Motion correction facilitates the automation of cardiac ASL perfusion imaging

作者：Ahsan Javed, Terrence Jao, Krishna S. Nayak · 发表于：Journal of Cardiovascular Magnetic Resonance · 年份：2015 · DOI：10.1186/1532-429x-17-s1-p51 · 被引用次数：7 · 研究领域：Advanced MRI Techniques and Applications、Cardiac Imaging and Diagnostics、Cardiovascular Function and Risk Factors

Cardiac arterial spin labeling (ASL) perfusion imaging requires subtraction of signals in tagged and control CMR images. In recent clinical studies, perfusion reserve mapping of a single short-axis slice has required laborious manual segmentation of the LV muscle [ 1 ]. Here we demonstrate that using free open source software for automatic motion correction reduces the required manual segmentation to just 4 images (2 rest, 2 stress). Images for rest and stress acquisition are processed similarly using the procedure in Figure 1 . The control and tagged image pair that has the highest correlation to other image pairs is chosen as a "reference" (C ref , T ref ), and is manually segmented to generate masks (M Cref , M Tref ). Remaining control and tagged images are then registered to their respective reference images. The resulting displacement fields are applied to the reference masks to generate masks for each image. A threshold (0.8) is applied to ensure masks are binary. Myocardial blood flow (MBF) is calculated through spatio-temporal filtering, as previously described [ 2 ]. Registrations are performed using advanced normalization tools (ANTS) [ 3 ] (settings: cross correlation; symmetric diffeomorphic transform; directly manipulated free form deformation regularization). Framework to semi-automatically generate masks for LV muscle. Reference images (C ref , T ref ) are automatically selected from the control {C j } and tagged {T j } images. The reference images are manuall...