dc.contributor.author | Gurney-Champion, OJ | |
dc.contributor.author | Collins, DJ | |
dc.contributor.author | Wetscherek, A | |
dc.contributor.author | Rata, M | |
dc.contributor.author | Klaassen, R | |
dc.contributor.author | van Laarhoven, HWM | |
dc.contributor.author | Harrington, KJ | |
dc.contributor.author | Oelfke, U | |
dc.contributor.author | Orton, MR | |
dc.date.accessioned | 2019-05-03T15:23:26Z | |
dc.date.issued | 2019-04-09 | |
dc.identifier.citation | Physics in medicine and biology, 2019, 64 (10), pp. 105015 - ? | |
dc.identifier.issn | 0031-9155 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3219 | |
dc.identifier.eissn | 1361-6560 | |
dc.identifier.doi | 10.1088/1361-6560/ab1786 | |
dc.description.abstract | Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method. | |
dc.format | Electronic | |
dc.format.extent | 105015 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | IOP PUBLISHING LTD | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Humans | |
dc.subject | Pancreatic Neoplasms | |
dc.subject | Diffusion Magnetic Resonance Imaging | |
dc.subject | Case-Control Studies | |
dc.subject | Movement | |
dc.subject | Algorithms | |
dc.subject | Principal Component Analysis | |
dc.subject | Image Processing, Computer-Assisted | |
dc.subject | Signal-To-Noise Ratio | |
dc.subject | Healthy Volunteers | |
dc.title | Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2019-04-09 | |
rioxxterms.versionofrecord | 10.1088/1361-6560/ab1786 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2019-05-17 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Physics in medicine and biology | |
pubs.issue | 10 | |
pubs.notes | No embargo | |
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/Cancer Biology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Biology/Targeted Therapy | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Targeted Therapy | |
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/Cancer Biology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Biology/Targeted Therapy | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Targeted Therapy | |
pubs.publication-status | Published | |
pubs.volume | 64 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Radiotherapy Physics Modelling | |
icr.researchteam | Targeted Therapy | |
dc.contributor.icrauthor | Gurney-Champion, Oliver | |
dc.contributor.icrauthor | Collins, David | |
dc.contributor.icrauthor | Wetscherek, Andreas | |
dc.contributor.icrauthor | Harrington, Kevin | |