Call for Papers - submission open Nov 1, 2022 thru February 15, 2023
The 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D 2023 for short) will be held on the campus of Stony Brook University from July 16 until July 21, 2023. Stony Brook University is a flagship of the State University of New York system, which is located on the North shore of Long Island with easy access to numerous beaches as well as to New York City and the Hamptons.
Fully3D is a biennial workshop-style meeting that is dedicated to the latest advances in algorithms and computational methods for the reconstruction of biomedical images in multiple dimensions, covering but not limited to the modalities of X-ray, CT, PET, SPECT, MRI, and Ultrasound. A volunteer-maintained website showing the rich history of this meeting can be found at: http://www.fully3d.org/index.html.The Fully3D 2023 organization Committee hopes to continue the tradition of hosting a congenial and focused meeting that attracts leaders in the field of image reconstruction as well as students, and encourages open and constructive dialogue. Fully3D 2023 welcomes submissions of innovative ideas with preliminary support data. The central theme is image reconstruction strategies and the topics include but not limited to:
- X-ray Imaging and Computed Tomography in various X-ray source and detector configurations, spectral imaging, phase contrast imaging, etc.
- PET Imaging including PET/CT, PET/MR, and other modality combinations
- SPECT Imaging including SPECT/CT, SPECT/MR, and others
- MR Imaging including combination with PET and SPECT
- Ultrasound Imaging including combination with other modalities
- Applications in Security and Industrial Imaging
- Impact of Machine Learning and Deep Learning
The final decision on whether a submission is accepted as an oral presentation or poster will be made by the Scientific Committee in coordination with the Local Organizing Committee.
Please note that all reviewers have signed a confidentiality agreement stating that they will not disclose the contents of the submissions to others and that they will treat them as confidential.