Blind Source Separation in Dynamic Contrast Enhanced Magnetic Resonance Imaging Renography.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) renography is a desirable kidney assessment methodology owing to the lack of ionizing radiation in MRI and its capability of producing high-resolution anatomical image data as well as physiological data. DCE-MRI renography emerged with the view to provide a minimally invasive framework to quickly and accurately assess kidney function, for example, to measure glomerular filtration rate (GFR). However, despite considerable developments, it is not yet considered a robust technique of renal assessment. This is due to a number of confounding factors ranging from optimization of data acquisition parameters to data post-processing challenges such as organ motion (mainly due to breathing), segmentation, partial volume (PV) effect (a signal mixing phenomenon) and tracer kinetic modelling. Prior works including registration-based motion correction techniques, semi-automatic segmentation based on similarity measures and a template-based PV correction method have not provided a complete and practical solution. In this work, a blind source separation (BSS) approach based on time-delayed decorrelation and temporal independent component analysis (ICA) was proposed to unmix physiological signals and remove the undesired motion artefacts. To evaluate the technique, test data were constructed using kidney, liver and nonspecific tissue dynamic MR signals. The source signals were correctly identified with small errors and coefficient of determination r2 values of 0.85 - 0.99 between the independent components (ICs) and source signals. The technique was then applied to a cohort of healthy volunteers’ DCE-MRI data, using a number of regions with different shape and size. The estimated GFRs, using IC signals, demonstrated improved consistency compared with the estimates that are conventionally produced using original or registration-based movement-corrected data. The technique, however, suffers from two drawbacks. First, it is limited by a search criterion based on the tracer perfusion time, which may need to be adjusted each time a new data set is used. Second, signal fluctuations associated with the kidney motion were not completely removed. To address the shortcomings, a different BSS approach was proposed to exploit spatial and temporal independence of physiological processes simultaneously via a spatio-temporal ICA (STICA) technique. In this technique, the image data are collapsed into 1-D vectors where the eigenvectors and IC vectors are updated at each sampling time, using time-integral samples, to produce a new ICA filter each time. A synthetic test object was constructed using independent source signals, randomly generated noise artefacts and randomly generated mixing filters. The source signals were identified correctly in a number of cases with relatively small errors and r2 values of 0.50 - 0.96. It was also observed that the noise artefacts were completely removed. The STIC A technique was applied to healthy volunteers’ DCE-MRI data. The independent components presented characteristics such as renal filtration and perfusion activities where the smooth curvature of the IC signals suggested that the motion artefacts have been completely removed. The GFR estimates produced by the independent components demonstrated significant consistency compared with the GFR estimates produced by the original and registration-based movement-corrected image data. The results imply that the STICA technique may have the potential of providing a complete and practical solution for the challenges involved in the post-processing steps of DCE-MRI renography