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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]

Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ 2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.

Background: Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification., Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves., Findings: End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001)., Interpretations: CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value., Competing Interests: Declaration of interests RJM received grant support and consulting fees from Pfizer. TDR received research grant support from GE Healthcare and Advanced Accelerator Applications. AJE received speaker fees from Ionetix, consulting fees from W. L. Gore & Associates, authorship fees from Wolters Kluwer Healthcare. AJE served on a scientific advisory board for Canon Medical Systems and received grants from Attralus, Bruker, Canon Medical Systems, Eidos Therapeutics, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. EJM received grant support from and is a consultant for GE Healthcare. MB was supported by a research award from the Kosciuszko Foundation – The American Centre of Polish Culture. DB and PS participate in software royalties for QPS software at Cedars-Sinai Medical Center. DB served as a consultant for GE Healthcare. PS received research grant support from Siemens Medical Systems, and consulting fees from Synektik S.A. Other Authors declared no competing interests.

Background: 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., Objectives: The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility., Methods: 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., Results: 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%., Conclusions: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging., Competing Interests: Funding Support and Author Disclosures This research was supported in part by the National Heart, Lung, and Blood Institute at the National Institutes of Health grant R35HL161195 (Principal Investigator: Dr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Mr Kavanagh has received software royalties for QPS software at Cedars-Sinai Medical Center. Dr Miller has received consulting fees and research support from Pfizer; and has served as a consultant for GE Healthcare. Dr Newby is supported by the British Heart Foundation; is a recipient of a Wellcome Trust Senior Investigator Award (WT103782AIA); and has received honoraria for consultancy and lectures from AstraZeneca. Dr Berman has received software royalties for QPS software at Cedars-Sinai Medical Center; and has served as a consultant for GE Healthcare. Dr Slomka has received software royalties for QPS software at Cedars-Sina Medical Center, research grant support from Siemens Medical Systems, and consulting fees from Synektik., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Aims: Vessel-specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram (ECG) gated and attenuation correction (AC) computed tomography (CT) in a large multi-centre registry., Methods and Results: Vessel-specific CAC was assessed in the left main/left anterior descending (LM/LAD), left circumflex (LCX), and right coronary artery (RCA) using a DL model trained on 3000 gated CT and tested on 2094 gated CT and 5969 non-gated AC CT. Vessel-specific agreement was assessed with linear weighted Cohen's Kappa for CAC zero, 1-100, 101-400, and >400 Agatston units (AU). Risk of major adverse cardiovascular events (MACE) was assessed during 2.4 ± 1.4 years follow-up, with hazard ratios (HR) and 95% confidence intervals (CI). There was strong to excellent agreement between DL and expert ground truth for CAC in LM/LAD, LCX and RCA on gated CT [0.90 (95% CI 0.89 to 0.92); 0.70 (0.68 to 0.73); 0.79 (0.77 to 0.81)] and AC CT [0.78 (0.77 to 0.80); 0.60 (0.58 to 0.62); 0.70 (0.68 to 0.71)]. MACE occurred in 242 (12%) undergoing gated CT and 841(14%) of undergoing AC CT. LM/LAD CAC >400 AU was associated with the highest risk of MACE on gated (HR 12.0, 95% CI 7.96, 18.0, P < 0.001) and AC CT (HR 4.21, 95% CI 3.48, 5.08, P < 0.001)., Conclusion: Vessel-specific CAC assessment with DL can be performed accurately and rapidly on gated CT and AC CT and provides important prognostic information., Competing Interests: Conflict of interest: M.C.W. has given talks for Canon Medical Systems and Siemens Healthineers. Dr. Robert Miller has received consulting fees and research support from Pfizer. Drs. Berman and Slomka and Mr. Kavanagh participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr. Berman is a consultant for GE Healthcare and Dr. Edward Miller has served as Pfizer, Eidos, CSL Behring, Anylam, and GE Healthcare consultant with grant support from Eidos, Pfizer, and Anylam. Dr. Slomka has received research grant support from Siemens Medical Systems. The remaining authors have no relevant disclosures., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and imaging parameters could predict hospitalization for acute HF exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging. Methods: The HF risk prediction model was developed using data from 4,766 patients who underwent SPECT/CT at a single center (internal cohort). The algorithm used clinical risk factors, stress variables, SPECT imaging parameters, and fully automated deep learning-generated calcium scores from attenuation CT scans. The model was trained and validated using repeated hold-out (10-fold cross-validation). External validation was conducted on a separate cohort of 2,912 patients. During a median follow-up of 1.9 y, 297 patients (6%) in the internal cohort were admitted for HF exacerbation. Results: The final model demonstrated a higher area under the receiver-operating-characteristic curve (0.87 ± 0.03) for predicting HF admissions than did stress left ventricular ejection fraction (0.73 ± 0.05, P < 0.0001) or a model developed using only clinical parameters (0.81 ± 0.04, P < 0.0001). These findings were confirmed in the external validation cohort (area under the receiver-operating-characteristic curve: 0.80 ± 0.04 for final model, 0.70 ± 0.06 for stress left ventricular ejection fraction, 0.72 ± 0.05 for clinical model; P < 0.001 for all). Conclusion: Integrating SPECT myocardial perfusion imaging into an artificial intelligence-based risk assessment algorithm improves the prediction of HF hospitalization. The proposed method could enable early interventions to prevent HF hospitalizations, leading to improved patient care and better outcomes., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making., (© 2024. The Author(s).)

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging., (© 2024. The Author(s).)

Purpose: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI)., Methods: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m 2 , prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis., Results: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m 2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m 2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m 2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28])., Conclusions: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m 2 . These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Motion correction (MC) affects myocardial blood flow (MBF) measurements in 82 Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). Methods: In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training ( n = 112) and validation ( n = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. Results: In the validation group ( n = 112), MBF measurements with automated and manual MC showed strong correlations ( r = 0.98 for stress MBF and r = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group ( n = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all P < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; P < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; P = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; P = 0.067). Conclusion: Automated MC on 82 Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

We investigated the prognostic utility of visually estimated coronary artery calcification (VECAC) from low dose computed tomography attenuation correction (CTAC) scans obtained during SPECT/CT myocardial perfusion imaging (MPI), and assessed how it compares to coronary artery calcifications (CAC) quantified by calcium score on CTACs (QCAC). From the REFINE SPECT Registry 4,236 patients without prior coronary stenting with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 47% female). VECAC in each coronary artery (left main, left anterior descending, circumflex, and right) were scored separately as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe), yielding a possible score of 0-12 for each patient (overall VECAC grade zero:0, mild:1-2, moderate: 3-5, severe: >5). CAC scoring of CTACs was performed at the REFINE SPECT core lab with dedicated software. VECAC was correlated with categorized QCAC (zero: 0, mild: 1-99, moderate: 100-399, severe: ≥400). A high degree of correlation was observed between VECAC and QCAC, with 73% of VECACs in the same category as QCAC and 98% within one category. There was substantial agreement between VECAC and QCAC (weighted kappa: 0.78 with 95% confidence interval: 0.76-0.79, p < 0.001). During a median follow-up of 25 months, 372 patients (9%) experienced major adverse cardiovascular events (MACE). In survival analysis, both VECAC and QCAC were associated with MACE. The area under the receiver operating characteristic curve for 2-year-MACE was similar for VECAC when compared to QCAC (0.694 versus 0.691, p = 0.70). In conclusion, visual assessment of CAC on low-dose CTAC scans provides good estimation of QCAC in patients undergoing SPECT/CT MPI. Visually assessed CAC has similar prognostic value for MACE in comparison to QCAC., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: 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., Objectives: The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans., Methods: 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., Results: 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)., Conclusions: 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., Competing Interests: Funding Support and Author Disclosures This research was supported in part by grant R01HL089765 from the National Heart, Lung, and Blood Institute/National Institutes of Health (principal investigator, Piotr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr Pieszko was supported by a research scholarship from the Polish National Agency for Academic Exchange. Cedars-Sinai has a pending patent application on the use of convolutional long short-term memory for multislice medical image segmentation. Drs Berman, Slomka, and Van Kriekinge and Mr Kavanagh participate in software royalties for nuclear cardiology software at Cedars-Sinai Medical Center. Dr Slomka has received research grant support from Siemens Medical Systems. Dr Berman has served as a consultant for GE Healthcare. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model, including correlation with expert annotations and associations with major adverse cardiovascular events (MACE). Methods: We trained a convolutional long short-term memory DL model to automatically quantify CAC on CTAC scans using 6,608 studies (2 centers) and evaluated the model in an external cohort of patients without known coronary artery disease ( n = 2,271) obtained in a separate center. We assessed agreement between DL and expert annotated CAC scores. We also assessed associations between MACE (death, revascularization, myocardial infarction, or unstable angina) and CAC categories (0, 1-100, 101-400, or >400) for scores manually derived by experienced readers and scores obtained fully automatically by DL using multivariable Cox models (adjusted for age, sex, past medical history, perfusion, and ejection fraction) and net reclassification index. Results: In the external testing population, DL CAC was 0 in 908 patients (40.0%), 1-100 in 596 (26.2%), 100-400 in 354 (15.6%), and >400 in 413 (18.2%). Agreement in CAC category by DL CAC and expert annotation was excellent (linear weighted κ, 0.80), but DL CAC was obtained automatically in less than 2 s compared with about 2.5 min for expert CAC. DL CAC category was an independent risk factor for MACE with hazard ratios in comparison to a CAC of zero: CAC of 1-100 (2.20; 95% CI, 1.54-3.14; P < 0.001), CAC of 101-400 (4.58; 95% CI, 3.23-6.48; P < 0.001), and CAC of more than 400 (5.92; 95% CI, 4.27-8.22; P < 0.001). Overall, the net reclassification index was 0.494 for DL CAC, which was similar to expert annotated CAC (0.503). Conclusion: DL CAC from SPECT/CT attenuation maps agrees well with expert CAC annotations and provides a similar risk stratification but can be obtained automatically. DL CAC scores improved classification of a significant proportion of patients as compared with SPECT myocardial perfusion alone., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

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., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-AC (NC) SPECT, without the need for CT. Methods: SPECT myocardial perfusion imaging was performed using 99m Tc-sestamibi or 99m Tc-tetrofosmin on contemporary scanners with solid-state detectors. We developed a conditional generative adversarial neural network that applies a deep learning model (DeepAC) to generate simulated AC SPECT images. The model was trained with short-axis NC and AC images performed at 1 site ( n = 4,886) and was tested on patients from 2 separate external sites ( n = 604). We assessed the diagnostic accuracy of the stress total perfusion deficit (TPD) obtained from NC, AC, and DeepAC images for obstructive coronary artery disease (CAD) with area under the receiver-operating-characteristic curve. We also quantified the direct count change among AC, NC, and DeepAC images on a per-voxel basis. Results: DeepAC could be obtained in less than 1 s from NC images; area under the receiver-operating-characteristic curve for obstructive CAD was higher for DeepAC TPD (0.79; 95% CI, 0.72-0.85) than for NC TPD (0.70; 95% CI, 0.63-0.78; P < 0.001) and similar to AC TPD (0.81; 95% CI, 0.75-0.87; P = 0.196). The normalcy rate in the low-likelihood-of-coronary-disease population was higher for DeepAC TPD (70.4%) and AC TPD (75.0%) than for NC TPD (54.6%, P < 0.001 for both). The positive count change (increase in counts) was significantly higher for AC versus NC (median, 9.4; interquartile range, 6.0-14.2; P < 0.001) than for AC versus DeepAC (median, 2.4; interquartile range, 1.3-4.2). Conclusion: In an independent external dataset, DeepAC provided improved diagnostic accuracy for obstructive CAD, as compared with NC images, and this accuracy was similar to that of actual AC. DeepAC simplifies the task of artifact identification for physicians, avoids misregistration artifacts, and can be performed rapidly without the need for CT hardware and additional acquisitions., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: We sought to evaluate inter-scan and inter-reader agreement of coronary calcium (CAC) scores obtained from dedicated, ECG-gated CAC scans (standard CAC scan) and ultra-low-dose, ungated computed tomography attenuation correction (CTAC) scans obtained routinely during cardiac PET/CT imaging., Methods: From 2928 consecutive patients who underwent same-day 82 Rb cardiac PET/CT and gated CAC scan in the same hybrid PET/CT scanning session, we have randomly selected 200 cases with no history of revascularization. Standard CAC scans and ungated CTAC scans were scored by two readers using quantitative clinical software. We assessed the agreement between readers and between two scan protocols in 5 CAC categories (0, 1-10, 11-100, 101-400, and > 400) using Cohen's Kappa and concordance., Results: Median age of patients was 70 (inter-quartile range: 63-77), and 46% were male. The inter-scan concordance index and Cohen's Kappa for readers 1 and 2 were 0.69; 0.75 (0.69, 0.81) and 0.72; 0.8 (0.75, 0.85) respectively. The inter-reader concordance index and Cohen's Kappa (95% confidence interval [CI]) was higher for standard CAC scans: 0.9 and 0.92 (0.89, 0.96), respectively, vs. for CTAC scans: 0.83 and 0.85 (0.79, 0.9) for CTAC scans (p = 0.02 for difference in Kappa). Most discordant readings between two protocols occurred for scans with low extent of calcification (CAC score < 100)., Conclusion: CAC can be quantitatively assessed on PET CTAC maps with good agreement with standard scans, however with limited sensitivity for small lesions. CAC scoring of CTAC can be performed routinely without modification of PET protocol and added radiation dose., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 99m Tc-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., (© 2021. American Society of Nuclear Cardiology.)

We aimed to develop a novel deep-learning based method for automatic coronary artery calcium (CAC) quantification in low-dose ungated computed tomography attenuation correction maps (CTAC). In this study, we used convolutional long-short -term memory deep neural network (conv-LSTM) to automatically derive coronary artery calcium score (CAC) from both standard CAC scans and low-dose ungated scans (CT-attenuation correction maps). We trained convLSTM to segment CAC using 9543 scans. A U-Net model was trained as a reference method. Both models were validated in the OrCaCs dataset (n=32) and in the held-out cohort (n=507) without prior coronary interventions who had CTAC standard CAC scan acquired contemporarily. Cohen's kappa coefficients and concordance matrices were used to assess agreement in four CAC score categories (very low: <10, low:10-100; moderate:101-400 and high >400). The median time to derive results on a central processing unit (CPU) was significantly shorter for the conv-LSTM model- 6.18s (inter quartile range [IQR]: 5.99, 6.3) than for UNet (10.1s, IQR: 9.82, 15.9s, p<0.0001). The memory consumption during training was much lower for our model (13.11Gb) in comparison with UNet (22.31 Gb). Conv-LSTM performed comparably to UNet in terms of agreement with expert annotations, but with significantly shorter inference times and lower memory consumption.

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., (Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Ejection fraction (EF) reserve has been found to be a useful adjunct for identifying high risk coronary artery disease in cardiac positron emission tomography (PET). We aimed to evaluate EF reserve obtained from technetium-99m sestamibi (Tc-99m) high-efficiency (HE) SPECT., Methods: Fifty patients (mean age 69 years) undergoing regadenoson same-day rest (8-11 mCi)/stress (32-42 mCi) Tc-99m gated HE SPECT were enrolled. Stress imaging was started 1 minute after sequential intravenous regadenoson .4 mg and Tc-99m injections, and was composed of five 2 minutes supine gated acquisitions followed by two 4 minutes supine and upright images. Ischemic total perfusion deficit (ITPD) ≥5 % was considered as significant ischemia., Results: Significantly lower mean EF reserve was obtained in the 5th and 9th minute after regadenoson bolus in patients with significant ischemia vs patients without (5th minute: -4.2 ± 4.6% vs 1.3 ± 6.6%, P = .006; 9th minute: -2.7 ± 4.8% vs 2.0 ± 6.6%, P = .03)., Conclusions: Negative EF reserve obtained between 5th and 9th minutes of regadenoson stress demonstrated best concordance with significant ischemia and may be a promising tool for detection of transient ischemic functional changes with Tc-99m HE-SPECT.

Purpose: To describe selected common health beliefs and practices among Mexican American immigrants with type II diabetes., Data Sources: Selected clinical trials, qualitative studies, and systematic reviews., Conclusions: The Hispanic folk illness belief susto refers to an episode of severe fright, and Mexican American immigrants hold varying views on its relation to diabetes. Culturally and in the research, susto has also been linked with depression. Sabila (aloe vera) and nopal (prickly pear cactus) are herbal remedies that have had widespread, longstanding use in Mexican culture and while this is not the gold standard of research, it does provide ample evidence and a strong cultural belief that these therapies work. There is some evidence in the literature to support their efficacy as glucose-lowering agents, but lack of Food and Drug Administration (FDA) regulation, potential side effects, and a dearth of rigorous clinical trials preclude aloe vera and nopal from being recommended therapy., Implications for Practice: Awareness about susto beliefs, commonly used herbal remedies, and development of culturally sensitive communication skills are essential for nurse practitioners to effectively assist patients in this population achieve their glycemic goals. Research on the effects of nopal and aloe vera on diabetes is needed to guide clinical decisions., (©2014 American Association of Nurse Practitioners.)

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(-2), range 35-79.7 kg·m(-2)) 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.

Unlabelled: High-sensitivity dedicated cardiac camera systems provide an opportunity to lower the injected doses for SPECT myocardial perfusion imaging (MPI), but the exact limits for lowering doses have not been determined. List-mode data acquisition allows for reconstruction of various fractions of acquired counts, enabling a simulation of gradually lower administered dose. We aimed to determine the feasibility of very low dose MPI by exploring the minimal count level in the myocardium required for accurate MPI., Methods: Seventy-nine patients were studied (mean body mass index, 30.0 ± 6.6; range, 20.2-54.0 kg/m(2)) who underwent 1-d standard-dose (99m)Tc-sestamibi exercise or adenosine rest-stress MPI for clinical indications using a cadmium-zinc-telluride dedicated cardiac camera. The imaging time was 14 min, with averaged 803 ± 200 MBq (21.7 ± 5.4 mCi) of (99m)Tc injected at stress. To simulate clinical scans with a lower dose at that imaging time we reframed the list-mode raw data. Accordingly, 6 stress-equivalent datasets were reconstructed containing various count fractions of the original scan. Automated quantitative perfusion and gated SPECT software was used to quantify total perfusion deficit (TPD) and ejection fraction for all 553 datasets (7 × 79). The minimal acceptable left ventricular region counts were determined on the basis of a previous report with repeatability of same-day, same-injection Anger camera studies. Pearson correlation coefficients and the SD of differences in TPD for all scans were calculated., Results: The correlations of quantitative perfusion and function analysis were excellent for both global and regional analysis between original scans and all simulated low-count scans (all r ≥ 0.95, P < 0.0001). The minimal acceptable counts were determined to be 1.0 million for the left ventricular region. At this count level, the SD of differences was 1.7% for TPD and 4.2% for ejection fraction. This count level would correspond to a 92.5-MBq (2.5-mCi) injected dose for the 14-min acquisition or 125.8-MBq (3.4-mCi) injected dose for the 10-min acquisition., Conclusion: 1.0 million counts appear to be sufficient to produce myocardial images that agree well with 8.0-million-count images on quantitative perfusion and function parameters. With a dedicated cardiac camera, these images can be obtained over 10 min with an effective radiation dose of less than 1 mSv without significant sacrifice of accuracy.

Unlabelled: We compared the performance of fully automated quantification of attenuation-corrected (AC) and noncorrected (NC) myocardial perfusion SPECT (MPS) with the corresponding performance of experienced readers for detection of coronary artery disease (CAD)., Methods: Rest-stress (99m)Tc-sestamibi MPS studies (n = 995; 650 consecutive cases with coronary angiography and 345 with likelihood of CAD < 5%) were obtained by MPS with AC. The total perfusion deficit (TPD) for AC and NC data was compared with the visual summed stress and rest scores of 2 experienced readers. Visual reads were performed in 4 consecutive steps with the following information progressively revealed: NC data, AC + NC data, computer results, and all clinical information., Results: The diagnostic accuracy of TPD for detection of CAD was similar to both readers (NC: 82% vs. 84%; AC: 86% vs. 85%-87%; P = not significant) with the exception of the second reader when clinical information was used (89%, P < 0.05). The receiver-operating-characteristic area under the curve (ROC AUC) for TPD was significantly better than visual reads for NC (0.91 vs. 0.87 and 0.89, P < 0.01) and AC (0.92 vs. 0.90, P < 0.01), and it was comparable to visual reads incorporating all clinical information. The per-vessel accuracy of TPD was superior to one reader for NC (81% vs. 77%, P < 0.05) and AC (83% vs. 78%, P < 0.05) and equivalent to the second reader (NC, 79%; and AC, 81%). The per-vessel ROC AUC for NC (0.83) and AC (0.84) for TPD was better than that for the first reader (0.78-0.80, P < 0.01) and comparable to that of the second reader (0.82-0.84, P = not significant) for all steps., Conclusion: For detection of ≥70% stenoses based on angiographic criteria, a fully automated computer analysis of NC and AC MPS data is equivalent for per-patient and can be superior for per-vessel analysis, when compared with expert analysis.

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(ed)) 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(ed) 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(ed)/TID(es): 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.

Unlabelled: Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies., Methods: A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive (99m)Tc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers., Results: The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 +/- 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 +/- 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 +/- 0.01; sensitivity, 100%; and specificity, 70%., Conclusion: A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.