Feasibility and clinical usefulness of deep learning-accelerated MRI for acute painful fracture patients wearing a splint: A prospective comparative study

Abstract

Objective To evaluate the feasibility and clinical usefulness of deep learning (DL)-accelerated turbo spin echo (TSEDL) sequences relative to standard TSE sequences (TSES) for acute radius fracture patients wearing a splint. Methods This prospective consecutive study investigated 50 patients’ preoperative wrist MRI scans acquired between July 2021 and January 2022. Examinations were performed at 3 Tesla MRI with body array coils due to the wrist splint. Besides TSES obtained according to the routine protocol, TSEDL sequences for axial T2-, coronal T1-, and coronal PD-weighted TSE sequences were scanned for comparison. For quantitative assessment, the relative signal-to-noise ratio (rSNR), the relative contrast-to-noise ratio (rCNR), and the relative contrast ratio (rCR) were measured. For qualitative assessment, all images were assessed by two independent musculoskeletal radiologists in terms of perceived SNR, image contrast, image sharpness, artifacts disturbing evaluation, overall image quality and diagnostic confidence for injuries using a four- or five-point Likert scale. Results The scan time was shortened approximately by a factor of two for TSEDL compared to TSES. TSEDL images showed significantly better rSNR, rCNR, and rCR values for all sequences, and scored significantly better in terms of both image quality and diagnostic confidence for both readers than TSES images (all p < .05). Interrater reliabilities were in almost perfect agreement. Conclusion The DL-accelerated technique proved to be very helpful not only to reduce scan time but also to improve image quality for acute painful fracture patients wearing a splint despite using body array coils instead of a wrist-specific coil. Based on our study, the DL-accelerated technique can be very useful for MRI of any part of the extremities in trauma settings just with body array coils.

The promise and limitations of artificial intelligence in musculoskeletal imaging

“…Similarly, Wu et al developed an eightfold-accelerated DL model capable of up-sampling sparsely sampled MRI data to output images with minimal artifacts and a permissible signal-to-noise ratio ( 30 ). In one study by Roh et al, DL-accelerated turbo spin echo sequences were assessed for their ability to depict acute fractures of the radius in patients wearing a splint and were shown to be effective for both increasing acquisition speed by a factor of 2 as well as improving image quality when compared to standard sequences ( 31 ). Studies are still ongoing, with AI-driven 10-fold accelerated MRI increasingly becoming within reach ( 32 ) and other exciting ML applications being explored such as the production of MR images from CT images ( 33 ) and the post-processing of a single MRI acquisition to obtain other planes and tissue weightings ( 34 ).…”