Development of physiologically-informed computational coronary artery plaques for use in virtual imaging trials
Thomas J Sauer1, Andrew J Buckler2, Ehsan Abadi1
1Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, the Duke University Medical Center, Durham, North Carolina, USA.
Insights
Researchers developed realistic coronary plaque models using deep learning for virtual imaging trials. These models enable quantitative evaluation and optimization of cardiac imaging technologies, offering a faster and more cost-effective alternative to traditional clinical trials for cardiovascular disease.
Area of Science:
- Biomedical imaging
- Computational modeling
- Cardiovascular disease research
Background:
- Cardiovascular disease (CVD) is a leading global cause of death, with coronary artery disease (CAD) being a major contributor.
- Traditional clinical trials for validating cardiac imaging technologies are time-consuming, expensive, and require large patient cohorts.
- Virtual imaging trials (VITs) offer a promising alternative by using virtual patients and accurate imaging device models.
Purpose of the Study:
- To develop physiologically-informed, realistic coronary plaque models for cardiac imaging VITs.
- To create anatomically variable plaque models with clinical realism for simulation purposes.
Main Methods:
- A deep convolutional generative adversarial network (DC-GAN) was trained on high-resolution histology images to generate plaque models.
- Finite element analysis (FEA) was used to assess the stability of the generated plaque models under physiological conditions.
- The plaque models were integrated with the XCAT computational phantom for simulations comparing energy-integrating detector (EID) CT and photon-counting detector (PCD) CT.
Main Results:
- The DC-GAN successfully generated realistic and anatomically diverse coronary plaque models.
- Simulation results demonstrated the utility of these models for comparing different cardiac imaging devices.
Conclusions:
- Generated CAD pathologies can be incorporated into computational phantoms for VITs.
- These models provide a "known truth" for quantitative optimization and evaluation of cardiac imaging technologies.
Background:
As a leading cause of death, worldwide, cardiovascular disease is of great clinical importance. Among cardiovascular diseases, coronary artery disease (CAD) is a key contributor, and it is the attributed cause of death for 10% of all deaths annually. The prevalence of CAD is commensurate with the rise in new medical imaging technologies intended to aid in its diagnosis and treatment. The necessary clinical trials required to validate and optimize these technologies require a large cohort of carefully controlled patients, considerable time to complete, and can be prohibitively expensive. A safer, faster, less expensive alternative is using virtual imaging trials (VITs), utilizing virtual patients or phantoms combined with accurate computer models of imaging devices.
Purpose:
In this work, we develop realistic, physiologically-informed models for coronary plaques for application in cardiac imaging VITs.
Methods:
Histology images of plaques at micron-level resolution were used to train a deep convolutional generative adversarial network (DC-GAN) to create a library of anatomically variable plaque models with clinical anatomical realism. The stability of each plaque was evaluated by finite element analysis (FEA) in which plaque components and vessels were meshed as volumes, modeled as specialized tissues, and subjected to the range of normal coronary blood pressures. To demonstrate the utility of the plaque models, we combined them with the whole-body XCAT computational phantom to perform initial simulations comparing standard energy-integrating detector (EID) CT with photon-counting detector (PCD) CT.
Results:
Our results show the network is capable of generating realistic, anatomically variable plaques. Our simulation results provide an initial demonstration of the utility of the generated plaque models as targets to compare different imaging devices.
Conclusions:
Vast, realistic, and variable CAD pathologies can be generated to incorporate into computational phantoms for VITs. There they can serve as a known truth from which to optimize and evaluate cardiac imaging technologies quantitatively.
