Cellular recovery after prolonged warm ischaemia of the whole body.

After cessation of blood flow or similar ischaemic exposures, deleterious molecular cascades commence in mammalian cells, eventually leading to their death1,2. Yet with targeted interventions, these processes can be mitigated or reversed, even minutes or hours post mortem, as also reported in the isolated porcine brain using BrainEx technology3. To date, translating single-organ interventions to intact, whole-body applications remains hampered by circulatory and multisystem physiological challenges. Here we describe OrganEx, an adaptation of the BrainEx extracorporeal pulsatile-perfusion system and cytoprotective perfusate for porcine whole-body settings. After 1 h of warm ischaemia, OrganEx application preserved tissue integrity, decreased cell death and restored selected molecular and cellular processes across multiple vital organs. Commensurately, single-nucleus transcriptomic analysis revealed organ- and cell-type-specific gene expression patterns that are reflective of specific molecular and cellular repair processes. Our analysis comprises a comprehensive resource of cell-type-specific changes during defined ischaemic intervals and perfusion interventions spanning multiple organs, and it reveals an underappreciated potential for cellular recovery after prolonged whole-body warm ischaemia in a large mammal.

Restoration of brain circulation and cellular functions hours post-mortem.

The brains of humans and other mammals are highly vulnerable to interruptions in blood flow and decreases in oxygen levels. Here we describe the restoration and maintenance of microcirculation and molecular and cellular functions of the intact pig brain under ex vivo normothermic conditions up to four hours post-mortem. We have developed an extracorporeal pulsatile-perfusion system and a haemoglobin-based, acellular, non-coagulative, echogenic, and cytoprotective perfusate that promotes recovery from anoxia, reduces reperfusion injury, prevents oedema, and metabolically supports the energy requirements of the brain. With this system, we observed preservation of cytoarchitecture; attenuation of cell death; and restoration of vascular dilatory and glial inflammatory responses, spontaneous synaptic activity, and active cerebral metabolism in the absence of global electrocorticographic activity. These findings demonstrate that under appropriate conditions the isolated, intact large mammalian brain possesses an underappreciated capacity for restoration of microcirculation and molecular and cellular activity after a prolonged post-mortem interval.

Clinical physiology: the crucial role of MRI in evaluation of peripheral artery disease.

Peripheral artery disease (PAD) is a common vascular disease that primarily affects the lower limbs and is defined by the constriction or blockage of peripheral arteries and may involve microvascular dysfunction and tissue injury. Patients with diabetes have more prominent disease of microcirculation and develop peripheral neuropathy, autonomic dysfunction, and medial vascular calcification. Early and accurate diagnosis of PAD and disease characterization are essential for personalized management and therapy planning. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and multiplanar imaging capabilities and is useful as a noninvasive imaging tool in the comprehensive physiological assessment of PAD. This review provides an overview of the current state of the art of MRI in the evaluation and characterization of PAD, including an analysis of the many applicable MR imaging techniques, describing the advantages and disadvantages of each approach. We also present recent developments, future clinical applications, and future MRI directions in assessing PAD. The development of new MR imaging technologies and applications in preclinical models with translation to clinical research holds considerable potential for improving the understanding of the pathophysiology of PAD and clinical applications for improving diagnostic precision, risk stratification, and treatment outcomes in patients with PAD.

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

PURPOSE: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/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 (> 90 days after SPECT) was evaluated with survival analysis. RESULTS: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/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 ≥ 35 kg/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-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

Links Between Obstructive Sleep Apnea and Myocardial Blood Flow Changes Impacting Adverse Cardiovascular Disease-related Outcomes.

PURPOSE OF REVIEW: Recent studies have demonstrated an association between obstructive sleep apnea (OSA) and abnormal myocardial blood flow (MBF), myocardial flow reserve (MFR), and coronary microvascular dysfunction (CMD). Here, we review the evidence and describe the potential underlying mechanisms linking OSA to abnormal MBF. Examining relevant studies, we assess the impact of OSA-specific therapy, such as continuous positive airway pressure (CPAP), on MBF. RECENT FINDINGS: Recent studies suggest an association between moderate to severe OSA and abnormal MBF/MFR. OSA promotes functional and structural abnormalities of the coronary microcirculation. OSA also promotes the uncoupling of MBF to cardiac work. In a handful of studies with small sample sizes, CPAP therapy improved MBF/MFR. Moderate to severe OSA is associated with abnormal MFR, suggesting an association with CMD. Evidence suggests that CPAP therapy improves MBF. Future studies must determine the clinical impact of improved MBF with CPAP.

Multiaxial pressure-strain analysis of regional myocardial work in the setting of graded coronary stenoses and dobutamine stress.

We present a detailed analysis of regional myocardial blood flow and work to better understand the effects of coronary stenoses and low-dose dobutamine stress. Our analysis is based on a unique open-chest model in anesthetized canines that features invasive hemodynamic monitoring, microsphere-based blood flow analysis, and an extensive three-dimensional (3-D) sonomicrometer array that provides multiaxial deformational assessments in the ischemic, border, and remote vascular territories. We use this model to construct regional pressure-strain loops for each territory and quantify the loop subcomponent areas that reflect myocardial work contributing to the ejection of blood and wasted work that does not. We demonstrate that reductions in coronary blood flow markedly alter the shapes and temporal relationships of pressure-strain loops, as well as the magnitudes of their total and subcomponent areas. Specifically, we show that moderate stenoses in the mid-left anterior descending coronary artery decrease regional midventricle myocardial work indices and substantially increase indices of wasted work. In the midventricle, these effects are most pronounced along the radial and longitudinal axes, with more modest effects along the circumferential axis. We further demonstrate that low-dose dobutamine can help to restore or even improve function, but often at the cost of increased wasted work. This detailed, multiaxial analysis provides unique insight into the physiology and mechanics of the heart in the presence of ischemia and low-dose dobutamine, with potential implications in many areas, including the detection and characterization of ischemic heart disease and the use of inotropic support for low cardiac output.NEW & NOTEWORTHY Our unique experimental model assesses cardiac pressure-strain relationships along multiple axes in multiple regions. We demonstrate that moderate coronary stenoses decrease regional myocardial work and increase wasted work and that low-dose dobutamine can help to restore myocardial function, but often with further increases in wasted work. Our findings highlight the significant directional variation of cardiac mechanics and demonstrate potential advantages of pressure-strain analyses over traditional, purely deformational measures, especially in characterizing physiological changes related to dobutamine.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.

BACKGROUND: The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A deep learning method is also proposed to generate synthetic dense-view image volumes from few-view counterparts. METHODS: By moving the detector array, a total of four projection angle sets were acquired and combined for image reconstructions. A deep neural network is proposed to generate synthetic four-angle images with 76 ([Formula: see text]) projections from corresponding one-angle images with 19 projections. Simulated data, pig, physical phantom, and human studies were used for network training and evaluation. Reconstruction results were quantitatively evaluated using representative image metrics. The myocardial perfusion defect size of different subjects was quantified using an FDA-cleared clinical software. RESULTS: Multi-angle reconstructions and network results have higher image resolution, improved uniformity on normal myocardium, more accurate defect quantification, and superior quantitative values on all the testing data. As validated against cardiac catheterization and diagnostic results, deep learning results showed improved image quality with better defect contrast on human studies. CONCLUSION: Increasing angular sampling can substantially improve image quality on DNM, and deep learning can be implemented to improve reconstruction quality in case of stationary imaging.

Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging.

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

Impaired left-ventricular global longitudinal strain by feature-tracking cardiac MRI predicts mortality in systemic sclerosis.

Impaired left-ventricular (LV) and right-ventricular (RV) cardiac magnetic resonance (CMR) strain has been documented in systemic sclerosis (SSc). However, it is unknown whether the CMR strain is predictive of adverse outcomes in SSc. Therefore, we set out to investigate the prognostic value of CMR strain in SSc. Patients with SSc who underwent CMR for clinical indications between 11/2010 and 07/2020 were retrospectively studied. LV and RV strain was evaluated by feature tracking. The association between strain, late gadolinium enhancement (LGE), and survival was evaluated with time to event and Cox-regression analyses. During the study period, 42 patients with SSc (age: 57 ± 14 years, 83% female, 57% limited cutaneous SSc, SSc duration: 7 ± 8 years) underwent CMR. During the median follow-up of 3.6 years, 11 patients died (26%). Compared to surviving patients, patients who died had significantly worse LV GLS (- 8.2 ± 6.2% versus - 12.1 ± 2.9%, p = 0.03), but no difference in LV global radial, circumferential, or RV strain values. Patients within the quartile of most impaired LV GLS (≥ - 12.8%, n = 10) had worse survival when compared to patients with preserved LV GLS (< - 12.8%, n = 32, log-rank p = 0.02), which persisted after controlling for LV cardiac output, LV cardiac index, reduced LV ejection fraction, or presence of LGE. In addition, patients who had both impaired LV GLS and LGE (n = 5) had worse survival than patients with LGE or impaired GLS alone (n = 14) and compared to those without any of these features (n = 17, p = 0.003). In our retrospective cohort of patients with SSc undergoing CMR for clinical indications, LV GLS and LGE were found to be predictive of overall survival.

The Potential Role of PET in the Management of Peripheral Artery Disease.

PURPOSE OF REVIEW: Current non-invasive tests for evaluating patients with peripheral artery disease (PAD) have significant limitations for early detection and management of patients with PAD and are generally focused on the evaluation of large vessel disease. PAD often involves disease of microcirculation and altered metabolism. Therefore, there is a critical need for reliable quantitative non-invasive tools that can assess limb microvascular perfusion and function in the setting of PAD. RECENT FINDINGS: Recent developments in positron emission tomography (PET) imaging have enabled the quantification of blood flow to the lower extremities, the assessment of the viability of skeletal muscles, and the evaluation of vascular inflammation and microcalcification and angiogenesis in the lower extremities. These unique capabilities differentiate PET imaging from current routine screening and imaging methods. The purpose of this review is to highlight the promising role of PET in the early detection and management of PAD providing a summary of the current preclinical and clinical research related to PET imaging in patients with PAD and related advancement of PET scanner technology.

Abnormal left atrial body stiffness is predicted by appendage size: impact of appendage occlusion on left atrial mechanics assessed by pressure-volume analysis.

Atrial cardiomyopathy has been recognized as having important consequences for cardiac performance and clinical outcomes. The pathophysiological role of the left atrial (LA) appendage and the effect of percutaneous left atrial appendage occlusion (LAAO) upon LA mechanics is incompletely understood. We evaluated if changes in LA stiffness due to endocardial LAAO can be detected by LA pressure-volume (PV) analysis and whether stiffness parameters are associated with baseline characteristics. Patients undergoing percutaneous endocardial LAAO (n = 25) were studied using a novel PV analysis using near-simultaneous three-dimensional LA volume measurements by transesophageal echocardiography (TEE) and direct invasive LA pressure measurements. LA stiffness (dP/dV, change in pressure with change in volume) was calculated before and after LAAO. Overall LA stiffness significantly increased after LAAO compared with baseline (median, 0.41-0.64 mmHg/mL; P ≪ 0.001). LA body stiffness after LAAO correlated with baseline LA appendage size by indexed maximum depth (Spearman's rank correlation coefficient Rs = 0.61; P < 0.01). LA stiffness change showed an even stronger correlation with baseline LA appendage size by indexed maximum depth (Rs = 0.70; P < 0.001). We found that overall LA stiffness increases after endocardial LAAO. Baseline LA appendage size correlates with the magnitude of increase and LA body stiffness. These findings document alteration of LA mechanics after endocardial LAAO and suggest that the LA appendage modulates overall LA compliance.NEW & NOTEWORTHY Our study documents a correlation of LA appendage remodeling with the degree of chronically abnormal LA body stiffness. In addition, we found that LA appendage size was the baseline parameter that best correlated with the magnitude of a further increase in overall LA stiffness after appendage occlusion. These findings offer insights about the LA appendage and LA mechanics that are relevant to patients at risk for adverse atrial remodeling, especially candidates for LA appendage occlusion.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

PURPOSE: Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (µ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without µ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS: For dedicated SPECT, we developed strategies to predict truncated µ-maps from NAC images reconstructed with a small matrix, or full µ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict µ-maps or AC images. RESULTS: For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full µ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS: We developed strategies of generating µ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Prototype device for endoventricular beta-emitting radiotracer detection and molecularly-guided intervention.

BACKGROUND: We have set out to develop a catheter-based theranostic system that: (a) identifies diseased and at-risk myocardium via endocardial detection of systemically delivered ß-emitting radiotracers and (b) utilizes molecular signals to guide delivery of therapeutics to appropriate tissue via direct intramyocardial injection. METHODS: Our prototype device consists of a miniature ß-radiation detector contained within the tip of a flexible intravascular catheter. The catheter can be adapted to incorporate an injection port and retractable needle for therapeutic delivery. The performance of the ß-detection catheter was assessed in vitro with various ß-emitting radionuclides and ex vivo in hearts of pigs following systemic injection of 18F-fluorodeoxyglucose (18F-FDG) at 1-week post-myocardial infarction. Regional catheter-based endocardial measurements of 18F activity were compared to regional tissue activity from PET/CT images and gamma counting. RESULTS: The ß-detection catheter demonstrated sensitive in vitro detection of ß-radiation from 22Na (ß+), 18F (ß+), and 204Tl (ß-), with minimal sensitivity to γ-radiation. For 18F, the catheter demonstrated a sensitivity of 4067 counts/s/µCi in contact and a spatial resolution of 1.1 mm FWHM. Ex vivo measurements of endocardial 18F activity with the ß-detection catheter in the chronic pig infarct model demonstrated good qualitative and quantitative correlation with regional tissue activity from PET/CT images and gamma counting. CONCLUSION: The prototype ß-detection catheter demonstrates sensitive and selective detection of ß- and ß+ emissions over a wide range of energies and enables high-fidelity ex vivo characterization of endocardial activity from systemically delivered 18F-FDG.

Anger recall mental stress decreases 123I-metaiodobenzylguanidine (123I-MIBG) uptake and increases heterogeneity of cardiac sympathetic activity in the myocardium in patients with ischemic cardiomyopathy.

BACKGROUND: Acute psychological stressors such as anger can precipitate ventricular arrhythmias, but the mechanism is incompletely understood. Quantification of regional myocardial sympathetic activity with 123I-metaiodobenzylguanidine (123I-mIBG) SPECT imaging in conjunction with perfusion imaging during mental stress may identify a mismatch between perfusion and sympathetic activity that may exacerbate a mismatch between perfusion and sympathetic activity that could create a milieu of increased vulnerability to ventricular arrhythmia. METHODS: Five men with ischemic cardiomyopathy (ICM), and five age-matched healthy male controls underwent serial 123I-mIBG and 99mTc-Tetrofosmin SPECT/CT imaging during an anger recall mental stress task and dual isotope imaging was repeated approximately 1 week later during rest. Images were reconstructed using an iterative reconstruction algorithm with CT-based attenuation correction. The mismatch of left ventricular myocardial 123I-mIBG and 99mTc-Tetrofosmin was assessed along with radiotracer heterogeneity and the 123I-mIBG heart-to-mediastinal ratios (HMR) were calculated using custom software developed at Yale. RESULTS: The hemodynamic response to mental stress was similar in both groups. The resting-HMR was greater in healthy control subjects (3.67 ± 0.95) than those with ICM (3.18 ± 0.68, P = .04). Anger recall significantly decreased the HMR in ICM patients (2.62 ± 0.3, P = .04), but not in normal subjects. The heterogeneity of 123I-mIBG uptake in the myocardium was significantly increased in ICM patients during mental stress (26% ± 8.23% vs. rest: 19.62% ± 9.56%; P = .01), whereas the 99mTc-Tetrofosmin uptake pattern was unchanged. CONCLUSION: Mental stress decreased the 123I-mIBG HMR, increased mismatch between sympathetic activity and myocardial perfusion, and increased the heterogeneity of 123I-mIBG uptake in ICM patients, while there was no significant change in myocardial defect size or the heterogeneity of 99mTc-Tetrofosmin perfusion. The changes observed in this proof-of-concept study may provide valuable information about the trigger-substrate interaction and the potential vulnerability for ventricular arrhythmias.

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

BACKGROUND: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. METHODS: CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance. RESULTS: Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net. CONCLUSIONS: Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.

Machine learning to predict abnormal myocardial perfusion from pre-test features.

BACKGROUND: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features. METHODS: We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation. RESULTS: In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750-0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001). CONCLUSION: ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.

Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry.

BACKGROUND: The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed. METHODS: We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia. RESULTS: In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center. CONCLUSION: The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia.

Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry.

BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.