Deep learning–accelerated HASTE for single breath-hold liver T2-weighted imaging

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Abstract

The HASTE sequence accelerated by deep learning (DL) reconstruction was used to perform liver T2-weighted imaging under a single breath-hold in a daily routine. Its image quality, including signal-to-noise ratio, contrast-to-noise ratio, artifacts, edge sharpness and slice continuity, was evaluated by comparing it with the conventional multi-breath-hold BLADE sequence. The result demonstrated that DL-accelerated HASTE could shorten the acquisition time remarkably while maintaining clinically satisfactory image quality.

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Liu et al. (Wed,) studied this question.

synapsesocial.com/papers/68e5c61db6db64358755c77d https://doi.org/https://doi.org/10.58530/2023/2444

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