Prediction of cardiovascular events using myocardial strain ratio derived from 13N-ammonia positron emission tomography.

OBJECTIVES: Myocardial flow reserve (MFR), derived from ammonia N-13 positron emission tomography (NH3-PET), can predict the prognosis of patients with various heart diseases. We aimed to investigate whether myocardial strain ratio (MSR) was useful in predicting MACE and allowed for further risk stratification of cardiovascular events in patients with ischemic heart disease (IHD) in addition to MFR. METHODS: Ninety-five patients underwent NH3-PET because of IHD. MFR was determined as the ratio of hyperemic to resting myocardial blood flow (MBF). MSR was defined as the ratio of strains at stress and rest. The endpoint was major adverse cardiac events (MACE), including all-cause death, acute coronary syndrome, heart failure hospitalization, and revascularization. The ability to predict MACE was assessed using receiver operating characteristic (ROC) analysis, and the predictability of ME was analyzed using Kaplan-Meier analysis. The Cox proportional hazards regression model was used to calculate the hazard ratio (HR) with 95% confidence interval (CI). RESULTS: The ROC curve analysis demonstrated a cutoff of 0.93 for MACE with MSR (sensitivity and specificity of 77% and 71%, respectively). Patients with MSR < 0.93 displayed a significantly higher MACE rate than those with MSR ≥ 0.93 (p = 0.0036). The Cox proportional hazards regression analysis indicated that MSR was an independent marker that could predict MACE in imaging and clinical parameters (HR, 7.32; 95% CI: 1.59-33.7, p = 0.011). CONCLUSIONS: MSR was an independent predictor of MACE and was useful for further risk stratification in IHD. MSR has the potential for a new indicator of revascularization in patients with IHD. KEY POINTS: • We hypothesized that combining myocardial flow reserve (MFR) with the myocardial strain ratio (MSR) obtained by applying the feature-tracking technique to ammonia N-13 PET would make it predictive of major adverse cardiac events (MACE) compared to MFR alone. • MSR was an independent predictor of MACE, allowing for further risk stratification in addition to MFR in patients with ischemic heart disease. • MSR is a potential new indicator of revascularization.

13 N-ammonia PET-derived right ventricular longitudinal strain and myocardial flow reserve in right coronary artery disease.

PURPOSE: We developed a feature-tracking algorithm for use with electrocardiography-gated high-resolution 13 N-ammonia positron emission tomography (PET) imaging, and we hypothesized it could be used to clarify the association between right ventricular (RV) longitudinal strain (LS) and right coronary artery (RCA) ischemia. The aim of this study was to investigate the association between the reduction of regional myocardial flow reserve (MFR) in RCA territories and PET-derived LS of the RV free wall. METHODS: Ninety-three patients with coronary artery stenosis > 50%, diagnosed by coronary computed tomography angiography, and 10 controls were retrospectively analyzed. RV-LS in the free wall was measured by a feature-tracking technique on the resting and stressed 13 N-ammonia PET images of horizontal long axis slices. The patients were sub-grouped according to regional MFR values at the territories of RCA, left anterior descending artery (LAD), and left circumflex coronary artery (LCx): RCA-MFR < 2.0 [n = 34], RCA-MFR ≥ 2.0 but MFR < 2.0 at LAD or LCx territories [n = 11], and MFR ≥ 2.0 for all territories [n = 48]. Stress and resting RV-LS were compared in each of the four groups. Multiple comparisons of RV-LS among the four groups were performed in the stress and resting state. RESULTS: Decreased stress RV-LS in patients with an RCA-MFR < 2.0 was observed. In the patients with MFR ≥ 2.0 for all territories, the stressed RV-LS was significantly increased compared to that in the resting state. Significantly decreased RV free wall LS during adenosine stress in patients with RCA-MFR < 2.0 was observed in the other three groups. CONCLUSIONS: We measured RV myocardial LS using feature tracking in cine imaging of 13 N-ammonia PET. The results of this study suggest that PET-derived stressed RV-LS is useful for detecting reduced RV myocardial motion due to ischemia in the RCA territory.

13N-ammonia positron emission tomography-derived endocardial strain for the assessment of ischemia using feature-tracking in high-resolution cine imaging.

BACKGROUND: Assessing endocardial strain using a single 13N-ammonia positron emission tomography (PET) scan would be clinically useful, given the association between ischemia and myocardial deformation. However, no software has been developed for strain analysis using PET. We evaluated the clinical potential of feature tracking-derived strain values measured using PET, based on associations with the myocardial flow reserve (MFR). METHODS AND RESULTS: This retrospective study included 95 coronary artery disease patients who underwent myocardial 13N-ammonia PET. Semi-automatic measurements were made using a feature-tracking technique during myocardial cine imaging, and values were calculated using a 16-segment model. Adenosine-stressed global circumferential strain (CS) and global longitudinal strain (LS) values were compared with global MFR values. Stressed and resting global strain values were also compared. Global strain values were significantly lower in 39 patients with abnormal MFRs [< 2.0] than in 56 patients with normal MFRs [≥ 2.0]. The global CS values in the stressed state were significantly decreased than the resting state values in patients with abnormal MFRs. CONCLUSIONS: This study applied endocardial feature-tracking to 13N-ammonia PET, and the results suggested that blood flow and myocardial motility could be clinically assessed in ischemic patients using a single PET scan.

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic resonance (CMR) images. Accordingly, the purpose of this study is to compare the accuracy of deep learning-based fully automatic segmentation of RLA images with the accuracy of conventional deep learning-based segmentation in SA orientation in terms of the measurements of RV strain parameters. MATERIALS AND METHODS: We compared the accuracies of the above-mentioned methods in RV segmentations and in measuring RV strain parameters by Dice similarity coefficients (DSCs) and correlation coefficients. RESULTS: DSC of RV segmentation of the RLA method exhibited a higher value than those of the conventional SA methods (0.84 vs. 0.61). Correlation coefficient with respect to manual RV strain measurements in the fully automatic RLA were superior to those in SA measurements (0.5-0.7 vs. 0.1-0.2). DISCUSSION: Our proposed RLA realizes accurate fully automatic extraction of the entire RV region from an available CMR cine image without any additional imaging. Our findings overcome the complexity of image analysis in CMR without the limitations of the RV visualization in echocardiography.

New transluminal attenuation gradient derived from dynamic coronary CT angiography: diagnostic ability of ischemia detected by 13N-ammonia PET.

Coronary computed tomography angiography (CCTA) has low specificity for detecting significant functional coronary stenosis. We developed a new transluminal attenuation gradient (TAG)-derived dynamic CCTA with dose modulation, and we investigated its diagnostic performance for myocardial ischemia depicted by 13N-ammonia positron emission tomography (PET). Data from 48 consecutive patients who had undergone both dynamic CCTA and 13N-ammonia PET were retrospectively analyzed. Dynamic CCTA was continuously performed in mid-diastole for five cardiac cycles with prospective electrocardiography gating after a 10-s contrast medium injection. One scan of the dynamic CCTA was performed as a boost scan for conventional CCTA at the peak phase of the ascending aorta. Absolute TAG values at five phases around the boost scan were calculated. The dynamic TAG index (DTI) was defined as the ratio of the maximum absolute TAG to the standard deviation of five TAG values. We categorized the coronary territories as non-ischemia or ischemia based on the 13N-ammonia PET results. A receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff of the DTI for identifying ischemia. The DTI was significantly higher for ischemia compared to non-ischemia (8.8 ± 3.9 vs. 4.6 ± 2.0, p < 0.01). The ROC analysis revealed 5.60 as the optimal DTI cutoff to detect ischemia, with an area under the curve of 0.87, 85.7% sensitivity, and 76.2% specificity. TAG provided no additional diagnostic value for the detection of ischemia. We propose the DTI derived from dynamic CCTA as a novel coronary flow index. The DTI is a valid technique for detecting functional coronary stenosis.

Clinical usefulness of right ventricular 3D area strain in the assessment of treatment effects of balloon pulmonary angioplasty in chronic thromboembolic pulmonary hypertension: comparison with 2D feature-tracking MRI.

OBJECTIVES: To evaluate the usefulness of right ventricular (RV) area strain analysis via cardiac MRI (CMRI) as a tool for assessing the treatment effects of balloon pulmonary angioplasty (BPA) in inoperable chronic thromboembolic pulmonary hypertension (CTEPH), RV area strain was compared to two-dimensional (2D) strain with feature-tracking MRI (FTMRI) before and after BPA. METHODS: We retrospectively analyzed 21 CTEPH patients who underwent BPA. End-systolic global area strain (GAS), longitudinal strain (LS), circumferential strain (CS), and radial strain (RS) were measured before and after BPA. Changes in GAS and RV ejection fraction (RVEF) values after BPA were defined as ΔGAS and ΔRVEF. Receiver operating characteristic (ROC) analyses were performed to determine the optimal cutoff of the strain at after BPA for detection of improved patients with decreased mean pulmonary artery pressure (mPAP) less than 30 mmHg and increased RVEF more than 50%. RESULTS: ROC analysis revealed the optimal cutoffs of strains (GAS, LS, CS, and RS) for identifying improved patients with mPAP < 30 mmHg (cutoff (%) = - 41.2, - 13.8, - 16.7, and 14.4: area under the curve, 0.75, 0.56, 0.65, and 0.75) and patients with RVEF > 50% (cutoff (%) = - 37.2, - 29.5, - 2.9, and 14.4: area under the curve, 0.81, 0.60, 0.56, and 0.56). CONCLUSIONS: Area strain analysis via CMRI may be a more useful tool for assessing the treatment effects of BPA in patients with CTEPH than 2D strains with FTMRI. KEY POINTS: • Area strain values can detect improvement of right ventricular (RV) pressure and function after balloon pulmonary angioplasty (BPA) equally or more accurately than two-dimensional strains. • Area strain analysis is a useful analytical method that reflects improvements in complex RV myocardial deformation by BPA. • Area strain analysis is a robust method with reproducibility equivalent to that of 2D strain analysis.

Relevance of increased negative T waves in apical hypertrophic cardiomyopathy with progressive myocardial damage: Insights from repeat cardiac magnetic resonance studies.

In patients with apical hypertrophic cardiomyopathy (HCM), progressive electrocardiographic changes are observed during long-term follow-up. However, it is difficult to correspond these changes to the specific myocardial changes. Cardiac magnetic resonance (CMR) imaging can elucidate myocardial changes by late gadolinium enhancement. Here, we present the long-term follow-up (>18 years) on a patient with apical HCM, whereupon, precise and continuous changes in the myocardium, causing ST segment and T wave changes on electrocardiography, were observed on CMR images. The combination of electrocardiography and CMR facilitates management of patients with apical HCM because it helps explain and understand the nature of electrocardiography changes over time.

Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

OBJECTIVE: Deep 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). METHODS: This 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. RESULTS: For 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. CONCLUSIONS: Our 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.

Risk Stratification Using Right Ventricular Longitudinal Strain Ratio Derived from 13N-Ammonia PET in Patients with Ischemic Heart Disease.

Purpose To investigate whether right ventricular (RV) myocardial strain ratio (RVMSR) assessed using nitrogen 13 ammonia (13N-NH3) PET can predict cardiovascular events in patients with ischemic heart disease (IHD). Materials and Methods This retrospective study included 480 consecutive patients (mean age, 66 years ± 12 [SD]; 334 males and 146 females) with IHD who underwent 13N-NH3 PET. RVMSR was defined as the ratio of RV strain during stress to that at rest. The primary end point was major adverse cardiac events (MACEs), defined as cardiac death or heart failure hospitalization. The ability of RVMSR to predict MACE was assessed using receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. Cox proportional hazards regression analysis was used to calculate hazard ratios (HRs) with 95% CIs. Results ROC curve analysis identified a sensitivity and specificity of 84% and 82%, respectively, for predicting MACE from RVMSR. Patients with reduced RVMSR (<110.2) displayed a significantly higher rate of MACE than those with a preserved RVMSR (34 of 240 vs four of 240; P < .001). Cox proportional hazards regression analysis of imaging parameters, including myocardial flow reserve, indicated that RVMSR was an independent predictor of MACE (HR, 0.94 [95% CI: 0.92, 0.97]; P < .001). Conclusion RVMSR was an independent predictor of MACE and has potential to aid in the risk stratification of patients with IHD. Keywords: Right Ventricular Myocardial Strain Ratio, Myocardial Flow Reserve, Ischemic Heart Disease, 13N-Ammonia Positron Emission Tomography Supplemental material is available for this article. © RSNA, 2024.

Gated SPECT-Derived Myocardial Strain Estimated From Deep-Learning Image Translation Validated From N-13 Ammonia PET.

RATIONALE AND OBJECTIVES: This study investigated the use of deep learning-generated virtual positron emission tomography (PET)-like gated single-photon emission tomography (SPECTVP) for assessing myocardial strain, overcoming limitations of conventional SPECT. MATERIALS AND METHODS: SPECT-to-PET translation models for short-axis, horizontal, and vertical long-axis planes were trained using image pairs from the same patients in stress (720 image pairs from 18 patients) and resting states (920 image pairs from 23 patients). Patients without ejection-fraction changes during SPECT and PET were selected for training. We independently analyzed circumferential strains from short-axis-gated SPECT, PET, and model-generated SPECTVP images using a feature-tracking algorithm. Longitudinal strains were similarly measured from horizontal and vertical long-axis images. Intraclass correlation coefficients (ICCs) were calculated with two-way random single-measure SPECT and SPECTVP (PET). ICCs (95% confidence intervals) were defined as excellent (≥0.75), good (0.60-0.74), moderate (0.40-0.59), or poor (≤0.39). RESULTS: Moderate ICCs were observed for SPECT-derived stressed circumferential strains (0.56 [0.41-0.69]). Excellent ICCs were observed for SPECTVP-derived stressed circumferential strains (0.78 [0.68-0.85]). Excellent ICCs of stressed longitudinal strains from horizontal and vertical long axes, derived from SPECT and SPECTVP, were observed (0.83 [0.73-0.90], 0.91 [0.85-0.94]). CONCLUSION: Deep-learning SPECT-to-PET transformation improves circumferential strain measurement accuracy using standard-gated SPECT. Furthermore, the possibility of applying longitudinal strain measurements via both PET and SPECTVP was demonstrated. This study provides preliminary evidence that SPECTVP obtained from standard-gated SPECT with postprocessing potentially adds clinical value through PET-equivalent myocardial strain analysis without increasing the patient burden.

Dynamic chest radiography for pulmonary vascular diseases: clinical applications and correlation with other imaging modalities.

Dynamic chest radiography (DCR) is a novel functional radiographic imaging technique that can be used to visualize pulmonary perfusion without using contrast media. Although it has many advantages and clinical utility, most radiologists are unfamiliar with this technique because of its novelty. This review aims to (1) explain the basic principles of lung perfusion assessment using DCR, (2) discuss the advantages of DCR over other imaging modalities, and (3) review multiple specific clinical applications of DCR for pulmonary vascular diseases and compare them with other imaging modalities.

Diagnostic accuracy and added value of dynamic chest radiography in detecting pulmonary embolism: A retrospective study.

Purpose: This study aimed to assess the diagnostic performance of dynamic chest radiography (DCR) and investigate its added value to chest radiography (CR) in detecting pulmonary embolism (PE). Methods: Of 775 patients who underwent CR and DCR in our hospital between June 2020 and August 2022, individuals who also underwent contrast-enhanced CT (CECT) of the chest within 72 h were included in this study. PE or non-PE diagnosis was confirmed by CECT and the subsequent clinical course. The enrolled patients were randomized into two groups. Six observers, including two thoracic radiologists, two cardiologists, and two radiology residents, interpreted each chest radiograph with and without DCR using a crossover design with a washout period. Diagnostic performance was compared between CR with and without DCR in the standing and supine positions. Results: Sixty patients (15 PE, 45 non-PE) were retrospectively enrolled. The addition of DCR to CR significantly improved the sensitivity, specificity, accuracy, and area under the curve (AUC) in the standing (35.6-70.0 % [P < 0.0001], 84.8-93.3 % [P = 0.0010], 72.5-87.5 % [P < 0.0001], and 0.66-0.85 [P < 0.0001], respectively) and supine (33.3-65.6 % [P < 0.0001], 78.5-92.2 % [P < 0.0001], 67.2-85.6 % [P < 0.0001], and 0.62-0.80 [P = 0.0002], respectively) positions for PE detection. No significant differences were found between the AUC values of DCR with CR in the standing and supine positions (P = 0.11) or among radiologists, cardiologists, and radiology residents (P = 0.14-0.68). Conclusions: Incorporating DCR with CR demonstrated moderate sensitivity, high specificity, and high accuracy in detecting PE, all of which were significantly higher than those achieved with CR alone, regardless of scan position, observer expertise, or experience.

Differentiation between mild and severe myocarditis using multiparametric cardiac magnetic resonance.

The pathophysiology of myocarditis is associated with mild inflammation and may progress silently, or in severe cases such as fulminant myocarditis, may lead to sudden hemodynamic compromise. An invasive myocardial biopsy is generally required for a definitive myocarditis diagnosis. Alternatively, cardiac magnetic resonance (CMR), which evaluates myocardial characteristics and cardiac function, can be used as a noninvasive tool for diagnosing myocarditis. We describe the cases of a 49-year-old woman with mild acute eosinophilic myocarditis and a 48-year-old man with severe acute lymphocytic myocarditis. CMR was performed during the acute and convalescent phases in both cases. Compared with mild myocarditis, CMR in severe myocarditis showed higher T2 values and decreased left ventricular and atrial volumes and strains; however, the right ventricular strain was preserved. Late gadolinium enhancement showed faint contrast enhancement in the whole and strong enhancement in the local myocardium. Follow-up CMR showed recovery from myocardial inflammation and cardiac function. Some late gadolinium enhancement persisted whereas acute inflammation-associated enhancement disappeared. This case report highlights the differences between the cardiac parameters of patients with mild and severe myocarditis. Severe myocardial inflammation can be caused by severe heart failure owing to the concurrent reduction of cardiac function and compliance. Additionally, preserved right ventricular strain may predict cardiac function recovery in acute myocarditis. Noninvasive and repeatable CMR provides information on myocardial characteristics, cardiac function, and hemodynamics in a single scan at that time, which is useful not only for diagnosis but also for severity assessment and patient management in acute myocarditis.

A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

PURPOSE: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation. METHODS: A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge. Then, another 800 images were used, i.e., 400 with and 400 without surgical sponge. The detection characteristics of retained sponges between the model and a general observer with 10-year clinical experience were analyzed using the receiver operator characteristics. RESULTS: The following values from the deep learning model and observer were, respectively, derived: Cutoff values of probability were 0.37 and 0.45; areas under the curves were 0.87 and 0.76; sensitivity values were 85% and 61%; and specificity values were 73% and 92%. CONCLUSION: For the detection of surgical sponges, we concluded that the deep learning model has higher sensitivity, while the human observer has higher specificity. These characteristics indicate that the deep learning system that is complementary to humans could support the clinical workflow in operation rooms for prevention of retained surgical items.

High-echoic line tracing of transthoracic echocardiography accurately assesses right ventricular enlargement in adult patients with atrial septal defect.

Accurate measurement of right ventricular (RV) size using transthoracic echocardiography (TTE) is important for evaluating the severity of congenital heart diseases. The RV end-diastolic area index (RVEDAi) determined using TTE is used to assess RV dilatation; however, the tracing line of the RVEDAi has not been clearly defined by the guidelines. This study aimed to determine the exact tracing method for RVEDAi using TTE. We retrospectively studied 107 patients with atrial septal defects who underwent cardiac magnetic resonance imaging (CMR) and TTE. We measured the RVEDAi according to isoechoic and high-echoic lines, and compared it with the RVEDAi measured using CMR. The isoechoic line was defined as the isoechoic endocardial border of the RV free wall, whereas the high-echoic line was defined as the high-echoic endocardial border of the RV free wall more outside than the isoechoic line. RVEDAi measured using high-echoic line (high-RVEDAi) was more accurately related to RVEDAi measured using CMR than that measured using isoechoic line (iso-RVEDAi). The difference in the high-RVEDAi was 0.3 cm2/m2, and the limit of agreement (LOA) was - 3.7 to 4.3 cm2/m2. With regard to inter-observer variability, high-RVEDAi was superior to iso-RVEDAi. High-RVEDAi had greater agreement with CMR-RVEDAi than with iso-RVEDAi. High-RVEDAi can become the standard measurement of RV size using two-dimensional TTE.

Myocardial Strain Derived from 13N-ammonia Positron Emission Tomography: Detection of Ischemia-Related Wall Motion Abnormality.

Background: Due to the limitation of spatial resolution, cardiac nuclear medicine images have not been applied to feature-tracking method to automatic extraction of myocardial contours. We have successfully applied the feature-tracking method to high-resolution cine 13N-ammonia positron emission tomography (PET) images to calculate the regional myocardial strains. Here, we investigate the potential of 13N-ammonia PET-derived strain to detect ischemia-related wall motion abnormality. Methods: Data of adenosine-stress/rest 13N-ammonia PET for 95 coronary artery disease patients was retrospectively analyzed. Using an original algorithm dedicated to 13N-ammonia PET, the longitudinal strain (LS) corresponding to the three main coronary artery territories [right coronary artery (RCA), left anterior descending artery (LAD), and left circumflex coronary artery (LCX)] was calculated from semi-automatic endocardial contours extraction on cine 13N-ammonia PET images of the left ventricular long-axis. The presence of ischemia in three main territories was determined from rest and stress-perfusion images. Results: In all three coronary territories, LS at stress was significantly smaller at rest in the ischemic region RCA: -19.2±8.0% vs. -22.7±6.1%, LAD: -19.0±6.9% vs. -24.4±6.4%, LCX: -20.5%±7.6% vs. -22.6±6.9%). In contrast, in the non-ischemic region, there was no significant difference between the LS at stress and at rest. Receiver-operating-characteristic analysis revealed that using the optimal cutoff of the LS ratio of stress to rest, ischemia could be diagnosed with area under the curve of 0.82 in the RCA, 0.86 in the LAD, and 0.69 in the LCX. Conclusions: Myocardial strain derived from endocardial feature-tracking of 13N-ammonia PET cine imaging is reduced in the ischemia induced by adenosine-stress. The LS ratio of stress to rest may detect wall motion abnormality related to ischemia.

Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation.

Cardiac cine magnetic resonance imaging (MRI) is a widely used technique for the noninvasive assessment of cardiac functions. Deep neural networks have achieved considerable progress in overcoming various challenges in cine MRI analysis. However, deep learning models cannot be used for classification because limited cine MRI data are available. To overcome this problem, features from cine image settings are derived by handcrafting and addition of other clinical features to the classical machine learning approach for ensuring the model fits the MRI device settings and image parameters required in the analysis. In this study, a novel method was proposed for classifying heart disease (cardiomyopathy patient groups) using only segmented output maps. In the encoder-decoder network, the fully convolutional EfficientNetB5-UNet was modified to perform the semantic segmentation of the MRI image slice. A two-dimensional thickness algorithm was used to combine the segmentation outputs for the 2D representation of images of the end-diastole (ED) and end-systole (ES) cardiac volumes. The thickness images were subsequently used for classification by using a few-shot model with an adaptive subspace classifier. Model performance was verified by applying the model to the 2017 MICCAI Medical Image Computing and Computer-Assisted Intervention dataset. High segmentation performance was achieved as follows: the average Dice coefficients of segmentation were 96.24% (ED) and 89.92% (ES) for the left ventricle (LV); the values for the right ventricle (RV) were 92.90% (ED) and 86.92% (ES). The values for myocardium were 88.90% (ED) and 90.48% (ES). An accuracy score of 92% was achieved in the classification of various cardiomyopathy groups without clinical features. A novel rapid analysis approach was proposed for heart disease diagnosis, especially for cardiomyopathy conditions using cine MRI based on segmented output maps.