Keywords: Artifacts, AI/ML Software
Motivation: Diffusion MRI of the liver has important clinical and research applications, but is often affected by unsuppressed fat, poor water excitation, and image distortions due to shimming challenges.
Goal(s): To optimize fat suppression, signal, and distortions for liver DWI.
Approach: Slice-specific chemical shift-encoded (CSE) data-informed optimization of shims is combined with liver object detection, and prospectively evaluated in 3 subjects imaged at 3T.
Results: Improved fat suppression, water signal excitation, and reduced distortion were observed for slice-specific CSE-guided optimized shimming that prioritized liver regions.
Impact: DWI of the abdomen with reliable fat suppression is demonstrated in challenging slices by optimizing for liver regions when using a slice-specific shimming approach. This combined method may enable improved detection and staging of cancer in the abdomen.
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