Department of Information Engineering, University of Padova, Padova 35131, Italy.
Magn Reson Imaging. 2011 Sep;29(7):927-36. doi: 10.1016/j.mri.2011.02.006. Epub 2011 May 25.
Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) allows the noninvasive assessment of brain hemodynamics alterations by quantifying, via deconvolution, the cerebral blood flow (CBF) and mean transit time (MTT). Singular value decomposition (SVD) and block-circulant SVD (cSVD) are the most widely adopted deconvolution method, although they bear some limitations, including unphysiological oscillations in the residue function and bias in the presence of delay and dispersion between the tissue and the arterial input function. A nonlinear stochastic regularization (NSR) has been proposed, which performs better than SVD and cSVD on simulated data both in the presence and absence of dispersion. Moreover, NSR allows to quantify the dispersion level. Here, cSVD and NSR are compared for the first time on a group of nine patients with severe atherosclerotic unilateral stenosis of internal carotid artery before and after carotid stenting to investigate the effect of arterial dispersion. According to region of interest-based analysis, NSR characterizes the pathologic tissue more accurately than cSVD, thus improving the quality of the information provided to physicians for diagnosis. In fact, in 7 (78%) of the 9 subjects, CBF and MTT maps provided by NSR allow to correctly identify the pathologic hemisphere to the physician. Moreover, by emphasizing the difference between pathologic and healthy tissues, NSR may be successfully used to monitor the subject's recovery after the treatment and/or surgery. NSR also generates dispersion level and non-dispersed CBF and MTT maps. The dispersion level provides information on CBF and MTT estimates reliability and may also be used as a clinical indicator of pathological tissue state complementary to CBF and MTT, thus increasing the clinical information provided by DSC-MRI analysis.
动态磁敏感对比磁共振成像（DSC-MRI）通过对脑血流（CBF）和平均通过时间（MTT）进行反卷积，可无创评估脑血流动力学变化。奇异值分解（SVD）和块循环奇异值分解（cSVD）是最广泛采用的反卷积方法，但它们存在一些局限性，包括残差函数中的非生理振荡和在存在延迟和组织与动脉输入函数之间的弥散时的偏差。已经提出了一种非线性随机正则化（NSR），它在存在和不存在弥散的情况下，在模拟数据上的表现均优于 SVD 和 cSVD。此外，NSR 允许量化弥散水平。在此，首次在 9 例严重动脉粥样硬化性颈内动脉单侧狭窄的患者在颈动脉支架置入术前后的组中比较了 cSVD 和 NSR，以研究动脉弥散的影响。根据基于感兴趣区域的分析，NSR 比 cSVD 更准确地描述病变组织，从而提高了为诊断提供给医生的信息质量。实际上，在 9 名患者中的 7 名（78％）中，NSR 提供的 CBF 和 MTT 图可让医生正确识别病变半球。此外，通过强调病变组织和健康组织之间的差异，NSR 可成功用于监测患者在治疗和/或手术后的恢复情况。NSR 还生成弥散水平和无弥散 CBF 和 MTT 图。弥散水平提供了有关 CBF 和 MTT 估计可靠性的信息，也可以用作补充 CBF 和 MTT 的病变组织状态的临床指标，从而增加了 DSC-MRI 分析提供的临床信息。
