Deep Learning MRI Reconstruction for Accelerating Turbo Spin Echo Hand and Wrist Imaging: A Comparison of Image Quality, Visualization of Anatomy, and Detection of Common Pathologies with Standard Imaging.

In Academic radiology

RATIONALE AND OBJECTIVES : Magnetic resonance imaging (MRI) of the hand and wrist is a routine MRI examination and takes about 15-20 minutes, which can lead to problems resulting from the relatively long scan time, such as decreased image quality due to motion artifacts and lower patient throughput. The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the hand and wrist regarding image quality, visualization of anatomy, and diagnostic performance concerning common pathologies.

MATERIALS AND METHODS : Twenty-one patients (mean age: 43 ± 19 [19-85] years, 10 men, 11 female) were prospectively enrolled in this study between October 2020 and June 2021. Each participant underwent two MRI protocols: first, standard fully sampled TSE sequences reconstructed with a standard GRAPPA reconstruction (TSE_S) and second, prospectively undersampled TSE sequences using a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSE_DL). Both protocols were acquired consecutively in one examination. Two experienced MSK-imaging radiologists qualitatively evaluated the images concerning image quality, noise, edge sharpness, artifacts, and diagnostic confidence, as well as the delineation of anatomical structures (triangular fibrocartilage complex, tendon of the extensor carpi ulnaris muscle, extrinsic and intrinsic ligaments, median nerve, cartilage) using a five-point Likert scale and assessed common pathologies. Wilcoxon signed-rank test and kappa statistics were performed to compare the sequences.

RESULTS : Overall image quality, artifacts, delineation of anatomical structures, and diagnostic confidence of TSE_DL were rated to be comparable to TSE_S (p > 0.05). Additionally, TSE_DL showed decreased image noise (4.90, median 5, IQR 5-5) compared to TSE_S (4.52, median 5, IQR 4-5, p < 0.05) and improved edge sharpness (TSE_DL: 4.10, median 4, IQR 3.5-5; TSE_S: 3.57, median 4, IQR 3-4; p < 0.05). Inter- and intrareader agreement was substantial to almost perfect (κ = 0.632-1.000) for the detection of common pathologies. Time of acquisition could be reduced by more than 60% with the protocol using TSE_DL.

CONCLUSION : Compared to TSE_S, TSE_DL provided decreased noise and increased edge sharpness, equal image quality, delineation of anatomical structures, detection of pathologies, and diagnostic confidence. Therefore, TSE_DL may be clinically relevant for hand and wrist imaging, as it reduces examination time by more than 60%, thus increasing patient comfort and patient throughput.

Herrmann Judith, Gassenmaier Sebastian, Keller Gabriel, Koerzdoerfer Gregor, Almansour Haidara, Nickel Dominik, Othman Ahmed, Afat Saif, Werner Sebastian

2023-Feb-14

Acceleration, Deep Learning Reconstruction, Hand, Image Processing, MRI, Wrist