Universität zu Köln

Thummerer, Adrian ORCID: 0000-0002-1874-5030, Oria, Carmen Seller, Zaffino, Paolo, Visser, Sabine ORCID: 0000-0001-7660-0545, Meijers, Arturs ORCID: 0000-0002-3647-6647, Marmitt, Gabriel Guterres, Wijsman, Robin, Seco, Joao, Langendijk, Johannes Albertus, Knopf, Antje Christin, Spadea, Maria Francesca and Both, Stefan (2022). Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy. Med. Phys., 49 (11). S. 6824 - 6840. HOBOKEN: WILEY. ISSN 2473-4209

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Abstract

Background Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. Purpose In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. Methods A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. Results 4D-sCTs resulted in average MAEs of 48.1 +/- 6.5 HU (single phase) and 37.7 +/- 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% +/- 3.2% (single phase) and 94.4% +/- 2.1% (average). The clinical target volume showed high agreement in D-98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. Conclusion In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Thummerer, Adrian UNSPECIFIED orcid.org/0000-0002-1874-5030 UNSPECIFIED Oria, Carmen Seller UNSPECIFIED UNSPECIFIED UNSPECIFIED Zaffino, Paolo UNSPECIFIED UNSPECIFIED UNSPECIFIED Visser, Sabine UNSPECIFIED orcid.org/0000-0001-7660-0545 UNSPECIFIED Meijers, Arturs UNSPECIFIED orcid.org/0000-0002-3647-6647 UNSPECIFIED Marmitt, Gabriel Guterres UNSPECIFIED UNSPECIFIED UNSPECIFIED Wijsman, Robin UNSPECIFIED UNSPECIFIED UNSPECIFIED Seco, Joao UNSPECIFIED UNSPECIFIED UNSPECIFIED Langendijk, Johannes Albertus UNSPECIFIED UNSPECIFIED UNSPECIFIED Knopf, Antje Christin UNSPECIFIED UNSPECIFIED UNSPECIFIED Spadea, Maria Francesca UNSPECIFIED UNSPECIFIED UNSPECIFIED Both, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-687880
DOI:	10.1002/mp.15930
Journal or Publication Title:	Med. Phys.
Volume:	49
Number:	11
Page Range:	S. 6824 - 6840
Date:	2022
Publisher:	WILEY
Place of Publication:	HOBOKEN
ISSN:	2473-4209
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BEAM COMPUTED-TOMOGRAPHY; DEFORMABLE IMAGE REGISTRATION; SCATTER CORRECTION; LUNG; RADIOTHERAPY; FEASIBILITY; RADIOGRAPHY; ALGORITHM; PHOTON; HEAD Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68788