Advances in Cardiac Computed Tomography Functional Imaging Technology

Tian, Xu-Wei; Ai-lin, MA; Zhou, Renbing; Jiang, Liu-Jiang; Hao, Yue Feng; Zou, Xiaoguang

doi:10.1159/000505317

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…Moreover, CCT provides global and regional functional analysis. Many prior studies validated, via comparisons with CMR, the global functional parameters, such as SV, CO, and EF, and the evaluation of regional functional findings, such as hypokinesia, akinesia, and dyskinesia [ 29 ].…”

Section: Cardiac-ctmentioning

confidence: 99%

“…Morphological and functional findings are comprehensively assessed using a retrospective ECG-gated helical scanning protocol, which is characterized by continuous data acquisition throughout some consecutive cardiac cycles. This retrospective technique allows the evaluation of any cardiac phase and it is particularly useful in patients with high or irregular heart rhythm and, even if it is associated with relatively high radiation doses, it allows with the same acquisition the evaluation of the coronary artery disease [ 8 , 29 ].…”

Section: Cardiac-ctmentioning

confidence: 99%

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Cardiac Imaging in Athlete’s Heart: The Role of the Radiologist

et al. 2021

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Athlete’s heart (AH) is the result of morphological and functional cardiac modifications due to long-lasting athletic training. Athletes can develop very marked structural myocardial changes, which may simulate or cover unknown cardiomyopathies. The differential diagnosis between AH and cardiomyopathy is necessary to prevent the risk of catastrophic events, such as sudden cardiac death, but it can be a challenging task. The improvement of the imaging modalities and the introduction of the new technologies in cardiac magnetic resonance (CMR) and cardiac computed tomography (CCT) can allow overcoming this challenge. Therefore, the radiologist, specialized in cardiac imaging, could have a pivotal role in the differential diagnosis between structural adaptative changes observed in the AH and pathological anomalies of cardiomyopathies. In this review, we summarize the main CMR and CCT techniques to evaluate the cardiac morphology, function, and tissue characterization, and we analyze the imaging features of the AH and the key differences with the main cardiomyopathies.

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Section: Cardiac-ctmentioning

confidence: 99%

Section: Cardiac-ctmentioning

confidence: 99%

Cardiac Imaging in Athlete’s Heart: The Role of the Radiologist

et al. 2021

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Noninvasive cardiovascular imaging techniques

Sinha

Uditi

Rathore

2022

Cardiovascular Toxicity and Therapeutic Modalities Targeting Cardio-Oncology

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Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!

Marey,

Serdysnki,

Killeen

et al. 2024

BJR|Artificial Intelligence

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Artificial intelligence (AI) has emerged as a prominent field in computer science. Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of artificial intelligence (AI) in cardiovascular imaging, focusing on echocardiography, cardiac magnetic resonance imaging (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly deep learning (DL) approaches like convolutional neural networks (CNNs), enhances standardization and reduces operator-dependent variations in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks (VNNs), improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The "black box" problem in deep learning models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI, addressing discrimination while preserving privacy. Ongoing research and development are vital to evolving AI ethics and ensuring ethical data handling in healthcare.

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Advances in Cardiac Computed Tomography Functional Imaging Technology

Cited by 3 publications

References 49 publications

Cardiac Imaging in Athlete’s Heart: The Role of the Radiologist

Cardiac Imaging in Athlete’s Heart: The Role of the Radiologist

Noninvasive cardiovascular imaging techniques

Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!

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