Tomographic Image Reconstruction and Quantification for PET/SPECT: Non-uniform Resolution and Partial Volume Recovery Methods - Couverture souple

AHMAD, Munir

 
9783639214215: Tomographic Image Reconstruction and Quantification for PET/SPECT: Non-uniform Resolution and Partial Volume Recovery Methods

Synopsis

Tomographic imaging devices, such as PET/SPECT, suffer from their low resolution capabilities and produce images having partial volume error and non- uniform reconstructed resolution across their field of view. This results in quantification errors and hinders the optimum use of these images in diagnostic and therapeutic applications. This writing provides a detailed insight into the tomographic imaging, image reconstruction methods and proposes recovery methods for non-uniform resolution and partial volume errors in Tomographic image reconstruction. Specifically, it details the resolution characteristics of median root based priors and their comparison with the standard quadratic priors and in general discusses their implementation for the recovery of above mentioned errors in reconstructed images for histogram and List-Mode data reconstruction methods. Readers in medical imaging and image reconstruction methods may benefit from this book including an understanding of basic imaging physics and mathematics.

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Présentation de l'éditeur

Tomographic imaging devices, such as PET/SPECT, suffer from their low resolution capabilities and produce images having partial volume error and non- uniform reconstructed resolution across their field of view. This results in quantification errors and hinders the optimum use of these images in diagnostic and therapeutic applications. This writing provides a detailed insight into the tomographic imaging, image reconstruction methods and proposes recovery methods for non-uniform resolution and partial volume errors in Tomographic image reconstruction. Specifically, it details the resolution characteristics of median root based priors and their comparison with the standard quadratic priors and in general discusses their implementation for the recovery of above mentioned errors in reconstructed images for histogram and List-Mode data reconstruction methods. Readers in medical imaging and image reconstruction methods may benefit from this book including an understanding of basic imaging physics and mathematics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.