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dc.contributor.authorZormpas-Petridis, K
dc.contributor.authorTunariu, N
dc.contributor.authorCurcean, A
dc.contributor.authorMessiou, C
dc.contributor.authorCurcean, S
dc.contributor.authorCollins, DJ
dc.contributor.authorHughes, JC
dc.contributor.authorJamin, Y
dc.contributor.authorKoh, D-M
dc.contributor.authorBlackledge, MD
dc.identifier.citationRadiology. Artificial intelligence, 2021, 3 (5), pp. e200279 - ?
dc.description.abstractPurpose To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA 1 ]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times. Materials and methods Both retrospective and prospective patient groups were used to develop a deep learning-based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA 1 and NOA 9 images (acquisition period, 2015-2017) were retrospectively included. An additional 22 prospective patients with advanced prostate cancer, myeloma, and advanced breast cancer were used for model testing (2019), and the radiologic quality of DNIF-processed NOA 1 (NOA 1-DNIF ) images were compared with NOA 1 images and clinical NOA 16 images by using a three-point Likert scale (good, average, or poor; statistical significance was calculated by using a Wilcoxon signed ranked test). The model was also retrained and tested in 28 patients with malignant pleural mesothelioma (MPM) who underwent lung MRI (2015-2017) to demonstrate feasibility in other body regions. Results The model visually improved the quality of NOA 1 images in all test patients, with the majority of NOA 1-DNIF and NOA 16 images being graded as either "average" or "good" across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA 1-DNIF images of bone disease deviated from those within NOA 9 images by an average of 1.9% (range, 1.1%-2.6%). The model was also successfully applied in the context of MPM; the mean ADCs from NOA 1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA 12 ) by 3.7% (range, 0.2%-10.6%). Conclusion Clinical-standard images were generated from subsampled images by using a DNIF. Keywords: Image Postprocessing, MR-Diffusion-weighted Imaging, Neural Networks, Oncology, Whole-Body Imaging, Supervised Learning, MR-Functional Imaging, Metastases, Prostate, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.
dc.format.extente200279 - ?
dc.titleAccelerating Whole-Body Diffusion-weighted MRI with Deep Learning-based Denoising Image Filters.
dc.typeJournal Article
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfRadiology. Artificial intelligence
pubs.notes12 months
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Computational Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Pre-Clinical MRI
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/16/17 Starting Cohort
pubs.embargo.terms12 months
icr.researchteamComputational Imaging
icr.researchteamPre-Clinical MRI
icr.researchteamRadiotherapy Physics Modelling
dc.contributor.icrauthorBlackledge, Matthewen_US
dc.contributor.icrauthorJamin, Yannen_US
dc.contributor.icrauthorZormpas Petridis, Konstantinosen_US
dc.contributor.icrauthorTunariu, Ninaen_US
dc.contributor.icrauthorMessiou, Christinaen_US
dc.contributor.icrauthorCollins, Daviden_US

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