A Semi-Quantitative Analysis Model with Parabolic Modelling for DCE-MRI Sequences of Prostate

Dynamic Contrast Enhanced Magnetic Resonance Resonance Imaging (DCE-MRI), also called perfusion Magnetic Resonance Imaging, is an advanced Magnetic Resonance Imaging (MRI) modality used in non-invasive diagnosis of Prostate Cancer. In this paper we propose a novel semi-quantitative model to represent perfusion behaviour of 3-dimensional prostate voxels in DCE-MRI sequences based on parametric evaluation of parabolic polynomials. Perfusion data of each prostate voxel is modelled on to a best fit parabolic function using second order non-linear regression. Then a single parameter is derived using geometric parameters of the parabola to represent the amount and rapidity of signal intensity enhancement of the voxel against the contrast enhancement agent. Finally prostate voxels are classified using k-means clustering based on the parameter derived by the proposed model. A qualitative evaluation was performed and the classification results represented as graphical summarizations of perfusion MR data for 70 axial DCE-MRI slices of 10 patients by an expert radiologist. The results show that the proposed semi- quantitative model and the parameter derived from the model have the potential to be used in manual observations or in Computer- Aided Diagnosis (CAD) systems for prostate cancer recognition.