dc.contributor.author | Zormpas-Petridis, K | |
dc.contributor.author | Tunariu, N | |
dc.contributor.author | Collins, DJ | |
dc.contributor.author | Messiou, C | |
dc.contributor.author | Koh, D-M | |
dc.contributor.author | Blackledge, MD | |
dc.coverage.spatial | United States | |
dc.date.accessioned | 2022-11-17T11:07:37Z | |
dc.date.available | 2022-11-17T11:07:37Z | |
dc.date.issued | 2022-10-01 | |
dc.identifier | ARTN 106091 | |
dc.identifier | S0010-4825(22)00799-5 | |
dc.identifier.citation | Computers in Biology and Medicine, 2022, 149 pp. 106091 - | |
dc.identifier.issn | 0010-4825 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/5565 | |
dc.identifier.eissn | 1879-0534 | |
dc.identifier.eissn | 1879-0534 | |
dc.identifier.doi | 10.1016/j.compbiomed.2022.106091 | |
dc.description.abstract | PURPOSE: To use deep learning to calculate the uncertainty in apparent diffusion coefficient (σADC) voxel-wise measurements to clinically impact the monitoring of treatment response and improve the quality of ADC maps. MATERIALS AND METHODS: We use a uniquely designed diffusion-weighted imaging (DWI) acquisition protocol that provides gold-standard measurements of σADC to train a deep learning model on two separate cohorts: 16 patients with prostate cancer and 28 patients with mesothelioma. Our network was trained with a novel cost function, which incorporates a perception metric and a b-value regularisation term, on ADC maps calculated by combinations of 2 or 3 b-values (e.g. 50/600/900, 50/900, 50/600, 600/900 s/mm2). We compare the accuracy of the deep-learning based approach for estimation of σADC with gold-standard measurements. RESULTS: The model accurately predicted the σADC for every b-value combination in both cohorts. Mean values of σADC within areas of active disease deviated from those measured by the gold-standard by 4.3% (range, 2.87-6.13%) for the prostate and 3.7% (range, 3.06-4.54%) for the mesothelioma cohort. We also showed that the model can easily be adapted for a different DWI protocol and field-of-view with only a few images (as little as a single patient) using transfer learning. CONCLUSION: Deep learning produces maps of σADC from standard clinical diffusion-weighted images (DWI) when 2 or more b-values are available. | |
dc.format | Print-Electronic | |
dc.format.extent | 106091 - | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | |
dc.relation.ispartof | Computers in Biology and Medicine | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Diffusion Magnetic Resonance Imaging | |
dc.subject | Humans | |
dc.subject | Male | |
dc.subject | Mesothelioma | |
dc.subject | Prostate | |
dc.subject | Prostatic Neoplasms | |
dc.subject | Uncertainty | |
dc.title | Deep-learned estimation of uncertainty in measurements of apparent diffusion coefficient from whole-body diffusion-weighted MRI. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2022-09-03 | |
dc.date.updated | 2022-11-17T11:06:46Z | |
rioxxterms.version | VoR | |
rioxxterms.versionofrecord | 10.1016/j.compbiomed.2022.106091 | |
rioxxterms.licenseref.startdate | 2022-10-01 | |
rioxxterms.type | Journal Article/Review | |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/36115298 | |
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/Royal Marsden Clinical Units | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Computational Imaging | |
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.publisher-url | http://dx.doi.org/10.1016/j.compbiomed.2022.106091 | |
pubs.volume | 149 | |
icr.researchteam | Computational Imaging | |
icr.researchteam | RMH Honorary Faculty | |
dc.contributor.icrauthor | Zormpas Petridis, Konstantinos | |
dc.contributor.icrauthor | Blackledge, Matthew | |
icr.provenance | Deposited by Mr Arek Surman on 2022-11-17. Deposit type is initial. No. of files: 1. Files: 1-s2.0-S0010482522007995-main.pdf | |