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Enhanced Image Quality and Comparable Diagnostic Performance of Prostate Fast Bi-MRI with Deep Learning Reconstruction.

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Title: Enhanced Image Quality and Comparable Diagnostic Performance of Prostate Fast Bi-MRI with Deep Learning Reconstruction.
Authors: Shen L; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Yuan Y; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Liu J; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Cheng Y; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Liao Q; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Shi R; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Xiong T; Department of Urology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (T.X.)., Xu H; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Wang L; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.)., Yang Z; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050, Beijing, China (L.S., Y.Y., J.L., Y.C., Q.L., R.S., H.X., L.W., Z.Y.). Electronic address: yangzhenghan@vip.163.com.
Source: Academic radiology [Acad Radiol] 2025 Oct; Vol. 32 (10), pp. 5964-5974. Date of Electronic Publication: 2025 Jul 18.
Publication Type: Journal Article; Comparative Study
Language: English
Journal Info: Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE
Imprint Name(s): Publication: Reston Va : Association Of University Radiologists
Original Publication: Reston, VA : Association of University Radiologists, c1994-
MeSH Terms: Deep Learning* , Prostatic Neoplasms*/diagnostic imaging , Prostatic Neoplasms*/pathology , Multiparametric Magnetic Resonance Imaging*/methods , Image Interpretation, Computer-Assisted*/methods , Magnetic Resonance Imaging*/methods, Male ; Humans ; Middle Aged ; Prospective Studies ; Aged ; Sensitivity and Specificity ; Prostate/diagnostic imaging ; Diffusion Magnetic Resonance Imaging/methods ; Signal-To-Noise Ratio
Abstract: Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Rational and Objectives: To evaluate image quality and diagnostic performance of prostate biparametric MRI (bi-MRI) with deep learning reconstruction (DLR).
Materials and Methods: This prospective study included 61 adult male urological patients undergoing prostate MRI with standard-of-care (SOC) and fast protocols. Sequences included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. DLR images were generated from FAST datasets. Three groups (SOC, FAST, DLR) were compared using: (1) five-point Likert scale, (2) signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), (3) lesion slope profiles, (4) dorsal capsule edge rise distance (ERD). PI-RADS scores were assigned to dominant lesions. ADC values were measured in histopathologically confirmed cases. Diagnostic performance was analyzed via receiver operating characteristic (ROC) curves (accuracy/sensitivity/specificity). Statistical tests included Friedman test, one-way ANOVA with post hoc analyses, and DeLong test for ROC comparisons (P<0.05).
Results: FAST scanning protocols reduced acquisition time by nearly half compared to the SOC scanning protocol. When compared to T2WI FAST , DLR significantly improved SNR, CNR, slope profile, and ERD (P < 0.05). Similarly, DLR significantly enhanced SNR, CNR, and image sharpness when compared to DWI FAST (P < 0.05). No significant differences were observed in PI-RADS scores and ADC values between groups (P > 0.05). The areas under the ROC curves, sensitivity, and specificity of ADC values for distinguishing benign and malignant lesions remained consistent (P > 0.05).
Conclusion: DLR enhances image quality in fast prostate bi-MRI while preserving PI-RADS classification accuracy and ADC diagnostic performance.
(Copyright © 2025 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Contributed Indexing: Keywords: Apparent diffusion coefficient value; Biparametric magnetic resonance imaging; Deep learning reconstruction; Prostate Imaging Reporting and Data System; Prostate cancer
Entry Date(s): Date Created: 20250719 Date Completed: 20251004 Latest Revision: 20251004
Update Code: 20251005
DOI: 10.1016/j.acra.2025.06.059
PMID: 40683764
Database: MEDLINE
Description
Abstract:Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Rational and Objectives: To evaluate image quality and diagnostic performance of prostate biparametric MRI (bi-MRI) with deep learning reconstruction (DLR).<br />Materials and Methods: This prospective study included 61 adult male urological patients undergoing prostate MRI with standard-of-care (SOC) and fast protocols. Sequences included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. DLR images were generated from FAST datasets. Three groups (SOC, FAST, DLR) were compared using: (1) five-point Likert scale, (2) signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), (3) lesion slope profiles, (4) dorsal capsule edge rise distance (ERD). PI-RADS scores were assigned to dominant lesions. ADC values were measured in histopathologically confirmed cases. Diagnostic performance was analyzed via receiver operating characteristic (ROC) curves (accuracy/sensitivity/specificity). Statistical tests included Friedman test, one-way ANOVA with post hoc analyses, and DeLong test for ROC comparisons (P&lt;0.05).<br />Results: FAST scanning protocols reduced acquisition time by nearly half compared to the SOC scanning protocol. When compared to T2WI <subscript>FAST</subscript> , DLR significantly improved SNR, CNR, slope profile, and ERD (P &lt; 0.05). Similarly, DLR significantly enhanced SNR, CNR, and image sharpness when compared to DWI <subscript>FAST</subscript> (P &lt; 0.05). No significant differences were observed in PI-RADS scores and ADC values between groups (P &gt; 0.05). The areas under the ROC curves, sensitivity, and specificity of ADC values for distinguishing benign and malignant lesions remained consistent (P &gt; 0.05).<br />Conclusion: DLR enhances image quality in fast prostate bi-MRI while preserving PI-RADS classification accuracy and ADC diagnostic performance.<br /> (Copyright © 2025 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
ISSN:1878-4046
DOI:10.1016/j.acra.2025.06.059