Free Surfer Development, Maintenance, and Hardening

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Abstract: FreeSurfer Development, Maintenance, and Hardening Imaging of the human brain has seen explosive growth in the last decade mainly through the various modalities of MRI. The massive amount of data requires automatic and robust tools for analysis. FreeSurfer (FS, surfer.nmr.mgh.harvard.edu) is one of the preeminent tools used for neuroimage analysis. FS has more than 20,000 downloads, and the core FS manuscripts have been cited more than 3000 times. FS is part of the analysis core for many NIH-funded large-scale data acquisition projects such as the Human Connectome Project (HCP), Alzheimer's Disease Neuroimaging Initiative (ADNI), and Frammingham Heart Study (FHS). One third of all ADNI-based publications cite FS. Simply put, much of the innovative research done in neuroimaging would not be possible without FS. Started in 1998, FS is best known for providing detailed and automated anatomical analysis of T1- weighted MRI images, especially for the cortical surface. However, FS anatomical analysis provides an ideal substrate for all modes of brain imaging including functional MRI, diffusion MRI, PET, optical/NIRS, EEG/MEG. FS provides tools to perform these analyses as well as software to integrate with other analysis tools (eg, SPM, FSL, AFNI). FS has been used for presurgical planning and even in the operating room. A software package with a scientific breadth and user based the size of FS’s requires a significant amount of effort just to maintain it. For example, the FS email list receives approximately 3000 posts a year. FS must be continuously and rigorously tested because it is such an integral part of the neuroimaging infrastructure. Users are constantly requesting new functionality and better performance. This proposal will be used to develop, maintain, and harden FS. Specifically, we will make FS more robust by incorporating multiple modalities instead of just T1. We will replace the whole-brain segmentation with an unsupervised method that simultaneously optimizes bias field correction in a multimodal setting. We will implements multivariate analysis tools to assist in the interpretation of data. We will also harden and optimize the FS code base. Finally, we will include tools to assist the user to easily find where the FS analysis fails.