Low-field MRI scanners are more viable in resource-strained regions such as Africa, sacrificing SNR for gains on the economics of purchase, siting and service. Pulse sequences need to be optimized for maximum efficiency on these scanners. This work introduces seq2prospa, a Python-based tool facilitating the conversion of pulse sequences designed using PyPulseq into a low-field spectrometer-friendly format. We perform an in-vitro and in-vivo acquisition experiment to image a structural phantom and a human hand respectively. Data acquired from a 2D GRE sequence designed natively in Prospa is compared with data acquired from a PyPulseq-converted 2D GRE sequence.
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