Quantitative susceptibility mapping in magnetically inhomogeneous tissues.

IF 3 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Thomas Jochmann, Fahad Salman, Michael G Dwyer, Niels Bergsland, Robert Zivadinov, Jens Haueisen, Ferdinand Schweser
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引用次数: 0

Abstract

Purpose: Conventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develop and evaluate DEEPOLE, a deep learning-based method that incorporates macroscopically nondipolar Larmor frequency shifts into QSM to enhance the quality and accuracy of susceptibility maps.

Methods: DEEPOLE integrates the QUASAR model into a deep convolutional neural network to account for frequency contributions neglected by conventional QSM. We trained DEEPOLE using synthesized data reflecting realistic power spectrum distributions. Its performance was evaluated against traditional QSM algorithms-including deep learning QSM, QUASAR (quantitative susceptibility and residual mapping), morphology-enabled dipole inversion (MEDI), fast nonlinear susceptibility inversion (FANSI), and superfast dipole inversion (SDI)-using realistic digital brain models with and without microstructure effects, as well as in vivo human brain data. Quantitative assessments focused on susceptibility estimation accuracy, artifact reduction, and anatomical consistency.

Results: In digital brain models, DEEPOLE outperformed conventional QSM methods by producing susceptibility maps with fewer artifacts and greater quantitative accuracy, especially in regions affected by microstructure effects. In vivo, DEEPOLE generated more anatomically consistent susceptibility maps and mitigated artifacts such as inhomogeneities and streaking, providing improved susceptibility estimates in deep gray matter and white matter.

Conclusion: Incorporating macroscopically nondipolar Larmor frequency shifts into QSM through DEEPOLE improves the quality and accuracy of susceptibility maps. This methodological advancement enhances the reliability of susceptibility measurements, particularly in studies of neurodegenerative and demyelinating conditions where macroscopically nondipolar contributions are substantial.

磁不均匀组织的定量敏感性作图。
目的:传统的定量敏感性制图(QSM)方法依赖于简化的物理模型,假设各向同性和均匀的组织特性,导致生物组织中的伪影和不准确性。本研究旨在开发和评估DEEPOLE,一种基于深度学习的方法,将宏观非偶极Larmor频移纳入QSM,以提高敏感性图的质量和准确性。方法:DEEPOLE将类星体模型集成到一个深度卷积神经网络中,以考虑传统QSM忽略的频率贡献。我们使用反映实际功率谱分布的合成数据训练DEEPOLE。利用真实的数字大脑模型以及体内人脑数据,对传统的QSM算法(包括深度学习QSM、QUASAR(定量敏感性和残差映射)、形态学偶极子反演(MEDI)、快速非线性敏感性反演(FANSI)和超高速偶极子反演(SDI))的性能进行了评估。定量评估侧重于敏感性估计的准确性、伪影减少和解剖一致性。结果:在数字脑模型中,DEEPOLE优于传统的QSM方法,其产生的敏感性图具有更少的伪影和更高的定量精度,特别是在受微观结构影响的区域。在体内实验中,DEEPOLE生成了解剖学上更加一致的易感性图谱,并减轻了诸如不均匀性和条纹等人为影响,从而改善了对深部灰质和白质的易感性估计。结论:通过DEEPOLE将宏观非偶极Larmor频移引入QSM,提高了敏感性图谱的质量和准确性。这种方法上的进步提高了敏感性测量的可靠性,特别是在宏观非偶极贡献很大的神经退行性和脱髓鞘疾病的研究中。
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来源期刊
CiteScore
6.70
自引率
24.20%
发文量
376
审稿时长
2-4 weeks
期刊介绍: Magnetic Resonance in Medicine (Magn Reson Med) is an international journal devoted to the publication of original investigations concerned with all aspects of the development and use of nuclear magnetic resonance and electron paramagnetic resonance techniques for medical applications. Reports of original investigations in the areas of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.
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