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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Vydáno v:	Physics in medicine & biology Ročník 54; číslo 3; s. 797
Hlavní autoři:	Faber, T L, Raghunath, N, Tudorascu, D, Votaw, J R
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England 07.02.2009
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ISSN:	0031-9155
DOI:	10.1088/0031-9155/54/3/021