A tailored ML-EM algorithm for reconstruction of truncated projection data using few view angles

Dedicated cardiac single photon emission computed tomography (SPECT) systems have the advantage of high speed and sensitivity at no loss, or even a gain, in resolution. The potential drawbacks of these dedicated systems are data truncation by the small field of view (FOV) and the lack of view angles...

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Vydáno v:Physics in medicine & biology Ročník 58; číslo 12; s. N157
Hlavní autoři: Mao, Yanfei, Zeng, Gengsheng L
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 21.06.2013
Témata:
Algorithms
Heart - diagnostic imaging
Image Processing, Computer-Assisted - methods
Phantoms, Imaging
Tomography, Emission-Computed, Single-Photon - methods
ISSN:1361-6560, 1361-6560
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Shrnutí:Dedicated cardiac single photon emission computed tomography (SPECT) systems have the advantage of high speed and sensitivity at no loss, or even a gain, in resolution. The potential drawbacks of these dedicated systems are data truncation by the small field of view (FOV) and the lack of view angles. Serious artifacts, including streaks outside the FOV and distortion in the FOV, are introduced to the reconstruction when using the traditional emission data maximum-likelihood expectation-maximization (ML-EM) algorithm to reconstruct images from the truncated data with a small number of views. In this note, we propose a tailored ML-EM algorithm to suppress the artifacts caused by data truncation and insufficient angular sampling by reducing the image updating step sizes for the pixels outside the FOV. As a consequence, the convergence speed for the pixels outside the FOV is decelerated. We applied the proposed algorithm to truncated analytical data, Monte Carlo simulation data and real emission data with different numbers of views. The computer simulation results show that the tailored ML-EM algorithm outperforms the conventional ML-EM algorithm in terms of streak artifacts and distortion suppression for reconstruction from truncated projection data with a small number of views.
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ISSN:1361-6560
1361-6560
DOI:10.1088/0031-9155/58/12/N157