Keerthi Sravan Ravi1,2, Thomas O'Reilly3, John Thomas Vaughan Jr.2, Andrew Webb3, and Sairam Geethanath2
1Biomedical Engineering, Columbia University, New York, NY, United States, 2Columbia Magnetic Resonance Research Center, New York, NY, United States, 3Leiden University Medical Center, Leiden, Netherlands
1Biomedical Engineering, Columbia University, New York, NY, United States, 2Columbia Magnetic Resonance Research Center, New York, NY, United States, 3Leiden University Medical Center, Leiden, Netherlands
A 2D GRE sequence programmed in PyPulseq was programmatically converted to a Prospa-friendly format using seq2prospa. In-vitro and in-vivo acquisitions were performed. The SNR values measured from the in-vitro acquisitions using the two sequences were 32 dB and 34 dB respectively.
Figure 1. Workflow for converting a 2D Gradient Recalled Echo (GRE) sequence. A 2D GRE sequence designed in Prospa (Gpr) was first manually programmed in PyPulseq (Gpy). Next, this PyPulseq 2D GRE was converted to a Prospa-friendly format (Gs2p) using seq2prospa. Finally, the two 2D GRE sequences (Gpr, Gs2p) were executed on a Kea2 spectrometer to image a human hand and a structural phantom. The acquired images (Ipr, Is2p) were reconstructed and visualized.
Figure 3. Image reconstructions of imaging the structural phantom using the standard Prospa and PyPulseq-converted sequences. The SNR values measured from the standard Prospa acquisition (a) and the PyPulseq-converted acquisition (b) were 32 dB and 34 dB respectively.