Four talks highlighted at SNMMI 2020!

Delighted to share that the four abstracts we had submitted to the SNMMI 2020 Annual Meeting were all accepted for talks and then also highlighted in the summary session of the PIDS council. The references are as below:

1. H.S. Moon, Z. Liu, M. Ponisio, R. Laforest, and A. K. Jha, A physics-guided and learning-based estimation method for segmenting 3D DaT-Scan SPECT images. Journal of Nuclear Medicine61(supplement 1), pp.10-10, 2020 (selected for Young Investigator Symposium, Highest scored abstract in the Data Sciences category) (link) (Presentation no. 10: link to virtual talk

2. Z. Yu, M. A. Rahman, T. H. Schindler, R. J. Gropler, R. Laforest, R. L. Wahl, A. K. Jha, AI-based methods for nuclear-medicine imaging: Need for objective task-specific evaluation,Journal of Nuclear Medicine, 61(supplement 1), pp. 575 , 2020 (link) (Presentation no. 575: link to virtual talk)

3. Z. Liu, R. Laforest, H. S. Moon, J. Mhlanga, T. Fraum, M. Itani, A. Mintz, F. Dehdashti, B. Siegel, A. K. Jha,   An estimation-based segmentation method to delineate tumors in PET images. Journal of Nuclear Medicine61(supplement 1), pp.447-447, 2020 (link) (Presentation no. 523: link to virtual talk)

4. J. Liu, Z. Liu, H. S. Moon, J. Mhlanga, B. Siegel and A. K. Jha, A no-gold-standard technique for objective evaluation of quantitative nuclear-medicine imaging methods in the presence of correlated noise. Journal of Nuclear Medicine61(supplement 1), pp.523-523, 2020 (link) (Presentation no. 447: link to virtual talk)

Excited to see this reception to our ongoing studies, and looking forward to the meeting.

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