果冻影院

Alexander鈥檚 background, before starting his PhD, is in Computer science. He pivoted to Medical Physics after working on and enjoying related projects during his time as a Master鈥檚 student. His particular areas of expertise, in the field of Computer Science聽are:聽computer graphics (including virtual environments and computer vision), electronics and embedded programming &聽compiler design. He also has an interest in the area of machine learning.
Alexander鈥檚 PhD project, 鈥淚mproved Quantification for Respiratory Gated PET/CT: Data-Driven Algorithms for Respiratory Motion Correction in PET/CT鈥�, focuses on attempting to correct the artefacts caused by respiratory motion in PET imaging. Respiratory motion is undesirable in medical imaging as breathing causes the organs in the abdomen to shift around causing the resulting image to be blurry and quantitatively inaccurate. This blurring occurs because PET imaging is not instantaneous, each section of a PET scan takes around two minutes to complete. It is not possible to avoid a patient breathing in a two minute period.
This project uses data driven motion modelling to correct the data. In this instance data driven means that the motion of the patient is tracked directly from the data from the scanner rather than an external device. Motion modelling means to attempt to fit a model over the data itself using the information from the data driven tracking as a reference. The motion model can then be used to correct the motion in a motion compensated image reconstruction, or included into the reconstruction process itself.