Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T

Judith Herrmann; Thomas Benkert; Andreas Brendlin; Sebastian Gassenmaier; Thomas Hölldobler; Simon Maennlin; Haidara Almansour; Andreas Lingg; Elisabeth Weiland; Saif Afat

doi:10.1016/j.acra.2023.06.035

Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T

Acad Radiol. 2024 Mar;31(3):921-928. doi: 10.1016/j.acra.2023.06.035. Epub 2023 Jul 25.

Affiliations

¹ Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany.
² MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
³ Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany. Electronic address: saif.afat@med.uni-tuebingen.de.

PMID: 37500416
DOI: 10.1016/j.acra.2023.06.035

Abstract

Rationale and objectives: To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI.

Materials and methods: A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWI_S) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWI_DL). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm²) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWI_S and DWI_DL were compared with the Wilcoxon signed-rank test.

Results: The overall image quality was evaluated to be significantly superior in DWI_DL compared to DWI_S for b = 0 s/mm², b = 800 s/mm², and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWI_DL compared to DWI_S for b = 0 s/mm², b = 800 s/mm², and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWI_S was 2:06 minutes, and simulated acquisition time for DWI_DL was 1:12 minutes.

Conclusion: DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible.

Keywords: Deep learning reconstruction; Diffusion-weighted imaging; MRI; Pelvic imaging.

MeSH terms

Aged
Artifacts
Deep Learning*
Diffusion Magnetic Resonance Imaging / methods
Female
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Pelvis / diagnostic imaging
Reproducibility of Results
Retrospective Studies
Signal-To-Noise Ratio