Michael Loecher1,2, Luigi E Perotti3, and Daniel B Ennis1,2,4,5
1Radiology, Stanford University, Stanford, CA, United States, 2Radiology, Veterans Affairs Health Care System, Palo Alto, CA, United States, 3Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States, 4Maternal & Child Health Research Institute, Stanford University, Stanford, CA, United States, 5Cardiovascular Institute, Stanford University, Stanford, CA, United States
1Radiology, Stanford University, Stanford, CA, United States, 2Radiology, Veterans Affairs Health Care System, Palo Alto, CA, United States, 3Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States, 4Maternal & Child Health Research Institute, Stanford University, Stanford, CA, United States, 5Cardiovascular Institute, Stanford University, Stanford, CA, United States
This work introduces a neural network for tracking
myocardial motion in cine grid tagged MRI images on a voxel-by-voxel basis,
which is trained from a large synthetic motion dataset. Voxel-wise displacement tracking is
demonstrated, as well as strain values that show improved quality.
Figure 2: Animation of the tracking network
output. The left panel shows a cropped
image of the input grid tagged data.
This middle panel shows the tracked points overlayed on the image
throughout cardiac cycle. The right
panel shows the displacement vectors of the tracked points (only 25% of points
included for visibility).
Figure 4: A) Ecc maps for both tracking
methods, where similar values can be seen, with less blurring on the voxel
tracked map. B) Corresponding Ecc
curve from (A), where very similar values are seen. C) Err maps from both methods,
where voxel tracking corresponds to higher values and less blurring. D) Err curves for this case, where
higher Err is evident.
