Cardiovascular diseases are among the leading causes of death worldwide, with more than 20 million deaths in 2021. These diseases also include inflammatory and infectious diseases of the blood vessels, such as atherosclerosis, large vessel vasculitis, and vascular graft infections. Early diagnosis and risk assessment are crucial in preventing and treating these diseases. Medical imaging, such as computed tomography (CT) and positron emission tomography (PET), plays an increasingly important role in this process. CT scans are used to visualise a patient's anatomy, while PET scans provide information about physiological processes, such as the activity of inflammatory cells. These techniques are developing rapidly. There are also many other technologies, such as artificial intelligence (AI), that can optimise the detection and quantification of these diseases in PET and CT scans. This opens up possibilities for updating/improving currently used clinical methods. The aim of this dissertation of Gijs van Praagh was to improve the quantification of inflammatory and infectious diseases in the blood vessels using PET and CT scans.
The dissertation consists of two parts. The first part focused on improving the settings used to make CT scans of coronary artery calcification. An update was proposed that, with a lower radiation dose, could reduce measurement variability between scans and better detect small, high-risk calcifications. This part also shared an automatic analysis method to facilitate and accelerate this type of research, reducing human error.
The second part focused on measuring inflammatory and infectious diseases using a combination of PET and CT scans. In the clinic, these scans are visually evaluated by a physician. Many studies show that quantifying disease activity in blood vessels in PET/CT scans is more objective and robust than visual evaluation. Moreover, it can lead to better diagnoses and more personalised treatments. However, automatic analyses are needed for this, as manual measurement is too time-consuming for physicians. To support this, an automated tool was developed in this dissertation that performs measurements of the aorta fully automatically. This tool, named SEQUOIA, allows large-scale research to be conducted more quickly and efficiently.
Subsequently, several studies were conducted to show what this automatic analysis of the aorta can be used for. For example, it was found that certain types of PET/CT scans (using a substance called sodium fluoride) have potential as a risk predictor for future adverse cardiovascular events, such as heart attacks or strokes. The potential of AI to provide better diagnoses in large vessel vasculitis and vascular graft infections was also demonstrated. This could prevent unnecessarily severe treatments in patients or detect the disease where it is currently not being identified.
In conclusion, the dissertation shows that technical advancements in imaging and AI can contribute to more accurate measurements and diagnosis of inflammatory and infectious diseases. Further studies are needed before these techniques can be fully implemented in the clinic. The dissertation demonstrates that there is significant potential for improved care and personalised treatments.
