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Descripción
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| Respiratory synchronized acquisitions lead to noisy images. Super-resolution techniques deal with the enhancement of several slightly different low-resolution images into a single high-resolution image. A maximum a-posteriori (MAP) superresolution algorithm has been implemented and applied to respiratory gated PET images for motion compensation. The algorithm was tested on a GATE simulated datasets. It consisted of 8 frames of the NeAT phantom with lesions between 1522mm placed throughout the lungs. Images were reconstructed using the OPLEM algorithm. Super-resolution was performed on the gated frames through a MAP algorithm, using a Huber prior as a regularization term to ensure convergence. The optimization of the function yjelded by the MAP method was performed through a steepest descent algorithm. Motion fields were recovered using a previously presented elastic registration algorithm. Image enhancement was assessed by estimating signal to noise ratio (SNR) and contrast in regions of interest. | |
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Internacional
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Si |
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Nombre congreso
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IEEE Nuclear Science Symposium and Medical Imaging Conference |
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Tipo de participación
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960 |
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Lugar del congreso
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Dresden, Alemania |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4244-2714-7 |
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DOI
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Fecha inicio congreso
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19/10/2008 |
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Fecha fin congreso
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25/10/2008 |
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Desde la página
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4285 |
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Hasta la página
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4287 |
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Título de las actas
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IEEE Nuclear Science Symposium Conference Record |