PURPOSE: Myocardial perfusion SPECT is an excellent tool for the assessment of coronary artery disease (CAD); however, it is affected by several artifacts, such as patient motion during acquisition, which increases false-positive rates. Therefore, the purpose of this work is to analyze changes in perfusion scores after motion-correction software application. METHODS: The population included 160 (99m)Tc-sestamibi CAD studies, divided into two groups: with and without perfusion defects, equally divided into subgroups according to movement during standard acquisition. A Siemens ECAM 180 was used for processing without correction and with automatic and manual e.soft 2.5 modalities. Visual interpretation as well as QPS software was compared using Pearson correlation and kappa agreement statistics. RESULTS: Moderate agreement was observed between SPECT interpretations after motion correction versus the original report, according to the presence of perfusion defects. Manual correction using the software obtained the lowest agreements. Perfusion summed stress scores (SSS) correlation from different processing modalities versus non-corrected studies differed significantly independent of the degree of motion. Mean SSS in 40 patients with no motion was 3.9 + or - 3.9 when no correction was applied; with automatic correction was 8.8 + or - 10 (p = 0.03) and with manual correction was 3.1 + or - 3.5 (p = ns versus non-corrected). Automatic correction was better when applied to patients with mild to moderate motion. In those with mild or no motion, software overestimated or created new perfusion defects. CONCLUSION: Motion-correction software must be used with caution when trying to optimize myocardial perfusion SPECT based on individual analysis. Acquisition should be always repeated in cases with severe motion and in no or mild motion it seems preferable to avoid correction.
PURPOSE: Myocardial perfusion SPECT is an excellent tool for the assessment of coronary artery disease (CAD); however, it is affected by several artifacts, such as patient motion during acquisition, which increases false-positive rates. Therefore, the purpose of this work is to analyze changes in perfusion scores after motion-correction software application. METHODS: The population included 160 (99m)Tc-sestamibi CAD studies, divided into two groups: with and without perfusion defects, equally divided into subgroups according to movement during standard acquisition. A Siemens ECAM 180 was used for processing without correction and with automatic and manual e.soft 2.5 modalities. Visual interpretation as well as QPS software was compared using Pearson correlation and kappa agreement statistics. RESULTS: Moderate agreement was observed between SPECT interpretations after motion correction versus the original report, according to the presence of perfusion defects. Manual correction using the software obtained the lowest agreements. Perfusion summed stress scores (SSS) correlation from different processing modalities versus non-corrected studies differed significantly independent of the degree of motion. Mean SSS in 40 patients with no motion was 3.9 + or - 3.9 when no correction was applied; with automatic correction was 8.8 + or - 10 (p = 0.03) and with manual correction was 3.1 + or - 3.5 (p = ns versus non-corrected). Automatic correction was better when applied to patients with mild to moderate motion. In those with mild or no motion, software overestimated or created new perfusion defects. CONCLUSION: Motion-correction software must be used with caution when trying to optimize myocardial perfusion SPECT based on individual analysis. Acquisition should be always repeated in cases with severe motion and in no or mild motion it seems preferable to avoid correction.
Authors: J M Links; L C Becker; P Rigo; R Taillefer; L Hanelin; F Anstett; D Burckhardt; L Mixon Journal: J Nucl Cardiol Date: 2000 Sep-Oct Impact factor: 5.952
Authors: R Eisner; A Churchwell; T Noever; D Nowak; K Cloninger; D Dunn; W Carlson; J Oates; J Jones; D Morris Journal: J Nucl Med Date: 1988-01 Impact factor: 10.057
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