BACKGROUND:Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation. METHODS AND RESULTS: A total of 492 patients with known or suspected coronary artery disease underwent both stress and rest MPS. The proposed perfusion analysis algorithm (Segment) was trained in 140 patients and validated in the remaining 352 patients usingvisual scoring in MPS by an expert reader as reference standard. Furthermore, validation was performed with simulated perfusion defects in an anthropomorphic computer model. Total perfusion deficit (TPD, range 0-100), including both extent and severity of the perfusion defect, was used as the global measurement of the perfusion defects. Mean bias ± SD between TPD by Segment and the simulated TPD was 3.6 ± 3.8 (R(2) = 0.92). Mean bias ± SD between TPD by Segment and the visual scoring in the patients was 1.2 ± 2.9 (R (2)= 0.64) for stress-induced ischemia and -0.3 ± 3.1 (R(2) = 0.86) for infarction. CONCLUSION: The proposed algorithm can detect and quantify perfusion defects in MPS with good agreement to expert readers and to simulated values in a computer phantom.
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BACKGROUND: Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation. METHODS AND RESULTS: A total of 492 patients with known or suspected coronary artery disease underwent both stress and rest MPS. The proposed perfusion analysis algorithm (Segment) was trained in 140 patients and validated in the remaining 352 patients using visual scoring in MPS by an expert reader as reference standard. Furthermore, validation was performed with simulated perfusion defects in an anthropomorphic computer model. Total perfusion deficit (TPD, range 0-100), including both extent and severity of the perfusion defect, was used as the global measurement of the perfusion defects. Mean bias ± SD between TPD by Segment and the simulated TPD was 3.6 ± 3.8 (R(2) = 0.92). Mean bias ± SD between TPD by Segment and the visual scoring in the patients was 1.2 ± 2.9 (R (2)= 0.64) for stress-induced ischemia and -0.3 ± 3.1 (R(2) = 0.86) for infarction. CONCLUSION: The proposed algorithm can detect and quantify perfusion defects in MPS with good agreement to expert readers and to simulated values in a computer phantom.
Authors: Manuel D Cerqueira; Neil J Weissman; Vasken Dilsizian; Alice K Jacobs; Sanjiv Kaul; Warren K Laskey; Dudley J Pennell; John A Rumberger; Thomas Ryan; Mario S Verani Journal: Circulation Date: 2002-01-29 Impact factor: 29.690
Authors: Milan Lomsky; Peter Gjertsson; Lena Johansson; Jens Richter; Mattias Ohlsson; Deborah Tout; Andries van Aswegen; S Richard Underwood; Lars Edenbrandt Journal: Eur J Nucl Med Mol Imaging Date: 2008-03-04 Impact factor: 9.236
Authors: E V Garcia; C D Cooke; R D Folks; C A Santana; E G Krawczynska; L De Braal; N F Ezquerra Journal: J Nucl Med Date: 2001-08 Impact factor: 10.057
Authors: S R Underwood; C Anagnostopoulos; M Cerqueira; P J Ell; E J Flint; M Harbinson; A D Kelion; A Al-Mohammad; E M Prvulovich; L J Shaw; A C Tweddel Journal: Eur J Nucl Med Mol Imaging Date: 2004-02 Impact factor: 9.236