Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application.

Abstract

In liver and pancreatobiliary MRI, mitigating respiratory motion-related artifacts has always been a major challenge in image acquisition. Motion reduction by breathing control schemes or scan time acceleration by k-space undersampling are two accessible approaches in clinical imaging. Parallel imaging is an indispensable everyday technique with well-known characteristics, but with drawbacks that limit acceleration factors to ≤4. Compressed sensing exploits the data sparsity of MR images, and pseudorandomly undersamples k-space data to iteratively reconstruct images using sophisticated complex computations within highly accelerated scanning time. Albeit, this is with long reconstruction time and complexity in parameter optimization. Deep learning reconstruction uses pretrained and validated convolutional neural networks to reconstruct undersampled data, with the main tasks being image acceleration, denoising, and superresolution. While promising, deep learning reconstruction requires further testing and practical experience with model stability, generalizability, and output image fidelity.