Motion correction of PET brain images through deconvolution: I. Theoretical development and analysis in software simulations

Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. Existing correction methods that use known patient motion obtained from tracking devices either require multi-frame acquisitions, detailed knowledge of the scanner, or specialized re...

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Bibliographic Details
Published in:Physics in medicine & biology Vol. 54; no. 3; p. 797
Main Authors: Faber, T L, Raghunath, N, Tudorascu, D, Votaw, J R
Format: Journal Article
Language:English
Published: England 07.02.2009
Subjects:
Algorithms
Computer Simulation
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Models, Neurological
Movement
Positron-Emission Tomography - methods
Reproducibility of Results
Sensitivity and Specificity
ISSN:0031-9155
Online Access:Get more information
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Summary:Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. Existing correction methods that use known patient motion obtained from tracking devices either require multi-frame acquisitions, detailed knowledge of the scanner, or specialized reconstruction algorithms. A deconvolution algorithm has been developed that alleviates these drawbacks by using the reconstructed image to estimate the original non-blurred image using maximum likelihood estimation maximization (MLEM) techniques. A high-resolution digital phantom was created by shape-based interpolation of the digital Hoffman brain phantom. Three different sets of 20 movements were applied to the phantom. For each frame of the motion, sinograms with attenuation and three levels of noise were simulated and then reconstructed using filtered backprojection. The average of the 20 frames was considered the motion blurred image, which was restored with the deconvolution algorithm. After correction, contrast increased from a mean of 2.0, 1.8 and 1.4 in the motion blurred images, for the three increasing amounts of movement, to a mean of 2.5, 2.4 and 2.2. Mean error was reduced by an average of 55% with motion correction. In conclusion, deconvolution can be used for correction of motion blur when subject motion is known.
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ISSN:0031-9155
DOI:10.1088/0031-9155/54/3/021