During my internship at VUmc's Nuclear Medicine department, I delved into medical imaging, focusing on enhancing the interpretation of PET-CT data in Alzheimer's research. I employed Independent Component Analysis (ICA) to simplify data interpretation, potentially advancing the field. This experience enhanced my professional skills, including communication and data analysis, while deepening my Alzheimer's disease and research knowledge.

PET-CT data, Alzheimer’s disease, Independent Component Analysis (ICA), image processing, data analysis, Matlab programming

During my four-month internship at VUmc's Nuclear Medicine department, I explored the field of medical imaging, specifically focusing on improving the interpretation of Positron Emission Tomography (PET) and Computed Tomography (CT) data of Alzheimer's patients. This experience not only provided me with hands-on experience but also expanded my understanding of the challenges surrounding Alzheimer's research.

Alzheimer's disease remains a puzzle, because it’s causes and course of development is still unknown. A probable theory suggests that the formation of insoluble amyloid beta plaques in the brain plays a role in the development of the disease’s symptoms. Given the belief that amyloid deposition begins a decade or more before symptom onset, early-stage research and diagnostics become vital. PET-CT imaging offers a means to visualise amyloid plaques by utilising a radioactive tracer that binds to the amyloid proteins in the brain. However, the dynamic nature of the tracer binding makes PET scan data interpretation challenging, as illuminated areas in the brain do not necessarily correspond to the location of amyloid. Therefore, decomposing PET scan data into physiologically significant independent components can simplify data interpretation.

Independent Component Analysis (ICA) emerged as a mathematical approach to extract spatially independent sources (Independent Components or ICs) from data. This approach relies on statistical assumptions about the data and requires no prior information. If the resulting ICs from PET scan data of Alzheimer's patients reveal physiological significance, they could significantly advance Alzheimer's research. The provided images depict some ICs that arose from my research and were analysed.

My internship at the VUmc was a transformative experience that improved my professional skills and enhanced my understanding of medical research. Because it was my first experience working in my field of eduction, it gave me an insight in the demands and expectations of professional life. A valuable skill I acquired during this process is effective communication with my supervisors, which allowed my to navigate the complexities of my research.

On a technical level, I gained proficiency in mathematical algorithms, Matlab programming, and data analysis. I obtained practical knowledge on the use of PET-CT scans and image processing techniques. Furthermore, I developed a deeper understanding of Alzheimer’s disease, PET-CT scan procedures, and independent component analysis. Most importantly, my internship taught me how to perform scientific research, requiring me to troubleshoot and adapt throughout the process.