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ESTRO 2020

Session Item

PH: Adaptive radiotherapy and inter-fraction motion management 1
9105
Poster Discussion
Physics
09:10 - 09:15
Comparison of CBCT based synthetic CT methods for adaptive proton therapy
PD-0309

Abstract

Comparison of CBCT based synthetic CT methods for adaptive proton therapy
Authors: Stefan Both.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Gabriel Guterres Marmitt.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Antje C. Knopf.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Johannes A. Langendijk.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Arturs Meijers.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Joao Seco .(Deutsches Krebsforschungszentrum DKFZ, Department of Biomedical Physics in Radiation Oncology, Heidelberg, Germany), Maria F. Spadea.(Magna Graecia University, Department of Experimental and Clinical Medicine, Catanzaro, Italy), Roel J.H.M. Steenbakkers.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Adrian Thummerer.(University Medical Center Groningen- University of Groningen, Department of Radiation Oncology, Groningen, The Netherlands), Paolo Zaffino.(Magna Graecia University, Department of Experimental and Clinical Medicine, Catanzaro, Italy)
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Purpose or Objective

Adaptive proton therapy (APT) aims to reduce dose to organs at risk (OAR) and ensures target dose coverage by frequently adapting treatment plans to anatomical changes. Repeated imaging, such as cone-beam computed tomography (CBCT), which in radiotherapy is commonly used for patient alignment purposes, also facilitates plan adaptation decisions. However, CBCT images suffer several image quality deficiencies (e.g. scatter) that prevent direct proton dose calculations. To overcome this limitation, several techniques to correct CT-numbers of CBCTs and to subsequently allow accurate dose calculations have been proposed in literature. In this study we compared three of these methods using a large head and neck dataset. Resulting synthetic CTs were not only evaluated in regard to their image quality and dosimetric accuracy, but also in terms of clinical suitability (e.g. conversion time) of each method.

Material and Methods

The comparison included a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction (AIC) method. Evaluation was performed on a dataset comprising 33 head and neck cancer patients treated with pencil-beam scanning proton therapy. For each patient a planning CT (pCT), weekly repeated CTs (rCT) and daily CBCTs were available. Image quality of synthetic CTs was quantified by calculating mean absolute error (MAE), mean error (ME) and the Dice similarity coefficient (DSC) of bone. Dosimetric characteristics were determined by intracranial proton dose calculations. Gamma pass ratios (2%/2mm and 3%/3mm) and relative range shifts were calculated to quantify dosimetric differences between the various sCT methods.

Results

On average, the lowest MAE and the highest DSC were observed for DCNN based sCTs (37 HU/0.96). Using DIR resulted in an average MAE of 44 HU and a DSC of 0.94. The highest MAE and the lowest DSC were observed for AIC based synthetic CTs, with values of 77 HU and 0.90 respectively. Figure 1 presents difference images between the various synthetic CTs and the reference rCT. The increased MAE for AIC is clearly visible. For a 2%/2mm passing criteria, gamma analysis resulted in average pass ratios of 99.30 %, 98.65 % and 97.35 % for DCNN, DIR and AIC respectively. A similar trend was observed for relative range shifts. Figure 2 shows a dose and CT-number profile comparison. The time to create a synthetic CT was 1 minute for AIC, 3 minutes for the DCNN, and 20 minutes for DIR. Contrary to DIR and AIC, the DCNN method did not require a pCT for sCT generation.


Conclusion

For the investigated set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and consecutively also dose calculation accuracy was reduced when compared to the other methods. The fast conversion times and the independence from the pCT favor the DCNN method for future clinical implementation.