Eleven presentations at the 2022 SNMMI Annual Meeting

Excited to share that eleven abstracts based on work done by our lab and with collaborators have been accepted at the 2022 Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Meeting to be held in Vancouver, Canada.

One of these abstracts was first-authored by Yan Liu, who accomplished this research during her rotation period in the lab! Great job everyone and thanks to all our collaborators!

Y. Liu, Z. Liu, D. Du, J. M. Luna, A. Rahmim, A. K. Jha Assessing linearity of PET-derived radiomics features across scanners: Implications for ComBat harmonization (supplemental material)

Z. Li, N. Benabdallah, R. Laforest, R. Wahl, D. L.J. Thorek, A. K. Jha, A multiple-energy-window projection-domain quantitative SPECT method for joint regional uptake quantification of Thorium-227 and Radium-223

Z. Liu, J. Mhlanga, B. Siegel, A. K. Jha, Need for objective task-based evaluation of segmentation methods in oncological PET: a study with ACRIN 6668/RTOG 0235 multi-center clinical trial data

Z. Liu, Z. Li, A. K. Jha, Objective task-based evaluation of a deep-learning-based segmentation method for quantitative SPECT

Z. Li, N. Benabdallah, R. Laforest, R. Wahl, D. L.J. Thorek, A. K. Jha, Variability in quantification between SPECT/CT scanners with a low-count quantitative SPECT method for alpha-particle radiopharmaceutical therapies

F. Yousefirizi, A. K. Jha,  S. Ahamed, I. Bloise, J. H. O, L.H. Sehn, K. J. Savage, C. F. Uribe, A. Rahmim, A novel loss function for improved deep learning-based segmentation: Implications for TMTV computation

K. Dutta, T. Whitehead, R. Laforest, A. K. Jha, K. I. Shoghi, Deep learning based generation of high-count preclinical [18F]-FDG PET images from low-count [18F]-FDG PET images

B. Saboury, T. Bradshaw, R. Boellaard, I. Buvat, J. Dutta, M. Hatt, A. K. Jha, Q. Li, C. Liu, H. McMeekin, M. A. Morris, P. J.H. Scott, E. Siegel, J. J. Sunderland, R. L. Wahl, S. Zuehlsdorff, A. Rahmim Artificial intelligence ecosystem in nuclear medicine: opportunities, challenges, and responsibilities

T. J. Bradshaw, R. Boellaard, J. Dutta, A. K. Jha, P. Jacobs, Q. Li, C. Liu, A. Sitek, B. Saboury, P. J. H. Scott, P. J. Slomka, J. J. Sunderland, R. L. Wahl, F. Yousefirizi, S. Zuehlsdorff, A. Rahmim, I. Buvat Pitfalls in the development of artificial intelligence algorithms in nuclear medicine and how to avoid them.

A. K. Jha, T. J. Bradshaw, I. Buvat, M. Hatt, P. KC, C. Liu, N. F. Obuchowski, B. Saboury, P. J. Slomka, J. J. Sunderland, R. L. Wahl, Z. Yu, S. Zuehlsdorff, A. Rahmim, R. Boellaard Best practices for evaluation of artificial intelligence-based algorithms for nuclear medicine: The RELAINCE guidelines

M. McCradden, J. Herington, K. Creel, R. Boellaard, A. K. Jha, A. Rahmim, P. J.H. Scott, J. J. Sunderland, R. L. Wahl, S. Zuehlsdorff, B. Saboury Ethical risks in the development and deployment of artificial intelligence systems in nuclear medicine

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