Bayesian Image Quality Transfer
Image quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However, the original framework gives no indication of confidence in its output, which is a significant barrier to adoption in clinical practice and downstream processing. In this article, we present a general Bayesian extension of IQT which enables efficient and accurate quantification of uncertainty, providing users with an essential prediction of the accuracy of enhanced images. We demonstrate the efficacy of the uncertainty quantification through super-resolution of diffusion tensor images of healthy and pathological brains. In addition, the new method displays improved performance over the original IQT and standard interpolation techniques in both reconstruction accuracy and robustness to anomalies in input images.
KeywordsDiffusion Tensor Image Diffusion Tensor Predictive Distribution Reconstruction Accuracy Human Connectome Project
This work was supported by Microsoft scholarship. Data were provided in part by the HCP, WU-Minn Consortium (PIs: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by NIH and Wash. U. The tumour data were acquired as part of a study lead by Alberto Bizzi, MD at his hospital in Milan, Italy. The MS data were acquired as part of a study at UCL Institute of Neurology, funded by the MS Society UK and the UCL Hospitals Biomedical Research Centre (PIs: David Miller and Declan Chard).
- 6.Alexander, D.C., Zikic, D., Zhang, J., Zhang, H., Criminisi, A.: Image quality transfer via random forest regression: applications in diffusion MRI. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 225–232. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10443-0_29 Google Scholar