July 6, 2022TomographyOpen Access

Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence

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Why the study?

The study was conducted to assess the technical feasibility and impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in extremity musculoskeletal imaging.

Does deep learning-accelerated T2w TSE improve image quality and reduce acquisition time compared to standard T2w TIRM in patients undergoing MRI of the extremities?

Structured PICO

Does deep learning-accelerated T2w TSE improve image quality and reduce acquisition time compared to standard T2w TIRM in patients undergoing MRI of the extremities?

Population

23 patients who underwent MRI of the extremities, mean age 60 years (range 30-86)

Intervention

Deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence (TSEDL)

Comparator

Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging

Outcome

Image quality, noise, sharpness, contrast, artifacts, lesion detectability, diagnostic confidence, and acquisition timesurrogate

Deep learning-accelerated T2-weighted MRI of the extremities significantly improves image quality while reducing acquisition time by over 50% compared to standard imaging.

Implication

May streamline extremity MRI workflows; leaves open routine adoption pending larger validation studies.

Abstract

Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in musculoskeletal imaging of the extremities. Methods: Twenty-three patients who underwent MRI of the extremities were prospectively included. Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging was compared to a deep learning-accelerated T2w TSE (TSEDL) sequence. Image analysis of 23 patients with a mean age of 60 years (range 30−86) was performed regarding image quality, noise, sharpness, contrast, artifacts, lesion detectability and diagnostic confidence. Pathological findings were documented measuring the maximum diameter. Results: The analysis showed a significant improvement for the T2 TSEDL with regard to image quality, noise, contrast, sharpness, lesion detectability, and diagnostic confidence, as compared to T2 TIRMStd (each p < 0.001). There were no differences in the number of detected lesions. The time of acquisition (TA) could be reduced by 52−59%. Interrater agreement was almost perfect (κ = 0.886). Conclusion: Accelerated T2 TSEDL was technically feasible and superior to conventionally applied T2 TIRMStd. Concurrently, TA could be reduced by 52−59%. Therefore, deep learning-accelerated MR imaging is a promising and applicable method in musculoskeletal imaging.