Evaluation of deep-learning TSE images in clinical musculoskeletal imaging.

Journal: Journal of medical imaging and radiation oncology
Published Date:

Abstract

In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scans. A total of 232 conventional-TSE and DL-TSE image pairs were acquired for comparison. For each consenting patient, one of the clinically acquired conventional-TSE proton density-weighted sequences in the sagittal or coronal planes (FS and non-FS), or in the axial plane (non-FS), was repeated using a research DL-TSE sequence. The DL-TSE reconstruction resulted in an image resolution that increased by at least 45% and scan times that were up to 52% faster compared to the conventional TSE. All images were acquired on a MAGNETOM Vida 3T scanner (Siemens Healthineers AG, Erlangen, Germany). The reporting radiologists, blinded to the acquisition time, were requested to qualitatively compare the DL-TSE against the conventional-TSE reconstructions. Despite having a faster acquisition time, the DL-TSE was rated to depict smaller structures better for 139/232 (60%) cases, equivalent for 72/232 (31%) cases and worse for 21/232 (9%) cases compared to the conventional-TSE. Overall, the radiologists preferred the DL-TSE reconstruction in 124/232 (53%) cases and stated no preference, implying equivalence, for 65/232 (28%) cases. DL-TSE reconstructions enabled faster acquisition times while enhancing spatial resolution and preserving the image contrast. From these results, the DL-TSE provided added or comparable clinical value and utility in less time. DL-TSE offers the opportunity to further reduce the overall examination time and improve patient comfort with no loss in diagnostic accuracy.

Authors

  • Rajat Vashistha
    ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
  • Mustafa M Almuqbel
    Pacific Radiology Group, Christchurch, New Zealand.
  • Nick J Palmer
    Pacific Radiology Group, Christchurch, New Zealand.
  • Ross J Keenan
    Pacific Radiology Group, Christchurch, New Zealand.
  • Kevin Gilbert
    Pacific Radiology Group, Christchurch, New Zealand.
  • Scott Wells
    Pacific Radiology Group, Christchurch, New Zealand.
  • Andrew Lynch
    Pacific Radiology Group, Christchurch, New Zealand.
  • Andrew Li
    Pacific Radiology Group, Christchurch, New Zealand.
  • Stephen Kingston-Smith
    Pacific Radiology Group, Christchurch, New Zealand.
  • Tracy R Melzer
    New Zealand Brain Research Institute, Christchurch, New Zealand.
  • Gregor Koerzdoerfer
    MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany.
  • Kieran O'Brien
    Center for Advanced Imaging, University of Queensland, St Lucia, QLD, Australia.