Imaging and Diagnostics

Jan 2nd, 2024

 

In the field of Cardiac Imaging and Diagnostics, several significant studies were conducted to enhance understanding and treatment strategies. One such study, part of the FLAVOUR trial, focused on physiology- or imaging-guided strategies for intermediate coronary stenosis. This cohort study involved 1619 patients with intermediate coronary stenosis and compared IVUS-guided and FFR-guided strategies. The results showed that the incidence of TVF in deferred vessels was 3.8% for IVUS and 4.1% for FFR. Importantly, it was found that specific parameters could be identified for low-risk deferral and high-risk revascularization, indicating that both FFR- and IVUS-guided strategies had comparable outcomes (1).

 

Another study in the area of Infective Endocarditis examined the use of 18F-Fluorodeoxyglucose Positron Emission Tomography/CT in diagnosing right-sided endocarditis. This retrospective analysis involved 29 patients with definitive or potential infective endocarditis. The findings highlighted the high efficacy of PET/CT in diagnosing right-sided endocarditis, which is often challenging to visualize using echocardiography. This study underscored the utility of PET/CT in this context (2).

 

In the realm of angina, a randomized controlled trial investigated invasive endotyping in patients with angina but no obstructive coronary arteries. The study included 250 outpatients and compared results between patients who underwent invasive endotyping and those who did not have their endotyping results disclosed. The intervention group showed a four-fold increase in the diagnosis of coronary vasomotor disorders and an improvement in treatment satisfaction. This indicated that invasive endotyping significantly aids in the accurate diagnosis and management of angina in the absence of obstructive coronary artery disease (3).

 

A study focusing on myocardial injury post-acute myocardial infarction (AMI) evaluated myocardial α(v)β(3) integrin expression using [(68)Ga]Ga-NODAGA-RGD PET imaging. In this prospective evaluation of 31 patients, increased α(v)β(3) integrin expression was correlated with regional and global systolic dysfunction and was predictive of improvement in LV function. This provided potential insights into myocardial recovery post-AMI (4).

 

For myocardial perfusion imaging, a study compared manual and automated motion correction in PET myocardial perfusion imaging. This study involved 565 patients undergoing PET myocardial perfusion imaging and found that automated motion correction showed a strong correlation with manual methods and improved diagnostic performance for significant CAD. This suggests that automated motion correction enhances the accuracy and efficiency of diagnosing coronary artery disease (5).

 

The diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC) was the focus of a retrospective study that evaluated right ventricular (RV) strain parameters using cardiovascular magnetic resonance (CMR) feature tracking. It involved 74 patients with ARVC and 37 controls. The study found that RV global longitudinal strain (GLS) was the strongest discriminator for ARVC. This indicates that RV strain analysis offers incremental diagnostic value, especially in borderline cases (6).

 

In the field of AI in Cardiology, a validation study focused on the use of deep learning for automated Agatston score calculation from ECG-gated cardiac computed tomography. This retrospective study involved 150 patients and compared automated calcium quantification using deep learning with manual evaluation. The results showed a high correlation between automated and manual scoring, demonstrating the efficacy of an automated deep learning model for calcium scoring in cardiac CT, which offers a time-efficient and accurate assessment for coronary artery disease (7).

 

Another AI-related study developed a deep learning-ECG model to predict right ventricular dysfunction and dilation. This prediction model study utilized data from the UK Biobank and a health system cohort. The results demonstrated the potential of AI in cardiac imaging, particularly for assessing right ventricular function (8).

 

Lastly, a technology development and validation study explored the use of deep learning for transesophageal echocardiography (TEE) view classification in patients undergoing cardiac surgery. This study showcased high accuracy across TEE views, emphasizing the enhancement of intraoperative and intraprocedural TEE imaging using deep learning (9).

 

  1. Yang S, Kang J, Hwang D, Zhang J, Jiang J, Hu X, et al. Physiology- or Imaging-Guided Strategies for Intermediate Coronary Stenosis. JAMA Netw Open. 2024;7(1):e2350036.

  2. Ugan Atik S, Arslan P, Bilgiç S, Sonmezoglu K, Cilsal E, Gokalp S, et al. 18F-fluorodeoxyglucose positron emission tomography/CT in the diagnosis of right-sided endocarditis in children and adults with infective endocarditis. Cardiol Young. 2024:1-6.

  3. Sidik NP, Stanley B, Sykes R, Morrow AJ, Bradley CP, McDermott M, et al. Invasive Endotyping in Patients With Angina and No Obstructive Coronary Artery Disease: A Randomized Controlled Trial. Circulation. 2024;149(1):7-23.

  4. Nammas W, Paunonen C, Teuho J, Siekkinen R, Luoto P, Käkelä M, et al. Imaging of Myocardial α(v)β(3) Integrin Expression for Evaluation of Myocardial Injury After Acute Myocardial Infarction. J Nucl Med. 2024;65(1):132-8.

  5. Kuronuma K, Wei CC, Singh A, Lemley M, Hayes SW, Otaki Y, et al. Automated Motion Correction for Myocardial Blood Flow Measurements and Diagnostic Performance of (82)Rb PET Myocardial Perfusion Imaging. J Nucl Med. 2024;65(1):139-46.

  6. Dong Z, Ma X, Wang J, Yang S, Yu S, Song Y, et al. Incremental Diagnostic Value of Right Ventricular Strain Analysis in Arrhythmogenic Right Ventricular Cardiomyopathy. J Am Heart Assoc. 2024;13(1):e031403.

  7. Gautam A, Raghav P, Subramaniam V, Kumar S, Kumar S, Jain D, et al. Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study. Angiology. 2024:33197231225286.

  8. Duong SQ, Vaid A, My VTH, Butler LR, Lampert J, Pass RH, et al. Quantitative Prediction of Right Ventricular Size and Function From the ECG. J Am Heart Assoc. 2024;13(1):e031671.

  9. Steffner KR, Christensen M, Gill G, Bowdish M, Rhee J, Kumaresan A, et al. Deep learning for transesophageal echocardiography view classification. Sci Rep. 2024;14(1):11.