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Deep learning-accelerated prostate MRI: improving speed, accuracy, and sustainability

Rationale and Objectives - This study aims to evaluate the effectiveness of a deep learning (DL)-enhanced four-fold parallel acquisition technique (P4) in improving prostate MR image quality while optimizing scan efficiency compared to the traditional two-fold parallel acquisition technique (P2). -...

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Main Authors: Reschke, Philipp (Author) , Koch, Vitali (Author) , Gruenewald, Leon D. (Author) , Bachir, Ahmed Ait (Author) , Gotta, Jennifer (Author) , Booz, Christian (Author) , Alrahmoun, Mohamed Alaa (Author) , Strecker, Ralph (Author) , Nickel, Dominik (Author) , D’Angelo, Tommaso (Author) , Dahm, Daniel M. (Author) , Konrad, Paul (Author) , Solim, Levent A. (Author) , Holzer, Maximilian (Author) , Al-Saleh, Saber (Author) , Scholtz, Jan-Erik (Author) , Sommer, Christof-Matthias (Author) , Hammerstingl, Renate M. (Author) , Eichler, Katrin (Author) , Vogl, Thomas J. (Author) , Leistner, David M. (Author) , Haberkorn, Sebastian Maximilian (Author) , Mahmoudi, Scherwin (Author)
Format: Article (Journal)
Language:English
Published: November 2025
In: Academic radiology
Year: 2025, Volume: 32, Issue: 11, Pages: 6718-6728
ISSN:1878-4046
DOI:10.1016/j.acra.2025.06.022
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.acra.2025.06.022
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S1076633225005719
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Author Notes:Philipp Reschke, MD, Vitali Koch, MD, Leon D. Gruenewald, MD, Ahmed Ait Bachir, MD, Jennifer Gotta, MD, Christian Booz, MD, Mohamed Alaa Alrahmoun, MD, Ralph Strecker, PhD, Dominik Nickel, PhD, Tommaso D’Angelo, MD, Daniel M. Dahm, cand med, Paul Konrad, cand med, Levent A. Solim, MD, Maximilian Holzer, cand. med., Saber Al-Saleh, Mr., Jan-Erik Scholtz, MD, Christof M. Sommer, MD, Renate M. Hammerstingl, MD, Katrin Eichler, MD, Thomas J. Vogl, MD, David M. Leistner, MD, Sebastian M. Haberkorn, MD, Scherwin Mahmoudi, MD
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Summary:Rationale and Objectives - This study aims to evaluate the effectiveness of a deep learning (DL)-enhanced four-fold parallel acquisition technique (P4) in improving prostate MR image quality while optimizing scan efficiency compared to the traditional two-fold parallel acquisition technique (P2). - Materials and Methods - Patients undergoing prostate MRI with DL-enhanced acquisitions were analyzed from January 2024 to July 2024. The participants prospectively received T2-weighted sequences in all imaging planes using both P2 and P4. Three independent readers assessed image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR). Significant differences in contrast and gray-level properties between P2 and P4 were identified through radiomics analysis (p <.05). - Results - A total of 51 participants (mean age 69.4 years ± 10.5 years) underwent P2 and P4 imaging. P4 demonstrated higher CNR and SNR values compared to P2 (p <.001). P4 was consistently rated superior to P2, demonstrating enhanced image quality and greater diagnostic precision across all evaluated categories (p <.001). Furthermore, radiomics analysis confirmed that P4 significantly altered structural and textural differentiation in comparison to P2. The P4 protocol reduced T2w scan times by 50.8%, from 11:48 min to 5:48 min (p <.001). - Conclusion - In conclusion, P4 imaging enhances diagnostic quality and reduces scan times, improving workflow efficiency, and potentially contributing to a more patient-centered and sustainable radiology practice.
Item Description:Online verfügbar: 14. Juli 2025, Artikelversion: 03. November 2025
Gesehen am 11.03.2026
Physical Description:Online Resource
ISSN:1878-4046
DOI:10.1016/j.acra.2025.06.022

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