An evaluation of techniques for dose calculation on cone beam computed tomography

Abstract

Objective: To assess the accuracy and efficiency of four different techniques, thus determining the optimum method for recalculating dose on cone beam CT (CBCT) images acquired during radiotherapy treatments. Methods: Four established techniques were investigated and their accuracy assessed via dose calculations: (1) applying a standard planning CT (pCT) calibration curve, (2) applying a CBCT site-specific calibration curve, (3) performing a density override and (4) using deformable registration. Each technique was applied to 15 patients receiving volumetric modulated arc therapy to one of three treatment sites, head and neck, lung and prostate. Differences between pCT and CBCT recalculations were determined with dose volume histogram metrics and 2.0%/0.1 mm gamma analysis using the pCT dose distribution as a reference. Results: Dose volume histogram analysis indicated that all techniques yielded differences from expected results between 0.0 and 2.3% for both target volumes and organs at risk. With volumetric gamma analysis, the dose recalculation on deformed images yielded the highest pass-rates. The median pass-rate ranges at 50% threshold were 99.6–99.9%, 94.6–96.0%, and 94.8.0-96.0% for prostate, head and neck and lung patients, respectively. Conclusion: Deformable registration, HU override and site-specific calibration curves were all identified as dosimetrically accurate and efficient methods for dose calculation on CBCT images. Advances in knowledge: With the increasing adoption of CBCT, this study provides clinical radiotherapy departments with invaluable information regarding the comparison of dose reconstruction methods, enabling a more accurate representation of a patient’s treatment. It can also integrate studies in which CBCT is used in image-guided radiation therapy and for adaptive radiotherapy planning processes.

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

“…Several methods to perform CBCT dose calculation have been proposed: (a) calibration curve between the HU and densities (HU-D curve), [4][5][6] (b) density assignment method (DAM), 5,[7][8][9][10] (c) deformable image registration (DIR) between CT and CBCT, 3,[10][11][12] and (d) machine learning to generate a pseudo-CT (pCT). [13][14][15] (a) The HU-D curve established from a CBCT image can be used to convert CBCT HUs to densities for dose calculation.…”

Adaptive radiotherapy based on statistical process control for oropharyngeal cancer

“…One of the problems preventing us from performing dose recalculation directly on CBCTs was the lack of CBCT HU/density calibration curve. Future work is underway to implement CBCT‐based dose recalculation using CBCT to synthetic CT conversion 23 or CBCT HU override 24 …”

Enhancement of Hounsfield unit distribution in cone-beam CT images for adaptive radiation therapy: Evaluation of a hybrid correction approach

“…In this study the accuracy of a hybrid method combining two pCTbased approaches to correct HU distribution in CBCT images was evaluated. In contrast to other works where these two approaches were applied singularly for correcting CBCT images for dose calculation [19,[23][24][25][26][27][28], our proposed method consisted of applying first a HM process, followed by MLT on CBCT images.…”

“…The common goal was to propose promising strategies to overcome the limitations related to the high amount of scatter that reduces the quality of CBCT images compared to the pCT [4] and ensure suitable calibration in ED for an accurate dose calculation. These strategies include: the integration of physical instruments as anti-scatter grids to reduce the impact of scatter on the CBCT reconstruction quality [5][6][7], scatter measurement and correction based on the imaging system simulation [8][9][10][11], calibration of CBCT system using phantoms [12][13][14][15][16][17][18][19] and finally the use of pCT-based approaches. The latter approaches aim to map hounsfield units (HU) from pCT to CBCT images [20,21], match the peaks of their histograms [22,23] or assign to the CBCT images exact HU values extracted from the pCT [19,[23][24][25][26][27][28].…”