Special Physics CERC Colloquium: Deep learning-assisted quantitative molecular imaging in the era of precision medicine

Deep learning-assisted quantitative molecular imaging in the era of precision medicine

Dr. Habib Zaidi*
Faculty of Medicine, Geneva University, Geneva, Switzerland
Head of PET Instrumentation and Neuroimaging Laboratory (PINLab)
Geneva University Hospital, Geneva, Switzerland

This talk presents the fundamental principles of multimodality medical imaging (PET/CT and PET/MRI) and reviews the major applications of deep learning approaches in multimodality medical imaging. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital. To this end, the applications of deep learning in five generic fields of multimodality medical imaging, including imaging instrumentation design, image denoising (low-dose imaging), image reconstruction quantification and segmentation, radiation dosimetry and computer-aided diagnosis and outcome prediction are discussed. Deep learning algorithms have been widely utilized in various medical image analysis problems owing to the promising results achieved in image reconstruction, segmentation, regression, denoising (low-dose scanning) and radiomics analysis. This talk reflects the tremendous increase in interest in quantitative molecular imaging using deep learning techniques in the past decade to improve image quality and to obtain quantitatively accurate data from dedicated standalone and combined imaging systems. Novel deep learning techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical medical imaging community. Future opportunities and the challenges facing the adoption of deep learning approaches and their role in molecular imaging research are also addressed.

*Dr. Habib Zaidi is being considered for an appointment as a Canada Excellence Research Chair in the Department of Physics