Accelerated median root prior reconstruction for pinhole single-photon emission tomography (SPET)

• Published in: Physics in medicine & biology Vol. 48; no. 13; pp. 1957 - 1969
• Main Authors: Sohlberg, Antti, Ruotsalainen, Ulla, Watabe, Hiroshi, Iida, Hidehiro, Kuikka, Jyrki T
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 07.07.2003; Institute of Physics
• Subjects: Algorithms; Animals; Bayes Theorem; Biological and medical sciences; Image Processing, Computer-Assisted - methods; Likelihood Functions; Male; Medical sciences; Models, Statistical; Myocardium - metabolism; Phantoms, Imaging; Rats; Rats, Sprague-Dawley; Tomography, Emission-Computed, Single-Photon - methods
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/0031-9155/48/13/308

Pinhole collimation can be used to improve spatial resolution in SPET. However, the resolution improvement is achieved at the cost of reduced sensitivity, which leads to projection images with poor statistics. Images reconstructed from these projections using the maximum likelihood expectation maximization (ML-EM) algorithms, which have been used to reduce the artefacts generated by the filtered backprojection (FBP) based reconstruction, suffer from noise/bias trade-off: noise contaminates the images at high iteration numbers, whereas early abortion of the algorithm produces images that are excessively smooth and biased towards the initial estimate of the algorithm. To limit the noise accumulation we propose the use of the pinhole median root prior (PH-MRP) reconstruction algorithm. MRP is a Bayesian reconstruction method that has already been used in PET imaging and shown to possess good noise reduction and edge preservation properties. In this study the PH-MRP algorithm was accelerated with the ordered subsets (OS) procedure and compared to the FBP, OS-EM and conventional Bayesian reconstruction methods in terms of noise reduction, quantitative accuracy, edge preservation and visual quality. The results showed that the accelerated PH-MRP algorithm was very robust. It provided visually pleasing images with lower noise level than the FBP or OS-EM and with smaller bias and sharper edges than the conventional Bayesian methods.