Joint Estimation and Smoothing of Clinical DT-MRI with a Log-Euclidean Metric
Year: 2005
Type: report
Abstract: Diffusion tensor MRI is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data has to be acquired rapidly at the detriment of the image quality. We propose a new variational framework that specifically targets low quality DT-MRI. The hypothesis of an additive Gaussian noise on the images leads us to estimate the tensor field directly on the image intensities. To further reduce the influence of the noise, we optimally exploit the spatial correlation by adding to the estimation an anisotropic regularization term. This criterion is easily optimized thanks to the use of the recently introduced Log-Euclidean metrics. Results on real clinical data show promising improvements of fiber tracking in the brain and we present the first successful attempt, up to our knowledge, to reconstruct the spinal cord.
Source: HAL
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2295481365