Stress Myocardial Perfusion Imaging Research Articles

ESR Essentials: imaging in stable chest pain - practice recommendations by ESCR.

Stable chest pain is a common symptom with multiple potential causes. Non-invasive imaging has an important role in diagnosis and guiding management through the assessment of coronary stenoses, atherosclerotic plaque, myocardial ischaemia or infarction, and cardiac function. Computed tomography (CT) provides the anatomical evaluation of coronary artery disease (CAD) with the assessment of stenosis, plaque type and plaque burden, with additional functional information available from CT fractional flow reserve (FFR) or CT myocardial perfusion imaging. Stress magnetic resonance imaging, nuclear stress myocardial perfusion imaging, and stress echocardiography can assess myocardial ischaemia and other cardiac functional parameters. Coronary CT angiography can be used as a first-line test for many patients with stable chest pain, particularly those with low to intermediate pre-test probability. Functional testing may be considered for patients with known CAD, where the clinical significance is uncertain based on anatomical testing, or in patients with high pre-test probability. This practice recommendations document can be used to guide the selection of non-invasive imaging for patients with stable chest pain and provides brief recommendations on how to perform and report these diagnostic tests.

Diagnostic value of stress myocardial perfusion imaging in combination with computed tomography angiography for coronary slow flow

Abstract Introduction: To date, there are few reports on computed tomography angiography (CTA) in combination with stress myocardial perfusion imaging (SMPI) in the diagnosis of coronary slow flow (CSF). Methods: A total of 62 patients with suspected CSF who were scheduled to undergo CTA, SMPI and coronary angiography (CAG) from June 2019 to June 2021 were selected. With the results of CAG as the gold standard, the value of CTA plus SMPI in the diagnosis of CSF was analyzed. Results: Of the 62 patients, 52 (83.87%) were diagnosed with CSF by CAG. The results of the Kappa consistency test showed that SMPI alone, CTA alone and SMPI plus CTA had 0.565, 0.396 and 0.881 consistency, respectively, with CAG in the diagnosis of CSF. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of SMPI plus CTA in the diagnosis of CSF were 98.08% (51/52), 90.00% (9/10), 96.47% (60/62), 98.08% (51/52), and 90.00% (9/10), respectively, all higher than those of SMPI or CTA alone (P<0.05). The areas under the receiver operating characteristic curves of SMPI alone, CTA alone and SMPI plus CTA in the diagnosis of CSF were 0.754, 0.771 and 0.940, respectively. Conclusion: CTA and SMPI have high diagnostic values for CSF, and the results of their combination have high consistency with those of CAG.

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.

Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities.

Cardiac allograft vasculopathy (CAV) is a distinct form of coronary artery disease that represents a major cause of death beyond the first year after heart transplantation. The pathophysiology of CAV is still not completely elucidated; it involves progressive circumferential wall thickening of both the epicardial and intramyocardial coronary arteries. Coronary angiography is still considered the gold-standard test for the diagnosis of CAV, and intravascular ultrasound (IVUS) can detect early intimal thickening with improved sensitivity. However, these tests are invasive and are unable to visualize and evaluate coronary microcirculation. Increasing evidence for non-invasive surveillance techniques assessing both epicardial and microvascular components of CAV may help improve early detection. These include computed tomography coronary angiography (CTCA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and vasodilator stress myocardial contrast echocardiography perfusion imaging. This review summarizes the current state of diagnostic modalities and their utility and prognostic value for CAV and also evaluates emerging tools that may improve the early detection of this complex disease.

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.

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.

Optimizing stress-only myocardial perfusion imaging: a clinical prediction model to improve patient selection.

Stress-only single photon emission computed tomography myocardial perfusion imaging (MPI) offers numerous advantages in terms of improved workflow, cost and radiation reduction but is currently not widely utilized due to challenges in selecting appropriate patients for this technique. Data from 5959 individuals were used to derive (N = 4018) and validate (N = 1941) a binomial logistic regression model to predict normal stress MPI studies (stress total perfusion deficit < 4%, ejection fraction ≥ 50%). Model performance was analyzed using receiver operator characteristic curves. A simplified point-scoring system was developed and its impact on imaging workflow was assessed. Significant predictors of abnormal vs. normal stress MPI included male sex, age > 65 years, cardiomyopathy, congestive heart failure, myocardial infarction, angina, and pharmacological stress. The final model and simplified scoring system were associated with areas under the curve of 0.81 (95% CI 0.79-0.83) and 0.80 (95% CI 0.79-0.82) in the validation group, respectively. Use of the scoring system was estimated to result in a decrease of 56.5% in the number of non-contributory imaging studies acquired with minimal patient rescheduling. A prediction tool derived from simple clinical information can identify candidates for stress-only MPI studies with a beneficial impact on departmental workflow.

Age-Specific Cardiovascular Risk Factors for Major Adverse Cardiac Events in Patients Undergoing Myocardial Perfusion Imaging.

The prevalence of traditional cardiovascular risk factors shows different age-specific patterns. It is not known whether the prognostic impact of risk factors is similarly age-specific. We evaluated the profiles of cardiovascular risk factors and their prognostic impact on coronary artery disease (CAD) in relation to age. We included 3667 patients with suspected or known CAD undergoing stress myocardial perfusion imaging (MPI). We evaluated the risk for major adverse cardiac events (MACE) within three years from the index MPI in patients belonging to three groups according to age tertile distribution: <59, 59-68, and >68 years. Gender, body mass index, diabetes, hypertension, dyslipidemia, family history of CAD, smoking, angina, dyspnea, previous CAD, and MPI outcome were assessed as risk factors by a multivariable Cox's regression. The three-year risk of MACE increased progressively with age and was 9%, 13%, and 18% for each group, respectively (p < 0.0001). Dyspnea and abnormal MPI outcome were significant risk factors for all age groups. Diabetes and smoking were significant from the age of 59 onwards, while hypertension resulted significant for patients older than 68 years. The number of risk factors was significantly associated with the occurrence of MACE increase with age. It is noteworthy that a personal history of CAD was not useful for risk stratification, while MPI results were.

Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study

This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.

Impact of appropriateness in clinical practice: data from a single-centre nuclear cardiology laboratory

Abstract Aims To verify the level of appropriateness of referral to our nuclear cardiology laboratory for stress myocardial perfusion imaging (MPI) and explore the correlation between test appropriateness patterns and ischaemia. Methods and results In 1870 consecutive patients (mean age 73 ± 12 years; 33% female) undergoing MPI, the level of imaging test appropriateness was evaluated according to the 2023 Appropriate Use Criteria (AUC) and the current European Society of Cardiology (ESC) guidelines for the management of chronic coronary syndromes. The evidence of moderate-to-severe ischaemia (i.e. summed difference score &amp;gt;7) was recorded. According to the AUC criteria, the MPI of 1638 (88%), 130 (7%), and 102 (5%) patients could be classified as ‘appropriate’, ‘inappropriate’, and ‘uncertain’, respectively. Similarly, in 1685 (90%) patients, the referral to MPI was adherent to ESC guidelines, while in 185 (10%), it was not. The majority of appropriate MPI tests showed the presence of moderate-to-severe ischaemia (55%), while only a limited number (10%; P &amp;lt; 0.05) of MPI tests with uncertain clinical appropriateness or clearly inappropriate indications did not. In patients managed adherently to ESC guidelines, invasive coronary angiography more frequently showed obstructive coronary artery disease (CAD) (93 vs. 47%, P &amp;lt; 0.001) and led to coronary revascularization (65 vs. 23%, P &amp;lt; 0.001) compared with patients managed non-adherently. Conclusion In a single-centre, single-national, single-modality population, the current rate of appropriate MPI tests is high. Appropriate referrals are associated with a higher probability of moderate-to-severe ischaemia and better downstream resource utilization than inappropriate ones.

External validation and update of the J-ACCESS model in an Italian cohort of patients undergoing stress myocardial perfusion imaging

BackgroundCardiovascular risk models are based on traditional risk factors and investigations such as imaging tests. External validation is important to determine reproducibility and generalizability of a prediction model. We performed an external validation of t the Japanese Assessment of Cardiac Events and Survival Study by Quantitative Gated SPECT (J-ACCESS) model, developed from a cohort of patients undergoing stress myocardial perfusion imaging. MethodsWe included 3623 patients with suspected or known coronary artery disease undergoing stress single-photon emission computer tomography (SPECT) myocardial perfusion imaging at our academic center between January 2001 and December 2019. ResultsIn our study population, the J-ACCESS model underestimated the risk of major adverse cardiac events (cardiac death, nonfatal myocardial infarction, and severe heart failure requiring hospitalization) within three-year follow-up. The recalibrations and updated of the model slightly improved the initial performance: C-statistics increased from 0.664 to 0.666 and Brier score decreased from 0.075 to 0.073. Hosmer–Lemeshow test indicated a logistic regression fit only for the calibration slope (P = .45) and updated model (P = .22). In the update model, the intercept, diabetes, and severity of myocardial perfusion defects categorized coefficients were comparable with J-ACCESS. ConclusionThe external validation of the J-ACCESS model as well as recalibration models have a limited value for predicting of three-year major adverse cardiac events in our patients. The performance in predicting risk of the updated model resulted superimposable to the calibration slope model.

Prognostic value of myocardial perfusion imaging by cadmium zinc telluride single-photon emission computed tomography in patients with suspected or known coronary artery disease: a systematic review and meta-analysis

BackgroundAim of this study was to define the prognostic value of stress myocardial perfusion imaging by cadmium zinc telluride (CZT) single-photon emission computed tomography (SPECT) for prediction of adverse cardiovascular events in patients with known or suspected coronary artery disease (CAD).Methods and resultsStudies published until November 2022 were identified by database search. We included studies using stress myocardial perfusion imaging by CZT-SPECT to evaluate subjects with known or suspected CAD and providing primary data of adverse cardiovascular events. Total of 12 studies were finally included recruiting 36,415 patients. Pooled hazard ratio (HR) for the occurrence of adverse events was 2.17 (95% confidence interval, CI, 1.78–2.65) and heterogeneity was 66.1% (P = 0.001). Five studies reported data on adjusted HR for the occurrence of adverse events. Pooled HR was 1.69 (95% CI, 1.44–1.98) and heterogeneity was 44.9% (P = 0.123). Seven studies reported data on unadjusted HR for the occurrence of adverse events. Pooled HR was 2.72 (95% CI, 2.00–3.70). Nine studies reported data useful to calculate separately the incidence rate of adverse events in patients with abnormal and normal myocardial perfusion. Pooled incidence rate ratio was 2.38 (95% CI, 1.39–4.06) and heterogeneity was 84.6% (P < 0.001). The funnel plot showed no evidence of asymmetry (P = 0.517). At meta-regression analysis, we found an association between HR for adverse events and presence of angina symptoms and family history of CAD.ConclusionsStress myocardial perfusion imaging by CZT-SPECT is a valuable noninvasive prognostic indicator for adverse cardiovascular events in patients with known or suspected CAD.

Pulmonary blood volume assessment from a standard cardiac rubidium-82 imaging protocol: impact of adenosine-induced hyperemia

BackgroundThis study aimed to assess the feasibility of estimating the pulmonary blood volume noninvasively using standard Rubidium-82 myocardial perfusion imaging (MPI) and characterize the changes during adenosine-induced hyperemia.MethodsThis study comprised 33 healthy volunteers (15 female, median age = 23 years), of which 25 underwent serial rest/adenosine stress Rubidium-82 MPI sessions. Mean bolus transit times (MBTT) were obtained by calculating the time delay from the Rubidium-82 bolus arrival in the pulmonary trunk to the arrival in the left myocardial atrium. Using the MBTT, in combination with stroke volume (SV) and heart rate (HR), we estimated pulmonary blood volume (PBV = (SV × HR) × MBTT). We report the empirically measured MBTT, HR, SV, and PBV, all stratified by sex [male (M) vs female (F)] as mean (SD). In addition, we report grouped repeatability measures using the within-subject repeatability coefficient.ResultsMean bolus transit times was shortened during adenosine stressing with sex-specific differences [(seconds); Rest: Female (F) = 12.4 (1.5), Male (M) = 14.8 (2.8); stress: F = 8.8 (1.7), M = 11.2 (3.0), all P ≤ 0.01]. HR and SV increased during stress MPI, with a concomitant increase in the PBV [mL]; Rest: F = 544 (98), M = 926 (105); Stress: F = 914 (182), M = 1458 (338), all P < 0.001. The following test–retest repeatability measures were observed for MBTT (Rest = 17.2%, Stress = 17.9%), HR (Rest = 9.1%, Stress = 7.5%), SV (Rest = 8.9%, Stress = 5.6%), and for PBV measures (Rest = 20.7%, Stress = 19.5%)ConclusionPulmonary blood volume can be extracted by cardiac rubidium-82 MPI with excellent test–retest reliability, both at rest and during adenosine-induced hyperemia.

Prognostic role of stress-CMR and SPECT-MPI in patients undergoing intermediate-to-high risk non-cardiac surgery

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Data supporting the role of stress test myocardial perfusion imaging (MPI) before non-cardiac surgery (NCS) are sparse and out of date. In particular, there is no specific evidence on the use of stress cardiac magnetic resonance (sCMR) in this setting. The aim of the present study was to evaluate the impact MPI stress test with sCMR and single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) on surgery-related cardiac events. Method In this single-centre retrospective study, we included all patients with CAD or at least two cardiovascular risk factors undergoing intermediate-to-high-risk non-cardiac surgery. After a pre-operative cardiological assessment, including EKG and echocardiography, the indication to perform a pre-operative MPI-stress-test relied on the clinical judgement of the cardiologist. Cardiac events occurring within 30 days from surgery were evaluated. A composite primary endpoint included myocardial infarction, unstable angina, cardiac death, cardiogenic shock and pulmonary oedema. The secondary endpoint was the rate of acute coronary syndromes. Results A total of 1590 patients were included, of whom 669 underwent an MPI stress test strategy (287 sCMR, 382 SPECT-MPI). The rate of 30-day cardiac events was lower in the stress test group vs. the non-stress test group (1.2% vs 3.4%; p 0,006; figure 1). Adopting a stress test strategy showed a significant reduction of composite endpoint (OR: 0.334, IC: 0.155 – 0.766, p 0.009) and acute coronary syndrome (OR: 0.414, IC: 0.174 – 0.984, p 0.046) at multivariable analysis. Stress CMR was non-inferior to SPECT in predicting cardiac events and showed a greater accuracy to predict coronary artery revascularizations (AUC for sCMR: 0.95 with a percentage of myocardial ischemia cut-point of 5.5%; figure 2). Conclusions A stress-test strategy is associated with a lower incidence of cardiac events in high-risk patients candidate to intermediate-to-high-risk non-cardiac surgery. The rate of cardiac complications is similarly predicted by sCMR and SPECT-MPI, although sCMR is more accurate in predicting coronary artery revascularizations.

Comparison of Regadenoson and Dipyridamole Safety Profiles During Stress Myocardial Perfusion Imaging.

The pharmacological stress test with vasodilator agents is an alternative cardiological diagnostic tool for patients with contraindications to the classical stress test provided by physical activity during single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). The study compared the frequency of the side effects of regadenoson and dipyridamole during a SPECT MPI. This retrospective study included data of 283 consecutive patients who underwent pharmacological stress tests in years 2015-2020. The study group consisted of 240 patients who had received dipyridamole and 43 patients who had received regadenoson. The collected data included the patients' characteristics, the occurrence of side effects (divided into mild: headache, vertigo, nausea, vomiting, dyspnea, chest discomfort, hot flushes, general weakness and severe: bradycardia, hypotension, loss of consciousness), and blood pressure values/measurements. Overall, complications occurred relatively often (regadenoson: 23.2%, dipirydamol: 26.7%, p=0.639). Procedure discontinuation was necessary in 0.7% of examinations, whereas pharmacological support was necessary in 4.7%. There was no difference in the prevalence of mild (regadenoson: 16.2%, dipirydamol: 18.3%, p=0.747) and severe complications (regadenoson: 11.6%, dipyridamole: 15.0%, p=0.563). However, regadenoson has been found to cause a significantly smaller mean decrease of systolic blood pressure (SBP) (regadenoson: -2.6±10.0 mmHg, dipyridamole: -8.7±9.6 mmHg, p=0.002), diastolic blood pressure (DBP) (regadenoson: -0.9±5.4 mmHg, dipyridamole: -3.6±6.2 mmHg, p=0.032), as well as mean arterial pressure (MAP) (regadenoson: -1.5±5.6 mmHg, dipyridamole: -5.4±6.5 mmHg, p=0.001). Regadenoson and dipyridamole presented a similar safety profile during SPECT MPI. However, regadenoson has been found to cause significantly smaller decreases in SBP, DBP, and MAP.

Fully automated pixel-wise quantitative CMR-myocardial perfusion with CMR-coronary angiography to detect hemodynamically significant coronary artery disease.

We applied a fully automated pixel-wise post-processing framework to evaluate fully quantitative cardiovascular magnetic resonance myocardial perfusion imaging (CMR-MPI). In addition, we aimed to evaluate the additive value of coronary magnetic resonance angiography (CMRA) to the diagnostic performance of fully automated pixel-wise quantitative CMR-MPI for detecting hemodynamically significant coronary artery disease (CAD). A total of 109 patients with suspected CAD were prospectively enrolled and underwent stress and rest CMR-MPI, CMRA, invasive coronary angiography (ICA), and fractional flow reserve (FFR). CMRA was acquired between stress and rest CMR-MPI acquisition, without any additional contrast agent. Finally, CMR-MPI quantification was analyzed by a fully automated pixel-wise post-processing framework. Of the 109 patients, 42 patients had hemodynamically significant CAD (FFR ≤ 0.80 or luminal stenosis ≥ 90% on ICA) and 67 patients had hemodynamically non-significant CAD (FFR ˃ 0.80 or luminal stenosis < 30% on ICA) were enrolled. On the per-territory analysis, patients with hemodynamically significant CAD had higher myocardial blood flow (MBF) at rest, lower MBF under stress, and lower myocardial perfusion reserve (MPR) than patients with hemodynamically non-significant CAD (p < 0.001). The area under the receiver operating characteristic curve of MPR (0.93) was significantly larger than those of stress and rest MBF, visual assessment of CMR-MPI, and CMRA (p < 0.05), but similar to that of the integration of CMR-MPI with CMRA (0.90). Fully automated pixel-wise quantitative CMR-MPI can accurately detect hemodynamically significant CAD, but the integration of CMRA obtained between stress and rest CMR-MPI acquisition did not provide significantly additive value. • Full quantification of stress and rest cardiovascular magnetic resonance myocardial perfusion imaging can be postprocessed fully automatically, generating pixel-wise myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) maps. • Fully quantitative MPR provided higher diagnostic performance for detecting hemodynamically significant coronary artery disease, compared with stress and rest MBF, qualitative assessment, and coronary magnetic resonance angiography (CMRA). • The integration of CMRA and MPR did not significantly improve the diagnostic performance of MPR alone.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images. Further, many SPECT systems do not include a CT component. To address these issues, we developed a Scatter-window projection and deep Learning-based AC (SLAC) method to perform AC without a separate transmission scan. To investigate the clinical efficacy of this method, we then objectively evaluated the performance of this method on the clinical task of detecting perfusion defects on MPI in a retrospective study with anonymized clinical SPECT/CT stress MPI images. The proposed method was compared with CT-based AC (CTAC) and no-AC (NAC) methods. Our results showed that the SLAC method yielded an almost overlapping receiver operating characteristic (ROC) plot and a similar area under the ROC (AUC) to the CTAC method on this task. These results demonstrate the capability of the SLAC method for transmission-less AC in SPECT and motivate further clinical evaluation.

Diagnostic value of quantitative myocardial blood flow assessment by NaI(Tl) SPECT in detecting significant stenosis: a prospective, multi-center study

ObjectivesThe aim of this prospective multi-center study was to investigate the diagnostic value of myocardial blood flow (MBF) quantification using NaI(Tl)-based single-photon emission computed tomography (SPECT) for determining coronary artery disease (CAD) defined by quantitative coronary angiography (QCA). BackgroundAbsolute quantitation of MBF and myocardial flow reserve (MFR) using SPECT is clinically feasible; however, whether flow quantification using NaI(Tl) SPECT is superior to commonly performed SPECT myocardial perfusion imaging (MPI) in determining CAD has not been evaluated. MethodsPatients with suspected or known CAD underwent pharmacological stress/rest dynamic SPECT imaging and routine SPECT MPI followed by QCA. Obstructive disease was defined as ≥ 50% reduction in luminal diameter on QCA. ResultsOne hundred fifty-four patients (462 vessels) were included in the analysis. Obstructive CAD was detected in 76/154 patients (49.4%) and 112/462 vessels (24.2%). Optimal cut-off values were 1.86 mL/min/g for stress MBF and 1.95 for MFR, respectively. Stress MBF and MFR were more sensitive than MPI in both individual patients (stress MBF vs MPI: 81.6% vs 51.3%; MFR vs MPI: 72.4% vs 51.3%) and in coronary vascular regions (stress MBF vs MPI: 78.6% vs 31.3%; MFR vs MPI: 75.9% vs 31.3%; all P < .01). In receiver operating characteristic curve analysis, quantification revealed a significantly greater area under the curve than MPI at the patient (stress MBF vs MPI: 0.761 vs 0.641; MFR vs MPI: 0.770 vs 0.641) and the vessel (stress MBF vs MPI: 0.745 vs 0.613; MFR vs MPI: 0.756 vs 0.613; all P < .05) levels. Integrating quantitative SPECT measures with MPI significantly increased the area under the curve and improved the discriminatory and reclassification capacity. ConclusionFlow quantification using NaI(Tl) SPECT provides superior sensitivity and discriminatory capacity to MPI in detecting significant stenosis.Clinical trial registration NCT03637725.

Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder

BackgroundSingle-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for patients with coronary heart disease. We tested the feasibility of feature extraction from MPI using a deep convolutional autoencoder (CAE) model. MethodsEight hundred and forty-three pairs of stress and rest myocardial perfusion images were collected from consecutive patients who underwent cardiac scintigraphy in our hospital between December 2019 and February 2022. We trained a CAE model to reproduce the input paired image data, so as the encoder to output a 256-dimensional feature vector. The extracted feature vectors were further dimensionally reduced via principal component analysis (PCA) for data visualization. Content-based image retrieval (CBIR) was performed based on the cosine similarity of the feature vectors between the query and reference images. The agreement of the radiologist’s finding between the query and retrieved MPI was evaluated using binary accuracy, precision, recall, and F1-score. ResultsA three-dimensional scatter plot with PCA revealed that feature vectors retained clinical information such as percent summed difference score, presence of ischemia, and the location of scar reported by radiologists. When CBIR was used as a similarity-based diagnostic tool, the binary accuracy was 81.0%. ConclusionThe results indicated the utility of unsupervised feature learning for CBIR in MPI. Graphical abstract▪

Complementary prognostic value of stress perfusion imaging and global coronary flow reserve derived from cardiovascular magnetic resonance: a long-term cohort study

BackgroundPhase-contrast cine cardiovascular magnetic resonance (CMR) quantifies global coronary flow reserve (CFR) by measuring blood flow in the coronary sinus (CS), allowing assessment of the entire coronary circulation. However, the complementary prognostic value of stress perfusion CMR and global CFR in long-term follow-up has yet to be investigated. This study aimed to investigate the complementary prognostic value of stress myocardial perfusion imaging and global CFR derived from CMR in patients with suspected or known coronary artery disease (CAD) during long-term follow-up. MethodsParticipants comprised 933 patients with suspected or known CAD who underwent comprehensive CMR. Major adverse cardiac events (MACE) comprised cardiac death, non-fatal myocardial infarction, unstable angina, hospitalization for heart failure, stroke, ventricular arrhythmia, and late revascularization. ResultsDuring follow-up (median, 5.3 years), there were 223 MACE. Kaplan–Meier curve analysis revealed a significant difference in event-free survival among tertile groups for global CFR (log-rank, p < 0.001) and between patients with and without ischemia (p < 0.001). The combination of stress perfusion CMR and global CFR enhanced risk stratification (p < 0.001 for overall), and prognoses were comparable between the subgroup with ischemia and no impaired CFR and the subgroup with no ischemia and impaired CFR (p = 0.731). Multivariate Cox proportional hazard regression analysis showed that impaired CFR remained a significant predictor for MACE (hazard ratio, 1.6; p = 0.002) when adjusted for coronary risk factors and CMR predictors, including ischemia. The addition of impaired CFR to coronary risk factors and ischemia significantly increased the global chi-square value from 88 to 109 (p < 0.001). Continuous net reclassification improvement and integrated discrimination with the addition of global CFR to coronary risk factors plus ischemia improved to 0.352 (p < 0.001) and 0.017 (p < 0.001), respectively. ConclusionsDuring long-term follow-up, stress perfusion CMR and global CFR derived from CS flow measurement provided complementary prognostic value for prediction of cardiovascular events. Microvascular dysfunction or diffuse atherosclerosis as shown by impaired global CFR may play a role as important as that of ischemia due to epicardial coronary stenosis in the risk stratification of CAD patients.