Motion Correction Algorithm for fMRI

Inventors

Erik Beall, PhD
Mark Lowe, PhD

Questions?


What is it? What does it do?

Head motion during functional MRI acquisition is a big problem that can lead to corrupted data. MRI users today use motion correction provided by MRI or software vendors. Unfortunately, most of the motion is invisible to these correction methods and the resulting data is often corrupted.  This algorithm utilizes a sliced-based motion correction technique to track and correct for these heretofore invisible motion effects in functional MRI studies. This algorithm, SLice-Oriented MOtion COrrection (SLOMOCO), is fully retrospective and can help reduce the need to repeat fMRI scans that have been corrupted by head motion.
 

Why is it better?

Functional MRI data consists of a volume of slices, where each slice is acquired at a different time within the volume. Because it takes seconds to acquire the volume, but only tenths of a second to acquire each slice, motion can impact individual slices differently within a single volume. However, accurately tracking the motion of individual slices has historically proven elusive. Therefore, existing volume-based algorithms simplify the correction by assuming the entire volume is affected equally and summing motion across all slices in a given volume. This can lead to under-correcting motion in some or all slices, or amplifying motion in some slices, and that is why SLOMOCO detects motion of individual slices to more accurately detect motion, resulting in intra-volume motion correction.
 

What is its current status?

Patent pending and effectiveness of software has been highlighted in the following publication:
Beall, Lowe. SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction. Neuroimage. June 2014. 
 


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