Congratulations to Roberto Fedrigo who has been awarded the 2020 Summer Undergraduate Fellowship by the American Association of Physicists in Medicine (AAPM). Roberto, an undergraduate student at UBC Physics & Astronomy, has been a very active member of our team since September of 2019. The awarded fellowship is a 10 week summer program designed to provide opportunities for undergraduate university students to gain experience in medical physics by performing research in a medical physics laboratory or assisting with clinical service at a clinical facility.
It is a pleasure to announce our very own Cassandra Miller to have won an NSERC Postgraduate Scholarships-Doctoral (PDG D) award, to pursue her PhD studies in “Monte Carlo Simulations of SPECT Imaging in Peptide Receptor Radionuclide Therapy with Lu177 and Y90”.
We are pleased to announce being awarded an NSERC Discovery Grant. Our proposal (funded for 5 years; $250,000) is entitled, “Quantitative Oncological PET Image Generation and Analysis”. Our aims are to explore: (i) novel data acquisition methods in PET imaging, (ii) advanced 3D and 4D image reconstruction methods for improved image quality and/or dose reduction, integrating advanced models, dynamic as well as motion information; and (iii) advanced radiomics / AI-based image processing towards improved clinical task performance. The grant, aside from its scientific dimensions, emphasizes high-quality training of the next generation of scientist and experts, which is a very important mission of our team.
We are pleased to announce being awarded a CIHR project grant. Our proposal (funded for 4 years; $631,124) is entitled, “Quantitative PSMA Targeted Imaging of Prostate Cancer Patients”. We aim to improve assessment of disease for prostate cancer patients in the context of our ongoing clinical trials involving prostate-specific membrane antigen (PSMA) radioligand therapy (also known as radiopharmaceutical therapy). We will pursue advanced PSMA PET data acquisition (particularly dynamic whole-body imaging), as well as improved image reconstruction and enhancement. Our efforts will also involve automated deep-learning based segmentation of PET images, as well as predictive modeling of prostate cancer using radiomics and machine learning methods.
Congratulations to Saeed Ashrafinia who has been awarded the 2018 Society of Nuclear Medicine and Molecular Imaging (SNMMI) Bradley-Alavi Student Fellowship!
Saeed, an Electrical & Computer Engineering PhD candidate in the lab, is actively pursuing research in quantitative PET and SPECT imaging. The awarded fellowship, entitled, “Radiomics Analysis of Clinical Myocardial Perfusion SPECT Images to Identify Subclinical Coronary Artery Disease” proposes to translate radiomics analyses (which has been largely absent in SPECT) to the domain of clinical cardiac imaging.
Bradley-Alavi Fellows are named in honor of the late Stanley E. Bradley, Professor of Medicine at Columbia University College of Physicians and Surgeons and a prominent researcher in the fields of renal physiology and liver disease, and Abass Alavi, M.D., Professor and Director of Research Education at the Department of Radiology at the University of Pennsylvania.
We had a truly exciting and fruitful meeting on March 3rd for our BRAIN initiative effort. Our collaborators are converging to a couple of very promising approaches to enable transcranial, real time, in vivo imaging of brain network activity. When we first put in this grant, it had the appearance of science fiction, to improve temporal resolution in the in vivo imaging of neurotransmission from minutes in PET imaging, by ~4 orders of magnitude (!), to the scale of 10 milliseconds. But our results are very promising, and suggestive that this is indeed possible.
We are pleased to have secured funding (NIH R21) towards advanced image generation for amyloid & tau PET scans. The grant, entitled “Partial Volume Correction Methods to Improve Quantitation and Interpretation of Amyloid & Tau PET Imaging in Aging and Dementia”, seeks to develop and apply state-of-the-art image generation methods, and thoroughly assess their performance in ongoing clinical and longitudinal studies.