Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging

Takahiro Ueda, Yoshiharu Ohno, Kaori Yamamoto, Kazuhiro Murayama, Masato Ikedo, Masao Yui, Satomu Hanamatsu, Yumi Tanaka, Yuki Obama, Hirotaka Ikeda, Hiroshi Toyama

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Background: Deep learning reconstruction (DLR) may improve image quality. However, its impact on diffusion-weighted imaging (DWI) of the prostate has yet to be assessed. Purpose: To determine whether DLR can improve image quality of diffusion-weighted MRI at b values ranging from 1000 sec/mm2 to 5000 sec/mm2 in patients with prostate cancer. Materials and Methods: In this retrospective study, images of the prostate obtained at DWI with a b value of 0 sec/mm2, DWI with a b value of 1000 sec/mm2 (DWI1000), DWI with a b value of 3000 sec/mm2 (DWI3000), and DWI with a b value of 5000 sec/mm2 (DWI5000) from consecutive patients with biopsy-proven cancer from January to June 2020 were reconstructed with and without DLR. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) from region-of-interest analysis and qualitatively assessed using a five-point visual scoring system (1 [very poor] to 5 [excellent]) for each high-b-value DWI sequence with and without DLR. The SNR, CNR, and visual score for DWI with and without DLR were compared with the paired t test and the Wilcoxon signed rank test with Bonferroni correction, respectively. Apparent diffusion coefficients (ADCs) from DWI with and without DLR were also compared with the paired t test with Bonferroni correction. Results: A total of 60 patients (mean age, 67 years; age range, 49-79 years) were analyzed. DWI with DLR showed significantly higher SNRs and CNRs than DWI without DLR (P,.001); for example, with DWI1000 the mean SNR was 38.7 6 0.6 versus 17.8 6 0.6, respectively (P,.001), and the mean CNR was 18.4 6 5.6 versus 7.4 6 5.6, respectively (P,.001). DWI with DLR also demonstrated higher qualitative image quality than DWI without DLR (mean score: 4.8 6 0.4 vs 4.0 6 0.7, respectively, with DWI1000 [P = .001], 3.8 6 0.7 vs 3.0 6 0.8 with DWI3000 [P = .002], and 3.1 6 0.8 vs 2.0 6 0.9 with DWI5000 [P,.001]). ADCs derived with and without DLR did not differ substantially (P..99). Conclusion: Deep learning reconstruction improves the image quality of diffusion-weighted MRI scans of prostate cancer with no impact on apparent diffusion coefficient quantitation with a 3.0-T MRI system.

Original languageEnglish
Pages (from-to)373-381
Number of pages9
JournalRadiology
Volume303
Issue number2
DOIs
Publication statusPublished - 05-2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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