Abstract #1863

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

Haoran Sun^1,2, Lixia Wang¹, Timothy Daskivich³, Shihan Qiu^1,2, Fei Han¹, Alessandro D'Agnolo⁴, Rola Saouaf⁵, Hyung Kim³, Debiao Li^1,2, and Yibin Xie¹

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

Synopsis

Keywords: Prostate, Quantitative Imaging

Prostate cancer (PCa) is one of the most common types of cancer with a considerable morbidity and mortality. Multiparametric MRI as a noninvasive imaging tool in PCa diagnosis has limitations. Recent studies suggest that quantitative T2 information is helpful in PCa diagnosis and lesion characterization but is not generally available due to the need for additional scans. Here, we developed a DL-based method to estimate T2 maps retrospectively from clinically acquired T1- and T2-weighted images. The developed technique has the potential to improve PCa diagnosis and lesion characterization using quantitative T2 information estimated from conventional clinical scans.

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