{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:43:13Z","timestamp":1777869793247,"version":"3.51.4"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:00:00Z","timestamp":1774656000000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006775","name":"GE HealthCare","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this prospective, single-center study, 24 consecutive men referred for prostate multiparametric MRI over 16\u2009months were enrolled (age 47\u201379\u2009years; mean, 68.1\u2009years). Axial T2-weighted images and DWI were obtained using a prototype 50\u2011channel coil and standard clinical phased array (3\u2009T Architect, GE HealthCare, USA). The DWI acquisitions were reconstructed with the vendor\u2019s 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\u2009=\u20091500\u2009s\/mm2 images. Combined reader scores were compared using the Wilcoxon matched\u2011pairs signed\u2011rank test, inter\u2011reader variability was assessed using Cohen\u2019s \u03ba, and quantitative SNR\/CNR values were compared using 2\u2011tailed paired t\u2011tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Twenty men were analyzable for qualitative and 18 for quantitative metrics (reported as mean \u00b1 SD). 50\u2011channel pelvic coil with DLPC+ARDL produced the highest SNR (99.70\u2009\u00b1\u200928.50) and CNR (91.68\u2009\u00b1\u200944.39), exceeding 50\u2011channel with ARDL alone (SNR 56.44\u2009\u00b1\u200928.50; CNR 51.11\u2009\u00b1\u200928.67), 30\u2011channel anterior array with ARDL (SNR 31.41\u2009\u00b1\u200913.18; CNR 26.26\u2009\u00b1\u200913.52), and DLPC + ARDL (SNR 49.4\u2009\u00b1\u200918.6; CNR 44.7\u2009\u00b1\u200918.3) (all P\u202f&amp;lt;\u202f.0001). Reader scores favored DLPC+ARDL in prostate border definition, peripheral\/transition zone distinction, lesion conspicuity, and confidence of extraprostatic extension (all P values for DWI at b\u2009=\u20091500\u202fs\/mm2 &amp;lt; 0.0001; synthetic DWI at b\u2009=\u20092000\u202fs\/mm2: P\u2009=\u20097.5\u2009\u00d7\u200910\u22127\u20130.01). Inter\u2011reader agreement was fair for acquired DWI (quadratic\u2011weighted \u03ba\u2009=\u20090.34) and lower for synthetic DWI (\u03ba\u2009=\u20090.19).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DWI images acquired using the 50\u2011channel pelvic coil and reconstructed with the DLPC + ARDL pipeline yield the highest image quality compared to ARDL only pipeline and to all 30-channel coil imaging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/radadv\/umag019","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:48:59Z","timestamp":1774529339000},"source":"Crossref","is-referenced-by-count":0,"title":["Improved prostate diffusion imaging using deep learning denoising and phase correction with ultra-high-density coil 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