  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally
invasive imaging technique which can be used for characterizing tumor biology
and tumor response to radiotherapy. Pharmacokinetic (PK) estimation is widely
used for DCE-MRI data analysis to extract quantitative parameters relating to
microvascu- lature characteristics of the cancerous tissues. Unavoidable noise
corruption during DCE-MRI data acquisition has a large effect on the accuracy
of PK estimation. In this paper, we propose a general denoising paradigm called
gather- noise attenuation and reduce (GNR) and a novel temporal-spatial
collaborative filtering (TSCF) denoising technique for DCE-MRI data. TSCF takes
advantage of temporal correlation in DCE-MRI, as well as anatomical spatial
similar- ity to collaboratively filter noisy DCE-MRI data. The proposed TSCF
denoising algorithm decreases the PK parameter normalized estimation error by
57% and improves the structural similarity of PK parameter estimation by 86%
com- pared to baseline without denoising, while being an order of magnitude
faster than state-of-the-art denoising methods. TSCF improves the univariate
linear regression (ULR) c-statistic value for early prediction of pathologic
response up to 18%, and shows complete separation of pathologic complete
response (pCR) and non-pCR groups on a challenge dataset.
