Rest Myocardial Perfusion Imaging Research Articles

A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.

Background To assess the feasibility of a machine learning (ML) approach using radiomics features of perfusion defects on rest myocardial perfusion imaging (MPI) to detect the presence of hibernating myocardium. Methodology Data of patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for myocardial viability assessment were retrieved. Rest MPI data were processed on ECToolbox, and polar maps were saved using theNFile PMap tool. The reference standard for defining hibernating myocardium was the presence of mismatched perfusion-metabolism defect with impaired myocardial contractility at rest. Perfusion defects on the polar maps were delineated with regions of interest (ROIs) after spatial resampling and intensity discretization. Replicable random sampling allocated 80% (257) of the perfusion defects of the patients from January 2017 to September 2022 to the training set and the remaining 20% (64) to the validation set. An independent dataset of perfusion defects from 29 consecutive patients from October 2022 to January 2023 was used as the testing set for model evaluation. One hundred ten first and second-order texture features were extracted for each ROI. After feature normalization and imputation, 14 best-ranked features were selected using a multistep feature selection process including the Logistic Regression and Fast Correlation-Based Filter. Thirteen supervised ML algorithms were trained with stratified five-fold cross-validation on the training set and validated on the validation set. The ML algorithms with a Log Loss of <0.688 and <0.672 in the cross-validation and validation steps were evaluated on the testing set. Performance matrices of the algorithms assessed included area under the curve (AUC), classification accuracy (CA), F1 score, precision, recall, and specificity. To provide transparency and interpretability, SHapley Additive exPlanations (SHAP) values were assessed and depicted as beeswarm plots. Results Two hundred thirty-nine patients (214 males; mean age 56 ± 11 years) were enrolled in the study. There were 371 perfusion defects (321 in the training and validation sets; 50 in the testing set). Based on the reference standard, 168 perfusion defects had hibernating myocardium (139 in the training and validation sets; 29 in the testing set). On cross-validation, six ML algorithms with Log Loss <0.688 had AUC >0.800. On validation, 10 ML algorithms had a Log Loss value <0.672, among which six had AUC >0.800. On model evaluation of the selected models on the unseen testing set, nine ML models had AUC >0.800 with Gradient Boosting Random Forest (xgboost) [GB RF (xgboost)] achieving the highest AUC of 0.860 and could detect the presence of hibernating myocardium in 21/29 (72.4%) perfusion defects with a precision of 87.5% (21/24), specificity 85.7% (18/21), CA 78.0% (39/50) and F1 Score 0.792. Four models depicted a clear pattern of model interpretability based on the beeswarm SHAP plots. These were GB RF (xgboost), GB (scikit-learn), GB (xgboost), and Random Forest. Conclusion Our study demonstrates the potential of ML in detecting hibernating myocardium using radiomics features extracted from perfusion defects on rest MPI images. This proof-of-concept underscores the notion that radiomics features capture nuanced information beyond what is perceptible to the human eye, offering promising avenues for improved myocardial viability assessment.

Assessment of myocardial injury by SPECT myocardial perfusion imaging in patients with COVID-19 infection in a single center after lifting the restrictions in China.

To assess myocardial injury using rest single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) in patients with COVID-19 and to evaluate whether myocardial injury detected by rest MPI predict the prognosis of symptoms after 6months follow-up. Patients suspected of myocarditis between December 2022 and March 2023, after the lifting of COVID-19 pandemic restrictions, and between December 2018 and March 2019, prior to the pandemic, were referred to our study. All patients underwent rest MPI. One hundred and sixty four patients with COVID-19 infection after the lifting of pandemic restrictions and 101 patients before the pandemic were included as the study and control groups, respectively. One hundred and fifty three patients of the study group and 83 of the control group presented symptoms when they initially visit to our department. Compare the parameters of myocardial injury detected by rest SPECT MPI between the two groups and then investigate the association between myocardial injury and symptom prognosis in symptomatic patients of both groups. Total perfusion defect (TPD) (4.2% ± 3.3% vs. 2.3% ± 2.2%, P < 0.001), summed rest score (SRS) (5.3 ± 5.4 vs. 2.7 ± 2.0, P < 0.001), the proportion of patients with TPD > 4% (43.3% vs. 17.8%, P < 0.001), TPD > 10% (6.71% vs 0, P < 0.001), SRS > 4 (40.2% vs 15.8%, P < 0.001), SRS > 10 (12.8% vs 0, P < 0.001), the number of abnormal perfusion segments (3.9 ± 3.1 vs. 2.4 ± 1.7, P < 0.001) were all significantly higher in the study group. All the parameters of rest MPI were not associated with the prognosis of symptoms in symptomatic patients of both groups after 6months follow-up. Myocardial injury in COVID-19 patients could be assessed by rest SPECT MPI. The COVID-19 patients could exhibited a higher frequency and greater severity of myocardial injury than uninfected control patients. Myocardial injury assessed by rest MPI did not predict for the prognosis of symptoms in symptomatic patients of both COVID-19 patients and uninfected patients.

Association of epicardial fat volume with subclinical myocardial damage in patients with type 2 diabetes mellitus.

In type 2 diabetes mellitus (T2DM) patients, left ventricular systolic dyssynchrony (LVSD) with normal left ventricular ejection fraction (LVEF) and normal myocardial perfusion could referred to as subclinical myocardial damage, which is difficult to diagnose at an early stage. Epicardial adipose tissue, a distinctive heart-specific visceral fat, is closely related to various cardiovascular diseases. The objective of this study was to investigate the correlation between epicardial fat volume (EFV) and subclinical myocardial damage in T2DM patients. This retrospective cross-sectional study included 117 T2DM patients with normal myocardial perfusion by single photon emission computed tomography-computed tomography (SPECT-CT) and normal LVEF by echocardiography. The study was conducted from January 2018 to December 2022. Patient data were collected through electronic medical records including basic patient information, medical history, laboratory tests, and medication data. The EFV was quantified through a non-contrast CT scan. Quantitative indicators of LVSD including phase standard deviation (PSD) and phase histogram bandwidth (PBW) were obtained through phase analysis of the gated rest myocardial perfusion imaging (MPI). Additionally, 83 healthy individuals at the same time were selected to gain the reference threshold of LVSD indicators (13.1° for PSD and 37.6° for PBW). Univariate and multivariable logistic regression models were performed to analyze factors influencing LVSD. A generalized additive model (GAM) was applied to explore the relationship between EFV and LVSD. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of EFV for LVSD. Among all patients, 32 (27.4%) patients had LVSD. Compared with the non-LVSD group, the body mass index (BMI) and EFV were higher in the LVSD group (25.83±2.66 vs. 23.94±3.13 kg/m2; 142.41±44.17 vs. 108.01±38.24 cm3, respectively, both P<0.05). Multivariate regression analysis revealed that EFV was independently associated with LVSD [odds ratio (OR) =1.19; 95% confidence interval (CI): 1.06-1.34; P=0.003]. Age, BMI, incidence of hypertension, and LVSD were increased with tertiles of EFV (all P<0.05). The GAM indicated a linear association between EFV and LVSD. The ROC curve analysis concluded that the area under the curve (AUC) of EFV for predicting subclinical myocardial damage in T2DM patients was 0.732 (95% CI: 0.633-0.831, P<0.001), with the optimal threshold of 122.26 cm3, sensitivity of 71.9%, and specificity of 69.4%. EFV is an independent risk factor for LVSD in T2DM patients with normal LVEF and normal MPI, which could potentially serve as a novel imaging marker and a potential therapeutic target for subclinical myocardial damage.

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

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

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

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

Assessing Myocardial Involvement in Systemic Lupus Erythematosus Patients without Cardiovascular Symptoms by Technetium-99m-sestamibi Myocardial Perfusion Imaging: A Correlation Study on NT-proBNP.

In patients with systemic lupus erythematosus (SLE), myocardial involvement is the third leading course of death after lupus nephropathy (LN) and infections. Previous autopsy studies have demonstrated a high incidence of cardiovascular abnormalities in the myocardium. However, the patients with typical symptoms are far much fewer than expected from post-mortem examinations. The current study aimed to evaluate the technetium-99m-sestamibi (99mTc-MIBI) gated myocardial perfusion imaging (GMPI) characteristics of lupus patients without cardiovascular symptoms, and the relationships between GMPI characteristics and biochemical markers of myocardial injury, and to explore the role of GMPI in assessing myocardial involvement. Thirty patients were studied with rest myocardial perfusion imaging, and summed rest score (SRS), summed motion score (SMS), and summed thickening score (STS) were calculated automatically. Biomarkers, including N-terminal prohormone of brain natriuretic peptide (NT-proBNP) and creatine-kinase-MB (CK-MB), were detected simultaneously. GMPI parameters, LV functions and biomarkers were compared between two NT-proBNP groups. The relationships between these parameters were studied by correlation analysis. SMS, STS, and glomerular filtration rate (eGFR) were the main influencing factors of NTproBNP level (p = 0.001, <0.001, 0.042, respectively). Thirteen patients with an evaluated concentration of NT-proBNP had the lower left ventricular ejection fraction (LVEF), peak filling rate (PFR), eGFR and higher levels of CK-MB (in all comparisons, p < 0.05), and SRS was the only influencing factor of NT-proBNP (p = 0.007). Within thirteen patients with SRS≥2, there was a significant correlation between SRS and NT-proBNP (p < 0.001). 99mTc-MIBI GMPI could evaluate the left ventricular function and prompt the cardiomyocyte function at the cellular level. SMS and STS were the main influencers for plasma NT-proBNP, and SRS was the independent factor for elevated NT-proBNP. This radionuclide imaging method could provide additional diagnostic information on myocardial involvement in patients with SLE.

FDG-PET/CT and rest myocardial perfusion imaging to predict high-degree atrioventricular block recovery in cardiac sarcoidosis.

High-degree atrioventricular block (AVB) recovery in CS has been shown to be highly variable despite immunosuppressive treatment, with no reliable tool available to predict odds of reversibility. This study sought to evaluate the potential of combined fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and resting myocardial perfusion imaging (rMPI) to predict reversibility of newly diagnosed high-grade AVB in cardiac sarcoidosis (CS). We performed a single-center, retrospective analysis of patients with CS presenting with high-grade AVB who underwent combined FDG-PET/CT and rMPI. The 2016 JCS and the 2014 HRS diagnostic criteria were used for the diagnosis of CS. Patients with a history of coronary artery disease or prior immunosuppressive treatment were excluded. Patients were divided into AVB recovery and non-recovery subgroups. CS disease staging was based on FDG-PET and rMPI findings: (Stage 0) normal FDG-PET and rMPI (Stage 1) positive FDG-PET and normal rMPI (Stage 2) positive FDG-PET with perfusion deficits on rMPI (Stage 3) normal FDG-PET with perfusion deficits on rMPI. Twenty-seven patients, including 13 demonstrating AVB recovery, were identified. Eleven out of fourteen (78.6%) patients presenting with stage 1 CS demonstrated AVB recovery. Stage 1 CS was significantly more present in the recovery group compared to the non-recovery group (84.6% vs 21.4%, P = .002). Eleven presented with stage 2 CS, with only 2 (18.2%) recovering AV nodal conduction. Stage 2 CS presented more frequently in the non-recovery group (64.3% vs 15.4%, P = .020). Combined FDG-PET and rMPI employed to stage CS disease presenting with high-degree AVB appears to have good performance for predicting likelihood of recovery.

P150 STRESS–REST DYNAMIC–CT MYOCARDIAL PERFUSION IMAGING IN THE MANAGEMENT OF MYOCARDIAL BRIDGING: A “ONE–STOP SHOP” EXAM

Abstract Myocardial bridging (MB) is a congenital anomaly characterized by intramyocardial coronary course that can cause coronary compression during systole and can lead to myocardial ischemia. The improvement in computed tomography (CT) scanner technology has increased the detection of MB during coronary–CT (cCT) exam but the assessment of their functional significance is often challenging. Tha stress–rest myocardial perfusion imaging (MPI) single–photon emission CT (SPECT) is often required to decide patient’s management after anatomical evaluation with cCT or Invasine Coronary Angiography (ICA). However, SPECT has long acquisition protocols, poor spatial resolution, and the anatomical/Functional protocols expose patients to significant radiation doses. Thus, dynamic–CT MPI after cCGT can represent a valid alternative. We report the cases of seven patients in which we detected MBs during cCT exam and we evaluated their functional significance using a stress–rest dynamic–CT MPI to decide the correct patient’s management. Three patients showed reversible perfusion defect in the left anterior descending (LAD) territory segments involved with MB and they were referred for treatment. The remnants four patients with MB in the LAD revealed normal MPI and they were conservatively managed. In all patients the results of the stress perfusion were completely overlapping, but the study with SPECT after anatomical evaluation required a longer process. The cCT and stress–rest dynamic–CT MPI allowed to detect and to evaluate the functional significance and to decide the management of MB in a “one–stop shop” exam.

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

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

Evaluation of myocardial viability in patients with myocardial ischemia reperfusion injury using the dual-energy CT myocardial blood pool imaging.

To evaluate myocardial viability in patients with myocardial ischemia reperfusion injury (MIRI) via dual-energy computed tomography myocardial blood pool imaging (DECT MBPI). Between September 2017 and January 2019, we prospectively recruited 59 patients with acute myocardial infarction (AMI) who developed MIRI after revascularization during invasive coronary angiography (ICA). Then, they received DECT MBPI, SPECT, and PET sequentially within 1 week after the onset of MIRI. A total of 1003 myocardial segments of 59 patients were recruited for this study. The iodine reduction areas and delayed enhancement areas were calculated by cardiac iodine map with SPECT rest myocardial perfusion imaging (MPI) + PET myocardial metabolism imaging (MMI) as reference criteria. The paired sample t-test was used to measure the difference of the myocardial iodine value. Cohen's Kappa analysis was used to test the consistency among different observers. ROC analysis was used to calculate the myocardial viability of DECT MBPI. ROC showed the AUCs of DECT MBPI iodine value to identify a normal myocardium, an ischemic myocardium, and an infarcted myocardium were 0.957, 0.900, and 0.906 (p < 0.001). The sensitivity, specificity, and accuracy of DECT MBPI in identifying an ischemic myocardium were 87.6%, 89.3%, and 97.9% (p < 0.001). The sensitivity, specificity, and accuracy of DECT MBPI in identifying an infarcted myocardium were 88.9%, 92.2%, and 98.6% (p < 0.001). The cutoff value for DECT MBPI to differentiate between an ischemic and a normal myocardium was 0.84 mg I/mL. The cutoff value for DECT MBPI to differentiate between an infarct and a normal myocardium was 2.01 mg I/mL. DECT MBPI can be used to assess myocardial viability in patients with MIRI with high sensitivity and specificity. • Dual-energy computed tomography myocardial blood pool imaging (DECT MBPI) can evaluate myocardial viability of myocardial ischemia-reperfusion injury (MIRI). • DECT MBPI is a non-invasive and timesaving method for evaluation on myocardial ischemia-reperfusion injury in patients with acute myocardial infarction after coronary intervention.

Impact of different obesity patterns on coronary microvascular function in male patients with non-obstructive coronary artery disease

Objective: This study sought to investigate the impact of different obesity patterns on coronary microvascular function in male patients with non-obstructive coronary artery disease. Methods: We retrospectively analyzed clinical data of male patients diagnosed with suspected coronary microvascular dysfunction (CMD) in the First Hospital of Shanxi Medical University between December 2015 and August 2021. All patients underwent the one-day rest and stress 13N-ammonia positron emission tomography myocardial perfusion imaging. Overall obesity was defined by body mass index (BMI) ≥28 kg/m2 and abdominal obesity was defined by waist circumference ≥90 cm. Hyperemic myocardial blood flow (MBF)<2.3 ml·min-1·g-1 or coronary flow reserve (CFR)<2.5 were referred as CMD. All patients were grouped based on their BMI and waist circumference. MBF, CFR, the incidence of CMD, hemodynamic parameters, and cardiac function were compared among the groups. Results: A total of 136 patients were included. According to BMI and waist circumference, patients were categorized into 3 groups: control group (n=45), simple abdominal obesity group (n=53) and compound obesity group (n=38). Resting MBF did not differ between groups (F=0.02,P=0.994). Compared with the control group, hyperemic MBF was significantly lower in the simple abdominal obesity and compound obesity groups ((2.82±0.64) ml·min-1·g-1, (2.44±0.85) ml·min-1·g-1 and (2.49±0.71) ml·min-1·g-1, both P<0.05, respectively). Hyperemic MBF was comparable among the groups of patients with obesity (P=0.772). CFR was significantly lower in the simle abdominal obesity group compared with the control group (2.87±0.99 vs. 3.32±0.62,P=0.012). Compared with the control group, CFR tended to be lower in the compound obesity group (3.02±0.91 vs. 3.32±0.62,P=0.117). The incidence of CMD was significantly higher in both the simple abdominal obesity and compound obesity groups than in the control group (62.3%, 52.6% vs. 22.2%, both P<0.01, respectively). Waist circumference was an independent risk factor for male CMD (OR=1.057, 95%CI: 1.013-1.103, P=0.011). Conclusions: In male patients with non-obstructive coronary artery disease, abdominal obesity is associated with decreased coronary microvascular function. Male patients with simple abdominal obesity face the highest risk of CMD.

National Diagnostic Reference Levels for Nuclear Medicine in Qatar.

Nuclear medicine (NM) started in Qatar in the mid-1980s with a 1-head γ-camera in Hamad General Hospital. However, Qatar is expanding, and now Hamad Medical Corp. has 2 NM departments and 1 PET/CT Center for Diagnosis and Research, with several hybrid SPECT/CT and PET/CT cameras. Furthermore, 2 new NM departments will be established in Qatar in the coming 3 y. Therefore, there is a need to optimize radiation protection in NM imaging and establish diagnostic reference levels (DRLs) for the first time in Qatar. This need is not only for the NM part of the examination but also for the CT part, especially in hybrid SPECT/CT and PET/CT. Methods: Data for adult patients were collected from the 3 SPECT/CT machines in the 2 NM facilities and from the 2 PET/CT machines in the PET/CT center. The 75th percentile values (also known as the third quartile) were considered preliminary DRLs and were consistent with the most commonly administered activities. The results for various general NM protocols were described, especially 99mTc-based radiopharmaceuticals and PET/CT protocols including mainly oncologic applications. Results: The first DRLs for NM imaging in Qatar adults were established. The values agreed with other published DRLs, as was the case, for example, for PET oncology using 18F-FDG, with DRLs of 258, 230, 370, 400, and 461-710 MBq for Qatar, Kuwait, Korea, the United Kingdom, and the United States, respectively. Similarly, for cardiac stress or rest myocardial perfusion imaging using 99mTc-methoxyisobutylisonitrile, the DRLs were 926, 976, 1,110, 800, and 945-1,402 MBq for Qatar, Kuwait, Korea, the United Kingdom, and the United States, respectively. Conclusion: The optimization of administered activity that this study will enable for NM procedures in Qatar will be of great value, especially for new departments that adhere to these DRLs.

Optimization of the left ventricle ejection fraction estimate obtained during cardiac adenosine stress 82Rubidium-PET scanning: impact of different reconstruction protocols

BackgroundLeft ventricular ejection fraction (LVEF) estimation using adenosine stress myocardial perfusion imaging (MPI) can be challenging. The short half-life of adenosine and the guideline-recommended adenosine infusion stop during Rubidium-82 acquisition protocol may affect the accuracy and repeatability of the LVEF measures. MethodsThis study comprised 25 healthy volunteers (median age 23 years) who underwent repeat myocardial perfusion imaging (MPI) sessions employing Rubidium-82 PET/CT. A guideline-recommended reconstruction protocol was used for both rest and adenosine stress MPI (150-360 s post-radiotracer injection, standardrecon). For the stress MPI protocol, two additional reconstruction protocols were considered; one was employing 60 seconds data (150-210 seconds, shortfixed) and the other a dynamic frame window based on the bolus arrival of Rubidium-82 in the heart until 210 seconds (x-210 seconds, shortindividual). We report rest and stress LVEF, the LVEF reserve, and the LVEF reserve repeatability. ResultsDifferences in the LVEF assessments were observed between the guideline recommended and alternative reconstruction protocol (LVEF stress MPI: standardrecon = 68 ± 7%, shortfixed = 71 ± 7% (P = .08), shortindividual = 72 ± 7% (P = .04)), and the LVEF reserve was reduced for the guideline-recommended protocol (standardrecon = 7.8 ± 3.5, shortfixed = 10.1 ± 3.7, shortindividual = 10.5 ± 3.6, all P < .001). The best repeatability measures were obtained for the shortindividual protocol (repeatability: standardrecon = 45.3%, shortfixed = 41.2%, shortindividual = 31.7%). ConclusionWe recommend using the shortindividual reconstruction protocol for improved LVEF repeatability and reserve assessment. Alternatively, in centers with limited technical support we recommend the use of the shortfixed protocol.

Nuklearmedizinische Funktionsdiagnostik in der Kardiologie

Cardiac nuclear medicine comprises various diagnostic techniques using radiopharmaceuticals for functional imaging in vivo. This article provides an overview of current clinical use of cardiac imaging in nuclear medicine in Germany: Myocardial perfusion imaging using SPECT is a well-established noninvasive tool to semi-quantitatively measure left ventricular myocardial perfusion. Ischemia and chronic myocardial scars can be idenified with a high diagnostic accuracy. Gated SPECT enables measuring left ventricular function. With new dedicated solid-state camera systems examinations have become faster and better while radiation exposure has been minimized. These new camera systems allow quantitative calculations of myocardial blood flow, which will further improve diagnostic accuracy.For patients with severe chronic coronary artery disease and myocardial dysfunction analyzing myocardial viability is crucial for guiding therpeutic decisions. For detection of hibernating myocardium and its differentiation from scar tissue, two nuclear cardiac methods are combined: Rest myocardial perfusion imaging detects perfusion defects and cardiac 18F-FDG-PET/CT detects glucose metabolism in the hypoperfused area. As long as glucose metabolism is intact therapeutic interventions can be beneficial.In general 18F-FDG-PET/CT allows visualization and quantification of celluar glucose metabolism in oncologic and inflammatory processes. For analysis of cardiac inflammation (e. g. endocarditis or sarcoidosis) a no-carb and high-protein diet is needed at leat 24 hours prior to imaging in order to suppress the physiologic myocardial glucose metabolism. Then, specific inflammatory tracer uptake can be assessed.Cardiac amyloidosis is a rare but dangerous condition. With a specific amyloidosis scintigraphy (bone scintigraphy), cardiac ATTR-amyloidosis can be diagnosed with high accuracy. A potenitally harmful myocardial biopsy often is not needed any more and specific therapy can be initiated.In summary, diagnostic methods in cardiac nuclear medicine non-invasively allow visualization and function analysis of biological processes and are essential for diagnosis finding and therapy guidance. The continuous advancement of diagnostic tools makes nuclear cardiology a highly relevant and interesting field.

Value of SiPM PET in myocardial perfusion imaging using Rubidium-82

BackgroundPET scanners using silicon photomultipliers with digital readout (SiPM PET) have an improved temporal and spatial resolution compared to PET scanners using conventional photomultiplier tubes (PMT PET). However, the effect on image quality and visibility of perfusion defects in myocardial perfusion imaging (MPI) is unknown. Our aim was to determine the value of a SiPM PET scanner in MPI. MethodsWe prospectively included 30 patients who underwent rest and regadenoson-induced stress Rubidium-82 (Rb-82) MPI on the D690 PMT PET (GE Healthcare) and within three weeks on the Vereos SiPM PET (Philips Healthcare). Two expert readers scored the image quality and assessed the existence of possible defects. In addition, interpreter’s confidence, myocardial blood flow (MBF), and myocardial flow reserve (MFR) values were compared. ResultsImage quality improved (P = 0.03) using the Vereos as compared to the D690. Image quality of the Vereos and the D690 was graded fair in 20% and 10%, good in 60% and 50%, and excellent in 20% and 40%, respectively. Defect interpretation and interpreter’s confidence did not differ between the D690 and the Vereos (P > 0.50). There were no significant differences in rest MBF (P ≥ 0.29), stress MBF (P ≥ 0.11), and MFR (P ≥ 0.51). ConclusionSiPM PET provides an improved image quality in comparison with PMT PET. Defect interpretation, interpreter’s confidence, and absolute blood flow measurements were comparable between both systems. SiPM PET is therefore a reliable technique for MPI using Rb-82. Trial registrationToetsingOnline NL63853.075.17. Registered 13 November, 2017.

Evaluation of the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with obstructive coronary artery disease

BackgroundTo investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD). MethodsEighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to 13N-NH3 PET/CT myocardial perfusion imaging (MPI) and 18F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans. Different combinations of indicators and seven machine learning methods were used to construct diagnostic models. Diagnostic performance was evaluated using the sum of four metrics (noted as sumScore), namely, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. ResultsIn univariate analysis, MIS outperformed other individual indicators in terms of sumScore (2.816-3.042 vs 2.138-2.908). In multivariate analysis, support vector machine (SVM) consisting of three indicators (MBF, SCR, and MIS) achieved the best performance (AUC 0.856, accuracy 0.810, sensitivity 0.838, specificity 0.757, and sumScore 3.261). This model consistently achieved significantly higher AUC compared with the SRS method for four specific subgroups (0.897, 0.839, 0.875, and 0.949 vs 0.775, 0.606, 0.713, and 0.744; P = 0.041, 0.005, 0.034 0.003, respectively). ConclusionsThe joint evaluation of PET rest MPI and MMI could improve the diagnostic performance for obstructive CAD. The multivariate model (MBF, SCR, and MIS) combined with SVM outperformed other methods.

Body weight-dependent Rubidium-82 activity results in constant image quality in myocardial perfusion imaging with PET

BackgroundClinical practice shows degrading image quality in heavier patients who undergo myocardial perfusion imaging (MPI) with Rubidium-82 (Rb-82) PET when using a fixed tracer activity. Our aim was to derive and validate a patient-specific activity protocol resulting in a constant image quality in PET MPI. MethodsWe included 251 patients who underwent rest MPI with Rb-82 PET (Discovery 670, GE Healthcare). 132 patients were included retrospectively and were scanned using a fixed activity of 740 MBq. The total number of measured prompts was normalized to activity and correlated to body weight, mass per body length and body mass index to find the best predicting parameter. Next, a patient-specific activity was derived and subsequently validated in 119 additional patients. Image quality was scored by three experts on a four-point scale. ResultsBoth image quality and prompts decreased in heavier patients when using a fixed activity (p < .005). Body weight was used to derive a new activity formula: Activity = 8.3 MBq/kg. When applying this formula, both measured prompts and scored image quality became independent of body weight (p > .60). ConclusionAdministrating a Rb-82 activity that linearly depends on body weight resulted in a constant image quality across all patients and is recommended.

Entropy-based myocardial blood flow measurements using PET: a way to improve reproducibility

Abstract Funding Acknowledgements Type of funding sources: None. Background and purpose Myocardial blood flow (MBF) measurements using PET are increasingly used to guide the management of patients with (suspected) coronary artery disease (CAD). Day-to-day variability of these measurements is poor with a 21% standard deviation or 40% 95%-confidence interval [Reference: JACC Cardiovasc Imaging, 2017;10(5):565]. This limits clinical applicability in diagnosis, risk stratification and follow-up as these all depend on comparison of flow values with fixed cut-off values. We expect that reproducibility can be improved by combining flow measurements with the variation of flow values within the myocardium. As entropy is a measure of variability of the associated distribution, we compared the reproducibility of an entropy-based flow parameter with that of conventional myocardial flow reserve (MFR) measurements. Methods We performed a study using intra-individual comparison in 24 patients who underwent rest and regadenoson-induced stress myocardial perfusion imaging using Rubidium-82 on two different PET systems (PET1: Discovery 690, GE Healthcare, and PET2: Vereos, Philips Healthcare) within 3 weeks. MBF for both rest and stress was calculated using Lortie’s one-tissue compartment model (Corridor4DM, INVIA). MFR (ratio of MBF stress/rest) was determined for the myocardial as a whole (MFRglobal), for the three vascular territories: LAD, LCX and RCA (MFRregional) and for the 17 segments. Next, we calculated Shannon’s entropy to measure the variation of the 17 MFR segmental values. We multiplied Shannon’s entropy by the mean of the MFR segmental values resulting in an entropy-based MFR (MFRentropy). For each patient MFRglobal, MFRregional and MRFentropy were compared between both PET systems. For each of the three parameters the test-retest precision was calculated as the SD of the relative difference between measurements. Results The mean difference in MFR measurements between both cameras did not differ from zero (p &amp;gt; 0.05). Mean values for PET1 were MFRglobal = 2.4, MFRregional = 2.4 (LAD), 2.4 (LCX) and 2.5 (RCA), and MFRentropy = 2.4. For PET2 we found MFRglobal = 2.5, MFRregional = 2.5 (LAD), 2.4 (LCX) and 2.6 (RCA), and MFRentropy = 2.5. Test-retest precision was lower for MFRentropy with 11% compared to that of MFRglobal (21%), MFRregional LAD (22%), MFRregional LCX (23%) and MFRregional RCA (24%) (p &amp;lt; 0.01). Conclusion The reproducibility of myocardial flow reserve measurements using Rubidium-82 PET improved by a factor of 2 when an entropy-based flow parameter instead of global or regional MFR parameters is used. This entropy-based flow-parameter may be used to better discriminate ischemia from non-ischemia and may therefore improve CAD management.

Reverse redistribution-like change on dipyridamole-stress 99mTc-tetrofosmin imaging in a patient with angiographically mild coronary artery stenosis

A 50-year-old man with a 1-month history of chest pain presented to our hospital. Transthoracic echocardiography showed normal left ventricular wall motion. Coronary computed tomography angiography revealed a mild stenosis with low-density non-calcified plaque in the proximal left anterior descending coronary artery (LAD) (Figure 1A, yellow arrows); therefore, the patient underwent a 1-day protocol of dipyridamole stress and rest 99mTc-tetrofosmin myocardial perfusion imaging (MPI). Baseline electrocardiography was normal; however, he developed angina 8 minutes after the start of .56 mg·kg−1 (.14 mg·kg−1·min−1 for 4 minutes) of intravenous dipyridamole infusion and his electrocardiogram showed ST-segment elevation in the precordial leads (Figure 2). After intravenous aminophylline with sublingual nitroglycerin was given, the ST-segment elevation was gradually resolved. Stress MPI showed no perfusion defect (Figure 1B) with abnormal wall motion in the anterior and septal walls on gated MPI (Supplementary Material). He had recurrent angina after the stress MPI. Rest MPI at 2 hours after the stress test showed reverse redistribution-like reduced uptake in the LAD territory (Figure 1B) with normal left ventricular wall motion on gated MPI (Supplementary Material). As with the coronary computed tomography, invasive coronary angiography via the right radial artery demonstrated the mild stenosis in the proximal LAD (Figure 3, yellow arrow). Intravascular ultrasound and optical coherence tomography images showed coronary plaque with neovascularization (Figure 3, red arrows) and small thrombi (Figure 3, white arrows) at the minimum lumen area site, indicating the increased vulnerability of the coronary plaque.1 Percutaneous coronary intervention (PCI) with a drug-eluting stent (4.0 × 33 mm) was successfully performed (Figure 3, red arrows). On the day following the PCI, he underwent cardiopulmonary exercise testing and did not present with chest pain at peak exercise (VO2 at peak was 22.5 mL·kg−1·min−1 [6.4 METs]). Open in a separate window Figure 1 (A) Coronary computed tomography angiography revealed a mild stenosis (yellow arrows) with low-density non-calcified plaque in the proximal left anterior descending coronary artery (LAD). (B) Dipyridamole stress and rest 99mTc-tetrofosmin myocardial perfusion imaging (MPI). Stress MPI (300 MBq of 99mTc-tetrofosmin) showed no perfusion defect, whereas rest MPI (900 MBq of 99mTc-tetrofosmin) 2 hours after the stress test showed reduced uptake (reverse redistribution) in the LAD territory

Head-to-head comparison of diagnostic accuracy of stress-only myocardial perfusion imaging with conventional and cadmium-zinc telluride single-photon emission computed tomography in women with suspected coronary artery disease

BackgroundBreast attenuation may impact the diagnostic accuracy of stress myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We compared the performance of conventional (C)-SPECT and cadmium-zinc-telluride (CZT)-SPECT systems in women with low-intermediate likelihood of coronary artery disease (CAD). Methods and ResultsA total of 109 consecutive women underwent stress-optional rest MPI by both C-SPECT and CZT-SPECT. In the overall study population, a weak albeit significant correlation between total perfusion defect (TPD) measured by C-SPECT and CZT-SPECT was observed (r = 0.38, P < .001) and at Bland-Altman analysis the mean difference in TPD (C-SPECT minus CZT-SPECT) was 2.40% (P < .001). Overall concordance of semi-quantitative diagnostic performance between C-SPECT and CZT-SPECT was observed in 52 (48%) women with a κ value of 0.09. Normalcy rate was significantly higher using CZT-SPECT compared to C-SPECT (P < .001). Machine learning analysis performed through the implementation of J48 algorithm proved that CZT-SPECT has higher sensitivity, specificity, and accuracy than C-SPECT. ConclusionsIn women with low-intermediate likelihood of CAD, there is a poor concordance of diagnostic performance between C-SPECT and CZT-SPECT, and CZT-SPECT allows better normalcy rate detection compared to C-SPECT.