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Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T

Marth, Adrian Alexander; von Deuster, Constantin; Sommer, Stefan; Feuerriegel, Georg Constatin; Goller, Sophia S; Sutter, Reto; Nanz, Daniel (2024). Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T (2024/07/04). Investigative Radiology, 59(12):831-837.

Abstract

OBJECTIVES
The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms.

MATERIALS AND METHODS
This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 x 0.31 x 1.5 mm 3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria.

RESULTS
The mean age of the participants was 32.0 +/- 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938 ) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939 ). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions ( P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality ( P </= 0.031). Image contrast of PD-fs images at 2-fold acceleration ( P = 0.087), image noise of T1-weighted images at 2-fold acceleration ( P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration ( P >/= 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions ( P >/= 0.082).

CONCLUSIONS
This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiology, Nuclear Medicine and Imaging
Uncontrolled Keywords:Humans Female Male *Deep Learning Prospective Studies Adult *Magnetic Resonance Imaging/methods Knee Joint/diagnostic imaging Image Processing, Computer-Assisted/methods Algorithms Healthy Volunteers Knee/diagnostic imaging Image Interpretation, Computer-Assisted/methods specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Two of the co-authors (C.v.D., S.S.) are employees of Siemens Healthineers who provided technical support but were not involved in data acquisition and analysis. The remaining authors do not have any conflict of interest to declare.
Language:English
Date:1 December 2024
Deposited On:27 Jan 2025 09:38
Last Modified:30 Jun 2025 02:07
Publisher:Lippincott Williams & Wilkins
Edition:2024/07/04
ISSN:0020-9996
Additional Information:Marth, Adrian Alexander von Deuster, Constantin Sommer, Stefan Feuerriegel, Georg Constantin Goller, Sophia Samira Sutter, Reto Nanz, Daniel eng Invest Radiol. 2024 Dec 1;59(12):831-837. doi: 10.1097/RLI.0000000000001095. Epub 2024 Jul 4.
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1097/RLI.0000000000001095
PubMed ID:38960863
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  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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