3D Multimodal Visualization of Medical Data: Applied to Perfusion-Weighted MRI

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging method used in radiology to create detailed images of anatomy and provide physiological data about the brain. Perfusion-weighted magnetic resonance imaging (PW-MRI) is one of MRI techniques used to study blood flow in the brain. This technique aims to estimate the parameters that characterize cerebral blood flow in a regular grid formed by large volume voxels, called the acquisition grid. One of the primary metrics assessed in PW-MRI is cerebral blood volume (CBV), which quantifies the amount of blood flowing through a specific brain region within a defined time frame. The accuracy of the quantification of CBV in each voxel of the acquisition grid is affected by the partial volume effect (PVE), which introduces a significant bias in cerebral perfusion measurements. In this research, we introduce an innovative multimodal technique aimed at enhancing the modeling of cerebral blood volume (CBV). This method integrates diverse tissue characteristics into the acquisition volume, effectively tackling and rectifying the issue of partial volumes of brain substances within the acquisition area. Our novel approach effectively mitigates the partial volume effect within the acquisition grid, ultimately providing a high-resolution 3D visualization of CBV. The first results show that we can qualify with more relevance whether a region contains a tumor or not.