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Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

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Bibliographic Details
Title: Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.
Authors: Jurka M; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic., Macova I; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic., Wagnerova M; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic., Capoun O; Department of Urology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic., Jakubicek R; Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic., Ourednicek P; Department of Medical Imaging, St. Anna University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic., Lambert L; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic., Burgetova A; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
Source: Quantitative imaging in medicine and surgery [Quant Imaging Med Surg] 2024 May 01; Vol. 14 (5), pp. 3534-3543. Date of Electronic Publication: 2024 Apr 11.
Publication Type: Journal Article
Journal Info: Publisher: AME Pub Country of Publication: China NLM ID: 101577942 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2223-4292 (Print) Linking ISSN: 22234306 NLM ISO Abbreviation: Quant Imaging Med Surg Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2223-4292
DOI:10.21037/qims-23-1488