Descripción
|
|
---|---|
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. | |
Internacional
|
Si |
Nombre congreso
|
IEEE Nuclear Science Symposium and Medical Imaging Conference |
Tipo de participación
|
960 |
Lugar del congreso
|
Dresden, Alemania |
Revisores
|
Si |
ISBN o ISSN
|
978-1-4244-2714-7 |
DOI
|
|
Fecha inicio congreso
|
19/10/2008 |
Fecha fin congreso
|
25/10/2008 |
Desde la página
|
4285 |
Hasta la página
|
4287 |
Título de las actas
|
IEEE Nuclear Science Symposium Conference Record |