Annals of nuclear cardiology最新文献

Background: Single-photon emission computed tomography (SPECT) encounters difficulties in diagnosing severe multi-vessel coronary artery disease (svMVD) because of balanced ischemia. We estimated the predictive value of electrocardiogram-gated SPECT for svMVD and improved it using machine learning (ML). Methods and results: We enrolled consecutive 335 patients (median age, 74 years; 255 men) who underwent adenosine stress-gated SPECT (^99mTechnesium) and coronary angiography. svMVD was defined as three-vessel disease or left main tract stenosis. Predictive models were constructed using statistical and ML methods. Eighteen cases (5%) showed svMVD, and diabetes, summed stress score (SSS), and the max difference among segmental time of stroke volume per cardiac cycle (MDSV: a parameter of left ventricular [LV] end-systolic dyssynchrony) on adenosine stress were independent significant predictors. The area under the receiver operating characteristic curve (AUC) of SSS and MDSV on stress were 0.759 and 0.763, respectively. Conversely, the extra trees classifier and light gradient boosting machine had improved AUC values of 0.826 and 0.870, respectively, and the MDSV on stress and diabetes showed high feature values in the ML models. Conclusion: ML on SPECT helped to improve the diagnostic performance of svMVD and diabetes, and the parameters of LV dyssynchrony played essential roles in the ML predictive models.

Background: ¹²³I-metaiodobenzylguanidine (MIBG) scintigraphy evaluates the severity and prognosis of patients with heart failure. A prognostic model has been proposed using a multicenter study data of ¹²³I-MIBG scintigraphy. We evaluated the usefulness of the model using a database. Methods: The study included 208 patients with noncompensated heart failure requiring hospitalization. ¹²³I-MIBG scintigraphy and echocardiography were performed predischarge and 6 months postdischarge. The 5-year mortality rate was calculated by the model and classified into tertiles. Results: In 208 patients, 56 cardiac deaths occurred within the observation period (median, 4.83 years). In the evaluation of predischarge parameters, the predicted 5-year mortality was 15.5% ± 5.0%, 33.5% ± 3.9%, and 51.2% ± 8.2%, and 11 (16.2%), 18 (27.3%), and 27 (36.5%) cardiac deaths occurred in groups 1, 2, and 3, respectively. At the 6-month postdischarge evaluation, the estimated mortality was 8.2% ± 2.2%, 18.5% ± 4.8%, and 43.0% ± 12.1%, and 6 (9.4%), 21 (29.2%), and 29 (40.3%) cardiac deaths occurred, respectively. The predischarge Kaplan-Meier survival analysis showed significant difference between groups 1 and 3 (P value 0.014). Moreover, the 6-month postdischarge evaluation showed significant difference between group 1 and 2, and between groups 1 and 3 (P value 0.016, <0.001, respectively). For groups 1 and 3, the 6-month postdischarge difference was more significant than the predischarge difference (Chi-square 16.7 and 8.1, respectively). Conclusions: The prognostic model using ¹²³I-MIBG scintigraphy was useful in predicting mortality risk in patients with heart failure. The estimated mortality at 6 months postdischarge was more useful than the predischarge estimation for heart failure hospitalization.

Both exercise single photon emission computed tomography (SPECT) imaging and myocardial perfusion imaging with positron emission tomography produce multiple outcome variables. These include the stress electrocardiogram (ECG), visual perfusion assessment and quantitative myocardial blood flow. Bayes' analysis using conditional probability allows the distillation of multiple test results into a single probability of disease for individual patients. This paper examines the application of conditional probability analysis to two noninvasive modalities that generate multiple outcome results: exercise ECG combined with SPECT imaging and vasodilator RB-82 positron emission tomography perfusion imaging combined with quantitative measure of absolute myocardial blood flow. In this manner, a single probability of disease incorporating all the available data is generated for an individual patient.

Background: Myocardial blood flow quantification (MBF) is one of the distinctive features for cardiac positron emission tomography. The MBF calculation is mostly obtained by estimating the input function from the time activity curve in dynamic scan. However, there is a substantial risk of count-loss because the high radioactivity pass through the left ventricular (LV) cavity within a short period. We aimed to determine the optimal intraventricular activity using the noise equivalent count rate (NECR) analysis with simplified phantom model. Methods: Positron emission tomography computed tomography scanner with LYSO crystal and time of flight was used for phantom study. 150 MBq/mL of ¹³N was filled in 10 mL of syringe, placed in neck phantom to imitate end-systolic small LV. 3D list-mode acquisition was repeatedly performed along radioactive decay. Net true and random count rate were calculated and compared to the theoretical activity in the syringe. NECR curve analysis was used to determine the optimal radioactive concentration. Result: The attenuation curves showed good correlation to the theoretical activity between 20 to 370, and 370 to 740 MBq (r²=1.0 ± 0.0001, p<0.0001; r²=0.99 ± 0.0001, p<0.0001 for 20 to 370, and 370 to 740, respectively), while did not over 740 MBq (p=0.62). NECR analysis revealed that the peak rate was at 2.9 Mcps, there at the true counts were significantly suppressed. The optimal radioactive concentration was determined as 36 MBq/mL. Conclusion: Simulative analysis for high-dose of ¹³N using the phantom imitating small LV confirmed that the risk of count-loss was increased. The result can be useful information in assessing the feasibility of MBF quantification in clinical routine.