I am a postdoc working with Dr. Michael Salerno. My research focus is developing advanced imaging techniques for cardiac magnetic resonance imaging and using deep learning to advance the clinical workflow.
Free-breathing self-gated continuous-IR spiral T1 mapping: Comparison of dual flip-angle and Bloch-Siegert B1-corrected techniques.
Magnetic resonance in medicine
PURPOSE: To develop a B1-corrrected single flip-angle continuous acquisition strategy with free-breathing and cardiac self-gating for spiral T1 mapping, and compare it to a previous dual flip-angle technique.METHODS: Data were continuously acquired using a spiral-out trajectory, rotated by the golden angle in time. During the first 2s, off-resonance Fermi RF pulses were applied to generate a Bloch-Siegert shift B1 map, and the subsequent data were acquired with an inversion RF pulse applied every 4s to create a T1* map. The final T1 map was generated from the B1 and the T1* maps by using a look-up table that accounted for slice profile effects, yielding more accurate T1 values. T1 values were compared to those from inversion recovery (IR) spin echo (phantom only), MOLLI, SAturation-recovery single-SHot Acquisition (SASHA), and previously proposed dual flip-angle results. This strategy was evaluated in a phantom and 25 human subjects.RESULTS: The proposed technique showed good agreement with IR spin-echo results in the phantom experiment. For in-vivo studies, the proposed technique and the previously proposed dual flip-angle method were more similar to SASHA results than to MOLLI results.CONCLUSIONS: B1-corrected single flip-angle T1 mapping successfully acquired B1 and T1 maps in a free-breathing, continuous-IR spiral acquisition, providing a method with improved accuracy to measure T1 using a continuous Look-Locker acquisition, as compared to the previously proposed dual excitation flip-angle technique.
View details for DOI 10.1002/mrm.29269
View details for PubMedID 35481596
DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging.
NMR in biomedicine
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net-based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3T. The SS and SMS MB=2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB=2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB=2 DESIRE, and MB=2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p=1 and p= 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB=2 whole-heart high-resolution spiral perfusion imaging.
View details for DOI 10.1002/nbm.4661
View details for PubMedID 34939246