Regional dopamine transporter (DaT) uptake in sub-cortical regions, as derived from DaT SPECT images, may help provide markers to measure severity of Parkinson disease. However, to achieve this goal, there is a need for methods to reliably reconstruct, segment, and then quantify these uptakes from DaT-SPECT images. To address this need, we have developed new methods for SPECT reconstruction that can provide improved quantification. We have also developed novel deep-learning-based techniques to segment regions such as globus pallidus, which are visually impossible to demarcate in SPECT images.

References:

Z. Liu, H. Moon, R. Laforest, J. Perlmutter, S. A. Norris, A. K. Jha, “Fully automated 3D segmentation of dopamine transporter SPECT images using an estimation-based approach”, (arxiv)

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)

M. A. Rahman, R. Laforest, A. K. Jha, “A list-mode OSEM-based attenuation and scatter compensation method for SPECT”, IEEE Symposium on Biomedical Imaging 2020 (link)

Z. Yu, M. A. Rahman, A. K. Jha, “A Transmission-less Attenuation Compensation Method for Brain SPECT Imaging”, IEEE International Symposium on Biomedical Imaging 2020