Application of independent component analysis to dynamic contrast-enhanced imaging for assessment of cerebral blood perfusion

Dynamic contrast-enhanced (DCE) imaging is widely used for in vivo assessment of the cerebral blood perfusion. In this work, we investigate the use of independent component analysis (ICA) on DCE imaging data for assessment of cerebral blood perfusion, without any prior knowledge of the underlying tissue vasculature and arterial input function. The minimum description length (MDL) criterion and principle component analysis (PCA) were employed to reduce the dimension of the data. An oscillating index method was used to select the components of interest. Numerical simulation and patient case studies were carried out to investigate the performance of ICA. The results show that ICA is able to extract physiologically meaningful components from the DCE imaging data. The advantages of ICA include its efficiency of computation, clarity of obtained component maps, and no need of the manually selected input function. The obtained independent component maps can provide reliable reference to identify the arterial and venous structure, and allow better demarcation of the tumor territories. The potential of ICA to be a useful clinical tool for diagnosis of cerebral vascular disease and for the assessment of treatment response has been demonstrated.