Motivation: Current acceleration techniques for head and neck MRI face trade-offs between acquisition speed and image quality. Goal(s): To evaluate a novel deep learning-based reconstruction framework integrating compressed sensing and super-resolution techniques for T1- and T2-weighted head and neck MRI. Approach: We prospectively enrolled 54 patients who underwent paired conventional and DL-reconstructed sequences, with quantitative and qualitative assessment by two radiologists. Results: The framework achieved 46.3% and 26.9% reduction in acquisition time for T1WI and T2WI respectively, with significant improvements in SNR (both P<0.001), CR (both P<0.001), and superior qualitative scores in image sharpness, lesion conspicuity, and overall image quality (all P<0.05). Impact: This integrated deep learning framework offers a clinically viable solution for accelerated head and neck MRI acquisition while enhancing image quality, potentially improving workflow efficiency and patient comfort in routine clinical practice.
Li et al. (2025) studied this question.