Interpretation Of Myocardial Perfusion Images Research Articles

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

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

Comparison of positional artifacts in myocardial perfusion imaging in supine and semi-reclining position using dedicated D-SPECT cardiac camera: validation using CT based attenuation correction

BackgroundSoft-tissue attenuation remains a major limitation of SPECT-MPI which interferes with the diagnosis of CAD. The current study aims to evaluate the pattern of attenuation artifacts in supine and semi-reclining positions on CZT cardiac camera and their interaction with gender, BMI and stress protocols. MethodsWe prospectively analysed 150 patients acquired in supine and semi-reclining positions on CZT camera. The images were evaluated for severity and extent of defect using 17-segment model. An additional CT scan was acquired to generate AC image in the first 50 patients studied to assist investigator learning for comparison of artifact vs true defects in the two SPECT systems. The defects present in one position or showing change in severity within two positions were considered as positional artifacts and further validated using CTAC supine image. ResultsIn overall analysis, higher extent and severity of positional artifacts were observed more in semi-reclining position affecting the apex, apico-inferior, inferolateral and inferoseptal segments. Females showed more positional artifacts than males with inferior wall attenuation in the semireclining position and anterior wall attenuation in the supine position. A positive correlation of the extent and severity of positional artifacts was noted with an increasing BMI. In patients with BMI > 30, mid inferior and inferolateral segments were most affected followed by anterior wall segments. Highest correction of artifactual perfusion defects by CTAC was noted in inferior wall followed by inferolateral segments. ConclusionThe incidence of positional artifacts was greater in semi-reclining position in females, higher BMI groups and adenosine stress subsets. Knowledge of the pattern of positional artifacts appears to be a reliable alternative of CTAC for correct interpretation of myocardial perfusion images.

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction – in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Utility of gated SPECT parameters in detection of coronary artery disease in patients with normal perfusion images

BackgroundFalse negative myocardial perfusion images on single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a substantial problem in the interpretation of MPI. To explore possible parameters from gated SPECT that could detected coronary artery disease (CAD) in patients with normal perfusion images, we retrospectively selected patients who underwent a 2-day Tc-99 m MIBI adenosine stress/rest MPI and a subsequent invasive coronary angiography. Gated SPECT parameters, including end systolic volume (ESV), end diastolic volume (EDV), left ventricular ejection fraction (LVEF), and transient ischemic dilation (TID) ratio of patients with and without CAD were compared and assessed for their respective diagnostic performance using receiver operating characteristics (ROC) area under the curve (AUC).ResultsAmong 22 patients with normal perfusion images, 7 patients had CAD. Despite the small number of patients, we found significant differences between the ESV, the EDV, and the LVEF between patients with and without CAD. The analysis of ROC curve showed the stress ESV could excellently detect CAD (AUC = 0.900). The rest ESV, the stress EDV, the rest EDV, the stress LVEF and the rest LVEF could likewise perform well in the detection of CAD (AUC = 0.833, 0.819, 0.790, 0.862, and 0.838, respectively). In contrast, the change in LVEF and the TID ratio (AUC = 0.667 and 0.524, respectively) did not seem as reliable as other parameters. Optimal cutoffs for detection of CAD in patients with normal perfusion images from our study were ≥ 20.0 mL for the stress ESV, ≥ 71.0 mL for the stress EDV, ≤ 66.3 EF units for the stress LVEF, ≥ 18.0 mL for the rest ESV, ≥ 67.0 mL for the rest EDV and ≤ 70.0 EF unit for the rest LVEF.ConclusionsGated SPECT parameters could facilitate detection of CAD in patient with normal perfusion images on Tc-99m MIBI MPI. These parameters should be carefully interpreted to improve diagnostic accuracy and reduce false negative MPI.

Improving Detection of CAD and Prognosis with PET/CT Quantitative Absolute Myocardial Blood Flow Measurements.

The purpose of this review is to provide an overview of the role of PET MPI in the detection of CAD, focussing on the added value of MBF for diagnosis and prognostication. Positron emission tomography (PET) myocardial perfusion imaging (MPI) is increasingly used for the risk stratification of patients with suspected or established coronary artery disease (CAD). PET MPI provides accurate and reproducible non-invasive quantification of myocardial blood flow (MBF) at rest and during hyperemia, providing incremental information over conventional myocardial perfusion alone. Inclusion of MBF in PET MPI interpretation improves both its sensitivity and specificity. Moreover, quantitative MBF measurements have repeatedly been shown to offer incremental and independent prognostic information over conventional clinical markers in a broad range of conditions, including in CAD. Quantitative MBF measurement is now an established and powerful tool enabling accurate risk stratification and guiding patients' management. The role of PET MPI and flow quantification in cardiac allograft vasculopathy (CAV), which represents a particular form of CAD, will also be reviewed.

Added value of coronary artery calcium score in the reporting of SPECT versus PET myocardial perfusion imaging

BackgroundKnowledge of coronary artery calcium score (CACS) influences the interpretation of myocardial perfusion imaging (MPI) with SPECT; however, the impact on PET interpretation remains unclear. We compared the added value of CACS to reporting MPI using SPECT vs PET. MethodsWe retrospectively included 412 patients. 206 patients who underwent Rb-82 PET were propensity-based matched to a cohort of 4018 patients who underwent cadmium–zinc–telluride SPECT MPI to obtain a comparable group of 206 SPECT patients. Next, we created four image sets: SPECT MPI-only, PET-only, SPECT + CACS, and PET + CACS. Two physicians interpreted the 824 images as normal, equivocal, or abnormal for ischemia or irreversible defects. Additionally, event rates were compared between PET and SPECT groups during 30-month follow-up. ResultsPET yielded more scans interpreted as normal than SPECT (88% vs 80%, respectively, P = 0.015). Adding CACS to SPECT increased the percentage of normal scans to 86% (P = 0.014), whereas this effect was absent for PET (90%, P = 0.77). Annualized event rates for images interpreted as normal did not differ and varied between 0.7 and 2.0% (P > 0.084). ConclusionAdding CACS correctly increased the percentage of normal scans for SPECT MPI but not for PET, possibly limiting the effect of adding CACS to reporting PET.

Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.

Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC). Results: In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, P < 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results (P < 0.001), but not compared with readers interpreting with DL results (P = 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%, P < 0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI.

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

BackgroundObese 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. MethodsParticipants 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. ResultsPatients 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). ConclusionsAutomated 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.

Comparison of myocardial blood flow and flow reserve with dobutamine and dipyridamole stress using rubidium-82 positron emission tomography

BackgroundThe objective of this study was to compare the hyperemic myocardial blood flow (MBF) and myocardial flow reserve (MFR) obtained with dobutamine to those of dipyridamole in patients referred for myocardial perfusion imaging (MPI) using 82Rb positron emission tomography. MethodsOne hundred and fifty-six patients who underwent a 82Rb PET MPI study with dobutamine stress were included. A matching cohort of patients who underwent a 82Rb PET MPI study with dipyridamole stress was created, accounting for sex, age, history of coronary artery disease (CAD), prior revascularization, CAD risk factors, body mass index, and MPI interpretation. ResultsGlobal rest MBF (median [interquartile range] 0.84 [0.64-1.00] vs 0.69 [0.59-0.85]), stress MBF (2.36 [1.73-3.08] vs 1.66 [1.25-2.06]), MFR (2.75 [2.19-3.64] vs 2.29 [1.78-2.84]), and corrected MFR (2.85 [2.14-3.64] vs 2.20 [1.65-2.75]) were all significantly higher (P < 0.0001) in the dobutamine cohort compared to the dipyridamole cohort. ConclusionThe results of this study suggest that dobutamine produces higher MBF compared to dipyridamole in a representative population referred to nuclear cardiology laboratories.

Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation.

To investigate a deep learning technique, more specifically state-of-the-art convolutional neural networks (CNN), for automatic characterization of polar maps derived from myocardial perfusion imaging (MPI) studies for the diagnosis of coronary artery disease. Stress and rest polar maps corresponding to 216 patient cases from the database of the department of Nuclear Medicine of our institution were analyzed. Both attenuation-corrected (AC) and non-corrected (NAC) images were included. All patients were subjected to invasive coronary angiography within 60 days from MPI. As the initial dataset of this study was small to train a deep learning model from scratch, two strategies were followed. The first is called transfer learning. For this, we employed the state-of-the-art CNN called VGG16, which has been broadly exploited in medical imaging classification tasks. The second strategy involves data augmentation, which is achieved by the rotation of the polar maps, to expand the training set. We evaluated VGG16 with 10-fold cross-validation on the original set of images performing separate experiments for AC and NAC polar maps, as well as for their combination. The results were compared to the standard semi-quantitative polar map analysis based on summed stress and summed difference scores, as well as to the medical experts' diagnostic yield. With reference to the findings of coronary angiography, VGG16 achieved an accuracy of 74.53%, sensitivity 75.00% and specificity 73.43% when the AC and NAC polar maps were incorporated into one single image set. Respective figures of MPI interpretation by experienced Nuclear Medicine physicians were 75.00%, 76.97% and 70.31%. The accuracy of semi-quantitative polar map analysis was lower, 66.20% and 64.81% for AC and NAC technique, respectively. The proposed deep learning model with data augmentation techniques performed better than the conventional semi-quantitative polar map analysis and competed with doctor's expertise in this particular patient cohort and image set. The model could potentially serve as an assisting tool to support interpretation of MPI studies or could be used for teaching purposes.

Prone Myocardial Perfusion Imaging and Breast Attenuation: A Phantom Study.

Soft tissue attenuation artifacts are the most common cause of misinterpretation in myocardial perfusion Imaging (MPI). Few studies assessing the value of prone imaging in women have been published. Breast attenuation artifacts can be present in up to 40% of the MPI studies in women. This study aimed at evaluating the potential impact of prone MPI on breast attenuation, with a critical analysis of activity optimization and breast size influence. MPI of an Anthropomorphic Torso Phantom with silicone breast prostheses and equivalent adipose tissue was compared to a standard MPI database. A medical qualitative and semiquantitative analysis demonstrated higher uptake in the LV anterior segments in the prone position for all injected activities. An artificial myocardium lesion was diagnosable in the right segment in all images, which shows that prone positioning would not mask a true lesion and it assists the cardiologist with a more accurate analysis. These results showed that it is possible to optimize the activity to be injected by up to 55.6% when using combined supine-prone images. Prone position has a high impact on the interpretation of MPI in female patients since it reduces the breast attenuation artifacts, and optimizes the radiation protection of the patient and all staff involved in the procedure, making it more cost-effective.

Multi-aspect Interpretation of Myocardial Perfusion Imaging in Patients with Suspected Coronary Artery Disease

Multi-aspect Interpretation of Myocardial Perfusion Imaging in Patients with Suspected Coronary Artery Disease

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry

To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting. A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed. In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient).

Automated Interpretation of Myocardial Perfusion Images with Multilayer Perceptron Network as a Decision Support System

Aim: Bull's eye pattern recognition with artificial neural networks (ANNs) has the potential to assist interpretation of myocardial perfusion images (MPIs). We aimed to develop a model for interpretation of MPI based on the clinical variables and imaging data. Materials and Methods: The study included 208 patients referred to the department of nuclear medicine for 2-day stress-rest ECG-gated MPI. Several ANN models were designed with the following input variables: average count of 20 segments of the bull's eye images of stress and rest MPIs, gender, the constellation of coronary artery disease risk factors and scintigraphic cardiac ejection fraction. The procedure was repeated excluding the data of the rest phase scan. Data of 150 subjects were used for training, 21 subjects for cross-validation and 37 subjects for final operation testing. Several ANN models were examined with different hidden layers and processing elements and functions. The target output variable was the conclusion of the nuclear physician (i.e., normal vs. abnormal scan). Results: A multilayer perceptron (MLP) with two hidden layers trained with both stress and rest data demonstrated the best performance to classify the normal and abnormal MPIs. It showed an overall accuracy of 91.9%, sensitivity of 91.3% and specificity of 92.9%. The accuracy of the similar MLP trained using stress-only myocardial perfusion images reduced to 67.6%. Conclusion: The automated interpretation of MPIs with a 2 hidden layer MLP trained with stress and rest images could be an accurate support system either for the interpretation or quality assurance.

Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study.

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99mTc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (P = 0.3). Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods.

Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study

ObjectivesThe study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). BackgroundDeep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. MethodsA total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99mTc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure. ResultsA total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01). ConclusionsDeep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease

PurposeTraditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD).Methods206 Patients (64% men, age 58.2 ± 8.7 years) with suspected CAD were included prospectively. All patients underwent 99mTc-tetrofosmin single-photon emission computed tomography (SPECT) and invasive coronary angiography with fractional flow reserve (FFR) measurements. Non-corrected (NC) and attenuation-corrected (AC) SPECT images were analyzed both visually as well as automatically by commercially available SPECT software. Automated analysis comprised a segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD), representing the extent and severity of hypoperfused myocardium. Subsequently, software was optimized with an institutional normal database and thresholds. Diagnostic performances of automated and visual analysis were compared taking FFR as a reference.ResultsSensitivity did not differ significantly between visual reading and most automated scoring parameters, except for SDS, which was significantly higher than visual assessment (p < 0.001). Specificity, however, was significantly higher for visual reading than for any of the automated scores (p < 0.001 for all). Diagnostic accuracy was significantly higher for visual scoring (77.2%) than for all NC images scores (p < 0.05), but not compared with SSS AC and S-TPD AC (69.8% and 71.2%, p = 0.063 and p = 0.134). After optimization of the automated software, diagnostic accuracies were similar for visual (73.8%) and automated analysis. Among the automated parameters, S-TPD AC showed the highest accuracy (73.5%).ConclusionAutomated analysis of myocardial perfusion SPECT can be as accurate as visual interpretation by an expert reader in detecting significant CAD defined by FFR.

Artifacts in Quantitative analysis of myocardial perfusion SPECT, using Cedars-Sinai QPS Software

Quantitative analysis of myocardial perfusion Single photon emission computerized tomography (SPECT) images is increasingly applied in modern nuclear cardiology practice, assisting in the interpretation of myocardial perfusion images (MPI). There are different extensively validated state-of-the-art software packages, including QPS (cedars-Sinai), Corridor 4DM (University of Michigan) and Emory cardiac toolbox (Emory university), providing highly accurate and reproducible data. However, these software packages may suffer from potential artifacts related to patient or technical factors. By recognizing the source of such artifacts, the interpreting physician can avoid misinterpretation of MPI study. In this review, we discuss some of technical pitfalls that may occur in Quantitative Perfusion SPECT software (QPS, cedars-Sinai Medical center).

AUTOMATIC SPECT ANALYSIS COMPARED TO EXPERT VISUAL SCORING FOR THE DETECTION OF CORONARY ARTERY DISEASE

Background: Traditionally, the interpretation of myocardial perfusion imaging is based on visual analysis. A computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study is to compare the diagnostic

Visual Estimation of Coronary Artery Calcium and the Effect of the Absence of Coronary Artery Calcium on Cardiac Events and the Interpretation of Myocardial Perfusion in Hybrid SPECT/CT Scans

The use of visual estimation of coronary artery calcium (CAC) on hybrid SPECT/CT imaging can be to provide additional source of information to assist in the interpretation of borderline myocardial perfusion images (MPI). We assessed the effect of the absence of CAC on cardiac events and the interpretation of non extensive perfusion defects. Background: At the time of MPI, hybrid systems obtain a low dose, non-ECG-gated CT scan that is used to perform attenuation correction. The utility of visual coronary CT attenuation correction scan (CTAC) in estimating actual CAC as measured by the Shemesh score (SS) was also validated. Methods: At our hospital two experienced readers, blinded to CAC scores, classified 150 patients using visual estimation of the SS. Each of the four main coronary arteries (left main, left anterior descending, left circumflex and right coronary artery) were scored 0 to 3. Calcification scoring was as follows: none (0); mild (1), when less than 1/3 of the length of the entire artery was calcified; moderate (2), when 1/3 to 2/3 of the artery was calcified; and severe (3), when more than 2/3 of the artery showed calcification. The scores for the four arteries were added to produce the total SS (0-12). To assess the effect of absence of calcium on interpretation of MPI scans we measured the size of the perfusion defect using the sum stress score (SSS) in a 17 segment model. Six month phone interviews for cardiac events was conducted at six months on 30/150 patients whose CAC score was read as zero. Results: Visual assessment of CAC had a excellent intraclass correlation coefficient of 0.973 for total calcium score, with 95% confidence interval of 0.963 to 0.980. 20% (30/150) patients had a CAC score read as zero. 97% (29/30) MPI scans read with a CAC score of zero were also read as having no inducible myocardial perfusion abnormality despite 70% (21/30) having a abnormal SSS. 10% (3/30) patients had normal coronary angiography. No patient (0/30) had a cardiac event at six months whose CAC Score was read as zero. Conclusion: CAC can be visually estimated from a CTAC scan with high degree of inter-observer reproducibility. Absence of CAC influences the interpretation of borderline MPI and may better define the risk of cardiac events during follow-up.