Highly constrained backprojection-local reconstruction (HYPR-LR) has made a dramatic impact on magnetic resonance angiography (MRA) and shows promise for positron emission tomography(PET) because of the improvements in the signal-to-noise ratio(SNR) it provides dynamic images. For PET in particular, HYPR-LR could improve kinetic analysis methods that are sensitive to noise. In this work, the authors closely examine the performance of HYPR-LR in the context of kinetic analysis, they develop an implementation of the algorithm that can be tailored to specific PETimaging tasks to minimize bias and maximize improvement in variance, and they provide a framework for validating the use of HYPR-LR processing for a particular imaging task.Methods:
HYPR-LR can introduce errors into non sparse PET studies that might bias kinetic parameter estimates. An implementation of HYPR-LR is proposed that uses multiple temporally summed composite images that are formed based on the kinetics of the tracer being studied (HYPR-LR-MC). The effects of HYPR-LR-MC and of HYPR-LR using a full composite formed with all the frames in the study (HYPR-LR-FC) on the kinetic analysis of Pittsburgh compound-B ([11C]-PIB) are studied. HYPR-LR processing is compared to spatial smoothing. HYPR-LR processing was evaluated using both simulated and human studies. Nondisplaceable binding potential (BPND) parametric images were generated from fifty noise realizations of the same numerical phantom and eight [11C]-PIB positive human scans before and after HYPR-LR processing or smoothing using the reference region Logan graphical method and receptor parametric mapping (RPM2). The bias and coefficient of variation in the frontal and parietal cortex in the simulated parametric images were calculated to evaluate the absolute performance of HYPR-LR processing. Bias in the human data was evaluated by comparing parametric image BPND values averaged over large regions of interest (ROIs) to Logan estimates of the BPND from TACs averaged over the same ROIs. Variance was assessed qualitatively in the parametric images and semiquantitatively by studying the correlation between voxel BPND estimates from Logan analysis and RPM2.Results:
Both the simulated and human data show that HYPR-LR-FC overestimates BPND values in regions of high [11C]-PIB uptake. HYPR-LR-MC virtually eliminates this bias. Both implementations of HYPR-LR reduce variance in the parametric images generated with both Logan analysis and RPM2, and HYPR-LR-FC provides a greater reduction in variance. This reduction in variance nearly eliminates the noise-dependent Logan bias. The variance reduction is greater for the Logan method, particularly for HYPR-LR-MC, and the variance in the resulting Logan images is comparable to that in the RPM2 images. HYPR-LR processing compares favorably with spatial smoothing, particularly when the data are analyzed with the Logan method, as it provides a reduction in variance with no loss of spatial resolution.Conclusions
: HYPR-LR processing shows significant potential for reducing variance in parametric images, and can eliminate the noise-dependent Logan bias. HYPR-LR-FC processing provides the greatest reduction in variance but introduces a positive bias into the BPND of high-uptake border regions. The proposed method for forming HYPR composite images, HYPR-LR-MC, eliminates this bias at the cost of less variance reduction.
The authors would like to thank Dr. Sterling Johnson of the University of Wisconsin-Madison for providing the human [11C]-PIB data, the UW Cyclotron group for the production of the [11C]-PIB, Dustin Wooten and Ansel Hilmer for assisting in the acquisition of the human data, and Kevin Cheng from Washington University, St. Louis for the helpful discussions.The authors would also like to acknowledge financial support from the University of Wisconsin Medical Scientist Training Program, the University of Wisconsin Department of Radiology, and the NIH Radiological Sciences Training Grant No. T32 CA009206.
III.A. Creation of numerical phantoms
III.B. Acquisition of real data
III.C. HYPR-LR processing and smoothing
III.D. Kinetic analysis
III.D.1. Logan graphical analysis
III.D.2. Receptor parametric mapping (RPM2)
III.E. Data evaluation
III.E.1. Bias and variance in the simulated data
III.E.2. Evaluation of human data
IV.A. Evaluation bias and variance in the simulated data
IV.B. Evaluation of human [11C]-PIB data
- Medical imaging
- Data analysis
- Positron emission tomography
- Medical image noise
- Image analysis
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