Edge-enhanced Dynamic MR imaging using compressed sensing

The critical challenge in Dynamic MR imaging is the trade-off between spatial and temporal resolution due to the limited availability of acquisition time. To address this it is imperative to under-sample k-space and develop specific reconstruction techniques. We propose an improvement to the well-known compressed sensing-based Total Variation (TV) minimization constrained reconstruction scheme by utilizing the gradient information obtained by a static high resolution data set to improve the quality of the dynamic volumes in the time series. This high resolution data set is assumed to be obtained before the start of the dynamic acquisitions. For every dynamic data acquisition, a weighting factor is estimated based on the gradient image and time elapsed, to compute the modified value of the pixels. Under sampled data is acquired using variable density sampling technique and TV minimization is used for reconstruction. The proposed method is tested on 6 real dynamic time-series consisting of 2 breast data sets and 4 abdomen data sets spanning 840 images in all. For data availability of only 10%, quality assessment metrics shows an average improvement in SSIM quality index of up to 36% on breast data and about 7% on abdomen data, for the proposed method as against the baseline TV-reconstruction.