TY  - JOUR
AU  - Thummerer, Adrian
AU  - Oria, Carmen Seller
AU  - Zaffino, Paolo
AU  - Meijers, Arturs
AU  - Marmitt, Gabriel Guterres
AU  - Wijsman, Robin
AU  - Seco, Joao
AU  - Langendijk, Johannes Albertus
AU  - Knopf, Antje-Christin
AU  - Spadea, Maria Francesca
AU  - Both, Stefan
TI  - Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.
JO  - Medical physics
VL  - 48
IS  - 12
SN  - 2473-4209
CY  - College Park, Md.
PB  - AAPM
M1  - DKFZ-2021-02395
SP  - 7673-7684
PY  - 2021
N1  - 2021 Dec;48(12):7673-7684
AB  - Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.A dataset of 33 thoracic cancer patients, containing CBCTs, same-day repeat CTs (rCT), planning-CTs (pCTs) and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT-based correction method. Mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU-accuracy of sCTs in terms of range errors.On average, sCTs without correction resulted in a MAE of 34±6 HU and ME of 4±8 HU. The correction reduced the MAE to 31±4HU (ME to 2±4HU). Average 3
KW  - CBCT (Other)
KW  - Deep learning (Other)
KW  - adaptive proton therapy (Other)
KW  - lung cancer (Other)
KW  - synthetic CT (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:34725829
DO  - DOI:10.1002/mp.15333
UR  - https://inrepo02.dkfz.de/record/177262
ER  -