Improved fMRI Time-Series Registration Using Joint Probability Density Priors

Functional MRI (fMRI) time-series studies are plagued by varying degrees of subject head motion. Faithful head motion correction is essential to accurately detect brain activation using statistical analyses of these time-series. Mutual information (MI) based slice-to-volume (SV) registration is used for motion estimation when the rate of change of head position is large. SV registration accounts for head motion between slice acquisitions by estimating an independent rigid transformation for each slice in the time-series. Consequently each MI optimization uses intensity counts from a single time-series slice, making the algorithm susceptible to noise for low complexity endslices (i.e., slices near the top of the head scans). This work focuses on improving the accuracy of MI-based SV registration of end-slices by using joint probability density priors derived from registered high complexity centerslices (i.e., slices near the middle of the head scans). Results show that the use of such priors can significantly improve SV registration accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85928/1/Fessler236.pd

Validation of an automated method to quantify stress-induced ischemia and infarction in rest-stress myocardial perfusion SPECT.

Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation

Image Fusion and Axial Labeling of the Spine

In order to improve radiological diagnosis of back pain and spine disease, two new algorithms have been developed to aid the 75% of Canadians who will suffer from back pain in a given year. With the associated medical imaging required for many of these patients, there is a potential for improvement in both patient care and healthcare economics by increasing the accuracy and efficiency of spine diagnosis. A real-time spine image fusion system and an automatic vertebra/disc labeling system have been developed to address this. Both magnetic resonance (MR) images and computed tomography (CT) images are often acquired for patients. The MR image highlights soft tissue detail while the CT image highlights bone detail. It is desirable to present both modalities on a single fused image containing the clinically relevant detail. The fusion problem was encoded in an energy functional balancing three competing goals for the fused image: 1) similarity to the MR image, 2) similarity to the CT image and 3) smoothness (containing natural transitions). Graph-Cut and convex solutions have been developed. They have similar performance to each other and outperform other fusion methods from recent literature. The convex solution has real-time performance on modern graphics processing units, allowing for interactive control of the fused image. Clinical validation has been conducted on the convex solution based on 15 patient images. The fused images have been shown to increase confidence of diagnosis compared to unregistered MR and CT images, with no change in time for diagnosis based on readings from 5 radiologists. Spinal vertebrae serve as a reference for the location of surrounding tissues, but vertebrae have a very similar appearance to each other, making it time consume for radiologist to keep track of their locations. To automate this, an axial MR labeling algorithm was developed that runs in near real-time. Probability product kernels and fast integral images combined with simple geometric rules were used to classify pixels, slices and vertebrae. Evaluation was conducted on 32 lumbar spine images and 24 cervical spine images. The algorithm demonstrated 99% and 79% accuracy on the lumbar and cervical spine respectively