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

Physics in Medicine and Biology
T L FaberJ R Votaw

Abstract

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 ...Continue Reading

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Citations

May 6, 2016·Medical Physics·J M MukherjeeR Licho
Sep 23, 2018·Medical & Biological Engineering & Computing·A MirandaJ Verhaeghe
Nov 27, 2016·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Nicolas A KarakatsanisHabib Zaidi
May 1, 2021·Sensors·Héctor Espinós-MoratóMaría José Rodríguez-Álvarez
Jun 9, 2021·Physics in Medicine and Biology·Andre Z Kyme, Roger R Fulton

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