- Oral presentation
- Open Access
Nonlinear myocardial perfusion imaging with motion corrected reconstruction: validation via quantitative flow mapping
© Xue et al. 2016
- Published: 27 January 2016
- Myocardial Perfusion Imaging
- Myocardial Blood Flow
- Saturation Recovery
- Data Fidelity
- Time Intensity Curve
Myocardial perfusion imaging typically uses saturation recovery to generate T1 contrast during the Gd passage. Imaging protocols lead to a tradeoff between spatial/temporal resolution, myocardial coverage, and SNR. To improve resolution while maintaining the image quality we propose a nonlinear iterative reconstruction. This method explicitly integrates respiratory motion correction into the reconstruction to permit spatio-temporal regularization in the presence of motion. Unlike most k-t methods, the motion corrected images are directly output. In this way, complete free-breathing acquisition is achieved. Nonlinear iterative reconstruction is difficult to characterize since implicit filtering resulting from regularization is signal dependent. We propose to validate the proposed method by comparing quantitative myocardial blood flow (MBF) against linear reconstruction.
In this study, the SPIRiT scheme  is extended to incorporate a motion correction operator which includes a forward and backward deformation. The unknowns are the motion corrected multi-channel complex images. A wavelet based L1-norm spatio-temporal regularization term is added, together with data fidelity and parallel imaging terms, to enforce signal consistency across different heart beats. Since motion fields are incorporated, the regularization can be more effective in suppressing random noise and aliasing as the tissue remains stationary. The regularization strength was experimentally selected to preserve dynamic changes of perfusion signal. All patients were approved by local IRB and written consent was collected. Imaging experiments were performed on a 3T clinical MRI system (MAGNETOM Skyra, Siemens). The administrated Gd dose was 0.075 mmol/kg for FLASH and 0.05 mmol/kg for SSFP. The proposed algorithm was implemented in C++ using the Gadgetron framework  and integrated inline on the scanner. A fully integrated Gadgetron Cloud based reconstruction  was used to further parallelize the computation.
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