Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images.
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Date
2019-04-09Author
Gurney-Champion, OJ
Collins, DJ
Wetscherek, A
Rata, M
Klaassen, R
van Laarhoven, HWM
Harrington, KJ
Oelfke, U
Orton, MR
Type
Journal Article
Metadata
Show full item recordAbstract
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.
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Subject
Humans
Pancreatic Neoplasms
Diffusion Magnetic Resonance Imaging
Case-Control Studies
Movement
Algorithms
Principal Component Analysis
Image Processing, Computer-Assisted
Signal-To-Noise Ratio
Healthy Volunteers
Research team
Radiotherapy Physics Modelling
Targeted Therapy
Language
eng
Date accepted
2019-04-09
License start date
2019-05-17
Citation
Physics in medicine and biology, 2019, 64 (10), pp. 105015 - ?
Publisher
IOP PUBLISHING LTD