Improved prostate diffusion imaging using deep learning denoising and phase correction with ultra-high-density coil array

作者：S. Huang, Xinzeng Wang, Patricia Lan, Milica Medved, Nurullah Kaya, Clyve Follante, Yunjeong Stickle, Jonathan Taylor, Ambereen Yousuf, Roger Engelmann, Fraser Robb, A. Guidon, Grace H. Lee, Aytekin Oto · 发表于：Radiology Advances · 年份：2026 · DOI：10.1093/radadv/umag019 · 研究领域：Prostate Cancer Diagnosis and Treatment、MRI in cancer diagnosis、Advanced Neuroimaging Techniques and Applications

Abstract Background MR diffusion-weighted imaging (DWI), especially at high b-value, is a key acquisition to help identify clinically significant prostate cancer; however, it suffers from low signal-to-noise ratio (SNR), high noise floor, and susceptibility artifact. Purpose To demonstrate the feasibility of improving DWI quality using a novel 50-channel pelvic coil in conjunction with a deep learning (DL)-based phase correction and a DL-denoising algorithm. Methods In this prospective, single-center study, 24 consecutive men referred for prostate multiparametric MRI over 16 months were enrolled (age 47–79 years; mean, 68.1 years). Axial T2-weighted images and DWI were obtained using a prototype 50‑channel coil and standard clinical phased array (3 T Architect, GE HealthCare, USA). The DWI acquisitions were reconstructed with the vendor’s deep learning denoising algorithm (ARDL). The same raw data were reconstructed offline using an investigational DL Phase Correction algorithm with ARDL (DLPC+ARDL). Two independent readers scored DWI and ADC series using 4 qualitative criteria. SNR and contrast-to-noise ratio (CNR) were measured on b = 1500 s/mm2 images. Combined reader scores were compared using the Wilcoxon matched‑pairs signed‑rank test, inter‑reader variability was assessed using Cohen’s κ, and quantitative SNR/CNR values were compared using 2‑tailed paired t‑tests. Results Twenty men were analyzable for qualitative and 18 for quantitative metrics (reported as mean ± SD)...