TY - JOUR
T1 - Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging
AU - Ueda, Takahiro
AU - Ohno, Yoshiharu
AU - Yamamoto, Kaori
AU - Murayama, Kazuhiro
AU - Ikedo, Masato
AU - Yui, Masao
AU - Hanamatsu, Satomu
AU - Tanaka, Yumi
AU - Obama, Yuki
AU - Ikeda, Hirotaka
AU - Toyama, Hiroshi
N1 - Funding Information:
The authors thank Akiyoshi Iwase, RT, and Takashi Fukuba, RT (Department of Radiology, Fujita Health University Hospital), Tetsuya Tsukamoto, MD, PhD (Department of Diagnostic Pathology, Fujita Health University School of Medicine), and Ryoichi Shiroki, MD, PhD (Department of Urology, Fujita Health University School of Medicine) for their outstanding contributions to this work.
Funding Information:
This retrospective study was approved by the institutional review board of Fujita Health University Hospital and compliant with the Health Insurance Portability and Accountability Act. Written informed consent was waived. This study was technically and financially supported by Canon Medical Systems. Three of the authors were employees of Canon Medical Systems (K.Y., M.I., and M.Y.) but did not have control over any of the data used in this study.
Funding Information:
Supported by Canon Medical Systems. Conflicts of interest are listed at the end of this article. See also the editorial by Turkbey in this issue.
Publisher Copyright:
© RSNA, 2022
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
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U2 - 10.1148/RADIOL.204097
DO - 10.1148/RADIOL.204097
M3 - Article
C2 - 35103536
AN - SCOPUS:85128453319
SN - 0033-8419
VL - 303
SP - 373
EP - 381
JO - Radiology
JF - Radiology
IS - 2
ER -