Purpose To develop and evaluate an image reconstruction technique for cardiac MRI (CMR)perfusion that utilizes localized spatio-temporal constraints. conventional dynamic-by-dynamic reconstruction and sparsity regularization using a temporal principal-component (pc) basis as well as zerofilled data in multi-slice 2D and 3D CMR perfusion. Qualitative image scores are used (1=poor 4 to evaluate the technique in 3D perfusion in 10 patients and 5 healthy subjects. On 4 healthy subjects the proposed technique was also compared to a breath-hold multi-slice 2D acquisition with parallel imaging in terms of signal intensity curves. Results The proposed technique results in images that are superior in terms of spatial and temporal blurring compared to the other techniques even in free-breathing datasets. The image scores indicate a significant improvement compared to other techniques in 3D perfusion (2.8±0.5 vs. 2.3±0.5 for x-pc regularization 1.7 for dynamic-by-dynamic 1.1 for zerofilled). Signal intensity curves indicate comparable dynamics of uptake between the proposed method with a 3D acquisition and the breath-hold multi-slice 2D acquisition with parallel imaging. Conclusion The proposed reconstruction utilizes sparsity regularization based on localized Bay 11-7821 information in both spatial and temporal domains for highly-accelerated CMR perfusion with potential Rabbit polyclonal to HERC4. power in free-breathing 3D acquisitions. domain name (Fourier transform of images along the time direction) using adaptive temporal filtering with signal correlation information derived from low-resolution training data as well as multi-coil information (9). For perfusion imaging the central a part of k-space is usually fully-sampled in each dynamic to generate the training data. These techniques were used to acquire multi-slice 2D images with 5-fold acceleration and 1.4 × Bay 11-7821 1.4 mm2 in-plane resolution with four slices acquired over two R-R intervals Bay 11-7821 (10). Compressed sensing (CS) which utilizes the compressibility of images in a transform domain name for reconstruction from incoherently undersampled data (achieved by random undersampling for Cartesian acquisition) has also been applied to perfusion CMR (11). Using a B1-weighted approach utilizing multi-coil information and sparsity in the domain name up to 8-fold acceleration was achieved for the acquisition of 10 slices covering the LV (11). Other advanced reconstruction techniques based on a combination of low-rank regularization and total variation (TV) norm regularization (12) as well as group sparsity (13) have also been used in this context. While the aforementioned k-t based techniques can be used for high acceleration rates the use of temporal correlations require that the subsequent dynamics be spatially aligned. This necessitates a prolonged breath-hold acquisition which may be difficult for many patients. Translational respiratory motion-correction based on an initial reconstruction generated by space regularization has been proposed as a way of facilitating free-breathing 2D perfusion acquisitions (14). However the reliance on an initial estimate generated by space regularization may reduce Bay 11-7821 the applicability of this technique to highly-accelerated acquisitions especially in patients with irregular breathing patterns. Rank-based regularization has also been used in acquisitions with breath-holding at the time of injection and free-breathing in later dynamics (12). Larger coverage of the LV is necessary to fully evaluate the extent of ischemia which is a strong predictor of outcome (15). 3D CMR perfusion has been proposed for its superior contiguous coverage and higher SNR to potentially improve the estimation of the extent of hypo-perfused tissue (16 17 The contiguous coverage reduces slice misregistration errors compared to 2D imaging facilitating accurate quantification. However for adequate spatio-temporal resolution in 3D perfusion CMR accelerated imaging is required. Due to the enhanced SNR parallel imaging techniques that are commonly used for 2D multi-slice imaging can be applied Bay 11-7821 with higher acceleration factors. In (16) a six-fold acceleration factor was used with adaptive sensitivity encoding (6 18 where time-varying coil sensitivity maps are generated using sliding-window reconstructions to achieve a spatial resolution of 2.3×3.6×10 mm3 with a 312 ms acquisition window on a Bay 11-7821 1.5T scanner. In (17) an acceleration factor of six was.