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Evaluation of amplitude-based sorting algorithm to reduce lung tumor blurring in PET images using 4D NCAT phantom.

Abstract：

PURPOSE:develop and validate a PET sorting algorithm based on the respiratory amplitude to correct for abnormal respiratory cycles. METHOD AND MATERIALS:using the 4D NCAT phantom model, 3D PET images were simulated in lung and other structures at different times within a respiratory cycle and noise was added. To validate the amplitude binning algorithm, NCAT phantom was used to simulate one case of five different respiratory periods and another case of five respiratory periods alone with five respiratory amplitudes. Comparison was performed for gated and un-gated images and for the new amplitude binning algorithm with the time binning algorithm by calculating the mean number of counts in the ROI (region of interest). RESULTS:an average of 8.87+/-5.10% improvement was reported for total 16 tumors with different tumor sizes and different T/B (tumor to background) ratios using the new sorting algorithm. As both the T/B ratio and tumor size decreases, image degradation due to respiration increases. The greater benefit for smaller diameter tumor and lower T/B ratio indicates a potential improvement in detecting more problematic tumors.

journal_name

Comput Methods Programs Biomed

journal_title

Computer methods and programs in biomedicine

authors

Wang J,Byrne J,Franquiz J,McGoron A

doi

10.1016/j.cmpb.2007.05.004

subject

Has Abstract

pub_date

2007-08-01 00:00:00

pages

112-22

issue

2

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(07)00108-3

journal_volume

87

pub_type

杂志文章

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