Evaluation of reconstruction techniques for lung single photon emission tomography: a Monte Carlo study.

Abstract:

BACKGROUND:In studies of the distribution of lung function, the image quality of lung single photon emission computed tomography (SPECT) is important and one factor influencing it is the reconstruction algorithm. AIM:To systematically evaluate ordered subsets expectation maximization (OSEM) and compare it with filtered back-projection (FBP) for lung SPECT with Tc. METHODS:The evaluation of the number of iterations used in OSEM was based on the image quality parameter contrast. The comparison between OSEM and FBP was based on trade-off plots between statistical noise and spatial resolution for different filter parameters, collimators and count-levels. A Monte Carlo technique was used to simulate SPECT studies of a digital thorax phantom containing two sets of activity: one with a homogeneous activity distribution within the lungs and the other with superposed high- and low-activity objects. Statistical noise in the reconstructed images was calculated as the coefficient of variation (CV) and spatial resolution as full width at half-maximum (FWHM). RESULTS:For the configuration studied, the OSEM reconstruction in combination with post-filtering should be used in lung SPECT studies with at least 60 MLEM equivalent iterations. Compared to FBP the spatial resolution was improved by about 1 mm. For a constant level of CV, a four-fold increase in count level resulted in an increased resolution of about 2 mm. Spatial resolution and cut-off frequency depends on what value of noise in the image is acceptable also increased by using a low-energy, high-resolution collimator for CV values above 3%. The choice of noise-reducing filter and cut-off frequency depends on what value of noise in the image is acceptable.

journal_name

Nucl Med Commun

authors

Norberg P,Bake B,Jacobsson L,Carlsson GA,Gustafsson A

doi

10.1097/MNM.0b013e3282f1acac

subject

Has Abstract

pub_date

2007-12-01 00:00:00

pages

929-36

issue

12

eissn

0143-3636

issn

1473-5628

pii

00006231-200712000-00009

journal_volume

28

pub_type

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