dc.contributor.author | Zormpas-Petridis, K | |
dc.contributor.author | Tunariu, N | |
dc.contributor.author | Curcean, A | |
dc.contributor.author | Messiou, C | |
dc.contributor.author | Curcean, S | |
dc.contributor.author | Collins, DJ | |
dc.contributor.author | Hughes, JC | |
dc.contributor.author | Jamin, Y | |
dc.contributor.author | Koh, D-M | |
dc.contributor.author | Blackledge, MD | |
dc.date.accessioned | 2021-11-03T15:25:02Z | |
dc.date.available | 2022-11-03T15:25:02Z | |
dc.date.issued | 2021-09-01 | |
dc.identifier.citation | Radiology. Artificial intelligence, 2021, 3 (5), pp. e200279 - ? | |
dc.identifier.issn | 2638-6100 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/4871 | |
dc.identifier.eissn | 2638-6100 | |
dc.identifier.doi | 10.1148/ryai.2021200279 | |
dc.description.abstract | PURPOSE: To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA1]) 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 NOA1 and NOA9 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 NOA1 (NOA1-DNIF) images were compared with NOA1 images and clinical NOA16 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 NOA1 images in all test patients, with the majority of NOA1-DNIF and NOA16 images being graded as either "average" or "good" across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA1-DNIF images of bone disease deviated from those within NOA9 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 NOA1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA12) 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 | Electronic-eCollection | |
dc.format.extent | e200279 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | RADIOLOGICAL SOC NORTH AMERICA (RSNA) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.title | Accelerating Whole-Body Diffusion-weighted MRI with Deep Learning-based Denoising Image Filters. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2021-06-04 | |
rioxxterms.version | VoR | |
rioxxterms.versionofrecord | 10.1148/ryai.2021200279 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Radiology. Artificial intelligence | |
pubs.issue | 5 | |
pubs.notes | 12 months | |
pubs.organisational-group | /ICR | |
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 | |
pubs.organisational-group | /ICR/Students/PhD and MPhil | |
pubs.organisational-group | /ICR/Students/PhD and MPhil/16/17 Starting Cohort | |
pubs.publication-status | Published | |
pubs.volume | 3 | |
pubs.embargo.terms | 12 months | |
pubs.embargo.date | 2022-11-03T15:25:02Z | |
icr.researchteam | Computational Imaging | |
icr.researchteam | Pre-Clinical MRI | |
icr.researchteam | Radiotherapy Physics Modelling | |
dc.contributor.icrauthor | Zormpas Petridis, Konstantinos | |
dc.contributor.icrauthor | Collins, David | |
dc.contributor.icrauthor | Jamin, Yann | |
dc.contributor.icrauthor | Blackledge, Matthew | |