Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Dec 28, 2023; 15(12): 338-349
Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.338
Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas
Yukihisa Takayama, Keisuke Sato, Shinji Tanaka, Ryo Murayama, Nahoko Goto, Kengo Yoshimitsu
Yukihisa Takayama, Keisuke Sato, Shinji Tanaka, Ryo Murayama, Nahoko Goto, Kengo Yoshimitsu, Department of Radiology, Faculty of Medicine, Fukuoka University, Fukuoka 8140180, Japan
Author contributions: Takayama Y and Yoshimitsu K designed the research and wrote the paper; Sato K, Tanaka S, Murayama R, and Goto N contributed to data collection, data analysis, and all authors approved the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Fukuoka University [Approval No. U21-966].
Informed consent statement: The requirement for written informed consent was waived because this was a retrospective analysis of image post-processing of clinical MR data.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: No additional data is available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yukihisa Takayama, MD, PhD, Associate Professor, Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka 8140180, Japan. ytakayama@fukuoka-u.ac.jp
Received: October 22, 2023
Peer-review started: October 22, 2023
First decision: November 2, 2023
Revised: November 12, 2023
Accepted: December 4, 2023
Article in press: December 4, 2023
Published online: December 28, 2023
Processing time: 64 Days and 3.8 Hours
ARTICLE HIGHLIGHTS
Research background

A combination of these techniques would improve diffusion-weighted imaging (DWI) image quality without prolonging the scan time but would influence the apparent diffusion coefficient calculation.

Research motivation

The image quality of reduced-field-of-view DWI [field-of-view optimized and constrained undistorted single-shot (FOCUS)] of the pancreas suffers from a low signal-to-noise ratio and the limitation of not providing good results at higher b-value settings.

Research objectives

This study aimed to evaluate the efficacy of deep learning-based reconstruction (DLR) for image quality improvement of FOCUS of the pancreas.

Research methods

This was a retrospective study evaluated 37 patients with pancreatic cystic lesions who underwent magnetic resonance imaging between August 2021 and October 2021. We evaluated three types of FOCUS examinations: FOCUS with DLR (FOCUS-DLR+), FOCUS without DLR (FOCUS-DLR−), and conventional FOCUS (FOCUS-conv). The three types of FOCUS and their apparent diffusion coefficient (ADC) maps were compared qualitatively and quantitatively.

Research results

FOCUS-DLR+ (3.62, average score of two radiologists) showed significantly better qualitative scores for image noise than FOCUS-DLR− (2.62) and FOCUS-conv (2.88) (P < 0.05). Furthermore, FOCUS-DLR+ showed the highest contrast ratios (CRs) between the pancreatic parenchyma and adjacent fat tissue for b-values of 0 and 600 s/mm2 (0.72 ± 0.08 and 0.68 ± 0.08) and FOCUS-DLR− showed the highest CR between cystic lesions and the pancreatic parenchyma for the b-values of 0 and 600 s/mm2 (0.62 ± 0.21, and 0.62 ± 0.21) (P < 0.05), respectively. FOCUS-DLR+ provided significantly higher ADCs of the pancreas and lesion (1.44 ± 0.24 and 3.00 ± 0.66) compared to FOCUS-DLR− (1.39 ± 0.22 and 2.86 ± 0.61) and significantly lower ADCs compared to FOCUS-conv (1.84 ± 0.45 and 3.32 ± 0.70) (P < 0.05), respectively.

Research conclusions

DLR improved image noise and CRs on FOCUS without prolonging the scan time. However, caution should be exercised when interpreting the ADCs after DLR.

Research perspectives

This study revealed that DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution. However, DLR did not ameliorate pancreatic distortion and physicians should pay attention to the interpretation of ADCs after DLR application.