Jochmann Thomas, Salman Fahad, Dwyer Michael G, Bergsland Niels, Zivadinov Robert, Haueisen Jens, Schweser Ferdinand
Institute of Biomedical Engineering and Informatics, Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany.
Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, New York, USA.
Magn Reson Med. 2025 Sep;94(3):1044-1059. doi: 10.1002/mrm.30537. Epub 2025 May 1.
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.
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.
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.
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.
传统的定量磁化率成像(QSM)方法依赖于简化的物理模型,该模型假设组织特性是各向同性和均匀的,这会导致生物组织中出现伪影和不准确的结果。本研究旨在开发和评估DEEPOLE,这是一种基于深度学习的方法,它将宏观非偶极拉莫尔频率偏移纳入QSM,以提高磁化率图的质量和准确性。
DEEPOLE将QUASAR模型集成到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。使用具有和不具有微观结构效应的真实数字脑模型以及体内人脑数据,将其性能与传统QSM算法(包括深度学习QSM、QUASAR(定量磁化率和残余映射)、形态学启用偶极反演(MEDI)、快速非线性磁化率反演(FANSI)和超快速偶极反演(SDI))进行比较评估。定量评估集中在磁化率估计准确性、伪影减少和解剖一致性方面。
在数字脑模型中,DEEPOLE通过生成伪影更少且定量准确性更高的磁化率图,优于传统QSM方法,特别是在受微观结构效应影响的区域。在体内,DEEPOLE生成了更具解剖一致性的磁化率图,并减轻了诸如不均匀性和条纹等伪影,在深部灰质和白质中提供了改进的磁化率估计。
通过DEEPOLE将宏观非偶极拉莫尔频率偏移纳入QSM可提高磁化率图的质量和准确性。这一方法的进步提高了磁化率测量的可靠性,特别是在神经退行性和脱髓鞘疾病的研究中,其中宏观非偶极贡献很大。