Purpose Dynamic contrast-enhanced MRI of the heart is well-suited for acceleration

Purpose Dynamic contrast-enhanced MRI of the heart is well-suited for acceleration with compressed sensing (CS) due to its spatiotemporal sparsity; however respiratory motion can degrade sparsity and lead to image artifacts. tracks the regions through time and applies matrix low-rank sparsity to the tracked regions. BLOSM was evaluated using computer simulations and first-pass cardiac datasets from human subjects. Using rate-4 acceleration BLOSM was compared to CGK 733 other CS methods such as k-t SLR that employs matrix low-rank sparsity applied to the whole image dataset with and without motion tracking and to k-t FOCUSS with motion estimation and compensation that employs spatial CGK 733 and temporal-frequency sparsity. Results BLOSM was shown to reduce respiratory artifact compared to other methods qualitatively. Quantitatively using root mean squared error and the CGK 733 structural similarity index BLOSM was superior to other methods. Conclusion BLOSM which exploits regional low rank structure and uses region tracking for motion compensation provides improved image quality for CS-accelerated first-pass cardiac MRI. of each pixel was obtained as Δ= ?(= (= {is a singular value soft thresholding operator defined as was applied to every cluster generated by Φ?mand the block size = is fixed the weighting factor λ in Eq.6 controls the threshold and has a high impact on reconstruction quality. For each of the CS methods (BLOSM BLOSM w/o MG k-t SLR and k-t SLR w/ gMG) a range of λ (0~2000) was independently tested using a couple of datasets to find the optimal λ that gave the minimum rRMSE. For the IST algorithm and using a diminishing of λ through iterations we found that the final image quality was stable for a range of λ values (20~200) even with changes in other experimental parameters such as the norm p. When an aggressive λ value (>500) was chosen over-regularization was observed as block-like artifacts. Other recent methods exploiting regional sparsity (29 31 35 also have different regularization Rabbit Polyclonal to EPHB6. of different regions. A moderate filtering or denoising step is taken in these scholarly studies to ease the block artifacts. Our use of overlapping blocks is similar to these strategies. In our study all images were scaled to have a maximum value of 250 and no block artifacts were observed for a wide range of λ (0~500) with our experimental settings. Most of the datasets tested showed optimal behavior at λ=50. Thus a λ value of 50 was chosen to reconstruct all the datasets. BLOSM is a motion-adaptive regional-sparsity-based CS method. Other methods such as k-t FOCUSS (4) and recently MASTeR (16) also incorporate motion compensation into a CS reconstruction. In k-t FOCUSS with motion estimation and compensation although motions are estimated on a regional basis x-f sparsity is exploited. In MASTeR motion estimation varies and temporal-difference sparsity is used regionally. In contrast BLOSM uses regional motion estimation and exploits regional matrix low-rank structure. Also these three methods differ in the details of the motion estimation algorithms which likely effects resulting CGK 733 image quality. Previously most CS methods for dynamic imaging have exploited sparsity using either whole images (such as k-t SLR) or single pixels (such as temporal difference or x-f sparsity). BLOSM which exploits regional sparsity is in between these two extremes and provides the advantages of greater flexibility (compared to whole images) and use of more information (compared to single pixels). The k-t SLR method used in this study for comparison was modified to use the IST optimization algorithm and excluded the spatiotemporal total variance reported by Lingala et al (6). The modified k-t SLR method was used in order to make a fair comparison with BLOSM. Like k-t SLR BLOSM could be extended to include extra sparsity constraints and could be solved by alternating direction algorithms (42). In this manuscript we focused our efforts on evaluating BLOSM and other CS methods using only single-coil data. In the future we plan to combine BLOSM with parallel imaging such as SENSE (43) and Self-consistent Parallel Imaging (SPIRiT) (44) by exploiting joint sparsity (3 45 to achieve higher acceleration rates and better reconstruction quality. BLOSM is compatible with non-Cartesian k-space trajectories also. A limitation of our study was that when comparing various reconstruction.