Improving multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information

(Non-FAU Project)

Overall project:
Project leader: Maximilian Reymann
Project members: Andreas Maier, Maximilian Reymann
Start date: April 1, 2019
End date: April 30, 2022
Acronym: IMQSDL
Funding source:
URL:

Abstract

This project aims to improve multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information. Such improvements include noise reduction and artifact removal from data acquired in SPECT.

Publications

Reymann M., Würfl T., Stimpel B., Ritt P., Cachovan M., Vija AH., Maier A.:
U-Net for SPECT Image Denoising
2019 IEEE Nuclear Science Symposium (NSS) and Medical Imaging Conference (MIC) (Manchester, October 26, 2019 - November 2, 2019)
In: IEEE (ed.): 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) 2019
DOI: 10.1109/nss/mic42101.2019.9059879
BibTeX: Download
Reymann M., Würfl T., Ritt P., Cachovan M., Stimpel B., Maier A.:
Deep Image Denoising in SPECT
Annual Congress of the European Association of Nuclear Medicine (Barcelona, October 12, 2019 - October 16, 2019)
In: Springer-Verlag GmbH Germany, part of Springer Nature 2019 (ed.): European Journal of Nuclear Medicine and Molecular Imaging (2019) 46 (Suppl 1): S1–S952, 10.1007/s00259-019-04486-2, Berlin, Heidelberg: 2019
DOI: 10.1007/s00259-019-04486-2
URL: https://link.springer.com/article/10.1007/s00259-019-04486-2
BibTeX: Download