提出Dipole-lets多尺度分解法，有效抑制磁共振相位成像中的条纹伪影。
Dipole-lets: a new multiscale decomposition for MR phase and quantitative susceptibility mapping
- 基于偶极子核零值双锥面特征，多尺度提取相位数据中非偶极子成分
- 在真实与模拟数据上验证可定位并分离出导致伪影的非偶极子信号
- 可作为正则化项嵌入重建优化，适用于磁敏感图谱等医学成像场景
在磁敏感图谱中，识别并抑制条纹伪影是最具挑战性的问题之一。组织磁化产生的测量相位被假设为与磁偶极子核卷积所得；直接反演或标准正则化方法常在估计的磁敏感度中引入条纹伪影，这源于极端噪声及非偶极子相位贡献，其经偶极子核放大后呈现条纹模式。本文提出一种多尺度变换——Dipole-lets，作为最优分解方法，通过提取与偶极子核零值双锥面（即‘魔锥’）不同尺度和方向特征的成分，识别测量场数据中的偶极子不兼容性。实验表明，Dipole-lets可从相位数据中提取非偶极子内容，并实现伪影定位。同时，我们展示了将Dipole-lets作为优化函数正则项的实现方式，采用简单的Tikhonov和无穷范数正则化。
原文摘要 · Abstract (English)
Identifying and suppressing streaking artifacts is one of the most challenging problems in quantitative susceptibility mapping. The measured phase from tissue magnetization is assumed to be the convolution by the magnetic dipole kernel; direct inversion or standard regularization methods tend to create streaking artifacts in the estimated susceptibility. This is caused by extreme noise and by the presence of non-dipolar phase contributions, which are amplified by the dipole kernel following the streaking pattern. In this work, we introduce a multiscale transform, called Dipole-lets, as an optimal decomposition method for identifying dipole incompatibilities in measured field data by extracting features of different characteristic size and orientation with respect to the dipole kernel's zero-valued double-cone surface (the magic cone). We provide experiments that showcase that non-dipolar content can be extracted by Dipole-lets from phase data through artifact localization. We also present implementations of Dipole-lets as a optimization functional regularizator, through simple Tikhonov and infinity norm.
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