TY - GEN
T1 - Deep Learning Approaches for Contrast Removal from Contrast-enhanced CT Streamlining Personalized Internal Dosimetry
AU - Ganß, Marcel
AU - De Benetti, Francesca
AU - Brosch-Lenz, Julia
AU - Uribe, Carlos
AU - Shi, Kuangyu
AU - Eiber, Matthias
AU - Navab, Nassir
AU - Wendler, Thomas
N1 - Publisher Copyright:
© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.
PY - 2023
Y1 - 2023
N2 - In internal radiation therapy, dosimetry is essential to predict its efficacy and potential side effects. Contrast enhanced computed tomography (ceCT) is most commonly used as starting point for planning. However, native CT (nCT) is required for accurate dosimetry computations. In thiswork, we propose an in-silico method to remove the contrast agent from ceCT images so that the Hounsfield Units (HU) would be similar to those in nCT. Two approaches, one paired-image neural network (NN) and one un-paired NN, were applied to ceCT/nCT image pairs for contrast removal. We evaluated their performance in terms of HU values, and performed dosimetry calculations on the original nCT and ceCT, and on the in-silico nCTs to evaluate the impact on the dose rate. The two approaches yielded good results both in terms of HU reduction (more than 30%) and in the difference of dose rate against the original nCT (less than 1.38% vs. 4.76%).
AB - In internal radiation therapy, dosimetry is essential to predict its efficacy and potential side effects. Contrast enhanced computed tomography (ceCT) is most commonly used as starting point for planning. However, native CT (nCT) is required for accurate dosimetry computations. In thiswork, we propose an in-silico method to remove the contrast agent from ceCT images so that the Hounsfield Units (HU) would be similar to those in nCT. Two approaches, one paired-image neural network (NN) and one un-paired NN, were applied to ceCT/nCT image pairs for contrast removal. We evaluated their performance in terms of HU values, and performed dosimetry calculations on the original nCT and ceCT, and on the in-silico nCTs to evaluate the impact on the dose rate. The two approaches yielded good results both in terms of HU reduction (more than 30%) and in the difference of dose rate against the original nCT (less than 1.38% vs. 4.76%).
UR - http://www.scopus.com/inward/record.url?scp=85164927747&partnerID=8YFLogxK
U2 - 10.1007/978-3-658-41657-7_18
DO - 10.1007/978-3-658-41657-7_18
M3 - Conference contribution
AN - SCOPUS:85164927747
SN - 9783658416560
T3 - Informatik aktuell
SP - 70
EP - 75
BT - Bildverarbeitung für die Medizin 2023 Proceedings, German Workshop on Medical Image Computing, Braunschweig
A2 - Deserno, Thomas M.
A2 - Handels, Heinz
A2 - Maier, Andreas
A2 - Maier-Hein, Klaus
A2 - Palm, Christoph
A2 - Tolxdorff, Thomas
PB - Springer Science and Business Media Deutschland GmbH
T2 - Bildverarbeitung für die Medizin Workshop, BVM 2023
Y2 - 2 July 2023 through 4 July 2023
ER -