Myocardial Perfusion SPECT Research Articles

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions. We aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI-SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions. A total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI-SPECT images were included. Images were reconstructed using 15 combinations of filter cut-off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal-Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t-test, Wilcoxon rank-sum, and signed-rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features. Before applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC<0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first-order (FO) and gray-level co-occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray-tone difference matrix (NGTDM), GLCM, gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and gray-level dependence matrix (GLDM) under Cut-off, Filter, and Order batches. The Wilcoxon rank-sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups. The majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI-SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility.

Deep learning-based multi-frequency denoising for myocardial perfusion SPECT

BackgroundDeep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.MethodsFifty anonymized patients who underwent routine 99mTc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.ResultsAttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.ConclusionsAttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.

Comparison between the use of one and two CT scans for attenuation correction of rest-stress myocardial perfusion SPECT with Tc-99m sestamibi

Background: The standard protocol is to use separate computed tomography (CT) scans acquired during rest and stress for attenuation correction (AC) of myocardial perfusion (MP) single photon emission computed tomography (SPECT) imaging. Recently, there have been attempts to reduce the radiation dose by using one CT instead of two CTs. Objective: To compare between the use of one and two CTs for AC of rest-stress MP SPECT with Tc-99m sestamibi in quantification of MP and left ventricle (LV) function. Materials and Methods: Gated rest-stress MP SPECT images of 107 patients were reprocessed using 3 different AC methods: 1) rest CT for AC of rest SPECT and stress CT for AC of stress SPECT (2CT); 2) rest CT for AC of both rest and stress SPECT (1CT-rest); and 3) stress CT for AC of both rest and stress SPECT (1CT-stress). SPECT images obtained from 2CT and 1CT were used for quantification of MP values and LV function values. The values from 2CT and 1CT were compared. Results: The MP values of 2CT and 1CT showed a strong correlation (r≥0.712) and they did not differ significantly (p=0.106 to 0.931). In contrast, the LV function values of 2CT and 1CT exhibited a very strong correlation (r≥0.960), but they differ significantly (p=&lt;0.001 to 0.004). Conclusions: The use of one and two CTs for AC in rest-stress MP SPECT with Tc-99m sestamibi can be interchanged for the quantification of MP, but not for the quantification of LV function.

Short- and long-term prognostic performance of exercise ECG and myocardial perfusion SPECT.

Myocardial perfusion SPECT(MPS) and exercise electrocardiography(Ex-ECG) results are of prognostic importance for short-term follow up duration. However, the value of MPS or Ex-ECG findings for long-term risk assessment is less evident as underlying risk factors for ischemic heart disease (IHD) gain in importance. To assess the short- and long-term prognostic value of MPS and Ex-ECG in relation to known risk factors. An observational study of 908 patients (age 63 years, 49% male, 45% prior IHD) referred for MPS and Ex-ECG. Follow-up was divided into two periods (short-term: <5 years and long-term: >5 years). Cardiac events were defined as a composite of acute myocardial infarction, unstable angina, unplanned revascularization and cardiovascular death. The composite endpoint occurred in 95 patients (short-term follow up) and in 94 patients (long-term follow up). In multivariable models stress testing had a strong predictive value for short-term follow up (HR for MPS = 2.9, CI = 1.9-4.5, p < 0.001 and HR for Ex-ECG = 2.1, CI 1.3-3.3, p = 0.002), but no predictive value for long-term follow up (HR for MPS = 0.9, CI = 0.5-1.5, p = 0.70 and HR for Ex-ECG = 1.0, CI = 0.6-1.6, p = 0.92). Male sex and prior IHD were significant predictors regardless of follow up duration. Age, diabetes and decreased exercise capacity were risk factors for long-term follow up. The prognostic value of MPS and Ex-ECG results are strong for short-term follow up but diminish over time and do not contribute significantly in multivariable models after 5 years. Long-term prognosis is primarily governed by underlying risk factors and exercise capacity.

Abstract Mo036: DNA Damage and Repair in Patients Undergoing Myocardial Perfusion Single-Photon Emission Computed Tomography

Background: As medical imaging has continuously grown, patient exposure to ionizing radiation and the risks associated with it are important issues. Myocardial perfusion single-photon emission computed tomography (MP-SPECT) is one of the most frequently performed noninvasive cardiac imaging tests, generating concern about the potentially excessive radiation exposure for patients, including the cumulative doses of lifetime radiation. even though the individual levels of radiation exposure are considered low. Aim: To evaluate DNA damage and DNA repair markers after MP-SPECT. Methods: Thirty-two patients undergoing Tc-99m sestamibi MP-SPECT (mean injected Tc-99m sestamibi dose: 15.1 mCi) were studied. Peripheral blood was collected before radiotracer injection at rest and 60-90 minutes after injection. The comet assay (single-cell gel electrophoresis) was performed with blood lymphocytes to detect DNA strand breaks, which appear as DNA fragmentation (the comet tail; Figure1). Images of fluorescence microscopy were analyzed using the Comet Assay Software Project Lab (CASPLab 1.2.3 beta 2). Three parameters were evaluated: the percentage of DNA in the comet tail, tail length, and tail moment (the measure of migratory DNA and number of fragments). Quantitative PCR (qPCR) was performed to evaluate the expression of five genes related to signaling pathways in response to DNA damage and repair (ATM, ATR, BRCA1, CDKN1A, XPC). Data are expressed as medians, and Wilcoxon or Mann-Whitney tests were employed for statistical analysis. A p-value &lt;0.05 was considered statistically significant. Results: Figure 2 shows pre- and post- radiation exposure comet assay parameters. After radiotracer injection, there was a 10.7% increase of the DNA percentage in the tail (pre-exposure median 2.185 vs post-exposure median 2.418, p&lt;0.0065). Tail length increased 6.7% (15.0 pre-exposure vs 16.0 post-exposure, p&lt;0.0034). Tail moment increased 27.5% (0.3177 pre-exposure vs 0.4050 post-exposure, p&lt;0.0001). qPCR did not reveal increased expression of any gene after radiation exposure. Conclusion: An increase in DNA damage was detected after a single radiotracer injection for MP-SPECT, yet unaccompanied by activation of repair genes. This may suggest that DNA damage is not large enough to trigger intense repair responses and might not result in biologically significant DNA damage.

Sixteen-Frame Gated Myocardial Perfusion SPECT as a Surrogate for Equilibrium Radionuclide Angiography in Measurement of Systolic and Diastolic Indices: A Cross-Sectional Study

Abstract Introduction Equilibrium radionuclide angiography (ERNA) has long been assumed as the preferred method to assess cardiac volumes as well as left ventricular systolic and diastolic indices. ERNA was used to diagnose subtle changes in cardiac function during chemotherapy or early stages of heart failure. Gated myocardial perfusion SPECT (GMPS) was introduced as a more feasible and versatile alternative to ERNA, but the precision of GMPS to assess systolic and diastolic indices has not yet been fully reviewed. Method We studied the left ventricular systolic and diastolic functional indices measured by a 16-frame GMPS and compared the results with those of ERNA in 25 patients. All the images were analyzed visually, semi-quantitatively, and quantitatively using quantitative gated SPECT (QGS), quantitative blood pool SPECT (QBS), and planar gated blood pool (PGBP) software. The left ventricular functional indices calculated using QGS compared with those obtained using QBS and PGBP Result Our study found a significant correlation between the left ventricular ejection fraction (LVEF) calculated using the PGBP, QGS, and QBS methods. There was a significant correlation between the LV peak ejection rate (LVPER) calculated by the PGBP and QGS analyses, and there was no significant difference in the LVPER calculated with the QGS and QBS methods. This study also revealed a significant correlation between the LV peak filling rate (LVPFR) calculated by QBS and QGS, with no significant difference between them. We also found a significant correlation between LV end systolic volume (LVESV) calculated using QGS and QBS and between LV end diastolic volume (LVEDV) calculated using QGS and QBS software. This study also revealed a significant correlation between the LV mean filling rate over the first third of diastole (LVMFR/3) calculated using the QGS and QBS software. Conclusion Considering the significant correlation between LVEF, LVPER, LVPFR, LVESV, LVMFR/3, and LVEDV calculated using the QGS and QBS methods in our study, the 16-frame GMPS could be regarded as an acceptable substitute for ERNA in the investigation of systolic and diastolic indices.

Improvement of myocardial perfusion SPECT studies with a cardio-dedicated CZT gamma camera. Comparison with an analogical SPECT/CT gamma camera

Improvement of myocardial perfusion SPECT studies with a cardio-dedicated CZT gamma camera. Comparison with an analogical SPECT/CT gamma camera

Interpretable deep generative model in myocardial perfusion SPECT images based on the creation of patient-matched normal maps

Interpretable deep generative model in myocardial perfusion SPECT images based on the creation of patient-matched normal maps

Radiomics Nomogram Derived from Gated Myocardial Perfusion SPECT for Identifying Ischemic Cardiomyopathy.

Personalized management involving heart failure (HF) etiology is crucial for better prognoses. We aim to evaluate the utility of a radiomics nomogram based on gated myocardial perfusion imaging (GMPI) in distinguishing ischemic from non-ischemic origins of HF. A total of 172 heart failure patients with reduced left ventricular ejection fraction (HFrEF) who underwent GMPI scan were divided into training (n = 122) and validation sets (n = 50) based on chronological order of scans. Radiomics features were extracted from the resting GMPI. Four machine learning algorithms were used to construct radiomics models, and the model with the best performances were selected to calculate the Radscore. A radiomics nomogram was constructed based on the Radscore and independent clinical factors. Finally, the model performance was validated using operating characteristic curves, calibration curve, decision curve analysis, integrated discrimination improvement values (IDI), and the net reclassification index (NRI). Three optimal radiomics features were used to build a radiomics model. Total perfusion deficit (TPD) was identified as the independent factors of conventional GMPI metrics for building the GMPI model. In the validation set, the radiomics nomogram integrating the Radscore, age, systolic blood pressure, and TPD significantly outperformed the GMPI model in distinguishing ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) (AUC 0.853 vs. 0.707, p = 0.038). IDI analysis indicated that the nomogram improved diagnostic accuracy by 28.3% compared to the GMPI model in the validation set. By combining radiomics signatures with clinical indicators, we developed a GMPI-based radiomics nomogram that helps to identify the ischemic etiology of HFrEF.

Efficacy of a Novel Respiratory Motion Reduction Block in Reducing Motion Artifact on Myocardial Perfusion Single-Photon Emission Computed Tomography.

Motion artifacts caused by heart motion during myocardial perfusion single-photon emission computed tomography (SPECT) can compromise image quality and diagnostic accuracy. This study aimed to evaluate the efficacy of the novel respiratory motion reduction block (RRB) device in reducing motion artifacts by compressing the hypochondrium and improving SPECT image quality. In total, 91 patients who underwent myocardial perfusion SPECT with 99mTc-sestamibi were retrospectively analyzed. Patients (n = 28) who underwent SPECT without the RRB were included in the control group, and those (n = 63) who underwent SPECT with the RRB were in the RRB group. The distance of heart motion during dynamic acquisition was measured, and projection data were assessed for patient motion and motion artifacts. Patient motion was classified into various levels, and motion artifacts on SPECT images were visually examined. The distances of heart motion without and with the RRB were 15.4 ± 5.3 and 7.5 ± 2.3, respectively. Compared with the control group, the RRB group had a lower frequency of heart motion based on the projection data, particularly in terms of creep and shift motion. The RRB group had a significantly lower incidence of motion artifacts on SPECT images than the control group. The RRB substantially reduced specific types of motion, such as shift and creep, and had a low influence on bounce motion. However, it could effectively suppress respiratory-induced heart motion and reduce motion artifacts on myocardial perfusion SPECT, thereby emphasizing its potential for improving image quality.

Combining anatomical and biochemical markers in the detection and risk stratification of coronary artery disease.

We aimed to test the hypothesis if combining coronary artery calcium score (Ca-score) as a quantitative anatomical marker of coronary atherosclerosis with high-sensitivity cardiac troponin as a quantitative biochemical marker of myocardial injury provided incremental value in the detection of functionally relevant coronary artery disease (fCAD) and risk stratification. Consecutive patients undergoing myocardial perfusion single-photon emission computed tomography (MPS) without prior CAD were enrolled. The diagnosis of fCAD was based on the presence of ischaemia on MPS and coronary angiography; fCAD was centrally adjudicated in the diagnostic and prognostic domain. Diagnostic accuracy was evaluated using the area under the receiver-operating characteristic curve (AUC). The composite of cardiovascular death and non-fatal acute myocardial infarction (AMI) within 730 days was the primary prognostic endpoint. Among 1715 patients eligible for the diagnostic analysis, 399 patients had fCAD. The combination of Ca-score and high-sensitivity cardiac troponin T (hs-cTnT) had good diagnostic accuracy for the diagnosis of fCAD (AUC 0.79, 95% confidence interval (CI) 0.77-0.81), but no incremental value compared with the Ca-score alone (AUC 0.79, 95% CI 0.77-0.81, P = 0.965). Similar results were observed using high-sensitivity cardiac troponin I (AUC 0.80, 95% CI 0.77-0.82) instead of hs-cTnT. Among 1709 patients (99.7%) with available follow-up, 59 patients (3.5%) suffered the composite primary prognostic endpoint (non-fatal AMI, n = 34; CV death, n = 28). Both Ca-score and hs-cTnT had independent prognostic value. Increased risk was restricted to patients with elevation in both markers. The combination of the Ca-score with hs-cTnT increases the prognostic accuracy for future events but does not provide incremental value vs. the Ca-score alone for the diagnosis of fCAD. Clinical trial registration: NCT00470587.

Prognostic value of left ventricular mechanical dyssynchrony indices derived from gated myocardial perfusion SPECT in coronary artery disease: a systematic review and meta-analysis.

Left ventricular mechanical dyssynchrony (LVMD) is an important prognostic factor in coronary artery disease. A growing body of evidence indicates that LVMD parameters derived from phase analysis of gated myocardial SPECT may allow risk stratification for future cardiac events. We performed a systematic review and meta-analysis on the prognostic value of LVMD on gated SPECT in patients with coronary artery disease. PubMed, Embase, and the Cochrane library were searched until August 25, 2022, for studies reporting the prognostic value of LVMD on gated SPECT for outcomes of all-cause death, cardiac death, or major adverse cardiovascular event (MACE) in patients with coronary artery disease. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were meta-analytically pooled using a random-effects model. Nine studies (26,750 patients) were included in a qualitative synthesis. Among the SPECT LVMD parameters used in various studies, high phase standard deviation, phase bandwidth, and phase entropy were widely evaluated and reported to be associated with high rates of all-cause death, cardiac death, or MACE. For five studies (23,973 patients) in the quantitative synthesis, the pooled HR of LVMD for predicting MACE was 2.81 (95% CI 2.03-3.88). Studies using combined phase parameters to define LVMD showed higher HRs than a study using phase entropy (p = 0.0180). LVMD from gated myocardial SPECT is a significant prognostic factor for coronary artery disease. Phase analysis of gated SPECT may be useful for accurate risk stratification and could be applied for clinical decision-making in such patients.

Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia

BackgroundNoninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. ObjectivesThis study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). MethodsA post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. ResultsIn CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with aHR: 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. ConclusionsThis newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.

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.

Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease.

The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligence-enabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities. We studied 241 patients, 155 with suspected CAD and 86 with known CAD who were referred for MPS. The CSG was performed after the MPS acquisition. The CSG results (1) p-factor (perfusion, 0: normal, 1: mildly, 2: moderately, 3: highly abnormal) and (2) s-factor (structure, categories as p-factor) were compared with the MPS scores. The CSG system was not trained during the study. Considering the p-factor alone, a specificity of >78% and a negative predictive value of mostly >90% for all MPS variables were found. The sensitivities ranged from 17 to 56%, the positive predictive values from 4 to 38%. Combining the p- and the s-factor, significantly higher specificity values of about 90% were reached. The s-factor showed a significant correlation (p=0.006) with the MPS ejection fraction. The CSG system is able to exclude relevant perfusion abnormalities in patients with suspected or known CAD with a specificity and a negative predictive value of about 90% combining the p- and the s-factor. Since it is a learning system there is potential for further improvement before routine use.

Prognostic value of the left ventricular ejection fraction reserve acquired by gated myocardial perfusion SPECT in patients with CAD and reduced stress LVEF.

Left ventricular ejection fraction (LVEF) strongly predicts cardiac events. However, conflicting findings exist regarding the prognostic value of the LVEF reserve (ΔLVEF) when measured by gated single-photon emission computed tomography myocardial perfusion imaging (SPECT G-MPI). In particular, data related to the prognostic value of ΔLVEF when measured by SPECT in patients with reduced LVEF are scarce. In this study, we aimed to evaluate the prognostic value of ΔLVEF when acquired by SPECT G-MPI in patients with coronary artery disease (CAD) and a LVEFStress < 60%. We retrospectively recruited 260 consecutive patients diagnosed with CAD by coronary angiography (CAG) and a LVEFStress < 60%, as determined by SPECT G-MPI. These patients were followed up for 33.4 ± 7.6 months. The patients were divided into two groups (ΔLVEF > 0% and ΔLVEF ≤ 0%), and survival analyses were conducted. The primary endpoints were major adverse cardiac events (MACEs), a composite of all-cause death, nonfatal myocardial infarction, unplanned coronary revascularization, and hospitalization for unstable angina. We observed 69 MACEs (26.5%). The cumulative incidence of MACEs in patients with ΔLVEF ≤ 0% was significantly higher than in patients with ΔLVEF > 0% (P = 0.042). Multivariate Cox regression further revealed that a ΔLVEF ≤ 0% represented an independent predictor of MACEs (adjusted hazard ratio [HR]: 1.276; 95% confidence interval [CI]: (1.006, 1.618), P = 0.045). Adding a ΔLVEF ≤ 0% to traditional myocardial perfusion and function variables evaluated by MPI significantly improved the ability to predict MACEs (P = 0.044). Determining ΔLVEF by SPECT G-MPI was associated with MACEs and improved risk stratification compared to prediction models based on traditional perfusion and functional parameters in CAD patients with left ventricular dysfunction, particularly those with no or mild myocardial ischemia.

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.

Exercise ECG and myocardial perfusion SPECT show high prognostic value up to four years after examination

Abstract Background Abnormal results of myocardial perfusion SPECT (MPS) or exercise ECG (Ex-ECG) is known to be associated with increased risk of ischemic cardiac events. The predictive value of stress testing however, is influenced by underlying risk factors as well as follow up duration and may vary considerably over time. With traditional Cox proportional hazards analysis the time-varying effects are not sufficiently accounted for. Purpose To evaluate the prognostic longevity of MPS and Ex-ECG using statistical methods that allow hazard ratio changes over time. Methods 908 patients (age 63 years, 49% male, 45% prior ischemic heart disease (IHD)) referred for MPS were prospectively included. Ex-ECG was performed in conjunction with MPS. Registry data were used for follow up. The outcome was a composite of acute myocardial infarction, unstable angina, unplanned revascularization, and cardiovascular death. Cox proportional hazard analysis and smoothed hazard functions with splines were used for risk assessment during follow up. Results Ex-ECG or MPS were abnormal in 198 (22%) or 148 (16%) of patients respectively. The mean follow up time was 8.2±2.6 years and the composite end point was reached in 189 patients (21%). The Kaplan-Meier curves suggest an interaction with time with the risk of event for subjects with abnormal test results being highest in the 2-3 of years after which the curves are approximately parallel (Figure 1). In multivariable models the best fit (lowest Akaike information criterion (AIC) value) was obtained with a logarithmic interaction term for time. With this approach and controlling for known risk factors the hazard ratio (HR) drops to 1 after approximately four years for both Ex-ECG and MPS (Figure 2). Age (HR=1.04, p&amp;lt;0.001), sex (HR=2.0, p&amp;lt;0.001), IHD (HR=1.7, p&amp;lt;0.001) and reduced exercise capacity (HR=0.99, p&amp;lt;0.001) were independent significant predictors of risk. Conclusions The high risk associated with abnormal MPS and Ex-ECG results decrease rapidly over time and after approximately 4 years the predictive value is lost. Thus, the long-term impact on prognosis of stress testing appear questionable. The time-varying effects on risk are substantial and warrant statistical considerations when designing clinical follow up studies of chronic coronary syndrome.

The role of preparticipation screening in detecting silent coronary artery disease in master athletes: a way to prevent exercise-related adverse cardiovascular events

Abstract Introduction Regular exercise reduces morbidity and mortality from cardiovascular diseases through cardiac and systemic adaptations including the increased coronary blood flow, an improved endothelial function, a slower progression of atherosclerotic processes as well as the stabilization of pre-existing lesions. However, long-term exercise is frequently associated with adverse cardiovascular events in 35-year older subjects because of a cardiac remodeling that predisposes to malignant arrhythmias and to an earlier atherosclerosis. Moreover, in athletes, ischemia is often silent with a higher risk of sudden cardiac death. Purpose The aim of our observational study was to evaluate the efficacy of preparticipation screening in identifying the presence of coronary artery disease (CAD) in a population of asymptomatic master athletes. Methods Between 2018 and 2019, 420 35-year older athletes (mean age: 55 + 17) underwent a preparticipation screening at the University of Pisa. None of them reported cardiovascular risk factors or previous cardiovascular diseases. After a physical examination, all subjects were submitted to spirometry and ECG stress test. Results The ECG stress test demonstrated signs of myocardial ischemia (ST depression &amp;gt; 1 mm) in 41 subjects; 3 athletes showed a frequency-dependent left bundle-branch block; 24 subjects had exercise-induced premature ventricular complexes during the ECG stress test. These subjects underwent further evaluations (Tc-99 myocardial perfusion SPECT and coronary angio-CT) to detect the presence of perfusion defects or coronary abnormalities: 26 athletes had an important coronary atherosclerosis with significant stenosis in one or more vessels and later subjected to Percutaneous transluminal coronary angioplasty (PTCA) or to coronary artery bypass grafting (CABG). 5 athletes presented a coronary myocardial bridge and 1 subject was demonstrated to have an anomalous origin of the left coronary. Conclusions Preparticipation screening in 35-year-older athletes was able to detect the presence of obstructive coronary artery disease (CAD) in the 38% of suspected cases and anomalous origin or intramural coronary course in 8,8% of cases that showed electric abnormalities during the stress test. Despite recent conflicting opinions about the scientific value of the preparticipation screening in young athletes, the present study demonstrated that it was fundamental to early detect silent conditions that could predispose master athletes to adverse cardiovascular events.

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.