Literature DB >> 34877735

Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate.

Patricia M Johnson1, Angela Tong1, Awani Donthireddy1, Kira Melamud1, Robert Petrocelli1, Paul Smereka1, Kun Qian2, Mahesh B Keerthivasan3, Hersh Chandarana1, Florian Knoll1.   

Abstract

BACKGROUND: Early diagnosis and treatment of prostate cancer (PCa) can be curative; however, prostate-specific antigen is a suboptimal screening test for clinically significant PCa. While prostate magnetic resonance imaging (MRI) has demonstrated value for the diagnosis of PCa, the acquisition time is too long for a first-line screening modality.
PURPOSE: To accelerate prostate MRI exams, utilizing a variational network (VN) for image reconstruction. STUDY TYPE: Retrospective.
SUBJECTS: One hundred and thirteen subjects (train/val/test: 70/13/30) undergoing prostate MRI. FIELD STRENGTH/SEQUENCE: 3.0 T; a T2 turbo spin echo (TSE) T2-weighted image (T2WI) sequence in axial and coronal planes, and axial echo-planar diffusion-weighted imaging (DWI). ASSESSMENT: Four abdominal radiologists evaluated the image quality of VN reconstructions of retrospectively under-sampled biparametric MRIs (bp-MRI), and standard bp-MRI reconstructions for 20 test subjects (studies). The studies included axial and coronal T2WI, DWI B50 seconds/mm2 and B1000 seconds/mm (4-fold T2WI, 3-fold DWI), all of which were evaluated separately for image quality on a Likert scale (1: non-diagnostic to 5: excellent quality). In another 10 test subjects, three readers graded lesions on bp-MRI-which additionally included calculated B1500 seconds/mm2 , and apparent diffusion coefficient map-according to the Prostate Imaging Reporting and Data System (PI-RADS v2.1), for both VN and standard reconstructions. Accuracy of PI-RADS ≥3 for clinically significant cancer was computed. Projected scan time of the retrospectively under-sampled biparametric exam was also computed. STATISTICAL TESTS: One-sided Wilcoxon signed-rank test was used for comparison of image quality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for lesion detection and grading. Generalized estimating equation with cluster effect was used to compare differences between standard and VN bp-MRI. A P-value of <0.05 was considered statistically significant.
RESULTS: Three of four readers rated no significant difference for overall quality between the standard and VN axial T2WI (Reader 1: 4.00 ± 0.56 (Standard), 3.90 ± 0.64 (VN) P = 0.33; Reader 2: 4.35 ± 0.74 (Standard), 3.80 ± 0.89 (VN) P = 0.003; Reader 3: 4.60 ± 0.50 (Standard), 4.55 ± 0.60 (VN) P = 0.39; Reader 4: 3.65 ± 0.99 (Standard), 3.60 ± 1.00 (VN) P = 0.38). All four readers rated no significant difference for overall quality between standard and VN DWI B1000 seconds/mm2 (Reader 1: 2.25 ± 0.62 (Standard), 2.45 ± 0.75 (VN) P = 0.96; Reader 2: 3.60 ± 0.92 (Standard), 3.55 ± 0.82 (VN) P = 0.40; Reader 3: 3.85 ± 0.72 (Standard), 3.55 ± 0.89 (VN) P = 0.07; Reader 4: 4.70 ± 0.76 (Standard); 4.60 ± 0.73 (VN) P = 0.17) and three of four readers rated no significant difference for overall quality between standard and VN DWI B50 seconds/mm2 (Reader 1: 3.20 ± 0.70 (Standard), 3.40 ± 0.75 (VN) P = 0.98; Reader 2: 2.85 ± 0.81 (Standard), 3.00 ± 0.79 (VN) P = 0.93; Reader 3: 4.45 ± 0.72 (Standard), 4.05 ± 0.69 (VN) P = 0.02; Reader 4: 4.50 ± 0.69 (Standard), 4.45 ± 0.76 (VN) P = 0.50). In the lesion evaluation study, there was no significant difference in the number of PI-RADS ≥3 lesions identified on standard vs. VN bp-MRI (P = 0.92, 0.59, 0.87) with similar sensitivity and specificity for clinically significant cancer. The average scan time of the standard clinical biparametric exam was 11.8 minutes, and this was projected to be 3.2 minutes for the accelerated exam. DATA
CONCLUSION: Diagnostic accelerated biparametric prostate MRI exams can be performed using deep learning methods in <4 minutes, potentially enabling rapid screening prostate MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 5.
© 2021 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  accelerated imaging; deep learning; image reconstruction; prostate MRI

Mesh:

Year:  2021        PMID: 34877735      PMCID: PMC9170839          DOI: 10.1002/jmri.28024

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   5.119


  32 in total

1.  Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Authors:  Mark A Griswold; Peter M Jakob; Robin M Heidemann; Mathias Nittka; Vladimir Jellus; Jianmin Wang; Berthold Kiefer; Axel Haase
Journal:  Magn Reson Med       Date:  2002-06       Impact factor: 4.668

2.  Detection of Individual Prostate Cancer Foci via Multiparametric Magnetic Resonance Imaging.

Authors:  David C Johnson; Steven S Raman; Sohrab A Mirak; Lorna Kwan; Amirhossein M Bajgiran; William Hsu; Cleo K Maehara; Preeti Ahuja; Izak Faiena; Aydin Pooli; Amirali Salmasi; Anthony Sisk; Ely R Felker; David S K Lu; Robert E Reiter
Journal:  Eur Urol       Date:  2018-12-01       Impact factor: 20.096

3.  Prostate cancer: multiparametric MRI for index lesion localization--a multiple-reader study.

Authors:  Andrew B Rosenkrantz; Fang-Ming Deng; Sooah Kim; Ruth P Lim; Nicole Hindman; Thais C Mussi; Bradley Spieler; Jason Oaks; James S Babb; Jonathan Melamed; Samir S Taneja
Journal:  AJR Am J Roentgenol       Date:  2012-10       Impact factor: 3.959

4.  Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality.

Authors:  Sebastian Gassenmaier; Saif Afat; Dominik Nickel; Mahmoud Mostapha; Judith Herrmann; Ahmed E Othman
Journal:  Eur J Radiol       Date:  2021-02-15       Impact factor: 3.528

5.  Learning a variational network for reconstruction of accelerated MRI data.

Authors:  Kerstin Hammernik; Teresa Klatzer; Erich Kobler; Michael P Recht; Daniel K Sodickson; Thomas Pock; Florian Knoll
Journal:  Magn Reson Med       Date:  2017-11-08       Impact factor: 4.668

6.  Pathologic and clinical findings to predict tumor extent of nonpalpable (stage T1c) prostate cancer.

Authors:  J I Epstein; P C Walsh; M Carmichael; C B Brendler
Journal:  JAMA       Date:  1994-02-02       Impact factor: 56.272

7.  Prognostic Gleason grade grouping: data based on the modified Gleason scoring system.

Authors:  Phillip M Pierorazio; Patrick C Walsh; Alan W Partin; Jonathan I Epstein
Journal:  BJU Int       Date:  2013-03-06       Impact factor: 5.588

8.  Multiparametric MRI to improve detection of prostate cancer compared with transrectal ultrasound-guided prostate biopsy alone: the PROMIS study.

Authors:  Louise Clare Brown; Hashim U Ahmed; Rita Faria; Ahmed El-Shater Bosaily; Rhian Gabe; Richard S Kaplan; Mahesh Parmar; Yolanda Collaco-Moraes; Katie Ward; Richard Graham Hindley; Alex Freeman; Alexander Kirkham; Robert Oldroyd; Chris Parker; Simon Bott; Nick Burns-Cox; Tim Dudderidge; Maneesh Ghei; Alastair Henderson; Rajendra Persad; Derek J Rosario; Iqbal Shergill; Mathias Winkler; Marta Soares; Eldon Spackman; Mark Sculpher; Mark Emberton
Journal:  Health Technol Assess       Date:  2018-07       Impact factor: 4.014

9.  ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA.

Authors:  Martin Uecker; Peng Lai; Mark J Murphy; Patrick Virtue; Michael Elad; John M Pauly; Shreyas S Vasanawala; Michael Lustig
Journal:  Magn Reson Med       Date:  2014-03       Impact factor: 4.668

10.  Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study.

Authors:  Hashim U Ahmed; Ahmed El-Shater Bosaily; Louise C Brown; Rhian Gabe; Richard Kaplan; Mahesh K Parmar; Yolanda Collaco-Moraes; Katie Ward; Richard G Hindley; Alex Freeman; Alex P Kirkham; Robert Oldroyd; Chris Parker; Mark Emberton
Journal:  Lancet       Date:  2017-01-20       Impact factor: 79.321

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