Range probing as a quality control tool for CBCT-based synthetic CTs:In vivo application for head and neck cancer patients

PURPOSE: Cone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs. METHODS: A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images. RESULTS: Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs. CONCLUSIONS: The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification

SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy

Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images to facilitate the development and evaluation of sCT generation for radiotherapy planning. Acquisition and validation methods: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in CSV files provided with the datasets. Data format and usage notes: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260705) under the SynthRAD2023 collection. The images for each subject are available in nifti format. Potential applications: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.Comment: 15 pages, 4 figures, 9 tables, pre-print submitted to Medical Physics - dataset. The training dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7260705 from April, 1st 202

Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of -8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the 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 dose calculation accuracy was reduced compared to the other methods

Characterization and preparation of thermoluminescence dosimeters for surface and in vivo dose measurements

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersDue to their size, reusability and good accuracy thermoluminescent dosimeters (TLDs), are a valuable tool in radiation dosimetry. The properties of TLDs allow a convenient use in radiotherapy and radiation oncology not only for measurements in di↵erent types of phantoms but also for in-vivo dosimetry. The relatively small detectors can be conveniently placed in cavities and/or on surfaces to measure skin dose or dose close to organs at risk. This can help to verify treatment delivery. For future use of thermoluminescent detectors at MedAustron, the center for ion therapy and research in Wiener Neustadt, a set of TLD-100 detectors (LiF:Mg,Ti) was characterized and initial measurements were performed for use of TLDs in in-vivo and surface dose measurements. The sample-to-sample uniformity of TLDs was verified to be within the limits stated by the manufacturer. Individual sensitivity factors for TLDs were determined and the reproducibility limits, given by the manufacturer were fulfilled by most of the investigated TLDs. Furthermore TLDs were calibrated in 60Co and proton beams, where supralinearity was observed for both radiation types starting at the dose level of about 1 Gy. Individual sensitivity factors were applied to correct for varying sensitivity of TLDs. This reduced the standard deviation of measurements by 50 %. To investigate the response of thermoluminescent detectors in changing LET conditions, detectors were placed at several depths of a spread-out Bragg peak. The positioning of TLDs in multiple depths, without mutual shielding, was realized with in-house modified RW3 slabs. TLD results were compared to the response of radiochromic films, i.e. type EBT-3 and EBT-XD. For TLDs no quenching was observed in the investigated region of a spread-out Bragg peak while for EBT-3 and EBT-XD films the known LET dependence was confirmed. The investigated TLDs, corrected with corresponding sensitivity factors, had proven to be well suited for clinical applications. Nevertheless, the consistency in the process of using and treating TLDs is a very crucial aspect. In order to maintain the good outcome, regular quality assurance of all involved parts is strongly recommended.8

Deep learning-based cone beam CT correction for adaptive proton therapy

Cone beam computed tomography (CBCT) is an imaging modality frequently used in radiotherapy for daily patient alignment. Besides the use for patient positioning, CBCT images can also provide valuable information about changes of the patient anatomy. However, due to the relatively poor image quality of CBCTs compared to conventional computed tomography, the useability of CBCTs in radiotherapy is currently limited. Especially adaptive proton therapy workflows, which aim to adapt treatment plans to changes in patient anatomy, could greatly benefit from daily information about changes in patient anatomy seen on CBCTs. Therefore, this thesis focuses on (deep learning) approaches to improve CBCT image quality and to enable daily proton dose calculations. The thesis investigates various CBCT correction techniques and evaluates their proton dose calculations accuracy in head and neck, and lung cancer patients. For the lung, a dynamic 4D-scenario, accounting for respiratory motion, was also investigated. In both anatomical regions, the results showed that deep learning can correct CBCTs and enable accurate proton dose calculations. Furthermore, a deep learning-based correction seems promising for future implementation in adaptive proton therapy workflows. The thesis also addresses the need for robust quality control procedures of corrected CBCTs by investigating a patient specific quality control procedure based on proton radiography

Weekly dosimetric evaluation of CBCT-based synthetic CTs for proton therapy of head & neck patients:E-Poster

Purpose/ObjectiveIn proton therapy it is common practice to acquire weekly verification CTs to monitor treatment progress and recalculate treatment plans on updated patient anatomy. For daily adaptive proton therapy workflows however, repurposing in-room images such as cone-beam CTs (CBCT), is more suitable since it is not adding to the clinical workload and does not cause any additional dose burden to the patient. CBCT images, routinely acquired for pre-treatment position verification, provide a daily representation of the patient anatomy but suffer from severe imaging artefacts preventing accurate dose calculations. Recently deep neural networks have shown promising results to correct CBCT images and generate high quality synthetic CTs (sCT), for proton dose calculations. Therefore, the aim of this study was to compare weekly rCT and daily sCT images of head and neck cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs generated by a neural network.Materials and MethodsA dataset of 30 head and neck cancer patients was utilized to generate synthetic CTs from daily pre-treatment patient alignment CBCTs using a previously developed and trained UNet deep convolutional neural network. Afterwards, clinically used proton treatment plans were recalculated on sCTs and weekly rCTs to evaluate the dosimetric accuracy of sCTs. Dose to clinical target volumes (CTV) and selected organs-at-risk (OAR) were compared between pCTs and both weekly rCTs and same-day sCTs by calculating mean dose differences. The investigated organs-at-risk include submandibular glands, pharyngeal constrictor muscles, parotid glands and the oral cavity.ResultsFigure 1 shows the mean relative dose differences between sCT/pCT and rCT/pCT pairs per ROI. The best agreement between pCT and rCT/sCT was observed for the low dose CTV (CTV 5425) with mean dose difference values of 0.3±0.2 % [0.18±0.15 Gy](rCT) versus 0.5±0.7 % [0.32±0.45 Gy](sCT), and for 0.2±0.2 % [0.13±0.11 Gy](rCT) versus 0.3±0.2 % [0.19±0.16 Gy](sCT) for the high dose CTV (CTV 7000). For all OARs, significantly larger dose differences were found than for the CTVs. The largest difference between sCT and pCT doses were observed in the left parotid gland with 14.7±16.4 % [1.61±1.31 Gy], compared to 8.4±9.2 % [1.01±0.87 Gy] between rCT and pCT, and in the left submandibular gland with 4.1±5.3 % [1.32±1.37 Gy](rCT) compared to 8.3±11.6 % [2.43±2.69 Gy](sCT). Overall, rCTs showed lower dose differences for all regions of interest. Figure 2 shows a comparison of daily sCT/pCT and weekly rCT/pCT dose differences for target volumes and OARs of the entire treatment of an exemplary patient.ConclusionThe deep learning based sCTs showed high agreement for target volume doses

Evaluation of CBCT-based synthetic CTs for clinical adoption in proton therapy of head & neck patients.: E-Poster

Purpose In adaptive proton therapy, weekly verification CTs (rCTs) are commonly acquired and used to monitor patient anatomy. Cone-Beam CTs (CBCT) on the other hand are used for daily pre-treatment position verification. These CBCT images however suffer from severe imaging artifacts preventing accurate proton dose calculations, meaning that CBCTs are unsuitable for treatment planning purposes. Recent advances in converting CBCT images to high quality synthetic CTs (sCTs) using Deep Convolution Neural Networks (DCNN) show that these sCTs can be suitable for proton dose calculations and therefore assist clinical adaptation decisions. The aim of this study was to compare weekly high definition rCTs to same-day sCT images of head and neck cancer patients in order to verify dosimetric accuracy of DCNN generated CBCT-based sCTs. Materials and Methods A dataset of 46 previously treated head and neck cancer patients was used to generate synthetic CTs from daily pre-treatment patient alignment CBCTs using a previously developed and trained U-net like DCNN. Proton dose was then recalculated on weekly rCTs and same-day sCTs utilizing clinical treatment plans. To assess the dosimetric accuracy of sCTs, dose to the clinical target volumes (CTV D98) and mean dose in selected organs-at-risk (OAR; Oral cavity, Parotid gland left, Submandibular gland right) was calculated and compared between rCTs and same-day sCTs. Furthermore, Normal Tissue Complication Probability (NTCP) models for xerostomia and dysphagia were used to assess the clinical significance of dose differences. Results For target volumes, the average difference in D98% between rCT and sCT pairs (N=284) was 0.34±3.86 % [-0.18±2.06 Gy] for the low dose CTV (54.25 Gy) and 0.23±3.62 % [-0.16±2.48 Gy] for the high dose CTV (70 Gy). For the OARs the following mean dose differences were observed; Oral Cavity: 4.15±9.78 % [0.75±1.39 Gy], Parotid L: 5.34±11.6 % [0.58±1.40 Gy], Submandibular R: 2.17±8.55 % [0.55±2.57 Gy]. The average NTCP difference was -0.15±0.58 % for grade 3 dysphagia, -0.26±0.54 % for grade 3 xerostomia, -0.53±1.20 % for grade 2 dysphagia and -0.71±1.40 % for grade 2 xerostomia. Conclusion For target coverage and NTCP difference, the deep learning based sCTs showed high agreement with weekly verification CTs. However, some outliers were observed (also indicated by the increased standard deviation) and warrant further investigation and improvements before clinical implementation. Furthermore, stringent quality control tools for synthetic CTs are required to allow reliable deployment in adaptive proton therapy workflows

ScatterNet for projection-based 4D cone-beam computed tomography intensity correction of lung cancer patients

Background and purpose:In radiotherapy, dose calculations based on 4D cone beam CTs (4DCBCTs) require image intensity corrections. This retrospective study compared the dose calculation accuracy of a deep learning, projection-based scatter correction workflow (ScatterNet), to slower workflows: conventional 4D projection-based scatter correction (CBCTcor) and a deformable image registration (DIR)-based method (4DvCT).Materials and methods:For 26 lung cancer patients, planning CTs (pCTs), 4DCTs and CBCT projections were available. ScatterNet was trained with pairs of raw and corrected CBCT projections. Corrected projections from ScatterNet and the conventional workflow were reconstructed using MA-ROOSTER, yielding 4DCBCTSN and 4DCBCTcor. The 4DvCT was generated by 4DCT to 4DCBCT DIR, as part of the 4DCBCTcor workflow. Robust intensity modulated proton therapy treatment plans were created on free-breathing pCTs. 4DCBCTSN was compared to 4DCBCTcor and the 4DvCT in terms of image quality and dose calculation accuracy (dose-volume-histogram parameters and 3%/3mm gamma analysis).Results:4DCBCTSN resulted in an average mean absolute error of 87HU and 102HU when compared to 4DCBCTcor and 4DvCT respectively. High agreement was observed in targets with median dose differences of 0.4Gy (4DCBCTSN-4DCBCTcor) and 0.3Gy (4DCBCTSN-4DvCT). The gamma analysis showed high average 3%/3mm pass rates of 96% for both 4DCBCTSN vs. 4DCBCTcor and 4DCBCTSN vs. 4DvCT.Conclusions:Accurate 4D dose calculations are feasible for lung cancer patients using ScatterNet for 4DCBCT correction. Average scatter correction times could be reduced from 10min (4DCBCTcor) to 3.9s, showing the clinical suitability of the proposed deep learning-based method

Reduction of cone‐beam CT artifacts in a robotic CBCT device using saddle trajectories with integrated infrared tracking

International audienceAbstract Background Cone beam computed tomography (CBCT) is widely used in many medical fields. However, conventional CBCT circular scans suffer from cone beam (CB) artifacts that limit the quality and reliability of the reconstructed images due to incomplete data. Purpose Saddle trajectories in theory might be able to improve the CBCT image quality by providing a larger region with complete data. Therefore, we investigated the feasibility and performance of saddle trajectory CBCT scans and compared them to circular trajectory scans. Methods We performed circular and saddle trajectory scans using a novel robotic CBCT scanner (Mobile ImagingRing (IRm); medPhoton, Salzburg, Austria). For the saddle trajectory, the gantry executed yaw motion up to using motorized wheels driving on the floor. An infrared (IR) tracking device with reflective markers was used for online geometric calibration correction (mainly floor unevenness). All images were reconstructed using penalized least‐squares minimization with the conjugate gradient algorithm from RTK with voxel size. A disk phantom and an Alderson phantom were scanned to assess the image quality. Results were correlated with the local incompleteness value represented by , which was calculated at each voxel as a function of the source trajectory and the voxel's 3D coordinates. We assessed the magnitude of CB artifacts using the full width half maximum (FWHM) of each disk profile in the axial center of the reconstructed images. Spatial resolution was also quantified by the modulation transfer function at 10% (MTF10). Results When using the saddle trajectory, the region without CB artifacts was increased from 43 to 190 mm in the SI direction compared to the circular trajectory. This region coincided with low values for . When was larger than 0.02, we found there was a linear relationship between the FWHM and . For the saddle, IR tracking allowed the increase of MTF10 from 0.37 to 0.98 lp/mm. Conclusions We achieved saddle trajectory CBCT scans with a novel CBCT system combined with IR tracking. The results show that the saddle trajectory provides a larger region with reliable reconstruction compared to the circular trajectory. The proposed method can be used to evaluate other non‐circular trajectories