Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

Haoran Sun; Lixia Wang; Timothy Daskivich; Shihan Qiu; Fei Han; Alessandro D'Agnolo; Rola Saouaf; Anthony G Christodoulou; Hyung Kim; Debiao Li; Yibin Xie

doi:10.3389/fradi.2023.1223377

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

Front Radiol. 2023 Oct 11:3:1223377. doi: 10.3389/fradi.2023.1223377. eCollection 2023.

Authors

Haoran Sun^{1

2}, Lixia Wang¹, Timothy Daskivich³, Shihan Qiu^{1

2}, Fei Han¹, Alessandro D'Agnolo⁴, Rola Saouaf⁵, Anthony G Christodoulou^{1

2}, Hyung Kim³, Debiao Li^{1

2}, Yibin Xie¹

Affiliations

¹ Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
² Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States.
³ Minimal Invasive Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
⁴ Imaging/Nuclear Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
⁵ Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

Abstract

Purpose: To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.

Methods: Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.

Results: The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).

Conclusion: A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

Keywords: T2 mapping; active surveillance; deep learning; multiparametric MRI; peripheral zone; prostate cancer; quantitative imaging.

Abstract

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