Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude (mcQSM+qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping

Junghun Cho
* ORCID logo ,
Jinwei Zhang
,
Pascal Spincemaille
,
Hang Zhang
,
Thanh D. Nguyen
,
Shun Zhang
,
Ajay Gupta
,
Yi Wang
ORCID logo
Version 1 : Received: 26 December 2023 / Approved: 27 December 2023 / Online: 27 December 2023 (09:10:19 CET)

A peer-reviewed article of this Preprint also exists.

Cho, J.; Zhang, J.; Spincemaille, P.; Zhang, H.; Nguyen, T.D.; Zhang, S.; Gupta, A.; Wang, Y. Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping. Bioengineering 2024, 11, 131. Cho, J.; Zhang, J.; Spincemaille, P.; Zhang, H.; Nguyen, T.D.; Zhang, S.; Gupta, A.; Wang, Y. Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping. Bioengineering 2024, 11, 131.

Abstract

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.

Keywords

oxygen extraction faction; quantitative susceptibility mapping; quantitative blood oxygen level-dependent imaging; multi-echo complex quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude; QSM+qBOLD; QQ; mcQQ; deep learning; magnetic resonance imaging; MRI

Subject

Engineering, Bioengineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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