventional learning; (4) Conclusion: We proposed a meta-learning framework consisting of the base network architecture, design of regularization, and bi-level optimization-based training. The network inherits the convergence property of the LOA and interpretation of the variational model. The generalization ability is improved by the designated regularization and bilevel optimization-based training algorithm.
ARTICLE | doi:10.20944/preprints202106.0543.v1
Subject: Medicine & Pharmacology, Allergology Keywords: radiomics; diffusion-weighted; MRI; cervical cancer
Online: 22 June 2021 (14:21:15 CEST)
Objectives: To explore the potential of Radiomics alone and in combination with diffusion-weighted derived quantitative parameter namely apparent diffusion co-efficient (ADC) using supervised classification algorithms in predicting outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was done for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection by taking the union of the features which had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis, analysis was done to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages and age range 28–79 (Median = 53 years) with median follow-up of 26.5 months (range, 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields AUC of 0.80 and kappa value as 0.55 and shows that addition of radiomics features on ADC values improves the statistical metrics by 40% approximately for AUC and 223% approximately for Kappa. Similarly, neural network model for prediction of metastasis re-turns AUC of 0.84 and kappa value as 0.65 over performs by 25% for AUC and 140% for Kappa approximately. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in stratification of uterine cervical cancer patients based on prognosis.
REVIEW | doi:10.20944/preprints201901.0133.v1
Online: 14 January 2019 (11:25:46 CET)
Early detection of pancreatic ductal adenocarcinoma (PDAC) requires further examination after selecting cases with risk factors for the condition, such as family history, hereditary pancreatic carcinoma syndrome, intraductal papillary mucinous neoplasms, or chronic pancreatitis. The Japan Study Group on the Early Detection of Pancreatic Cancer has investigated and clarified the clinicopathological features for the early diagnosis of PDAC. Further approaches for the early diagnosis of PDAC are warranted.
REVIEW | doi:10.20944/preprints202110.0232.v1
Subject: Medicine & Pharmacology, Psychiatry & Mental Health Studies Keywords: twice exceptionality; autism spectrum disorder; neuroimaging; MRI
Online: 15 October 2021 (16:17:13 CEST)
There is a long-standing association between exceptional cognitive abilities, of various sorts, and neuropsychiatric illness, but it has historically largely been investigated in an exploratory and non-systematic way. One group in which this association has been investigated with more rigor is in subjects who have been identified as twice exceptional; an educational term describing subjects who are both gifted and diagnosed with a neuropsychiatric disability. This term covers multiple conditions, but is of specific interest in particular in the study of autism spectrum disorder. Recent findings have led to the development of a hypothesis that a certain degree of the neurobiology associated with autism might even be advantageous for individuals and could lead to high giftedness, while becoming disadvantageous, once a certain threshold is surpassed. In this model, the same neurobiological mechanisms confer an increasing advantage up to a certain threshold, but become pathological past that point. Twice-exceptional individuals would be exactly at the inflection point, being highly gifted, but also symptomatic at the same time. Here, we review how existing neuroimaging literature on autism spectrum disorder can inform research on twice exceptionality specifically. We propose to study key neural networks with a robust implication in ASD to identify the neurobiology underlying twice-exceptionality. A better understanding of the neural mechanisms of twice exceptionality should help to better understand resilience and vulnerability to neurodevelopmental disorders and tofurther support affected individuals.
ARTICLE | doi:10.20944/preprints202103.0136.v1
Subject: Medicine & Pharmacology, Allergology Keywords: Sleep apnea; hypoxemia; cognitive; brain health; MRI
Online: 3 March 2021 (14:14:41 CET)
We aim to determine the sleep correlates of age-related brain loss in a sample of middle-aged to older males with obstructive sleep apnea. We evaluated consecutive treatment naïve male patients with OSA (AHI≥15 events/hr) without dementia, stroke or heart disease, from January to November of 2019. We collected demographic variables, vascular risk factors, and sleep questionnaires. We also obtained computerized neurocognitive testing with the Go-No-Go Response Inhibition Test, Stroop Interference Test, Catch Game Test, Staged Information Processing Speed Test, Verbal Memory Test and Non-Verbal Memory Test. We derived age and education adjusted domain-specific Z-scores for global cognition, memory, attention, processing speed and executive function. We used brain MRI T1-weighted images to derive total hippocampal and gray matter volumes. Partial correlations evaluated associations between the ISI, AHI, and oxygen level during sleep, with cognitive domains and brain volumes. Sixteen participants, age 40-76 years, 73% Hispanic/Latino, with mean AHI=48.9±25.5 and mean oxygen saturation of 91.4±6.9% during sleep. Hypertension was seen in 66% and diabetes in 27%. We observed that ISI and oxygen level during sleep had strong correlations with brain volumes and cognition. These preliminary findings may aid in developing future strategies to improve age-related brain loss in OSA.
ARTICLE | doi:10.20944/preprints201712.0090.v1
Subject: Mathematics & Computer Science, Probability And Statistics Keywords: PFG anomalous diffusion; fractional derivative; NMR; MRI
Online: 14 December 2017 (11:27:15 CET)
Pulsed-field gradient (PFG) diffusion experiments can be used to measure anomalous diffusion in many polymer or biological systems. However, it is still complicated to analyze PFG anomalous diffusion, particularly the finite gradient pulse width (FGPW) effect. In practical applications, the FGPW effect may not be neglected such as in clinical diffusion magnetic resonance imaging (MRI). Here, two significantly different methods are proposed to analyze PFG anomalous diffusion: the effective phase shift diffusion equation (EPSDE) method and an observing the signal intensity at the origin method. The EPSDE method describes the phase evolution in virtual phase space, while the method to observe the signal intensity at the origin describes the magnetization evolution in real space. However, these two approaches give the same general PFG signal attenuation including FGPW effect, which can be numerically evaluated by a direct integration method. The direct integration method is fast and without overflow. It is a convenient numerical evaluation method for Mittag-Leffler function type PFG signal attenuation. The methods here provide a clear view of spin evolution under field gradient, and their results will help the analysis of PFG anomalous diffusion.
ARTICLE | doi:10.20944/preprints202107.0189.v1
Subject: Medicine & Pharmacology, Allergology Keywords: MRI; deuterium metabolic imaging; tumor; 2H; glucose; choline
Online: 8 July 2021 (10:06:03 CEST)
: Increased glucose and choline uptake are hallmarks of cancer. We investigated if the uptake and conversion of [2H9]choline alone and together with that of [6,6’ 2H2]glucose can be assessed in subcutaneous tumors by deuterium metabolic imaging (DMI) after bolus administration of these compounds. Therefore tumors with human renal carcinoma cells were grown subcutaneously in mice up to ~0.5 cm3. Mice were anesthetized with isoflurane and IV infused in the MR magnet for ~20 sec with ~0.2 ml solutions containing either [2H9]choline (0.05g/kg) alone or together with [6,6’ 2H2]glucose (1.3g/kg). 2H MR was performed on a 11.7T MR system with a home-built 2H/1H coil using a 900 excitation pulse and 400ms repetition time. 3D DMI was recorded at high resolution (2x2x2mm) in 37 min or at low resolution (3.7x3.7x3.7mm) in 2:24 min. Absolute tissue concentrations were calculate assuming initial [HOD]=13.7mM. Within 5 minutes after [2H9]choline infusion its signal appeared in tumor spectra representing concentration increasing up to 0.3–1.2 mM and then slowly decreased or remained constant over 100 minutes. In plasma [2H9]choline disappeared within 15 minutes post-infusion implying that its tumor signal arises from tissue and not blood. After infusing a mixture of [2H9]choline and [6,6’ 2H2]glucose their signals were observed separately in tumor 2H spectra. Over time the [2H9]choline signal broadened, possibly due to conversion to other choline compounds, [[6,6’ 2H2]glucose] declined, [HOD] increased and a lactate signal appeared, reflecting glycolysis. Metabolic maps of all 2H compounds were reconstructed from high resolution DMIs. As choline infusion and glucose DMI is feasible in patients, their simultaneous detection has clinical potential for tumor characterization.
ARTICLE | doi:10.20944/preprints202007.0650.v1
Subject: Mathematics & Computer Science, Other Keywords: Myocarditis; Diagnosis; Convolutional Neural Network; Cardiac MRI; prediction
Online: 26 July 2020 (17:44:05 CEST)
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained as one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered as a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR is heavily dependent on the clinical presentation and non-specific features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis. The hybrid CNN-KCL method performs the early and accurate diagnosis of Myocarditis. To the best-of-our-knowledge, a Convolutional neural network has never been used before for the diagnosis of Myocarditis. In this study, we used 47 subjects to diagnose myocarditis patients from Tehran's Omid Hospital. The total number of data examined is 10425. Our results demonstrate that CNN-KCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.
ARTICLE | doi:10.20944/preprints202007.0267.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: mTBI, MRI; MRE; FE model; brain wave dynamics
Online: 12 July 2020 (16:46:04 CEST)
We extend our high-resolution MRI-based Finite Element (FE) head model, previously presented and validated in [1–3], by considering the heterogeneities of the white matter structures captured through the use of Magnetic Resonance Elastography (MRE). This approach imparts more sophistication to our numerical model and yields results that more closely match experimental results. It is found that the peak pressure more closely matches the experiments as compared to the heterogeneous case. Qualitatively, we find differences in stress wave propagation near the corpus callosum and the corona radiata, which are stiffer on average than the global white matter. We are able to study the effects of these stiff structures on transient stress wave propagation within the cerebrum, something that cannot be done with a homogenized material model.
REVIEW | doi:10.20944/preprints201805.0177.v1
Subject: Medicine & Pharmacology, Psychiatry & Mental Health Studies Keywords: hippocampus volume; mood Disorders; MRI; depression; subiculum; CA1
Online: 11 May 2018 (06:32:32 CEST)
Background and objectives: due to the neurotoxic effect caused by high levels of cortisol, studies suggest that stress and certain psychiatric disorders, such as mood disorders, have influences under the hippocampus, causing a decrease in volume and consequent memory changes. This study aims to evaluate the relationship between hippocampal volume in patients with mood disorders under therapy. Materials and Methods: the PRISMA protocol for systematic reviews was followed. Pubmed, Cochraine and Scielo databases were searched by terms “Hippocampus”, “Mood Disorders” and “MRI”, and variants in other languages, in human, from January 2011 to September 2016. The individual quality of the articles was analyzed using the Cochraine modified scale for clinical trials and the Agency for Healthcare Research and Quality scale for observational studies. Results: all studies showed reduction of hippocampal volume in depressive patients. Change in hippocampal volume is not related to the use of antidepressant. Particularly the sub-region of the subculum is more reduced, without lateralizations. Significant relationship between stress and right hippocampal reduction. The findings seem to point out: a common pathway of hippocampus reduction, mediated by stress, explaining memory deficits due to depression, where the cortisol pathway seems to act; alteration in the prefrontal cortex; reduction in the subiculum related to inhibition of the hypothalamic-pituitary-adrenal axis, corroborating the hypothesis of cortisol. Conclusions: the papers suggest: association between global hippocampal atrophy with mood disorders; reduction of hippocampal subiculum; refractoriness to clinical treatment among patients with lower hippocampal volume.
ARTICLE | doi:10.20944/preprints201703.0207.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: Activin-A; HIE; MRI; apparent diffusion coefficient; neonates
Online: 28 March 2017 (03:07:20 CEST)
Background: Early identification and prevention of hypoxic ischemic encephalopathy (HIE) may reduce neonatal mortality and morbidity. Objective: We aimed to correlate between urinary Activin-A and MRI (conventional and Diffusion-weighted) and the severity of HIE in full-term neonates. Methods: Forty-five full-term neonates with HIE admitted to NICU and 15 normal neonates were enrolled into the study. The concentration of urinary Activin-A was determined using enzyme immunoassay kits and MRI were done. Correlations between urinary Activin-A and MRI with the degree of HIE were done. Results: Urinary Activin-A levels were significantly higher in neonates with HIE than controls (P<0.001). It was positively correlated with the clinical grading of HIE and a cutoff value of 0.08µg/l on day-1 after birth had a sensitivity of 98.6% and specificity of 97.1% for prediction of HIE. DW-MRI detected HIE with a high sensitivity (85%) compared to the low sensitivity of conventional MRI (35%). An ADC value of ≤ 0.8 was the best sensitivity-specificity cutoff point for detecting severe ischemic injury. DW-MRI imaging was positively correlated with Urinary Activin-A and both of them were positively correlated with the clinical grades of HIE (P < 0.001). Conclusions: DW-MRI imaging is correlated well with urinary Activin-A in full-term neonates with HIE and both of them are correlated with the degree of HIE. Early determination of urinary Activin-A combined with DW-MRI imaging can early detect HIE and its severity in full-term neonates with HIE.
ARTICLE | doi:10.20944/preprints202011.0023.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: Computers in Medicine; Segmentation; Machine Learning; Deep Learning; MRI
Online: 2 November 2020 (11:02:44 CET)
Segmentation of Magnetic Resonance Images (MRI) of abdominal organs is useful for analysis prior to surgical procedures and for further processing. Deep Learning (DL) has become the standard, researchers have proposed improvements that include multiple views, ensembles and voting. Loss function alternatives, while being crucial to guide automated learning, have not been compared in detail. In this work we analyze limitations of popular metrics and their use as loss, study alternative loss variations based on those and other modifications and search for the best approach. An experimental setup was necessary to assess the alternatives. Results for the top scoring network and top scoring loss show improvements between 2 and 11 percentage points (pp) in Jaccard Index (JI), depending on organ and patient (sequence), for a total of 22 pp over 4 organs, all this being obtained just by choosing the best performing loss function instead of cross-entropy or dice. Our results apply directly to MRI of abdominal organs, with important practical implications for other architectures, as they can be applied easily to any of them. They also show the worth of variants of loss function and loss tuning, with future work needed to generalize and test in other contexts.
ARTICLE | doi:10.20944/preprints202007.0366.v1
Online: 17 July 2020 (06:21:09 CEST)
Tissues of the brain, especially white matter, are extremely heterogeneous - with constitutive response varying spatially. In this paper, we implement a high-resolution Finite Element (FE) head model where heterogeneities of white matter structures are introduced through Magnetic Resonance Elastography (MRE) experiments. Displacement of white matter under shear wave excitation is captured and the material properties determined though an inversion algorithm are directly used in the FE model. This approach is found to improve model predictions when compared to experimental results. In the first place, responses in the cerebrum near stiff structures such as the corpus callosum and corona radiata are markedly different compared with a homogenized material model. Additionally, the heterogeneities introduce additional attenuation of the shear wave due to wave scattering within the cerebrum.
REVIEW | doi:10.20944/preprints201709.0159.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: Key wordsbrest cancer screening, mammography, breast ultrasound, breast MRI
Online: 29 September 2017 (15:00:08 CEST)
Abstract On the background of new research results in screening of breast cancer, together with the expectation of the participants in a screening for breast cancer, the conventional mammography requires supplementation by means of tomosynthesis or additive ultrasound. Alternatively, ultrasound now seems to be an independent method of early detection of breast cancer because of its superior sensitivity, especially in the case of aggressive mammary carcinoma types. The MRI remains at present still a preferred method in high-risk cases and as an additive examination in case of insufficient presentation of the glandular tissue by conventional methods. MRI is also preoperatively valuable for more accurately measuring the extent of multifocal carcinomas.
ARTICLE | doi:10.20944/preprints202205.0357.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: prostate cancer; diffusion MRI; false positives; biophysical modelling; deep learning
Online: 26 May 2022 (08:30:28 CEST)
False positives on multiparametric (mp)-MRI result in a large number of unnecessary biopsies in men with clinically insignificant diseases. This study investigates whether quantitative diffusion MRI can improve differentiation between false positives, true positives and normal tis-sue. Twenty-three patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffu-sion for Cytometry in Tumours (VERDICT)-MRI, followed by transperineal biopsy. The patients were categorised into two groups following biopsy: (1) significant cancer - true positive (2) atro-phy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN) - false positive. The clinical apparent diffusion coefficient (ADC) values of the lesions were obtained, and the intravoxel inco-herent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted using a deep learning approach. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular-extravascular vol-ume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were only found for the VERDICT fIC. These results demonstrate that model-based diffusion MRI could reduce the number of unnecessary biopsies due to false positive prostate lesions and shows promising sensitivity to benign diseases that mimic cancer.
ARTICLE | doi:10.20944/preprints202205.0161.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: Pineal cyst; hydrocephalus; microsurgery; real-time MRI; respiration; glymphatic system
Online: 12 May 2022 (08:00:41 CEST)
Proposal: Pineal region cysts (PCs) may affect the tectum and aqueduct and cause deep central vein congestion and endocrine dysfunction. In addition to headaches, PC often causes a broad range of symptoms, leading to prolonged diagnosis and therapy. The aims of this study are to reveal parameters that might explain the ambiguity of the symptoms and to identify factors association with the respiration driven neurofluid preload system. Methods: This retrospective study included 28 paediatric patients (mean age 11.6 years) who received surgical treatment for pineal region cysts and 18 patients (mean age 11.3 years) who were followed conservatively. Multiple clinical patient characteristics, such as symptoms, time to neuroimaging diagnosis, cyst size, ventricular indices, head circumference and postoperative outcome, were analysed. Four patients were investigated for CSF dynamics with real-time MRI. The mean follow-up time was 1.6 years. Results: The most common early onset symptoms were headaches (92%), blurred vision (42.8%), sleep disturbances (39.3%) and vertigo (32.1%). Tectum contact was observed in 82% of patients, and MRI examinations revealed that imaging flow void signals were absent in 32.1% of patients. The mean cyst diameters were 13.7 mm for the axial axis and 15.6 mm for the longitudinal axis. Together with a postoperative flow void signal, 4 patients recovered their respiration-driven CSF upward flow, which was not detectable before OP. After operation in 92.1% of patients, the leading symptoms improved without any mortality or morbidity. Conclusion: Despite proximity to the tectum and aqueduct with frequently absent aqueductal flow void signals, hydrocephalic ventricular enlargement was never detected. Data from real-time MRI depicted a reduced preoperative filling of the ventricular CSF compartments, indicating a diminished fluid preload, which recovered postoperatively.
REVIEW | doi:10.20944/preprints202109.0149.v1
Subject: Medicine & Pharmacology, Ophthalmology Keywords: uveal melanoma; diagnosis; imaging; MRI; PET; CT; SPECT; OCT; ultrasonography
Online: 8 September 2021 (12:38:40 CEST)
Uveal melanoma is the most common primary intraocular malignancy in adults characterized by insidious onset and poor prognosis strongly associated with tumor size and the presence of distant metastases, most commonly in the liver. Contrary to most tumor identification, biopsy followed by pathological exam is not recommended in ophthalmic oncology. Therefore, early and non-invasive diagnosis is essential to enhance patients’ chances for early treatment possibilities. We reviewed imaging modalities currently used in the diagnosis of uveal melanoma, i.e., fundus imaging, ultrasonography (US), optical coherence tomography (OCT), single-photon emission computed tomography (SPECT), positron emission tomography/computed tomography (PET/CT), magnetic resonance imaging (MRI), fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), fundus autofluorescence (FAF). The principle of imaging techniques was briefly explained, along with their role in the diagnostic process and a summary of their advantages and limitations. Further, the experimental data and the advancements in imaging modalities were searched. We described their innovations, showed current usage and research, and explained the possibilities of utilizing them to diagnose uveal melanoma and their potential application in personalized medicine such as theranostics.
ARTICLE | doi:10.20944/preprints202209.0285.v1
Subject: Life Sciences, Cell & Developmental Biology Keywords: mesothelioma; chick embryo; CAM; xenograft; bioluminescence; fluorescence; histology; MRI; preclinical; 3Rs
Online: 20 September 2022 (02:48:17 CEST)
Malignant pleural mesothelioma (MPM) has limited treatment options and poor prognosis. Frequent inactivation of the tumour suppressors BAP1, NF2 and P16 may differentially sensitise tumours to treatments. We have established chick chorioallantoic membrane (CAM) xenograft models of low-passage MPM cell lines and protocols for evaluating drug responses. Ten cell lines, representing the spectrum of histological subtypes and tumour suppressor status, were dual labelled for fluorescence/bioluminescence imaging and implanted on the CAM at E7. Bioluminescence was used to assess viability of primary tumours, which were excised at E14 for immunohistological staining or real-time PCR. All MPM cell lines engrafted efficiently forming vascularised nodules, however their size, morphology and interaction with chick cells varied. MPM phenotypes including local invasion, fibroblast recruitment, tumour angiogenesis and vascular remodelling were evident. Bioluminescence imaging could be used to reliably estimate tumour burden pre- and post-treatment, correlating with tumour weight and Ki-67 staining. In conclusion, MPM-CAM models recapitulate important features of the disease and are suitable to assess therapies using a broad range of MPM cell lines that allow histological or genetic stratification. They are amenable to multi-modal imaging, offering a time and cost-efficient, 3Rs-compliant alternative to rodent xenograft models to prioritise candidate compounds from in vitro studies.
ARTICLE | doi:10.20944/preprints202201.0148.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: Neuroendoscopy; ETV; Hydrocephalus; ETVSS; T2 flow void; Real-time MRI; Inspiration
Online: 11 January 2022 (14:08:02 CET)
Purpose: ETV is indicated for treating obstructions of major CSF pathways. The outcome evaluation often yields success rates of only +- 70% for shunt independency. Hence, compromised CSF absorption seems to occur more often than expected. We searched for parameters suitable to assess the involved CSF dynamics. Material and Methods: This was a prospective study in 58 paediatric patients (7.7 yrs. mean age) between 2000 and 2020 with aqueductal stenosis (11/58), obstruction of the aqueduct due to tumor growth (22/58),and connatal hydrocephalus (9/58). The average follow-up interval was 4.7 years. Head circumferences, Evans- and fronto-occipital horn ratios before and 3 months after ETV were obtained as Delta-indices. Furthermore ETV success score (ETVSS), the patency of the aqueduct pre- and postoperatively as well as of the stoma were assessed by flow void signs on MRI. Evaluation on MRI also included the shape of the floor of the 3rd ventricle and whether or not the septum pellucidum showed signs of perforation. Four patients were analysed pre- and postoperatively via real-time MRI. At least the educational status regarding protected or unprotected education was analyzed. Results:The prevalence of a bowing of the floor of the 3rd ventricle was 72%, and the ETVSS was 71.0%. In 26 children a septal perforations or an open aqueduct prior to ETV (19) could be identified. Mean ER and FOHR were reduced by 0.03 and 0.05 , respectively. Maintained open (flow void on postop MRI) or perforation could successfully be carried out during endoscopic surgery in 44 patients (79%). The disproportionate increase of head circumference abated in 79.4% of patients. New shunt insertion occurred in 16 patients (27.5%). Intraoperatively upward CSF flow was detected in all cases. Statistical analyses(ANOVA) showed significant results for unprotected education, postoperative ER and FOHR but not for open stoma. Conclusion: The identification of flow through the stoma on postoperative MRI seems to be a necessary but not sufficient condition for ETV success. In our study, ventricular volumes were used as parameters to determine success rates as well as unprotected education. Furthermore, enabling upward CSF flow driven by inspiration seems crucial for successful ETV.
REVIEW | doi:10.20944/preprints202111.0461.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: juvenile idiopathic arthritis; X-Ray; ultrasound; MRI; passive microwave radiometry (MWR).
Online: 24 November 2021 (16:11:24 CET)
Juvenile idiopathic arthritis (JIA) is a disease with unknown causes within all forms of arthritis in children under 16 years of age. The diagnosis is made when another joint pathology is excluded. Difficulties in early and differential diagnosis lead to the rapid disability of patients and an unfavourable life prognosis. Therefore, timely diagnosis is necessary to prevent irreversible damage to the joints and preserve their function. Due to the widespread use of new technologies, modern multimodal imaging has gained recognition, which includes X-ray, ultrasound, and MRI. The combination of methods plays a key role in confirming the diagnosis, monitoring disease activity, prognosis during the course, and outcome in children with JIA. Each method has its own advantages and disadvantages. The introduction of the method of passive microwave radiometry (MWR), in combination with other imaging methods, makes it possible to expand the possibilities of screening the disease in the preclinical and early clinical phases.
ARTICLE | doi:10.20944/preprints202012.0789.v1
Subject: Life Sciences, Biochemistry Keywords: glioblastoma; DCE-MRI; interstitial flow; convection; diffusion; The Cancer Imaging Archive
Online: 31 December 2020 (11:36:28 CET)
Background: Glioblastoma is the deadliest, yet most common, brain tumor in adults, with poor survival and response to aggressive therapy. Therapeutic failure results from a number of causes inherent to these tumors. Imaging, computational, and drug delivery approaches can aid in the quest to access and kill each tumor cell in patients. One factor, interstitial fluid flow, is a driving force therapeutic delivery. However, convective and diffusive transport mechanisms are un-der-studied. In this study, we examine the application of a novel image analysis method to meas-ure fluid flow and diffusion in glioblastoma patients with MRI and compare to patient outcomes. Methods: Building on a prior imaging methodology tested and validated in vitro, in silico and in preclinical models of disease, here we apply our analysis method to archival patient data from the Ivy GAP dataset. Results: We characterize interstitial fluid flow and diffusion patterns in patients. We find strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Looking at overall magnitudes, there is significant correlation with both age and survival in this patient cohort. Additionally, we find that tumor size nor resection significantly alter the velocity magnitude. Last, we map the flow pathways in patient tumors and find variability in degree of directionality that we hypothesize in future studies may lead to information concerning treatment, invasive spread, and progression. Conclusions: Analysis of standard DCE-MRI in patients with glioblastoma offers more infor-mation regarding transport within and around tumor, can be measured post-resection and mag-nitudes correlate with patient prognosis.
ARTICLE | doi:10.20944/preprints201805.0386.v1
Subject: Physical Sciences, Radiation & Radiography Keywords: magnetic resonance imaging; arterial spin labelling; renal MRI; perfusion; renal ASL
Online: 28 May 2018 (06:26:31 CEST)
Purpose: A number of imaging readout schemes have been proposed for renal arterial spin labelling (ASL) to quantify kidney cortex perfusion, including gradient echo based methods of balanced fast field echo (bFFE) and gradient-echo echo-planar imaging (GE-EPI), or spin echo based schemes of spin-echo echo planar imaging (SE-EPI) and turbo spin-echo (TSE). Here, we compare these imaging schemes to evaluate the optimal imaging scheme for pulsed ASL (PASL) assessment of human kidney cortex perfusion at 3 T. Methods: Ten healthy volunteers with normal renal function were scanned using each 2D multislice imaging scheme, in combination with a respiratory triggered FAIR (flow-sensitive alternating inversion recovery) ASL scheme on a 3 T Philips Achieva scanner. All volunteers returned for a second identical scan session within two weeks of the first scan session. Comparisons were made between the imaging schemes in terms of perfusion weighted image (PWI) signal-to-noise ratio (SNR) and perfusion quantification, temporal SNR (tSNR), spatial coverage, and repeatability. Results: For each imaging scheme, renal cortex perfusion was calculated (bFFE: 276 ± 29 mL/100 g/min, GE-EPI: 222 ± 18 mL/100 g/min, SE-EPI: 201 ± 36 mL/100 g/min, TSE: 200 ± 20 mL/100 g/min). Perfusion was found to be higher for GE based readouts compared to SE based readouts, with significantly higher measured perfusion for the bFFE readout compared to all other schemes (P < 0.05), attributed to the greater vascular signal present. Despite the PWI-SNR being significantly lower for SE-EPI compared to all other schemes (P < 0.05), the SE-EPI readout gave the highest tSNR and was found to be the most reproducible scheme for the assessment of kidney cortex, with a CoV of 17.2%, whilst minimizing variability of the perfusion weighted signal across slices for whole kidney perfusion assessment. Conclusion: For the assessment of kidney cortex perfusion, SE-EPI provides optimal tSNR, minimal variability across slices and repeatable data acquired in a short scan time with low specific absorption rate.
ARTICLE | doi:10.20944/preprints201805.0070.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: Hyperpolarized gas MRI; xenon; gas retention; Alzheimer’s disease; wash out; vascular
Online: 3 May 2018 (12:02:44 CEST)
Biomarkers have the potential to aid in the study of Alzheimer’s disease (AD); unfortunately, AD biomarker values often have a high degree of overlap between healthy and AD individuals. This study investigates the potential utility of a series of novel AD biomarkers, the sixty second 129Xe retention time, and the xenon washout parameter, based on the washout of hyperpolarized 129Xe from the brain of AD participants following inhalation. The xenon washout parameter is influenced by cerebral perfusion, T1 relaxation of xenon, and the xenon partition coefficient, all factors influenced by AD. Participants with Alzheimer’s disease (n=4) and healthy volunteers (n=4) were imaged using hyperpolarized 129Xe magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to determine the amount of retain xenon in the brain. At 60 sec post breath hold, AD patients retained significantly higher amounts of 129Xe compared to healthy controls. Data was fit to a pharmacokinetic model and the xenon washout parameter was extracted. Xenon washout in white and grey matter occurs at a slower rate in Alzheimer’s participants (129Xe half-life time of 42s and 43s, respectively) relative to controls (20s and 16s, respectively). Following larger scale clinical trials for validation, the xenon washout parameter has the potential to become a useful biomarker for the support of an AD diagnosis.
REVIEW | doi:10.20944/preprints202207.0412.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: stroke; hypereosinophilia; hypereosinophilic syndrome (HES); brain MRI; embolic pattern; border zone stroke
Online: 27 July 2022 (05:30:13 CEST)
Background: Hypereosinophilic syndromes (HES) are a group of relatively rare disorders in which neurological manifestations are not uncommon including ischemic stroke. The hypothesized pathophysiological mechanisms are hypercoagulability, cardioembolism (mainly mediated by myocardial involvement) and damage to the endothelium. A variable ischemic pattern has been described, including an association of territorial and border zone ischemic stroke. Methods: Three patients who presented to our department with acute stroke were selected aiming to show these three different mechanisms inferred from the stroke pattern on brain Magnetic Resonance Imaging (MRI) and to simultaneously illustrate the three main causes of HES. Results and Discussion: The first patient is a 55-year-old man with an abrupt onset of aphasia due to an acute ischemic stroke involving the left parietal lobule and the angular gyrus; recent lab test had shown hypereosinophilia. An extensive workup excluded primary and secondary causes of hy-pereosinophilia so a diagnosis of idiopathic hypereosinophilia was formulated and he was treated with high doses of steroids. The second patient had severe hypereosinophilia and developed multiple small scattered ischemic lesions, mainly in watershed zones. The history of severe asthma and recurrent sinusitis supported the diagnosis of EGPA (Eosinophilic Granulomatosis with Polyangiitis); considering the severe clinical conditions and the presumptive role of hypereo-sinophilia in determining her symptoms, steroid treatment was promptly started, with good clinical response. The third patient also presented with multiple metachronous ischemic lesions, both in cortical and watershed distribution and marked eosinophilia; the diagnostic work-up found an ovarian cancer. She was treated with steroids and then underwent surgery and adjuvant chemotherapy. Conclusions: HES should be considered in stroke etiological evaluation, although it is a rare disorder, and border zones pattern on neuroimaging is quite suggestive. A thorough research of the sources of hypereosinophilia should be performed to select the appropriate therapy.
REVIEW | doi:10.20944/preprints202112.0119.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: resting state functional MRI; effective connectivity; dystonia; movement disorders; subcortical; basal ganglia
Online: 8 December 2021 (11:55:16 CET)
AbstractIn the evolving modern era of neuromodulation for movement disorders in adults and children, much progress has been made recently characterizing the human motor network (MN) with potentially important treatment implications. Herein is a focused review of relevant resting state fMRI functional and effective connectivity of the human motor network across the lifespan in health and disease. The goal is to examine how the transition from static functional to dynamic effective connectivity may be especially informative of network-targeted movement disorder therapies, with hopeful implications for children.Impact StatementWhile functional connectivity has elucidated much MN properties with relation to age, disease, and behavior, effective connectivity has been shown to be useful in MN-informed therapies in adults. Thus, effective connectivity may have potential to impact childhood movement disorder therapies, given the lower to no patient demand.
ARTICLE | doi:10.20944/preprints201810.0228.v1
Subject: Medicine & Pharmacology, Pediatrics Keywords: cerebellar hemorrhage; intraventricular hemorrhage; preterm; MRI; neurodevelopment; outcome prediction; white matter injury
Online: 11 October 2018 (04:21:24 CEST)
Although the most common forms of brain injury in preterm infants have been associated with adverse neurodevelopmental outcomes, existing MRI scoring systems lack specificity, do not incorporate clinical factors, and are technically challenging to perform. The objective of this study was to develop a web-based, clinically-focused prediction system which differentiates severe from normal-moderate neurodevelopmental outcomes at two years. Infants were retrospectively identified as those who were born ≤30 weeks gestation, had MR imaging at term-equivalent age, and neurodevelopmental testing at 18-24 months. Each MRI was scored on injury in three domains (intraventricular hemorrhage, white matter injury, and cerebellar hemorrhage) and clinical factors strongly predictive of outcome were investigated. A binary logistic regression model was then generated from the composite of clinical and imaging components. A total of 154 infants were included (mean GA = 26.1±1.8 weeks, BW = 889.1±226.2 grams). The final model (imaging score + ventilator days + delivery mode + antenatal steroids + ROP requiring surgery) had strong discriminatory power for severe disability (AUC=0.850), with a PPV of 76% and NPV of 90%. Available as a web-based tool, it can be useful for prognostication and targeting early intervention services to infants who may benefit most from such services.
ARTICLE | doi:10.20944/preprints201902.0078.v1
Subject: Medicine & Pharmacology, Psychiatry & Mental Health Studies Keywords: major depressive disorder, bipolar disorder, structural MRI, grey matter volume, voxel-based morphometry
Online: 8 February 2019 (09:30:12 CET)
Objective: The aim of the current study was to examine whether and to what extent mood disorders, comprising major depression and bipolar disorder, are accompanied by structural changes in the brain as measured using voxel-based morphometry (VBM). Methods: We have performed a VBM study using a 3Т MRI system (GE Discovery 750w) in patients with mood disorders (n=50), namely 39 with major depression and 11 with bipolar disorder, compared to 42 age, sex and education matched healthy controls. Results: Our results show that depression was associated with significant decreases in grey matter (GM) volume restricted to regions located in medial frontal and anterior cingulate cortex on the left side and middle frontal gyrus, medial orbital gyrus, inferior frontal gyrus (triangular and orbital parts), and middle temporal gyrus (extending to the superior temporal gyrus) on the right side. When the patient group was separated into bipolar disorder and major depression the reductions remained significant only for the patients with major depressive disorder. Conclusions: Using VBM the present study was able to replicate decreases in GM volume restricted to frontal and temporal regions in patients with mood disorders mainly major depression, as compared with healthy controls.
REVIEW | doi:10.20944/preprints202004.0490.v1
Subject: Keywords: nanomaterials; iron oxide nanoparticles; magnetic nanodiscs; synthetic antiferromagnetic nanostructures; nanowires; contrast agents; MRI; theragnosis
Online: 28 April 2020 (08:53:32 CEST)
Magnetic Resonance Imaging (MRI) is a powerful, non-invasive and nondestructive tool, capable of providing three-dimensional (3D) images of living organisms. The use of magnetic contrast agents has allowed clinical researchers and analysts to enormously increase the sensitivity and specificity of MRI since these substances change the intrinsic properties of the tissues within a living body, increasing the information present in the images. The advances in nanotechnology and materials science as well as the research of new magnetic effects have been the driving forces that propel the use of magnetic nanostructures as promising alternatives to the commercial contrast agents used in MRI. This review discusses the principles associated with the use of contrast agents in MRI as well as the most recent reports focused on nanostructured contrast agents. The potential applications of gadolinium (Gd) and manganese Mn-based nanomaterials and iron oxide nanoparticles in this imaging technique are discussed as well, from their magnetic behavior to the mainly used materials and nanoarchitectures. Then, it is also addressed the recent efforts made to develop new types of contrast agents based on synthetic antiferromagnetic and high-aspect ratio nanostructures. Furthermore, the application of these materials in theragnosis, either as contrast agents and controlled drug release, contrast agents and thermal therapy or contrast agents and radiosensitizers, is also presented.
REVIEW | doi:10.20944/preprints202208.0190.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: iron-deficient anemia; IDA; stroke; cerebral venous thrombosis; brain MRI; aortic thrombosis; pulmonary embolism; embolic pattern
Online: 10 August 2022 (04:31:26 CEST)
Background: Anemia is one of the most frequent diseases worldwide, affecting a third of the general population. Anemia in general and in particular, iron-deficient anemia (IDA), has been associated to a higher risk of thrombotic manifestations, including ischemic stroke and cerebral venous thrombosis (CVT), as well as systemic extra cerebral arterial and venous thrombosis. Despite these data, anemia is seldom considered as an etiological factor of stroke. Methods: An individual case encompassing all known neurovascular and systemic arterial and venous thrombotic manifestations related to IDA is presented with the focus on clinical reasoning issues in the diagnostic pathways, starting from the neuroradiological signs. The main questions have been identified and addressed in a narrative review of the most relevant data in the literature from a pragmatic and clinical viewpoint. Results and Discussion: The presented case concerns a 46 years old man admitted to the Stroke Unit because of acute is-chemic stroke with multiple thrombi in large intracranial and extracranial vessels, multifocal ischemic lesions in several arterial territories and the concurrent finding of asymptomatic CVT, pulmonary embolism with lung infarction and aortic thrombosis. An extended diagnostic work-up excluded the main etiologies (arterial dissection, cardiac embolism, genetic and acquired prothrombotic disorders, as cancer and antiphospholipid syndrome), except for a severe IDA, such as to require blood transfusions followed by anticoagulant therapy for the several thrombotic manifestations. Neuroimaging and systemic vascular findings have been analyzed and the main issues proposed by the case in the diagnostic pathway have been identified and discussed in a pragmatic clinical road map reviewing the data provided by the literature. Conclusions: IDA is a common but treatable condition that, independently or synergically, may increase the risk of thrombotic events. The diagnostic and therapeutic approach has not yet defined and each case should be individually addressed in a pragmatic clinical road map.
REVIEW | doi:10.20944/preprints202012.0322.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: Cerebrospinal fluid; real-time MRI; hydrocephalus; space flight disease; aquaporin; spontaneous intracranial hypotension; neural tube defect
Online: 14 December 2020 (10:21:21 CET)
New experimental and clinical findings question the historic view of hydrocephalus and its 100-year-old classification. In particular, real-time MRI evaluation of CSF flow and detailed insights into brain water regulation on the molecular scale indicate the existence of at least three main mechanisms that determine the dynamics of neurofluids. (i) Inspiration is a major driving force (ii) Adequate filling of brain ventricles by balanced cerebrospinal fluid upsurge is sensed by cilia (iii) The perivascular glial network connects the ependymal surface to the pericapillary Virchow-Robin spaces. Hitherto, these aspects have not been considered a common physiologic framework improving knowledge and therapy for severe disorders of normal-pressure and post-haemorrhagic hydrocephalus, spontaneous intracranial hypotension and spaceflight disease.
CASE REPORT | doi:10.20944/preprints202011.0286.v1
Subject: Medicine & Pharmacology, Allergology Keywords: Clinical Neurology; Neuroinvasive Disease; Encephalitis; Meningitis; MRI Brain Scans; West-Nile Virus; Infectious Diseases; Diagnostic Tools
Online: 9 November 2020 (17:36:58 CET)
A case report of the diagnosis of Long Term Sequelae of West Nile Neuroinvasive Disease in a patient with 9 years history. Empirical data of symptoms and test reports has been presented and based on the available data likely pathogenesis of the disease has been discussed. The empirical data has been compared to the published literature to reach a highly confident diagnosis.
Subject: Medicine & Pharmacology, Allergology Keywords: breast-specific gamma imaging (BSGI); neoadjuvant chemotherapy (NAC); breast cancer; Magnetic resonance imaging (MRI); residual tumor size
Online: 6 July 2021 (12:40:31 CEST)
Background: The present retrospective study was designed to evaluate the relative diagnostic utility of breast-specific gamma imaging (BSGI) and breast magnetic resonance imaging (MRI) as means of evaluating female breast cancer patients in China. Methods: A total of 229 malignant breast cancer patients underwent ultrasound, mammography, BSGI, and MRI between January 2015 and December 2018 for initial tumor staging. Of these patients, 73 were subsequently treated via definitive breast surgery following neoadjuvant chemotherapy (NAC), of whom 17 exhibited a complete pathologic response (pCR) to NAC. Results: BSGI and MRI were associated with respective 76.8% and 69.6% sensitivity values as a means of detecting residual tumors following NAC, while both of these approaches exhibited comparable specificity in this diagnostic context (58.8% vs 70.6%, P=0.473). Conclusion: These results demonstrate that BSGI is a useful auxiliary approach to evaluating pCR to NAC treatment.
ARTICLE | doi:10.20944/preprints202106.0259.v1
Subject: Medicine & Pharmacology, Allergology Keywords: Deep Learning (DP); Parkinson's disease (PD); Multiple System Atrophy (MSA); Magnetic Resonance Imaging (MRI); Neural Network (NN)
Online: 9 June 2021 (11:14:07 CEST)
Apathy in patients with Parkinson's disease (PD) is one of the least studied manifestations of a wide range of neuropsychiatric disorders (PND). The frequency of apathy in PD, according to various researchers, is 17–80%. Structural and neurochemical changes associated with PD are considered as the leading pathophysiological factors of apathy. Possible general pathophysiological mechanisms of apathy and hypokinesia, depression, executive (frontal) cognitive functions, sleep disorders in PD are discussed. The basis of the pathophysiological commonality of apathy, hypokinesia, and executive functions is probably bilateral disturbances in the functional connections of the stratum and dorsolateral, medial parts of the prefrontal cortex. The combination of apathy and depression in PD may be due to dysfunction of the structures of the limbic system and the medial orbital prefrontal cortex responsible for motivational determined behavior. The variability of the relationship between apathy and hypokinesia, depression, cognitive impairments, sleep disorders at different stages of PD may be associated with the phenomenological heterogeneity of apathy.Apathy worsens the quality of life, makes a significant contribution to disorders of both household and social adaptation of PD patients. It is promising to study the possibility of correcting apathy using dopaminergic therapy. Against the background of the appointment of pramipexole (1.5-3.0 mg / day) to 20 patients with PD (middle stage, according to Hen-Yar-2.5) to correct movement disorders after 4-6 weeks of therapy, a statistically significant positive dynamics of apathy were noted. According to the total assessment of the scale of apathy SE Starkstein (AS). As a result of the study, no statistically significant correlation was found between the dynamics of the total apathy indicator and the dynamics of motor symptoms of PD according to the unified rating scale for assessing PD (“Motor functions”), which, we believe, indicates an independent effect of pramipexole therapy in relation to motivational disorders.
ARTICLE | doi:10.20944/preprints202003.0299.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Data Mining; Alzheimer’s Dementia; Composite Hybrid Feature Selection; Machine learning; stack Hybrid Classification; AI; MRI; Neuroimaging; MPEG7 edge histogram feature extraction; CNN
Online: 19 March 2020 (11:25:01 CET)
Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of (CHFS) feature extraction lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.
REVIEW | doi:10.20944/preprints202001.0042.v1
Subject: Medicine & Pharmacology, Other Keywords: computed tomography (CT); magnetic resonance imaging (MRI); leukoaraiosis (LA); white matter hyperintensities (WMH); small vessels disease (SVD); periventricular white matter hyperintensities (PWMH); deep white matter hyperintensities (DWMH)
Online: 5 January 2020 (16:11:25 CET)
“White matter disease” identifies series of different conditions and pathological mechanisms: autoimmune, infectious, toxic-metabolic and vascular. Each of these leads to a global impairment of the neural myelination process through the secondary destruction of previously myelinated structures. To date, the imaging spectrum represents an irreplaceable tool to detect these lesions, describe their distribution patterns and stage them over time. This study aims presents a pictorial review of white matter disease, from pathology to imaging spectrum, reporting current main staging systems with greater emphasis to relationship between cerebrovascular disease and white matter hyper-intensity appearance, and the newest advances in this field.
ARTICLE | doi:10.20944/preprints201802.0165.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leuco encephalopathy (CADASIL); Carotid Endarterectomy (CEA); Modified Rankin Scale (MRS); Computed Tomography(CT); Tissue plasminogen activator (tPA); Diffusion weighted Imaging (DWI); Recognition of Stroke in the Emergency Room (ROSIER) scale; Magnetic resonance Imaging (MRI); Internal Carotid Artery (ICA)
Online: 26 February 2018 (11:46:47 CET)
In advanced world stroke is one of the disabling cause of death that can be managed with thrombolysis if presents early despite further risk of intracerebral haemorrhage. Secondary prevention is an important objective in ischaemic stroke where recurrence is very high with subsequent stroke. Carotid End Arterectomy impact a definitive and effective role for both symptomatic and asymptomatic carotid stenosis for secondary stroke prevention in selective cases. Thrombolysis is a potential primary management for certain group whereas carotid surgery employs secondary preventative measure in a specified ischaemic stroke group.