Image reconstruction is a fundamental part of PET allowing to generate 3D tomographic images of the tracer’s spatial distribution based on the position and timing of the detected annihilation gammas. PET reconstruction, common to other imaging modalities like CT and SPECT, apply algorithms whose mathematical details the general users may not need to understand. Nevertheless, a good knowledge on the performance characteristics of different algorithms can be highly beneficial to obtain reliable quantitative images more efficiently.
Overview
This webinar will be a guided tour to PET image reconstruction for the application-oriented user making sense of the sometimes complex reconstruction concepts. The webinar will cover the Pros & Cons and FAQs of PET image reconstruction, from analytical (FBP) to iterative methods (MLEM, OSEM and MAP), from the importance of physical corrections (scatter, attenuation etc) to reconstruction acceleration and noise reduction. In addition, the webinar will give you a glimpse of what to expect in the future with exciting insights into topics like how MRI can make better PET images and how AI can be applied to PET reconstruction.
Speakers
Dr. Charalampos (Harry) Tsoumpas
Lecturer of Medical Imaging, University of Leeds
Dr Charalampos (Harry) Tsoumpas is a Lecturer of Medical Imaging at the University of Leeds and Adjunct Professor at the Icahn School of Medicine at Mount Sinai, New York. He received his Ph.D. from Imperial College in 2008 and, then as researcher at Imperial College and King’s College.
Dr. Josep Oliver
Senior NMI Image Reconstruction Expert, Bruker
Dr. Josep F Oliver is working at Bruker corp. in the R&D pre-clinical division as a senior NMI image reconstruction expert. Along the last years, his research has focused on the development and implementation of image reconstruction algorithms for imaging systems based on emission tomography; specifically, PET scanners and Compton cameras.
Key Discussion Topics
- Fundamentals of FBP
- Fundamentals of MLEM
- Improvements to MLEM
- The importance of data corrections in image reconstruction
- Image reconstruction for quantification
- Current and future trends