Using measurements of water diffusion, dMRI can give unique insights into the microstructure and cellular orientation of tissues. In neurosurgical brain cancer research, dMRI is the only existing method that provides information about the trajectories of the white matter connections (fiber tracts). Neurosurgeons aim to preserve key fiber tracts when surgically removing tumors. dMRI also provides quantitative measurements that may aid in defining the borders of brain tumors, or in distinguishing tumor infiltration from edema. There is a growing awareness in the neurosurgery community that diffusion models must move beyond the current clinical standard of the diffusion tensor for better anatomical accuracy of fiber tracts. But several informatics challenges prevent advances in dMRI from easily reaching clinical cancer researchers: 1) advances in dMRI are not supported by commercial clinical software, 2) dMRI research software is not designed for clinical cancer settings, and 3) a lack of common file format standards prevents interoperability between dMRI software packages. Unlike other popular dMRI packages, the community software package 3D Slicer 4.0 (www.slicer.org) is uniquely positioned to enable novel clinical research in brain cancer because it was designed from the start for patient-specific cancer research. The 3D Slicer software package is an open-source community-based software platform, with 68629 total Slicer downloads around the world in 2013. While the current dMRI capabilities of Slicer are comparable to the technology available in commercial brain cancer neuron navigation software, the basic diffusion tensor model available in 3D Slicer is no longer state of the art for research. Its drawbacks include anatomical inaccuracies in fiber tracts and non-specificity of DTI-derived measurements. We propose to develop the open-source software infrastructure and key clinically-relevant workflows necessary to move toward more advanced dMRI technologies for open-source cancer research using 3D Slicer. In addition, we propose to improve file format interoperability by developing a standalone standards- compliant library for dMRI tractography file formats, based on the newly proposed DICOM supplement for MR diffusion tractography storage. We will collaborate with local and international neurosurgical brain cancer researchers as well as our prostate cancer research collaborators, all of whom use 3D Slicer in their research. Our software dissemination will leverage the infrastructure in place for the community-based Slicer software. The expected outcome is a state-of-the-art suite of dMRI tools in the open-source software 3D Slicer and a standards-compliant tractography file format library. We expect that this open source dMRI technology will enable novel clinical research in brain cancer.
In brain cancer research, diffusion MRI (dMRI) is the only existing method that provides information about the trajectories of the white matter connections, and it also provides novel quantitative measurements that may aid in defining the borders of brain tumors, or in distinguishing tumor infiltration from edema. But dMRI has not yet seen widespread clinical translation, in large part due to the limitations of the current clinical standrd of the diffusion tensor model. We propose to develop a state-of-the-art suite of diffusion MRI tools, incorporating advanced data and modeling beyond the diffusion tensor, into the 3D Slicer community-based open-source software package.
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