Mutual information optimization and evaluation of single photon emission computed tomography.

In this thesis we study the performance of Single Photon Emission Computed Tomography using concepts and techniques of information theory. Two specific tomographic tasks are considered: image reconstruction and image feature classification. For the image reconstruction problem we derive a necessary and sufficient condition for an aperture to be information optimal. The aperture is nearly a collimator over aperture regions of high fluence, hence sacrificing fluence for better resolution, while it is nearly transparent over regions of low fluence, hence sacrificing resolution for better fluence. Simulations are presented which show that the mutual information of conventional uniform parallel hole apertures can be significantly lower than the maximum achievable information using an optimal aperture. We then study the effect of count loss side information on the mutual information for the reconstruction problem. We derive a lower bound on the information gain achievable from using count corrections. This bound increases with a quantity, the information divergence, measuring spatial dependence of the probability of losing a count from a particular emitter location. It is established that if this spatial dependence is significant high gains can be achieved when the mean number of detected gamma-rays is low. In particular, this implies that count corrections side information can greatly improve performance in dynamic studies where the reconstruction of a time varying mean source distribution mandates multi-stage data acquisition over short time intervals. For the feature classification problem we study the channel cut-off rate which is related to the information transfer from the image features to the projections data. On the basis of the cut-off rate and the information theoretic Fano bound we propose a very simple approximation to the probability of classification error of the optimal Bayes classifier. For the special case of two features, i.e. detection, we determine by simulation that the approximation is quite close to the actual Bayes minimum probability of error. We then study apertures which minimize the minimum probability of error approximation by maximizing the cut-off rate. It is determined that the optimal detection aperture can be simply approximated by an aperture which is reconstruction optimal for the Bayes averaged source distribution.Ph.D.Applied SciencesBiological SciencesBiomedical engineeringBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/128450/2/9014013.pd

Myocardial blood flow measurement with a conventional dual-head SPECT/CT with spatiotemporal iterative reconstructions - a clinical feasibility study.

Cardiac single photon emission computed tomography (SPECT) cameras typically rotate too slowly around a patient to capture changes in the blood pool activity distribution and provide accurate kinetic parameters. A spatiotemporal iterative reconstruction method to overcome these limitations was investigated. Dynamic rest/stress (99m)Tc-methoxyisobutylisonitrile ((99m)Tc-MIBI) SPECT/CT was performed along with reference standard rest/stress dynamic positron emission tomography (PET/CT) (13)N-NH3 in five patients. The SPECT data were reconstructed using conventional and spatiotemporal iterative reconstruction methods. The spatiotemporal reconstruction yielded improved image quality, defined here as a statistically significant (p<0.01) 50% contrast enhancement. We did not observe a statistically significant difference between the correlations of the conventional and spatiotemporal SPECT myocardial uptake K 1 values with PET K 1 values (r=0.25, 0.88, respectively) (p<0.17). These results indicate the clinical feasibility of quantitative, dynamic SPECT/CT using (99m)Tc-MIBI and warrant further investigation. Spatiotemporal reconstruction clearly provides an advantage over a conventional reconstruction in computing K 1

Comparison of axial performance of cone-beam reconstruction algorithms for off-center flat-panel imaging with a SPECT system

In this paper, using Defrise Phantoms, we present our investigations of the axial limitations of an analytical cone-beam reconstruction algorithm (FDK), and the iterative ordered-subset transmission reconstruction (OSTR) iterative algorithm, for an axially extended version of the Philips Brightview XCT cone beam CT (CBCT) geometry. Simulations were preformed for head size and body size Defrise Phantoms of different axial dimensions to investigate limitations on axial extent as a function of these. OSTR yielded overall better axial performance than FDK. It may be possible to reconstruct a larger axial extent for brain studies; however, patients with larger body sizes pose more of a problem. © 2010 IEEE