Diffusion tensor imaging (DTI) is a non-invasive magnetic resonance imaging (MRI) modality used to map white matter fiber tracts for a variety of clinical applications; one of which is aiding preoperative assessments for tumor patients. DTI requires numerical computations on multiple diffusion weighted images to calculate diffusion tensors at each voxel and probabilistic tracking¹ to construct fiber tracts, or tractography. Greater accuracy in tractography is possible with larger, more advanced imaging and reconstruction algorithms. However, larger scans and advanced reconstruction is often computationally intensive. The post-processing pipeline involves significant computational resources and time and requires up to 40 minutes of computation time on state-of-the-art hardware. Parallel GPU computations can improve time for the resource-intensive tractography. A collaborative team from DIPY, NVIDIA, and UCSF recently developed a tool, GPUStreamlines, for GPU-enabled tractography² which has been expanded to support the constant solid angle (CSA) reconstruction algorithm³. This GPU-enabled tractography was applied to MRIs of brains with and without presence of lesions, with substantial increases in processing speed. We demonstrate that CSA GPU-enabled tractography in normal controls and patients are comparable to the existing gold standard tractography currently in place at UCSF.