Probabilistic deconvolution of PET images using informed priors.

ABSTRACT:

Purpose

We present a probabilistic approach to medical image analysis that requires, and makes use of, explicit prior information provided by a medical expert. Depending on the choice of prior model the method can be used for image enhancement, analysis, and segmentation.

Methods

The methodology is based on a probabilistic approach to medical image analysis, that allows integration of 1) arbitrarily complex prior information (for which realizations can be generated), 2) information about a convolution operator of the imaging system, and 3) information about the noise in the reconstructed image into a posterior probability density. The method was demonstrated on positron emission tomography (PET) images obtained from a phantom and a patient with lung cancer. The likelihood mo

SUBMITTER: Hansen TM 

PROVIDER: S-EPMC11459987 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature