Improving the Execution Performance of FreeSurfer
A scheme to significantly speed up the processing of MRI with FreeSurfer (FS) is presented. The scheme is aimed at maximizing the productivity (number of subjects processed per unit time) for the use case of research projects with datasets involving many acquisitions. The scheme combines the already existing GPU-accelerated version of the FS workflow with a task-level parallel scheme supervised by a resource scheduler. This allows for an optimum utilization of the computational power of a given hardware platform while avoiding problems with shortages of platform resources. The scheme can be executed on a wide variety of platforms, as its implementation only involves the script that orchestrates the execution of the workflow components and the FS code itself requires no modifications. The scheme has been implemented and tested on a commodity platform within the reach of most research groups (a personal computer with four cores and an NVIDIA GeForce 480 GTX graphics card). Using the scheduled task-level parallel scheme, a productivity above 0.6 subjects per hour is achieved on the test platform, corresponding to a speedup of over six times compared to the default CPU-only serial FS workflow.
freesurfer mri resource scheduler medical imaging gpu cuda