Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude (mcQSM+qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping
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..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Preprints.org - (2024) vom: 05. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cho, Junghun [VerfasserIn] |
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| Links: |
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| doi: |
10.20944/preprints202312.2063.v1 |
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| PPN (Katalog-ID): |
preprintsorg042000424 |
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| 100 | 1 | |a Cho, Junghun |e verfasserin |0 (orcid)0000-0002-0826-5463 |4 aut | |
| 245 | 1 | 0 | |a Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude (mcQSM+qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping |
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| 520 | |a 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. | ||
| 700 | 1 | |a Zhang, Jinwei |4 aut | |
| 700 | 1 | |a Spincemaille, Pascal |4 aut | |
| 700 | 1 | |a Zhang, Hang |4 aut | |
| 700 | 1 | |a Nguyen, Thanh D. |4 aut | |
| 700 | 1 | |a Zhang, Shun |4 aut | |
| 700 | 1 | |a Gupta, Ajay |4 aut | |
| 700 | 1 | |a Wang, Yi |0 (orcid)0000-0003-1404-8526 |4 aut | |
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