Purpose To provide joint calibrationless parallel imaging reconstruction of highly accelerated multislice 2D MR k‐space data. Methods Adjacent image slices in multislice MR data have similar coil sensitivity maps, spatial support, and image content. Such similarities can be utilized to improve image quality by reconstructing multiple slices jointly with low‐rank tensor completion. Specifically, the multichannel k‐space data from multiple slices are constructed into a block‐wise Hankel tensor and iteratively updated by promoting tensor low‐rankness through higher‐order SVD. This multislice block‐wise Hankel tensor completion was implemented for 2D spiral and Cartesian k‐space undersampling where sampling patterns vary between adjacent slices. The approach was evaluated with human brain MR data and compared to the traditional single‐slice simultaneous autocalibrating and k‐space estimation reconstruction. Results The proposed multislice block‐wise Hankel tensor completion approach robustly reconstructed highly undersampled multislice 2D spiral and Cartesian data. It produced substantially lower level of artifacts compared to the traditional single‐slice simultaneous autocalibrating and k‐space estimation reconstruction. Quantitative evaluation using error maps and root mean square error demonstrated its significantly improved performance in terms of residual artifacts and root mean square error. Conclusion Our proposed multislice block‐wise Hankel tensor completion method exploits the similar coil sensitivity and image content within multislice MR data through a tensor completion framework. It offers a new and effective approach to acquire and reconstruct highly undersampled multislice MR data in a calibrationless manner.
Abstract:In this study, we developed fluorescent dual pH and oxygen sensors loaded in multi-well plates for in-situ and high-throughput monitoring of oxygen respiration and extracellular acidification during microbial cell growth for understanding metabolism. Biocompatible PHEMA-co-PAM materials were used as the hydrogel matrix. A polymerizable oxygen probe (OS2) derived from PtTFPP and a polymerizable pH probe (S2) derived from fluorescein were chemically conjugated into the matrix to solve the problem of the probe leaching from the matrix. Gels were allowed to cure directly on the bottom of 96-well plates at room-temperature via redox polymerization. The influence of matrix's composition on the sensing behaviors was investigated to optimize hydrogels with enough robustness for repeatable use with good sensitivity. Responses of the dual sensing hydrogels to dissolved oxygen (DO) and pH were studied. These dual oxygen-pH sensing plates were successfully used for microbial cell-based screening assays, which are based on the measurement of fluorescence intensity changes induced by cellular oxygen consumption and pH changes during microbial growth. This method may provide a real-time monitoring of cellular respiration, acidification, and a rapid kinetic assessment of multiple samples for cell viability as well as high-throughput drug screening. All of these assays can be carried out by a conventional plate reader.
Purpose To provide simultaneous multislice (SMS) EPI reconstruction with k‐space implementation and robust Nyquist ghost correction. Methods 2D phase error correction SENSE (PEC‐SENSE) was recently developed for Nyquist ghost correction in SMS EPI reconstruction for which virtual coil simultaneous autocalibration and k‐space estimation (VC‐SAKE) was used to remove slice‐dependent Nyquist ghosts and intershot 2D phase variations in multi‐shot EPI reference scan. However, masking coil sensitivity maps to exclude background region in PEC‐SENSE and manually selecting slice‐wise target ranks in VC‐SAKE are cumbersome procedures in practice. To avoid masking, the concept of PEC‐SENSE is extended to k‐space implementation and termed as PEC‐GRAPPA. Furthermore, a singular value shrinkage scheme is incorporated in VC‐SAKE to circumvent the empirical slice‐wise target rank selection. PEC‐GRAPPA was evaluated and compared to PEC‐SENSE with/without masking and 1D linear phase correction GRAPPA. Results PEC‐GRAPPA robustly reconstructed SMS EPI images from 7T phantom and human brain data, effectively removing the phase error‐induced artifacts. The resulting residual artifact level and temporal SNR were comparable to those by PEC‐SENSE with careful tuning. PEC‐GRAPPA outperformed PEC‐SENSE without masking and 1D linear phase correction GRAPPA. Conclusion Our proposed PEC‐GRAPPA approach effectively removes the artifacts caused by Nyquist ghosts in SMS EPI without cumbersome tuning. This approach provides a robust and practical implementation of SMS EPI reconstruction in k‐space with slice‐dependent 2D Nyquist ghost correction.
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