SPECT Myocardial Perfusion Imaging Research Articles

Coronary calcification on invasive angiography and the Agatston score—a single-center experience

Aim: The pattern and severity of coronary artery calcification (CAC) can influence prognosis and outcome in percutaneous coronary intervention. An objective assessment of CAC during invasive angiography may provide additional prognostic information. This study aimed to assess the correlation between the angiographic Birmingham calcium score (BCS) and the Agatston coronary calcium score (CCS) performed as part of single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). Methods: In this retrospective observational study, patients undergoing SPECT-MPI and invasive coronary angiography as part of their routine management were included. BCS was calculated by reviewing angiography images in retrospect by an observer blinded to the SPECT-MPI calcium score. Spearman’s correlation was used to analyze the correlation between BCS and SPECT-MPI. Receiver operating characteristic curve was used to detect cut-off for BCS that would detect clinically significant CAC [> 400 Agatston units (AU)]. Kaplan-Meier was used to report on outcomes at 5 years follow-up. Results: In this cohort of 151 patients, there was a positive correlation between BCS and CCS [Spearman correlation coefficient (r) = 0.558, P < 0.001]. Cumulative BCS of 1 was able to identify clinically significant CAC [area under the curve 0.788, 95% confidence interval (CI) 0.714–0.863]. Cumulative BCS ≥ 3 was associated with major adverse outcomes at 5 years follow-up (log rank P = 0.013). Conclusion: BCS correlates well with established higher CCS. Application of BCS during invasive coronary angiography will aid risk stratification, management, and follow-up with no extra patient involvement, radiation, or costs.

Going beyond summed stress scores: Correlating global and territorial coronary flow reserve by single photon emission tomography with routine myocardial perfusion imaging

(a) BackgroundTechnological advancement in the recent years has enabled the application of single photon emission tomography (SPECT) to evaluate myocardial blood flow (MBF). This method offers increased sensitivity in the assessment of coronary health, quantifiable through non-invasive imaging beyond the more conventional methods such as with myocardial perfusion imaging (MPI). (b) AimsTo correlate MBF, derived by dynamic SPECT, both global and by coronary territories to the summed stress scores (SSS) on conventional MPI. (c) MethodsImages obtained from dipyridamole-gated SPECT MPI stress and rest studies performed on recruited subjects were examined. We calculated the global and regional coronary flow reserve (CFR) via a standard software package, taken as the ratio of stress MBF to rest MBF, using CFR<2.5 as the cut off. (d) ResultsAmongst the 90 recruited subjects (mean age 67 ± 8 years; of which 76% were males), 49% had MPI within normal limits (summed stress score (SSS) 0–3; Left ventricular ejection fraction (LVEF) > 50%). We observed a progressive reduction in global and regional CFR across the normal SSS category to that of severely abnormal (SSS >13). Reduced global CFR with correspondent lower CFR across the regional arteries were detected in scans within normal limits of MPI scans in subjects who were older (69 ± 7 vs. 62 ± 9 years, p = 0.034). Decreasing CFR was significantly associated with increasing age across the regional arteries. (e) ConclusionIn our study we depict the global and regional MBF values obtained via SPECT MPI in correlation to the respective SSS categories. Our data proposes that dynamic SPECT has a part in refining cardiac risk stratification, particularly in the older adult population, who are at greater risk.

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

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.

Impact of cardiac size on SPECT myocardial perfusion imaging performance: Insights from the REFINE-SPECT registry.

Variation in diagnostic performance of SPECT myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI's ability to detect obstructive coronary artery disease (CAD), and its interaction with age and sex. A total of 2,066 patients without known CAD (67% male, 64.7 ± 11.2 years) across 9 institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated performance of quantitative and visual assessments according to cardiac size (end- diastolic volume [EDV];  < 20th vs. ≥ 20th population or sex-specific percentiles), age (<75 vs. ≥ 75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared to those without (AUC: population 0.72 vs. 0.78, p = 0.03; sex-specific 0.72 vs. 0.79, p = 0.01) and elderly patients compared to younger patients (AUC 0.72 vs. 0.78, p = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, p = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, p = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.

Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility. We conducted a retrospective study of patients with suspected or known coronary artery disease. We proposed a DL-based image-to-image translation technique to transform non-attenuation-corrected images into CT-based attenuation-corrected (CT AC ) images. The model was trained using a modified U-Net with structural similarity index (SSIM) loss and mean squared error (MSE) loss and compared with other models. Segment-wise analysis using a polar map and visual assessment for the generated attenuation-corrected (GEN AC ) images were also performed to evaluate clinical feasibility. This study comprised 657 men and 328 women (age, 65 ± 11 years). Among the various models, the modified U-Net achieved the highest performance with an average mean absolute error of 0.003, an SSIM of 0.990, and a peak signal-to-noise ratio of 33.658. The performance of the model was not different between the stress and rest datasets. In the segment-wise analysis, the myocardial perfusion of the inferior wall was significantly higher in GEN AC images than in the non-attenuation-corrected images in both the rest and stress test sets ( P < 0.05). In the visual assessment of patients with diaphragmatic attenuation, scores of 4 (similar to CT AC images) or 5 (indistinguishable from CT AC images) were assigned to most GEN AC images (65/68). Our clinically feasible DL-based attenuation correction models can replace the CT-based method in Tl-201 MPS, and it would be useful in case SPECT/CT is unavailable for MPS.

Cardiovascular risk factors and development of nomograms in an Italian cohort of patients with suspected coronary artery disease undergoing SPECT or PET stress myocardial perfusion imaging

IntroductionSingle photon emission computed tomography (SPECT) and positron emission tomography (PET) are non-invasive nuclear medicine techniques that can identify areas of abnormal myocardial perfusion. We assessed the prevalence of cardiovascular risk factors in patients with suspected coronary artery disease (CAD) undergoing SPECT or PET stress myocardial perfusion imaging (MPI). Based on significant risk factors associated with an abnormal MPI, we developed a nomogram for each cohort, as a pretest that would be helpful in decision-making for clinicians.MethodsA total of 6,854 patients with suspected CAD who underwent stress myocardial perfusion imaging by SPECT or PET/CT was studied. As part of the baseline examination, clinical teams collected information on traditional cardiovascular risk factors: age, gender, body mass index, angina, dyspnea, diabetes, hypertension, hyperlipidemia, family history of CAD, and smoking.ResultsThe prevalence of cardiovascular risk factors was different in the two cohorts of patients undergoing SPECT (n = 4,397) or PET (n = 2,457) myocardial perfusion imaging. A statistical significance was observed in both cohorts for age, gender, and diabetes. At multivariable analysis, only age and male gender were significant covariates in both cohorts. The risk of abnormal myocardial perfusion imaging related to age was greater in patients undergoing PET (odds ratio 4% vs. 1% per year). In contrast, male gender odds ratio was slightly higher for SPECT compared to PET (2.52 vs. 2.06). In the SPECT cohort, smoking increased the risk of abnormal perfusion of 24%. Among patients undergoing PET, diabetes and hypertension increased the risk of abnormal perfusion by 63% and 37%, respectively. For each cohort, we obtained a nomogram by significant risk factors at multivariable logistic regression. The area under receiver operating characteristic curve associated with the nomogram was of 0.67 for SPECT and 0.73 for the PET model.ConclusionsPatients with suspected CAD belonging to two different cohorts undergoing SPECT or PET stress myocardial perfusion imaging can have different cardiovascular risk factors associated with a higher risk of an abnormal MPI study. As crude variables, age, gender, and diabetes were significant for both cohorts. Net of the effect of other covariates, age and gender were the only risk factors in common between the two cohorts. Furthermore, smoking and type of stress test were significant for the SPECT cohort, where diabetes and hypertension were significant for the PET cohort. Nomograms obtained by significant risk factors for the two cohorts can be used by clinicians to evaluate the risk of an abnormal study.

Spherization indices measured by resting SPECT improve risk stratification in patients with ischemia with non-obstructive coronary artery disease (INOCA)

BackgroundThe prevalence of ischemia with non-obstructive coronary artery disease (INOCA) is substantial, but its risk stratification has been suboptimal. Resting SPECT myocardial perfusion imaging (MPI) could provide useful heart information including spherical indices. We aimed to evaluate the prognostic value of spherical indices in individuals with INOCA.ResultsDuring a median follow-up of 47.2 ± 20.8 months, 49 (17.2%) patients experienced major adverse cardiac events (MACE). Compared to those without MACE, those with MACE had a higher shape index (SI) (0.60 ± 0.07 vs. 0.58 ± 0.06; P = 0.028) and a lower E2 (eccentricity index calculated by the QPS) (0.81 ± 0.05 vs. 0.83 ± 0.04; P = 0.019). MACE event-free survival analysis revealed significant differences in the SI and E2 among all patients (all log-rank P < 0.01). Multivariate Cox analysis showed abnormal SI (HR: 2.73, 95% CI 1.44–5.18, P = 0.002) and E2 (HR: 1.94, 95% CI 1.08–3.48, P = 0.026) were both independent predictors for MACE when they were put into the same model, respectively. The incorporation of the SI into the baseline model demonstrated a significant improvement in the predictive accuracy for MACEs (P = 0.026), whereas E2 did not exhibit a similar improvement (P > 0.05).ConclusionFor patients with INOCA, spherical indices (especially the SI) were associated with long-term MACE, which could be a preferable indicator for risk stratification and prognostic prediction.

Left ventricular mechanical dyssynchrony after chemotherapy in breast cancer patients with normal rest gated SPECT-MPI.

Early diagnosis of chemotherapy-induced cardiotoxicity plays an important role in preventing heart failure. The main aim of our study was to assess left ventricular (LV) dyssynchrony measured by phase analysis of gated single-photon emission computed tomography (SPECT) as an early sign of cardiotoxicity after breast cancer chemotherapy. This cross-sectional study was conducted on patients with stage ≤ 3 breast cancer and no history of cardiovascular disease or diabetes. After mastectomy, the patients underwent rest gated SPECT myocardial perfusion imaging (MPI). Sixty patients with normal gated SPECT-MPI were selected and the imaging was performed after chemotherapy with doxorubicin, cyclophosphamide and paclitaxel. LV function and contractility parameters were extracted by QGS software and the results were compared with the t test method. The abnormality of at least one of the three phase analysis indices was considered as left ventricular dyssynchrony (LVD). The average LV end-systolic volume and ejection fraction (LVEF) before and after chemotherapy were (16.2 ± 8.0ml and 21.6 ± 11.6ml) and (73.4 ± 7.9% and 67.5 ± 9.2%) respectively, which showed a significant decrease (P < 0.05). In 2 patients (3.3%), the LVEF decreased to less than 50% after chemotherapy. The average parameters of left ventricular contractility before and after chemotherapy were, respectively, as follows: PHB (24.1 ± 7.5 and 33.8 ± 16.4), PSD (9.4 ± 6.1 and 5.7 ± 1.9) and entropy (28.9 ± 7.1 and 35.6 ± 9.7), which showed a significant increase (P < 0.05). LVD was observed in 14 patients (23.4%) after chemotherapy and prevalence of LVD was significantly higher in patients who had received a cumulative dose of doxorubicin of more than 400mg/m2 (P = 0.005). There was no relationship between age and body mass index with the incidence of LVD after chemotherapy (P > 0.05). Using phase analysis of gated SPECT-MPI, chemotherapy-induced LVD was seen in a significant number of patients with breast cancer, especially with a high cumulative dose of doxorubicin. LVD might indicate chemotherapy-induced cardiotoxicity before LVEF becomes abnormal.

Comparative predictors of mortality among patients referred for stress single-photon emission computed tomography versus positron emission tomography myocardial perfusion imaging

BackgroundThere is currently little information regarding the usage and comparative predictors of mortality among patients referred for single-photon emission computed tomography (SPECT) versus positron emission tomography (PET) myocardial perfusion imaging (MPI) within multimodality imaging laboratories. MethodsWe compared the clinical characteristics and mortality outcomes among 15,718 patients referred for SPECT-MPI and 6202 patients referred for PET-MPI between 2008 and 2017. ResultsApproximately two-thirds of MPI studies were performed using SPECT-MPI. The PET-MPI group was substantially older and included more patients with known coronary artery disease (CAD), hypertension, diabetes, and myocardial ischemia. The annualized mortality rate was also higher in the PET-MPI group, and this difference persisted after propensity matching 3615 SPECT-MPI and 3615 PET-MPI patients to have similar clinical profiles. Among the SPECT-MPI patients, the most potent predictor of mortality was exercise ability and performance, including consideration of patients’ mode of stress testing and exercise duration. Among the PET-MPI patients, myocardial flow reserve (MFR) was the most potent predictor of mortality. ConclusionsIn our real-world setting, PET-MPI was more commonly employed among older patients with more cardiac risk factors than SPECT-MPI patients. The most potent predictors of mortality in our SPECT and PET-MPI groups were variables exclusive to each test: exercise ability/capacity for SPECT-MPI patients and MFR for PET-MPI patients.

Comparative Effectiveness of PET and SPECT MPI for Predicting Cardiovascular Events After Kidney Transplant.

Advanced chronic kidney disease is associated with high cardiovascular risk, even after kidney transplant. Pretransplant cardiac testing may identify patients who require additional assessment before transplant or would benefit from risk optimization. The objective of the current study was to determine the relative prognostic utility of pretransplant positron emission tomography (PET) and single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) for posttransplant major adverse cardiovascular events (MACEs). We retrospectively followed patients who underwent MPI before kidney transplant for the occurrence of MACE after transplant including myocardial infarction, stroke, heart failure, and cardiac death. An abnormal MPI result was defined as a total perfusion deficit >5% of the myocardium. To determine associations of MPI results with MACE, we utilized Cox hazard regression with propensity weighting for PET versus SPECT with model factors, including demographics and cardiovascular risk factors. A total of 393 patients underwent MPI (208 PET and 185 SPECT) and were followed for a median of 5.9 years post-transplant. Most were male (58%), median age was 58 years, and there was a high burden of hypertension (88%) and diabetes (33%). A minority had abnormal MPI (n=58, 15%). In propensity-weighted hazard regression, abnormal PET result was associated with posttransplant MACE (hazard ratio, 3.02 [95% CI, 1.78-5.11]; P<0.001), while there was insufficient evidence of an association of abnormal SPECT result with MACE (1.39 [95% CI, 0.72-2.66]; P=0.33). The explained relative risk of the PET result was higher than the SPECT result (R2 0.086 versus 0.007). Normal PET was associated with the lowest risk of MACE (2.2%/year versus 3.6%/year for normal SPECT; P<0.001). Kidney transplant recipients are at high cardiovascular risk, despite a minority having obstructive coronary artery disease on MPI. PET MPI findings predict posttransplant MACE. Normal PET may better discriminate lower risk patients compared with normal SPECT, which should be confirmed in a larger prospective study.

Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging

ObjectiveDeep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computed tomography (SPECT). In this study, we proposed a novel deep learning approach referring to positron emission tomography (PET). The aims of this study were to analyze the agreement of representative voxel values and perfusion scores of SPECT-to-PET translation model-generated SPECT (SPECTSPT) against PET in 17 segments according to the American Heart Association (AHA).MethodsThis retrospective study evaluated the patient-to-patient stress, resting SPECT, and PET datasets of 71 patients. The SPECTSPT generation model was trained (stress: 979 image pairs, rest: 987 image pairs) and validated (stress: 421 image pairs, rest: 425 image pairs) using 31 cases of SPECT and PET image pairs using an image-to-image translation network. Forty of 71 cases of left ventricular base-to-apex short-axis images were translated to SPECTSPT in the stress and resting state (stress: 1830 images, rest: 1856 images). Representative voxel values of SPECT and SPECTSPT in the 17 AHA segments against PET were compared. The stress, resting, and difference scores of 40 cases of SPECT and SPECTSPT were also compared in each of the 17 segments.ResultsFor AHA 17-segment-wise analysis, stressed SPECT but not SPECTSPT voxel values showed significant error from PET at basal anterior regions (segments #1, #6), and at mid inferoseptal regions (segments #8, #9, and #10). SPECT, but not SPECTSPT, voxel values at resting state showed significant error at basal anterior regions (segments #1, #2, and #6), and at mid inferior regions (segments #8, #9, and #11). Significant SPECT overscoring was observed against PET in basal-to-apical inferior regions (segments #4, #10, and #15) during stress. No significant overscoring was observed in SPECTSPT at stress, and only moderate over and underscoring in the basal inferior region (segment #4) was found in the resting and difference states.ConclusionsOur PET-supervised deep learning model is a new approach to correct well-known inferior wall attenuation in SPECT myocardial perfusion imaging. As standalone SPECT systems are used worldwide, the SPECTSPT generation model may be applied as a low-cost and practical clinical tool that provides powerful auxiliary information for the diagnosis of myocardial blood flow.

Is typical angina still prognostically important? The influence of “treatment bias” upon prognostic assessments

BackgroundSince typical angina has become less frequent, it is unclear if this symptom still has prognostic significance. MethodsWe evaluated 38,383 patients undergoing stress/rest SPECT myocardial perfusion imaging followed for a median of 10.9 years. After dividing patients by clinical symptoms, we evaluated the magnitude of myocardial ischemia and subsequent mortality among medically treated versus revascularized subgroups following testing. ResultsPatients with typical angina had more frequent and greater ischemia than other symptom groups, but not higher mortality. Among typical angina patients, those who underwent early revascularization had substantially greater ischemia than the medically treated subgroup, including a far higher proportion with severe ischemia (44.9% vs 4.3%, P < 0.001) and transient ischemic dilation of the LV (31.3% vs 4.7%, P < 0.001). Nevertheless, the revascularized typical angina subgroup had a lower adjusted mortality risk than the medically treated subgroup (HR = 0.72, 95% CI: 0.57-0.92, P = 0.009) ConclusionsTypical angina is associated with substantially more ischemia than other clinical symptoms. However, the high referral of patients with typical angina patients with ischemia to early revascularization resulted in this group having a lower rather than higher mortality risk versus other symptom groups. These findings illustrate the need to account for “treatment bias” among prognostic studies.

Enhancing the diagnostic performance of CZT SPECT myocardial perfusion imaging with myocardial blood flow information

Abstract Background The use of cadmium-zinc-telluride (CZT) single photon emission computed tomography (SPECT) for measuring myocardial blood flow (MBF) has been established as a valuable tool for improving diagnostic accuracy. However, the clinical significance of incorporating MBF into the interpretation of stress SPECT myocardial perfusion imaging (MPI) remains largely unknown. Purpose The objective of this study is to determine the effect of MBF information on the interpretation of CZT SPECT MPI. Methods A cohort of 411 patients underwent a rest/stress gated perfusion and MBF imaging procedure using full-dose 99mTc-tetrofosmin. MBF and flow reserve were calculated using the Corridor4DM v2018 software (INVIA, Ann Arbor, MI). The SPECT data were analyzed twice, once without and once with MBF results. The criteria for abnormal myocardial flow reserve were set at 2.0, while stress flow was set at 1.8 ml/min/g. The interpretations were categorized into normal, equivocal, or abnormal and interpretive certainty was classified into five groups: definitely normal/abnormal, probably normal/abnormal, or equivocal. The overall results were evaluated, along with differences between men and women. Statistical significance was determined using contingency table analysis and chi-squared testing. Results The study cohort had a mean age of 62.5 ± 9.8 years, with 71% of the participants being male. The addition of MBF altered the MPI interpretation in 10.7% of patients (44/411), with a change from abnormal or equivocal to normal in 45.5% (20/44) of cases. The frequency of equivocal MPI interpretations dropped significantly from 4.6% without MBF to 1.5% with MBF (P=0.008). Interpretive certainty improved with a significant decrease in equivocal scans (P=0.006). 89.1% of MPI with MBF were reported as definitely normal or abnormal, compared to 83.0% without MBF (P=0.012) (Figure 1). Further analysis by gender showed similar results for men, while no significant changes were observed in women's MPI interpretation and certainty (Figure 2). Conclusion The inclusion of MBF had a significant impact on the interpretation of CZT SPECT MPI scans, with changes observed in 10.7% of cases. Interpretative certainty improved with fewer equivocal scans and a higher proportion of definitely normal or definitely abnormal interpretations. These findings were evident in men, but not in women (possible due to smaller sample size).Figure 1Figure 2

Abstract 13473: 3D Fusion Between SPECT Myocardial Perfusion Imaging and Invasive Coronary Angiography to Guide Revascularization in Patients With Stable Coronary Artery Disease

Objective: We evaluated the value of 3-dimensional (3D) fusion of SPECT myocardial perfusion imaging (MPI) with invasive coronary angiography (ICA) to guide revascularization. Methods: A retrospective observational study of 621 patients who underwent SPECT MPI and ICA. Based on the location of perfusion defect and stenosis on ICA, patients were classified into matched, unmatched, or normal groups via 3D fusion. The concordance between treatments was defined as, matched if there was revascularization in concordance with 3D fusion recommendation or if patient did not require revascularization; otherwise, they were classified as discordant. Major adverse cardiac events (MACE) (cardiac death, myocardial infarction, unstable angina requiring hospitalization or ICA, and unplanned revascularization) after revascularization of lesion recommended by 3D fusion were compared with those that the patients actually received. Results: Over a five-year follow-up period, 16% experienced MACE. Rates of MACE were 69% for matched findings, 22% for unmatched findings, 9% for normal findings (P&lt;0.01). Among the 366 patients who had at least one vessel with a stenosis of &gt;=50%, those whose treatments were concordant with the fusion had significantly better outcomes compared to those who were not (P&lt;0.05), primarily observed in the intermediate stenosis group (stenosis: 50-79%) (P&lt;0.01) (Fig. A). Further, in the intermediate stenosis patients, the revascularization ratio in the concordant group was significantly higher in the LCX (31.2% vs. 14.3%, P&lt;0.01) than that in the discordant group. (Fig. B) Conclusions: 3D fusion prior to coronary revascularization can guide coronary revascularization to improve outcomes, among patients with stable CAD patients and intermediate stenosis.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

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). 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 ± 12years, 45% female, body mass index [BMI]: 30.0 ± 6.3kg/m2, 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 (> 90days after SPECT) was evaluated with survival analysis. During a median follow-up of 25months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35kg/m2 (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 ≥ 35kg/m2 (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-yearMACE. In patients with BMI ≥ 35kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

Impact of myocardial perfusion and coronary calcium on medical management for coronary artery disease.

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) remains one of the most widely used imaging modalities for the diagnosis and prognostication of coronary artery disease (CAD). Despite the extensive prognostic information provided by MPI, little is known about how this influences the prescription of medical therapy for CAD. We evaluated the relationship between MPI with computed tomography (CT) attenuation correction and prescription of acetylsalicylic acid (ASA) and statins. We performed a retrospective analysis of consecutive patients who underwent SPECT MPI at a single centre between 2015 and 2021. Myocardial perfusion abnormalities and coronary calcium burden were assessed, with attenuation correction imaging 77.8% of patients. Medication prescriptions before and within 180 days after the test were compared. Associations between abnormal perfusion and calcium burden with ASA and statin prescription were assessed using multivariable logistic regression. In total, 9908 patients were included, with a mean age 66.8 ± 11.7 years and 5337 (53.9%) males. The prescription of statins increased more in patients with abnormal perfusion (increase of 19.2 vs. 12.0%, P < 0.001). Similarly, the presence of extensive CAC led to a greater increase in statin prescription compared with no calcium (increase 12.1 vs. 7.8%, P < 0.001). In multivariable analyses, ischaemia and coronary artery calcium were independently associated with ASA and statin prescription. Abnormal MPI testing was associated with significant changes in medical therapy. Both calcium burden and perfusion abnormalities were associated with increased prescriptions of medical therapy for CAD.

Spatial-temporal V-Net for automatic segmentation and quantification of right ventricle on gated myocardial perfusion SPECT images.

Functional assessment of right ventricle (RV) using gated myocardial perfusion single-photon emission computed tomography (MPS) heavily relies on the precise extraction of right ventricular contours. In this paper, we present a new deep-learning-based model integrating both the spatial and temporal features in gated MPS images to perform the segmentation of the RV epicardium and endocardium. By integrating the spatial features from each cardiac frame of the gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we developed a Spatial-Temporal V-Net (ST-VNet) for automatic extraction of RV endocardial and epicardial contours. In the ST-VNet, a V-Net is employed to hierarchically extract spatial features, and convolutional long-term short-term memory (ConvLSTM) units are added to the skip-connection pathway to extract the temporal features. The input of the ST-VNet is ECG-gated sequential frames of the MPS images and the output is the probability map of the epicardial or endocardial masks. A Dice similarity coefficient (DSC) loss which penalizes the discrepancy between the model prediction and the manual annotation was adopted to optimize the segmentation model. Our segmentation model was trained and validated on a retrospective dataset with 45 subjects, and the cardiac cycle of each subject was divided into eight gates. The proposed ST-VNet achieved a DSC of 0.8914 and 0.8157 for the RV epicardium and endocardium segmentation, respectively. The mean absolute error, the mean squared error, and the Pearson correlation coefficient of the RV ejection fraction (RVEF) between the manual annotation and the model prediction were 0.0609, 0.0830, and 0.6985. Our proposed ST-VNet is an effective model for RV segmentation. It has great promise for clinical use in RV functional assessment.

Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion 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.

Stress-only versus rest-stress SPECT MPI in the detection and diagnosis of myocardial ischemia and infarction by machine learning.

Rest-stress SPECT myocardial perfusion imaging (MPI) is widely used to evaluate coronary artery disease (CAD). We aim to evaluate stress-only versus rest-stress MPI in diagnosing CAD by machine learning (ML). A total of 276 patients with suspected CAD were randomly divided into training (184 patients) and validation (92 patients) cohorts. Variables extracted from clinical, physiological, and rest-stress SPECT MPI were screened. Stress-only and rest-stress MPI using ML were established and compared using the training cohort. Then the diagnostic performance of two models in diagnosing myocardial ischemia and infarction was evaluated in the validation cohort. Six ML models based on stress-only MPI selected summed stress score, summed wall thickness score of stress%, and end-diastolic volume of stress as key variables and performed equally good as rest-stress MPI in detecting CAD [area under the curve (AUC): 0.863 versus 0.877, P = 0.519]. Furthermore, stress-only MPI showed a reasonable prediction of reversible deficit, as shown by rest-stress MPI (AUC: 0.861). Subsequently, nomogram models using the above-stated stress-only MPI variables showed a good prediction of CAD and reversible perfusion deficit in training and validation cohorts. Stress-only MPI demonstrated similar diagnostic performance compared with rest-stress MPI using 6 ML algorithms. Stress-only MPI with ML models can diagnose CAD and predict ischemia from scar.

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images

BackgroundSingle photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. MethodsA total of 254 patients were enrolled, including 226 stress SPECT MPIs and 247 rest SPECT MPIs. Fivefold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced nuclear cardiologist and used as the reference standard. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: (1) optimize the translation parameters while fixing the rotation parameters; (2) optimize rotation parameters while fixing the translation parameters; (3) optimize both translation and rotation parameters together. ResultsIn the test set, the Spearman determination coefficients of the translation distances and rotation angles between the model prediction and the reference standard were 0.993 in X axis, 0.992 in Y axis, 0.994 in Z axis, 0.987 along X axis, 0.990 along Y axis and 0.996 along Z axis, respectively. For the 46 stress MPIs in the test set, the Spearman determination coefficients were 0.858 in percentage of profusion defect (PPD) and 0.858 in summed stress score (SSS); for the 46 rest MPIs in the test set, the Spearman determination coefficients were 0.9 in PPD and 0.9 in summed rest score (SRS). ConclusionsOur deep learning-based LV reorientation method is able to accurately generate the SA images. Technical validations and subsequent evaluations of measured clinical parameters show that it has great promise for clinical use.