Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.

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.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

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.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.

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.

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

PURPOSE: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS: Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

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.

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.

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.

Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images.

BACKGROUND: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing. METHODS: A total of 4735 consecutive patients referred for stress and rest 82Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24-6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients scanned from 2017 to 2018 to simulate prospective clinical implementation. RESULTS: In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77-0.86]) was higher than ischemia (0.60 [95% CI, 0.54-0.66]; P <0.001), myocardial flow reserve (0.70 [95% CI, 0.64-0.76], P <0.001) or a comprehensive logistic regression model (0.75 [95% CI, 0.69-0.80], P <0.05). The highest quartile of patients by DL had an annual all-cause mortality rate of 11.87% and had a 16.8 ([95% CI, 6.12%-46.3%]; P <0.001)-fold increase in the risk of death compared with the lowest quartile patients. DL showed a 21.6% overall reclassification improvement as compared with established measures of ischemia. CONCLUSIONS: The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.

Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry.

BACKGROUND: Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk. METHODS: We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD). RESULTS: During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs. CONCLUSION: Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance.

Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry.

BACKGROUND: Phase analysis of single-photon emission computed tomography myocardial perfusion imaging provides dyssynchrony information which correlates well with assessments by echocardiography, but the independent prognostic significance is not well defined. This study assessed the independent prognostic value of single-photon emission computed tomography-myocardial perfusion imaging phase analysis in the largest multinational registry to date across all modalities. METHODS: From the REFINE SPECT (Registry of Fast Myocardial Perfusion Imaging With Next Generation SPECT), a total of 19 210 patients were included (mean age 63.8±12.0 years and 56% males). Poststress total perfusion deficit, left ventricular ejection fraction, and phase variables (phase entropy, bandwidth, and SD) were obtained automatically. Cox proportional hazards analyses were performed to assess associations with major adverse cardiac events (MACE). RESULTS: During a follow-up of 4.5±1.7 years, 2673 (13.9%) patients experienced MACE. Annualized MACE rates increased with phase variables and were ≈4-fold higher between the second and highest decile group for entropy (1.7% versus 6.7%). Optimal phase variable cutoff values stratified MACE risk in patients with normal and abnormal total perfusion deficit and left ventricular ejection fraction. Only entropy was independently associated with MACE. The addition of phase entropy significantly improved the discriminatory power for MACE prediction when added to the model with total perfusion deficit and left ventricular ejection fraction (P<0.0001). CONCLUSIONS: In a largest to date imaging study, widely representative, international cohort, phase variables were independently associated with MACE and improved risk stratification for MACE beyond the prediction by perfusion and left ventricular ejection fraction assessment alone. Phase analysis can be obtained fully automatically, without additional radiation exposure or cost to improve MACE risk prediction and, therefore, should be routinely reported for single-photon emission computed tomography-myocardial perfusion imaging studies.

"Same-Patient Processing" for multiple cardiac SPECT studies. 1. Improving LV segmentation accuracy.

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.

"Same-patient processing" for multiple cardiac SPECT studies. 2. Improving quantification repeatability.

OBJECTIVES: This paper investigates the ability of grouped quantification (an expression of the same-patient processing approach, or SPP) to improve repeatability of measurements in patients with multiple SPECT studies, and evaluates its performance compared to standard quantification in a population of 100 patients undergoing rest, stress, gated rest, and gated stress SPECT MPI. All acquisitions were performed twice, back-to-back, for a total of 800 image datasets (8 per patient). METHODS: Each dataset was automatically processed (a) independently, using standard quantitative software, and (b) as a group, together with the other 7 datasets belonging to the same patient, using an SPP-modified version of the software that registered the images to one another using a downhill simplex algorithm for the search of optimal translation, rotation, and scaling parameters. RESULTS: Overall, grouped quantification resulted in significantly lower differences between repeated measurements of stress ungated volumes (1.40 ± 2.76 mL vs 3.33 ± 5.06 mL, P < .05), end-diastolic volumes (1.78 ± 2.78 vs 3.49 ± 5.35 mL, P < .05), end-systolic volumes (1.17 ± 1.96 vs 2.44 ± 3.35 mL, P < .05), and LVEFs (-0.45 ± 2.29% vs -1.16 ± 3.30%, P < .05). Additionally, grouped quantification produced better repeatability (lower repeatability coefficients) for stress and rest ungated volumes (5.4 vs 9.9 and 5.2 vs 13.1, respectively), stress TPD (2.6 vs 3.6), stress and rest end-diastolic volumes (5.5 vs 10.5 and 7.2 vs 14.7, respectively), stress and rest end-systolic volumes (3.8 vs 6.6 and 5.3 vs 10.3, respectively), stress and rest LVEFs (4.5 vs 6.5 and 4.7 vs 8.2, respectively), and rest total motion deficit (5.6 vs 9.6). CONCLUSION: It is possible to improve the repeatability of quantitative measurements of parameters of myocardial perfusion and function derived from SPECT MPI studies of a same patient by group processing of image datasets belonging to that patient. This application of the same-patient processing approach is an extension of the "paired processing" technique already described by our group, and can be performed in automated fashion through incorporation in the quantitative algorithm.

Quantitation in gated perfusion SPECT imaging: the Cedars-Sinai approach.

Cedars-Sinai's approach to the automation of gated perfusion single photon emission computed tomography (SPECT) imaging is based on the identification of key procedural steps (processing, quantitation, reporting), each of which is then implemented, in completely automated fashion, by use of mathematic algorithms and logical rules combined into expert systems. Our current suite of software applications has been designed to be platform- and operating system-independent, and every algorithm is based on the same 3-dimensional sampling scheme for the myocardium. The widespread acceptance of quantitative software by the nuclear cardiology community (QGS alone is used at over 20,000 locations) has provided the opportunity for extensive validation of quantitative measurements of myocardial perfusion and function, in our opinion, helping to make nuclear cardiology the most accurate and reproducible modality available for the assessment of the human heart.

Left ventricular shape index assessed by gated stress myocardial perfusion SPECT: initial description of a new variable.

BACKGROUND: Ventricular remodeling is predictive of congestive heart failure (CHF). We aimed to automatically quantify a new myocardial shape variable on gated myocardial perfusion single photon emission computed tomography (SPECT) (MPS) and to evaluate the association of this new SPECT parameter with the risk of hospitalization for CHF. METHODS AND RESULTS: A computer algorithm was used to measure the 3-dimensional (3D) left ventricular (LV) shape index (LVSI), derived as the ratio of maximum 3D short- and long-axis LV dimensions, for end systole and end diastole. LVSI normal limits were obtained from stress technetium 99m sestamibi MPS images of 186 patients (60% of whom were men) (control subjects) with a low likelihood of CAD (< 5%). These limits were tested in a consecutive series of 93 inpatients (85% of whom were men) having MPS less than 1 week after hospitalization, of whom 25 were hospitalized for CHF exacerbation. Variables associated with CHF hospitalization were tested by receiver operating characteristic curve and multivariate logistic regression analyses. LVSI repeatability was assessed in 52 patients with ischemic cardiomyopathy who had sequential stress MPS within 60 days after the initial MPS without clinical events in the interval between MPS studies. Control subjects had lower end-systolic and end-diastolic LVSIs compared with patients with CHF and those without CHF (P < .001). Receiver operating characteristic curve areas for the prediction of hospitalization as a result of CHF were similar for LV ejection fraction and end-systolic LVSI. End-systolic and end-diastolic LVSIs were independent predictors of CHF hospitalization by multivariate analysis; however, end-systolic LVSI had the greatest added value among all tested variables. Repeatability was excellent for both end-systolic LVSI (R2 = 0.85, P < .0001) and end-diastolic LVSI (R2 = 0.82, P < .001). CONCLUSION: LVSI is a promising new 3D variable derived automatically from gated MPS providing highly repeatable ventricular shape assessment. Preliminary findings suggest that LVSI might have clinical implications in patients with CHF.

Assessment of diastolic function using 16-frame 99mTc-sestamibi gated myocardial perfusion SPECT: normal values.

UNLABELLED: The purposes of this study were (a) to assess the feasibility of diastolic function (DFx) evaluation using standard 16-frame postexercise gated (99m)Tc-sestamibi myocardial perfusion SPECT (MPS), (b) to determine the relationship of the 2 common DFx parameters, peak filling rate (PFR) and time to peak filling (TTPF), to clinical and systolic function (SFx) variables in patients with normal myocardial perfusion and SFx, and (c) to derive and validate normal limits. METHODS: Ninety patients (71 men; age, 30-79 y) with normal exercise gated MPS were studied. None had hypertension, diabetes, rest electrocardiogram abnormality, or known cardiac disease. All patients reached > or = 85% of maximum predicted heart rate (HR). The population was randomized into derivation (n = 50) and validation (n = 40) groups. Univariable and multivariable approaches were deployed to assess the influence of clinical and functional variables on DFx parameters. RESULTS: PFR and TTPF were assessed in all patients. Mean values of PFR and TTPF in the whole study population were 2.62 +/- 0.46 end-diastolic volumes per second (EDV/s) and 164.6 +/- 21.7 ms, respectively. By applying a 2-SD cutoff to the mean values in the derivation group, the threshold for abnormal PFR and the threshold for abnormal TTPF were < 1.71 EDV/s and > 216.7 ms, respectively. The normalcy rates in the validation group for PFR and TTPF were both 100%. The PFR showed weak but significant correlations with age, EDV, end-systolic volume, left ventricular ejection fraction (LVEF), and poststress HR. However, TTPF did not correlate with these parameters. Final normal thresholds determined from the combined populations were PFR = 1.70 EDV/s and TTPF = 208 ms. Multivariable analysis showed that age, sex, LVEF, and HR are strong predictors for PFR, whereas TTPF was not influenced by any clinical or SFx variable. CONCLUSION: With a new algorithm in QGS, assessment of LV DFx is feasible using 16-frame gated MPS even without bad-beat rejection, resulting in normal limits similar to those reported with gated blood-pool studies. However, due to the dependency of PFR on SFx parameters, sex, HR, and age, TTPF appears to be a stable and more useful parameter with this approach. The clinical usefulness of these findings requires further study.