A penalized-likelihood PET/CT reconstruction algorithm by using prior image model with adaptive beta adjustment

Pao Yue-kong Library Electronic Theses Database

A penalized-likelihood PET/CT reconstruction algorithm by using prior image model with adaptive beta adjustment

 

Author: See, Shiu-king Eric
Title: A penalized-likelihood PET/CT reconstruction algorithm by using prior image model with adaptive beta adjustment
Degree: Eng.D.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Tomography, Emission.
Tomography.
Image reconstruction.
Department: Dept. of Electronic and Information Engineering
Pages: 90 leaves : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2315424
URI: http://theses.lib.polyu.edu.hk/handle/200/5141
Abstract: PET (Positron Emission Tomography) and CT (Computed Tomography) scans are both standard imaging tools that physicians use to pinpoint disease states in the body. A PET scan demonstrates the biological function of the body before anatomical changes take place, while the CT scan provides information about the body's anatomy such as size, shape and location. By combining these two scanning technologies, a PET/CT scan enables physicians to more accurately diagnose and identify cancer, heart disease and brain disorders. As data demonstrating the advantages of PET/CT over MRI and CT in early disease detection continue to accumulate, it is reasonable to anticipate that PET/CT imaging systems could become the first line of defense in optimization of treatment for cancer patients. Due to the statistical nature of nuclear disintegration process and limitations of the detectors, noisy and low resolution PET images can only be obtained by using traditional reconstruction algorithms such as filter backprojection (FBP), expectation maximization - maximum likelihood estimator (EM-MLE) etc. The problem can be resolved in PET-CT system by combining functional and anatomical information from the two modalities at the image reconstruction stage. These include minimize penalized likelihood (PL), minimize cross-entropy (MCE) and maximize posterior (MAP) algorithms. Their main philosophy is that by using the CT image to explore the neighborhood tissue density, uniformity and texture to guide the PET image reconstruction, smooth out the random noise and improve spatial resolution at anatomical boundaries. The objective of this project is to develop a novel combination of the prior anatomical information and the projected data in PET image reconstruction. The new proposed algorithm uses penalized-likelihood regularization method with the prior image model to suppress the Poisson noise in data. A new novel mechanism was developed to adjust the beta factor adaptively. The simulation studies demonstrated that the proposed algorithm yielded significant improvement in the accuracies of standard uptakes value, recovery coefficient and total root mean square error of the reconstructed image as compared with the standard maximum likelihood expectation maximization with post filtering method when the lesion boundary was available in the co-registered CT image.

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