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. 2024 May 28;21(5):550-576.
doi: 10.26599/1671-5411.2024.05.002.

Cardiovascular computed tomography in cardiovascular disease: An overview of its applications from diagnosis to prediction

Zhong-Hua Sun  1   2
Affiliations

Cardiovascular computed tomography in cardiovascular disease: An overview of its applications from diagnosis to prediction

Zhong-Hua Sun. J Geriatr Cardiol. .

Abstract

Cardiovascular computed tomography angiography (CTA) is a widely used imaging modality in the diagnosis of cardiovascular disease. Advancements in CT imaging technology have further advanced its applications from high diagnostic value to minimising radiation exposure to patients. In addition to the standard application of assessing vascular lumen changes, CTA-derived applications including 3D printed personalised models, 3D visualisations such as virtual endoscopy, virtual reality, augmented reality and mixed reality, as well as CT-derived hemodynamic flow analysis and fractional flow reserve (FFRCT) greatly enhance the diagnostic performance of CTA in cardiovascular disease. The widespread application of artificial intelligence in medicine also significantly contributes to the clinical value of CTA in cardiovascular disease. Clinical value of CTA has extended from the initial diagnosis to identification of vulnerable lesions, and prediction of disease extent, hence improving patient care and management. In this review article, as an active researcher in cardiovascular imaging for more than 20 years, I will provide an overview of cardiovascular CTA in cardiovascular disease. It is expected that this review will provide readers with an update of CTA applications, from the initial lumen assessment to recent developments utilising latest novel imaging and visualisation technologies. It will serve as a useful resource for researchers and clinicians to judiciously use the cardiovascular CT in clinical practice.

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Figures

Figure 1
Figure 1
An 82-year-old man with coronary artery disease.
Figure 2
Figure 2
Cardiac PCCT visualisation of coronary stents and stented lumen.
Figure 3
Figure 3
VIE views of normal coronary ostium.
Figure 4
Figure 4
VIE visualisation of coronary plaques.
Figure 5
Figure 5
VIE visualisation of coronary stents in comparison with CCTA and ICA.
Figure 6
Figure 6
VIE views of aortic dissection.
Figure 7
Figure 7
VIE view of an infrarenal abdominal aortic aneurysm.
Figure 8
Figure 8
Pulmonary embolism involving bilateral pulmonary artery branches.
Figure 10
Figure 10
Virtual intravascular endoscopy views of right posterobasal segmental embolism.
Figure 11
Figure 11
Irregular entry tear on VIE and MPR.
Figure 12
Figure 12
CT virtual intravascular endoscopic image.
Figure 9
Figure 9
Virtual intravascular endoscopy views of left lower lobar embolism from proximal to distal segments of lobar artery.
Figure 13
Figure 13
Correlation of LAD-LCx angle with CAD.
Figure 14
Figure 14
RCA-aorta angles in two different cases.
Figure 15
Figure 15
VR completely immersing the user in a virtual 3D space.
Figure 16
Figure 16
AR integrates superimposed virtual elements into a real-world environment.
Figure 17
Figure 17
Mean difference between groups in knowledge scores (using percentages).
Figure 18
Figure 18
A screenshot using the HoloLens 2.
Figure 19
Figure 19
Learning materials provided to the study groups: phase 1 materials include plastinated cardiac specimens (top row) and their 3D printed replicas and the coronary vessels (bottom row).
Figure 20
Figure 20
A 3D-printed model of the tricuspid valve of a human heart specimen (HH 223).
Figure 21
Figure 21
Surgical and interventional planning on 3D-printed heart models.
Figure 22
Figure 22
Participants’ responses on how 3D-printed cardiac models improve communication with colleagues and patients/families.
Figure 23
Figure 23
Stent graft deployed in a 3D-printed model.
Figure 24
Figure 24
Sagittal reformatted images of CTA protocols.
Figure 25
Figure 25
Three-dimensional printed patient-specific coronary models based on the simulation of calcified plaques in the coronary arteries.
Figure 26
Figure 26
Correlation between wider angulation and hemodynamic changes by CCTA-derived CFD analysis.
Figure 27
Figure 27
Correlation between narrower angulation and CCTA-derived CFD analysis.
Figure 28
Figure 28
Three-dimensional reconstruction of complicated Stanford type B aortic dissection patients' geometry after SG repair.
Figure 29
Figure 29
Blood flow pattern of 5 patients with CFD analysis of type B aortic dissection.
Figure 30
Figure 30
Examples of FFRCT in assessing the hemodynamic significance of coronary lesions at three main coronary arteries (A, B).
Figure 31
Figure 31
Graph showing diagnostic performance of CTP-FFR, CTP, FFR-CT and CCTA. AUC of receiver operating characteristics curve analysis is shown on per vessel for CTP-FFR, CTP, FFR-CT and visual stenosis grading (stenosis ≥ 50%) at CCTA.
Figure 32
Figure 32
Example of a 47-year-old man who presented with atypical chest pain, hypertension and dyslipidemia.
Figure 33
Figure 33
The use of deep learning for plaque segmentation.
Figure 34
Figure 34
Multiple calcified plaques at the left anterior descending artery (LAD) in a 72-year-old female.
Figure 35
Figure 35
The performance of the proposed network framework.
Figure 36
Figure 36
The applications of artificial intelligence in clinical cardiology practice.
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