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Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging. [Display omitted] [ABSTRACT FROM AUTHOR]

Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging. The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans. The novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC score categories (0, 1-100, 101-400, and >400) was analyzed and CAC scores derived from electrocardiographically gated CT scans (standard scores) by expert observers were compared with automatic DL scores from CTAC scans. Automatic DL scoring required <6 seconds per scan. DL CTAC scores provided stepwise increase in the risk for MACE across the CAC score categories (HR up to 3.2; P < 0.001). Net reclassification improvement of standard CAC scores over DL CTAC scores was nonsignificant (−0.02; 95% CI: −0.11 to 0.07). The negative predictive values for MACE of zero CAC with standard (85%) and DL CTAC (83%) CAC scores were similar (P = 0.19). DL CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated electrocardiographically gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) has been previously applied for prognostication in patients undergoing SPECT myocardial perfusion imaging (MPI). We evaluated whether including attenuation CT coronary artery calcification (CAC) scoring improves ML prediction of major adverse cardiovascular events (MACE) in patients undergoing SPECT/CT MPI. Methods: From the REFINE SPECT Registry 4770 patients with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 45% female). ML algorithm (XGBoost) inputs were clinical risk factors, stress variables, SPECT imaging parameters, and expert-observer CAC scoring using CT attenuation correction scans performed to obtain CT attenuation maps. The ML model was trained and validated using tenfold hold-out validation. Receiver Operator Characteristics (ROC) curves were analyzed for prediction of MACE. MACE-free survival was evaluated with standard survival analyses. Results: During a median follow-up of 24.1 months, 475 patients (10%) experienced MACE. Higher area under the ROC curve for MACE was observed with ML when CAC scoring was included (CAC-ML score, 0.77, 95% confidence interval [CI] 0.75–0.79) compared to ML without CAC (ML score, 0.75, 95% CI 0.73–0.77, P =.005) and when compared to CAC score alone (0.71, 95% CI 0.68–0.73, P <.001). Among clinical, imaging, and stress parameters, CAC score had highest variable importance for ML. On survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (hazard ratio 5.3, 95% CI 4.3–6.5, P <.001). Conclusion: Integration of attenuation CT CAC scoring improves the predictive value of ML risk score for MACE prediction in patients undergoing SPECT MPI. [ABSTRACT FROM AUTHOR]

N survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (CI 5.2, 95% CI 4.2-6.5, I P i <.001)." The positive and negative NRI were 0.06 (95% CI 0.02, 0.1) and - 0.01 (95% CI - 0.02, - 0.004), respectively"."Patients with high CAC-ML score (> 0.091, had higher event rate when compared to patients with low CAC-ML score (< 0.091, HR 5.2, 95% CI 4.2-6.5, I P i <.001) (Figure 4B).". [Extracted from the article]

Background: We hypothesized early post-stress left ventricular ejection fraction reserve (EFR) on solid-state-SPECT is associated with major cardiac adverse events (MACE). Methods: 151 patients (70 ± 12 years, male 50%) undergoing same-day rest/regadenoson stress 99mTc-sestamibi solid-state SPECT were followed for MACE. Rest imaging was performed in the upright and supine positions. Early stress imaging was started 2 minutes after the regadenoson injection in the supine position and followed by late stress acquisition in the upright position. Total perfusion deficit (TPD) and functional parameters were quantified automatically. EFR, ∆end-diastolic volume (EDV), and end-systolic volume (ESV) were calculated as the difference between stress and rest values in the same position. EFR < 0%, ∆EDV ≥ 5 ml, or ∆ESV ≥ 5 ml was defined as abnormal. Results: During the follow-up (mean 3.2 years), 28 MACE occurred (19%). In Kaplan–Meier analysis, there was a significantly decreased event-free survival in patients with early EFR < 0% (P = 0.004). Similarly, there was a decreased event-free survival in patients with ∆ESV ≥ 5 ml at early stress (P = 0.003). However, EFR, ∆EDV, and ∆ESV at late stress were not associated with MACE-free survival. Cox proportional hazards model adjusting for clinical information and stress TPD demonstrated that EFR, ∆EDV, and ∆ESV at early stress were significantly associated with MACE (P < 0.05 for all). Conclusions: Reduced early post-stress EFR on vasodilator stress solid-state SPECT is associated with MACE. [ABSTRACT FROM AUTHOR]

Objectives: This paper describes a novel approach (same-patient processing, or SPP) aimed at improving left ventricular segmentation accuracy in patients with multiple SPECT studies, and evaluates its performance compared to conventional processing in a large population of 962 patients undergoing rest and stress electrocardiography-gated SPECT MPI, for a total of 5,772 image datasets (6 per patient).Methods: Each dataset was independently processed using a standard algorithm, and a shape quality control score (SQC) was produced for every segmentation. Datasets with a SQC score higher than a specific threshold, suggesting algorithmic failure, were automatically reprocessed with the SPP-modified algorithm, which incorporates knowledge of the segmentation mask location in the other datasets belonging to the same patient. Experienced operators blinded as to whether datasets had been processed based on the standard or SPP approach assessed segmentation success/failure for each dataset.Results: The SPP approach reduced segmentation failures from 219/5772 (3.8%) to 42/5772 (0.7%) overall, with particular improvements in attenuation corrected (AC) datasets with high extra-cardiac activity (from 100/962 (10.4%) to 12/962 (1.4%) for rest AC, and from 41/962 (4.3%) to 9/962 (0.9%) for stress AC). The number of patients who had at least one of their 6 datasets affected by segmentation failure decreased from 141/962 (14.7%) to 14/962 (1.7%) using the SPP approach.Conclusion: Whenever multiple image datasets for the same patient exist and need to be processed, it is possible to deal with the images as a group rather than individually. The same-patient processing approach can be implemented automatically, and may substantially reduce the need for manual reprocessing due to cardiac segmentation failure. [ABSTRACT FROM AUTHOR]

Objectives This study evaluated the added predictive value of combining clinical information and myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) data using machine learning (ML) to predict major adverse cardiac events (MACE). Background Traditionally, prognostication by MPI has relied on visual or quantitative analysis of images without objective consideration of the clinical data. ML permits a large number of variables to be considered in combination and at a level of complexity beyond the human clinical reader. Methods A total of 2,619 consecutive patients (48% men; 62 ± 13 years of age) who underwent exercise (38%) or pharmacological stress (62%) with high-speed SPECT MPI were monitored for MACE. Twenty-eight clinical variables, 17 stress test variables, and 25 imaging variables (including total perfusion deficit [TPD]) were recorded. Areas under the receiver-operating characteristic curve (AUC) for MACE prediction were compared among: 1) ML with all available data (ML-combined); 2) ML with only imaging data (ML-imaging); 3) 5-point scale visual diagnosis (physician [MD] diagnosis); and 4) automated quantitative imaging analysis (stress TPD and ischemic TPD). ML involved automated variable selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross validation. Results During follow-up (3.2 ± 0.6 years), 239 patients (9.1%) had MACE. MACE prediction was significantly higher for ML-combined than ML-imaging (AUC: 0.81 vs. 0.78; p < 0.01). ML-combined also had higher predictive accuracy compared with MD diagnosis, automated stress TPD, and automated ischemic TPD (AUC: 0.81 vs. 0.65 vs. 0.73 vs. 0.71, respectively; p < 0.01 for all). Risk reclassification for ML-combined compared with visual MD diagnosis was 26% (p < 0.001). Conclusions ML combined with both clinical and imaging data variables was found to have high predictive accuracy for 3-year risk of MACE and was superior to existing visual or automated perfusion assessments. ML could allow integration of clinical and imaging data for personalized MACE risk computations in patients undergoing SPECT MPI. [ABSTRACT FROM AUTHOR]

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Background: Obesity is a common source of artifact on conventional SPECT myocardial perfusion imaging (MPI). We evaluated image quality and diagnostic performance of high-efficiency (HE) cadmium-zinc-telluride parallel-hole SPECT MPI for coronary artery disease (CAD) in obese patients. Methods and Results: 118 consecutive obese patients at three centers (BMI 43.6 ± 8.9 kg·m, range 35-79.7 kg·m) had upright/supine HE-SPECT and invasive coronary angiography > 6 months (n = 67) or low likelihood of CAD (n = 51). Stress quantitative total perfusion deficit (TPD) for upright (U-TPD), supine (S-TPD), and combined acquisitions (C-TPD) was assessed. Image quality (IQ; 5 = excellent; < 3 nondiagnostic) was compared among BMI 35-39.9 (n = 58), 40-44.9 (n = 24) and ≥45 (n = 36) groups. ROC curve area for CAD detection (≥50% stenosis) for U-TPD, S-TPD, and C-TPD were 0.80, 0.80, and 0.87, respectively. Sensitivity/specificity was 82%/57% for U-TPD, 74%/71% for S-TPD, and 80%/82% for C-TPD. C-TPD had highest specificity ( P = .02). C-TPD normalcy rate was higher than U-TPD (88% vs 75%, P = .02). Mean IQ was similar among BMI 35-39.9, 40-44.9 and ≥45 groups [4.6 vs 4.4 vs 4.5, respectively ( P = .6)]. No patient had a nondiagnostic stress scan. Conclusions: In obese patients, HE-SPECT MPI with dedicated parallel-hole collimation demonstrated high image quality, normalcy rate, and diagnostic accuracy for CAD by quantitative analysis of combined upright/supine acquisitions. [ABSTRACT FROM AUTHOR]

Background: Transient ischemic dilation (TID) of the left ventricle in myocardial perfusion SPECT (MPS) has been shown to be a clinically useful marker of severe coronary artery disease (CAD). However, TID has not been evaluated for 99mTc-sestamibi rest/stress protocols (Mibi-Mibi). We aimed to develop normal limits and evaluate diagnostic power of TID ratio for Mibi-Mibi scans. Methods: TID ratios were automatically derived from static rest/stress MPS (TID) and gated rest/stress MPS from the end-diastolic phase (TID) in 547 patients who underwent Mibi-Mibi scans [215 patients with correlating coronary angiography and 332 patients with low likelihood (LLk) of CAD]. Scans were classified as severe (≥70% stenosis in proximal left anterior descending (pLAD) artery or left main (LM), or ≥90% in ≥2 vessels), mild to moderate (≥90% stenosis in 1 vessel or ≥70%-90% in ≥1 vessel except pLAD or LM), and normal (<70% stenosis or LLk group). Another classification based on the angiographic Duke prognostic CAD index (DI) was also applied: DI ≥ 50, 30 ≤ DI < 50 and DI < 30 or LLk group. Results: The upper normal limits were 1.19 for TID and 1.23 for TID as established in 259 LLk patients. Both ratios increased with disease severity ( P < .0001). Incidence of abnormal TID increased from 2% in normal patients to >36% in patients with severe CAD. Similarly, when DI was used to classify disease severity, the average ratios showed significant increasing trend with DI increase ( P < .003); incidence of abnormal TID also increased with increasing DI. The incidence of abnormal TID in the group with high perfusion scores significantly increased compared to the group with low perfusion scores (stress total perfusion deficit, TPD < 3%) ( P < .0001). The sensitivity for detecting severe CAD improved for TID when added to mild to moderate perfusion abnormality (3% ≤ TPD < 10%): 71% vs 64%, P < .05; and trended to improve for TID/TID: 69% vs 64%, P = .08, while the accuracy remained consistent if abnormal TID was considered as a marker in addition to stress TPD. Similar results were obtained when DI was used for the definition of severe CAD (sensitivity: 76% vs 66%, P < .05 when TID was combined with stress TPD). Conclusion: TID ratios obtained from gated or ungated Mibi-Mibi MPS and are useful markers of severe CAD. [ABSTRACT FROM AUTHOR]

Background: Attenuation corrected myocardial perfusion SPECT (AC-MPS) has been demonstrated to improve the specificity of detecting coronary artery disease (CAD) by visual analysis which utilizes both non-corrected (NC) and AC data. However, the combined automated quantification of NC and AC-MPS has not been previously described. We aimed to develop a combined quantitative analysis from AC and NC data to improve the accuracy of automated detection of CAD from AC-MPS.Methods: Stress total perfusion deficit (TPD) values were generated by standard analysis for NC (NC-TPD), AC (AC-TPD) and by combined NC-AC analysis (NA-TPD), in which the hypoperfusion severity in each polar map location was defined as the average of AC and NC severity computed by comparison with separate AC and NC normal limits. Ischemic TPD was also calculated as the difference between stress TPD and rest TPD for each measure. Stress/rest Tc-99m sestamibi MPS studies in 650 patients with correlating coronary angiography and in 345 patients with a low-likelihood (LLk) of CAD were used to assess diagnostic performance of combined NC-AC analysis.Results: NA-TPD had a higher receiver-operator-characteristic area under the curve (ROC-AUC) (0.87) than NC-TPD (0.85; P < .01) or AC-TPD (0.85; P < .01) for detection of stenosis >or=70% in angiographic group. It also had higher specificity (75%) vs NC-TPD (65%; P < .0001), or AC-TPD (70%; P = .016). In LLk group, the normalcy rate of NA-TPD (95%) was higher than for NC-TPD (90%; P < .01) and similar to AC-TPD (94%; P = NS). NA-TPD had higher ROC-AUC than that for 17-segment expert visual scoring of stress scans in angiographic group (0.84; P = .01), comparable accuracy (81%) and similar normalcy rates (95% vs 97%; P = NS). Ischemic TPD by combined NC-AC analysis had higher ROC-AUC than that for any ischemic measure. Similar to stress NA-TPD, it also obtained the similar performance results as compared with ischemic TPD based on NC or AC and higher sensitivity (89% vs 85%; P = .0295) as compared with ischemic visual score in angiographic group.Conclusion: Combined NC-AC MPS quantification using either stress or ischemic TPD shows significant improvements for ROC-AUC and specificity of MPS in the detection of CAD compared with standard NC-MPS or AC-MPS and comparable performance to expert visual scoring. This technique may lead to an enhancement in a fully automated quantification for the perfusion analysis by AC-MPS. [ABSTRACT FROM AUTHOR]

Attenuation corrected myocardial perfusion SPECT (AC-MPS) has been demonstrated to improve the specificity of detecting coronary artery disease (CAD) by visual analysis which utilizes both non-corrected (NC) and AC data. However, the combined automated quantification of NC and AC-MPS has not been previously described. We aimed to develop a combined quantitative analysis from AC and NC data to improve the accuracy of automated detection of CAD from AC-MPS. Stress total perfusion deficit (TPD) values were generated by standard analysis for NC (NC-TPD), AC (AC-TPD) and by combined NC-AC analysis (NA-TPD), in which the hypoperfusion severity in each polar map location was defined as the average of AC and NC severity computed by comparison with separate AC and NC normal limits. Ischemic TPD was also calculated as the difference between stress TPD and rest TPD for each measure. Stress/rest Tc-99m sestamibi MPS studies in 650 patients with correlating coronary angiography and in 345 patients with a low-likelihood (LLk) of CAD were used to assess diagnostic performance of combined NC-AC analysis. NA-TPD had a higher receiver-operator-characteristic area under the curve (ROC-AUC) (0.87) than NC-TPD (0.85; P < .01) or AC-TPD (0.85; P < .01) for detection of stenosis ≥70% in angiographic group. It also had higher specificity (75%) vs NC-TPD (65%; P < .0001), or AC-TPD (70%; P = .016). In LLk group, the normalcy rate of NA-TPD (95%) was higher than for NC-TPD (90%; P < .01) and similar to AC-TPD (94%; P = NS). NA-TPD had higher ROC-AUC than that for 17-segment expert visual scoring of stress scans in angiographic group (0.84; P = .01), comparable accuracy (81%) and similar normalcy rates (95% vs 97%; P = NS). Ischemic TPD by combined NC-AC analysis had higher ROC-AUC than that for any ischemic measure. Similar to stress NA-TPD, it also obtained the similar performance results as compared with ischemic TPD based on NC or AC and higher sensitivity (89% vs 85%; P = .0295) as compared with ischemic visual score in angiographic group. Combined NC-AC MPS quantification using either stress or ischemic TPD shows significant improvements for ROC-AUC and specificity of MPS in the detection of CAD compared with standard NC-MPS or AC-MPS and comparable performance to expert visual scoring. This technique may lead to an enhancement in a fully automated quantification for the perfusion analysis by AC-MPS. [ABSTRACT FROM AUTHOR]