Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.
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Date
2018-04-04ICR Author
Author
Gurney-Champion, OJ
Klaassen, R
Froeling, M
Barbieri, S
Stoker, J
Engelbrecht, MRW
Wilmink, JW
Besselink, MG
Bel, A
van Laarhoven, HWM
Nederveen, AJ
Type
Journal Article
Metadata
Show full item recordAbstract
The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman's rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.
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Subject
Humans
Pancreatic Neoplasms
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Bayes Theorem
Prospective Studies
Algorithms
Aged
Middle Aged
Female
Male
Research team
Radiotherapy Physics Modelling
Language
eng
Date accepted
2018-03-06
License start date
2018-01
Citation
PloS one, 2018, 13 (4), pp. e0194590 - ?
Publisher
PUBLIC LIBRARY SCIENCE