Coronary artery disease (CAD) and subsequent myocardial infarction are a global burden in terms of mortality and morbidity[1]. Underlying mechanism involves reduction of blood flow and oxygen to the myocardium due to atherosclerosis of the coronary arteries (Figure 1). CAD is good treatable with early and proper diagnosis.

Myocardial perfusion imaging (MPI) is a common test method to diagnose functional CAD. Many hospitals use techniques as SPECT and PET (or hybrid) imaging. Approaches that are more novel include MR and high-end CT perfusion imaging. In multimodal MPI, the distribution of a radiotracer / contrast agent is being measured at a specific time point (static) or over time (dynamic) (Figure 2). The latter enables ‘quantification’, which implies computation of absolute myocardial blood flow (MBF).

Incorporation of quantification algorithms in MPI workflows has great potential for validation and standardization of institutional MPI applications and is therefore already a well-established research tool. However, implementation in clinical practice remains hampered, since it requires a shift in quality control.

Figure 1 Etiology of coronary artery disease (CAD). Copied from Mayo Clinic.

Figure 2 Example time intensity curve obtained in CT.

Research focus

This research focuses on the role of phantom modeling in validating dynamic MPI, which encompasses the development of a novel dynamic myocardial perfusion phantom. In this way, we can execute ‘ground truth’ flow measurements in a controlled environment. Future applications of this next generation phantom are extensive: from facilitation in the validation process of quantification software packages, to realization of inter- and intramodal comparison of dynamic MPI applications and imaging protocol optimization in novel CT perfusion approaches. 

Current project status

Last year we have developed a first prototype dynamic myocardial perfusion phantom and performed initial tests in CT successfully (Figure 3). Next steps include further development of a second prototype, validation of the measurement setup and further testing in the clinical setting.

Figure 3 Overview of measurement setup in CT.

Collaborating partners

This project encompasses a multidisciplinary collaboration between the University of Twente (RaM, M3i), the University of Münster (EIMI), and regional and academic hospitals (Ziekenhuisgroep Twente and the University Medical Center Groningen).