Sparsity adaptive reconstruction for highly accelerated cardiac MRI. Issue 6 (21st January 2019) - Chen, Chong; Liu, Yingmin; Schniter, Philip; Jin, Ning; Craft, Jason; Simonetti, Orlando; Ahmad, Rizwan
Abstract : Purpose : To enable parameter‐free, accelerated cardiovascular magnetic resonance (CMR). Methods : Regularized reconstruction methods, such as compressed sensing (CS), can significantly accelerate MRI data acquisition but require tuning of regularization weights. In this work, a technique, called Sparsity adaptive Composite Recovery (SCoRe) that exploits sparsity in multiple, disparate sparsifying transforms is presented. A data‐driven adjustment of the relative contributions of different transforms yields a parameter‐free CS recovery process. SCoRe is validated in a dynamic digital phantom as well as in retrospectively and prospectively undersampled cine CMR data. Results : The results from simulation and 6 retrospectively undersampled datasets indicate that SCoRe with auto‐tuned regularization weights yields lower root‐mean‐square error (RMSE) and higher structural similarity index (SSIM) compared to state‐of‐the‐art CS methods. In 45 prospectively undersampled datasets acquired from 15 volunteers, the image quality was scored by 2 expert reviewers, with SCoRe receiving a higher average score ( p < 0.01) compared to other CS methods. Conclusions : SCoRe enables accelerated cine CMR from highly undersampled data. In contrast to other acceleration techniques, SCoRe adapts regularization weights based on noise power and level of sparsity in each transform, yielding superior performance without admitting any free parameters.