A review of cone-beam CT applications for adaptive radiotherapy of prostate cancer

Clinical evaluation of a deep learning CBCT auto-segmentation software for prostate adaptive radiation therapy

Purpose

Aim of the present study is to characterize a deep learning-based auto-segmentation software (DL) for prostate cone beam computed tomography (CBCT) images and to evaluate its applicability in clinical adaptive radiation therapy routine.

Materials and methods

Ten patients, who received exclusive radiation therapy with definitive intent on the prostate gland and seminal vesicles, were selected. Femoral heads, bladder, rectum, prostate, and seminal vesicles were retrospectively contoured by four different expert radiation oncologists on patients CBCT, acquired during treatment. Consensus contours (CC) were generated starting from these data and compared with those created by DL with different algorithms, trained on CBCT (DL-CBCT) or computed tomography (DL-CT). Dice similarity coefficient (DSC), centre of mass (COM) shift and volume relative variation (VRV) were chosen as comparison metrics. Since no tolerance limit can be defined, results were also compared with the inter-operator variability (IOV), using the same metrics.

Results

The best agreement between DL and CC was observed for femoral heads (DSC of 0.96 for both DL-CBCT and DL-CT). Performance worsened for low-contrast soft tissue organs: the worst results were found for seminal vesicles (DSC of 0.70 and 0.59 for DL-CBCT and DL-CT, respectively). The analysis shows that it is appropriate to use algorithms trained on the specific imaging modality. Furthermore, the statistical analysis showed that, for almost all considered structures, there is no significant difference between DL-CBCT and human operator in terms of IOV.

Conclusions

The accuracy of DL-CBCT is in accordance with CC; its use in clinical practice is justified by the comparison with the inter-operator variability.

Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers

BACKGROUND

Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCT images.

PURPOSE

To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinical applications.

METHODS

The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testing purposes.

RESULTS

The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results of CBCT.

CONCLUSIONS

We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.

Energy-guided diffusion model for CBCT-to-CT synthesis

Cone Beam Computed Tomography (CBCT) plays a crucial role in Image-Guided Radiation Therapy (IGRT), providing essential assurance of accuracy in radiation treatment by monitoring changes in anatomical structures during the treatment process. However, CBCT images often face interference from scatter noise and artifacts, posing a significant challenge when relying solely on CBCT for precise dose calculation and accurate tissue localization. There is an urgent need to enhance the quality of CBCT images, enabling a more practical application in IGRT. This study introduces EGDiff, a novel framework based on the diffusion model, designed to address the challenges posed by scatter noise and artifacts in CBCT images. In our approach, we employ a forward diffusion process by adding Gaussian noise to CT images, followed by a reverse denoising process using ResUNet with an attention mechanism to predict noise intensity, ultimately synthesizing CBCT-to-CT images. Additionally, we design an energy-guided function to retain domain-independent features and discard domain-specific features during the denoising process, enhancing the effectiveness of CBCT-CT generation. We conduct numerous experiments on the thorax dataset and pancreas dataset. The results demonstrate that EGDiff performs better on the thoracic tumor dataset with SSIM of 0.850, MAE of 26.87 HU, PSNR of 19.83 dB, and NCC of 0.874. EGDiff outperforms SoTA CBCT-to-CT synthesis methods on the pancreas dataset with SSIM of 0.754, MAE of 32.19 HU, PSNR of 19.35 dB, and NCC of 0.846. By improving the accuracy and reliability of CBCT images, EGDiff can enhance the precision of radiation therapy, minimize radiation exposure to healthy tissues, and ultimately contribute to more effective and personalized cancer treatment strategies.

Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS)

BACKGROUND

Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.

PURPOSE

The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections.

METHODS

To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method.

RESULTS

Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values (E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, theE RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively.

CONCLUSIONS

We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

Evaluation of the trend of set-up errors during the treatment period using set-up margin in prostate radiotherapy

Accurate information on set-up error during radiotherapy is essential for determining the optimal number of treatments in hypofractionated radiotherapy for prostate cancer. This necessitates careful control by the radiotherapy staff to assess the patient's condition. This study aimed to develop an evaluation method of the temporal trends in a patient's specific prostate movement during treatment using image matching and margin values. This study included 65 patients who underwent prostate volumetric modulated arc therapy (mean treatment time, 87.2 s). Set-up errors were assessed using bone, inter-, and intra-fraction marker matching across 39 fractions. The set-up margin was determined by dividing the four periods into 39 fractions using Stroom's formula and correlation coefficient. The intra-fraction set-up error was biased in the anterior-superior (AS) direction during treatment. The temporal trend of set-up errors during radiotherapy slightly increased based on bone matching and inter-fraction marker matching, with a 1.6-mm difference in the set-up margin fractions 11 to 20. The correlation coefficient of the mean prostate movement during treatment significantly decreased in the superior-inferior direction, while remaining high in the left-right and anterior-posterior directions. Image matching contributed significantly to the improvement of set-up errors; however, careful attention is needed for prostate movement in the AS direction, particularly during short treatment times. Understanding the trend of set-up errors during the treatment period is essential in numerical information sharing on patient condition and evaluating the margins for tailored hypo-fractionated radiotherapy, considering the facility's image-guided radiation therapy technology.

A carbon nanotube x-ray source array designed for a new multisource cone beam computed tomography scanner

Cone beam computed tomography (CBCT) is known to suffer from strong scatter and cone beam artifacts. The purpose of this study is to develop and characterize a rapidly scanning carbon nanotube (CNT) field emission x-ray source array to enable a multisource CBCT (ms-CBCT) image acquisition scheme which has been demonstrated to overcome these limitations. A CNT x-ray source array with eight evenly spaced focal spots was designed and fabricated for a medium field of view ms-CBCT for maxillofacial imaging. An external multisource collimator was used to confine the radiation from each focal spot to a narrow cone angle. For ms-CBCT imaging, the array was placed in the axial direction and rapidly scanned while rotating continuously around the object with a flat panel detector. The x-ray beam profile, temporal and spatial resolutions, energy and dose rate were characterized and evaluated for maxillofacial imaging. The CNT x-ray source array achieved a consistent focal spot size of 1.10 ± 0.04 mm × 0.84 ± 0.03 mm and individual beam cone angle of 2.4°±0.08 after collimation. The x-ray beams were rapidly switched with a rising and damping times of 0.21 ms and 0.19 ms, respectively. Under the designed operating condition of 110 kVp and 15 mA, a dose rate of 8245μGy s-1was obtained at the detector surface with the inherent Al filtration and 2312μGy s-1with an additional 0.3 mm Cu filter. There was negligible change of the x-ray dose rate over many operating cycles. A ms-CBCT scan of an adult head phantom was completed in 14.4 s total exposure time for the imaging dose in the range of that of a clinical CBCT scanner. A spatially distributed CNT x-ray source array was designed and fabricated. It has enabled a new multisource CBCT to overcome some of the main inherent limitations of the conventional CBCT.

Validation of complex radiotherapy techniques using polymer gel dosimetry

Modern radiotherapy delivers highly conformal dose distributions to irregularly shaped target volumes while sparing the surrounding normal tissue. Due to the complex planning and delivery techniques, dose verification and validation of the whole treatment workflow by end-to-end tests became much more important and polymer gel dosimeters are one of the few possibilities to capture the delivered dose distribution in 3D. The basic principles and formulations of gel dosimetry and its evaluation methods are described and the available studies validating device-specific geometrical parameters as well as the dose delivery by advanced radiotherapy techniques, such as 3D-CRT/IMRT and stereotactic radiosurgery treatments, the treatment of moving targets, online-adaptive magnetic resonance-guided radiotherapy as well as proton and ion beam treatments, are reviewed. The present status and limitations as well as future challenges of polymer gel dosimetry for the validation of complex radiotherapy techniques are discussed.

Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators

PURPOSE

The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions for intact prostate and prostate bed treatments.

METHODS AND MATERIALS

DLAS models were trained using 116 iCBCT data sets with manually delineated organs at risk (bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT data sets were used for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared with those obtained for an additional DLAS-based model trained on planning computed tomography (pCT) data sets and for a deformable image registration (DIR)-based automatic contour propagation method.

RESULTS

In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all organs at risk and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes of interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, and approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines.

CONCLUSIONS

The proposed method presents the potential for improved segmentation accuracy and efficiency compared with the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition

PURPOSE

The purpose of this study is to develop a virtual CBCT simulator with a head and neck (HN) human phantom library and to demonstrate the feasibility of elemental material decomposition (EMD) for quantitative CBCT imaging using this virtual simulator.

METHODS

The library of 36 HN human phantoms were developed by extending the ICRP 110 adult phantoms based on human age, height, and weight statistics. To create the CBCT database for the library, a virtual CBCT simulator that simulated the direct and scattered X-ray on a flat panel detector using ray-tracing and deep-learning (DL) models was used. Gaussian distributed noise was also included on the flat panel detector, which was evaluated using a real CBCT system. The usefulness of the virtual CBCT system was demonstrated through the application of the developed DL-based EMD model for case involving virtual phantom and real patient.

RESULTS

The virtual simulator could generate various virtual CBCT images based on the human phantom library, and the prediction of the EMD could be successfully performed by preparing the CBCT database from the proposed virtual system, even for a real patient. The CBCT image degradation owing to the scattered X-ray and the statistical noise affected the prediction accuracy, although these effects were minimal. Furthermore, the elemental distribution using the real CBCT image was also predictable.

CONCLUSIONS

This study demonstrated the potential of using computer vision for medical data preparation and analysis, which could have important implications for improving patient outcomes, especially in adaptive radiation therapy.

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept

The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the treatment couch using visual markers, followed by fine adjustments to the treatment couch position depending on the shift observed between the computed tomography (CT) image acquired for treatment planning and the CBCT image acquired immediately before commencing treatment. The field of view of CBCT images is limited to the size of the kV imager which leads to the acquisition of partial CBCT scans for lateralised tumors. The cone-beam geometry results in high amounts of streaking artifacts and in conjunction with limited anatomical information reduces the registration accuracy between planning CT and the CBCT image. This study proposes a methodology that can improve radiotherapy patient setup CBCT images by removing streaking artifacts and generating the missing patient anatomy with patient-specific precision. This research was split into two separate studies. In Study A, synthetic CBCT (sCBCT) data was created and used to train two machine learning models, one for removing streaking artifacts and the other for generating the missing patient anatomy. In Study B, planning CT and on-couch CBCT data from several patients was used to train a base model, from which a transfer of learning was performed using imagery from a single patient, producing a patient-specific model. The models developed for Study A performed well at removing streaking artifacts and generating the missing anatomy. The outputs yielded in Study B show that the model understands the individual patient and can generate the missing anatomy from partial CBCT datasets. The outputs generated demonstrate that there is utility in the proposed methodology which could improve the patient setup and ultimately lead to improving overall treatment quality.

Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy

Background and Purpose

Deformable image registration (DIR) is a core element of adaptive radiotherapy workflows, integrating daily contour propagation and/or dose accumulation in their design. Propagated contours are usually manually validated and may be edited, thereby locally invalidating the registration result. This means the registration cannot be used for dose accumulation. In this study we proposed and evaluated a novel multi-modal DIR algorithm that incorporated contour information to guide the registration. This integrates operator-validated contours with the estimated deformation vector field and warped dose.

Materials and Methods

The proposed algorithm consisted of both a normalized gradient field-based data-fidelity term on the images and an optical flow data-fidelity term on the contours. The Helmholtz-Hodge decomposition was incorporated to ensure anatomically plausible deformations. The algorithm was validated for same- and cross-contrast Magnetic Resonance (MR) image registrations, Computed Tomography (CT) registrations, and CT-to-MR registrations for different anatomies, all based on challenging clinical situations. The contour-correspondence, anatomical fidelity, registration error, and dose warping error were evaluated.

Results

The proposed contour-guided algorithm considerably and significantly increased contour overlap, decreasing the mean distance to agreement by a factor of 1.3 to 13.7, compared to the best algorithm without contour-guidance. Importantly, the registration error and dose warping error decreased significantly, by a factor of 1.2 to 2.0.

Conclusions

Our contour-guided algorithm ensured that the deformation vector field and warped quantitative information were consistent with the operator-validated contours. This provides a feasible semi-automatic strategy for spatially correct warping of quantitative information even in difficult and artefacted cases.

Impact of rectal gas on the planning target volume margin for pelvic bone and prostate matching in prostate cancer patients receiving volumetric-modulated arc therapy

We performed daily cone-beam computed tomography (CBCT) to determine the impact of rectal gas on the movements of prostate and seminal vesicles (SVs). We aimed to determine the relationship between planning target volume (PTV) margins and rectal gas. In 30 treatments of 15 prostate cancer patients, excessive rectal gas was removed and CBCT images were analyzed. Image registration between planning CT and daily CBCT images before and after rectal gas removal was performed for pelvic bone and prostate matching. The couch movement distance between each matching was considered the prostate movement. In addition, we measured SV tip movement between each matching. The anterior-posterior movement of the prostate before rectal gas removal (3.1 ± 2.9 mm) was significantly greater than that after rectal gas removal (1.2 ± 1.2 mm; p < 0.01). The left-right and superior-inferior movements were similar regardless of the presence or absence of rectal gas. The SV movement distances before and after rectal gas removal were 11.0 ± 5.8 mm and 4.6 ± 3.8 mm, respectively (p < 0.01), in pelvic bone matching, and 8.0 ± 4.2 mm and 3.8 ± 3.2 mm, respectively (p < 0.01), in prostate matching. After rectal gas removal, the SV position did not differ significantly between each matching. In 26 of the 30 treatments, SV movement distance in the presence of rectal gas was >6 mm, which is the minimum PTV margin at our institution. In comparison, after rectal gas removal and prostate matching, only 6 treatments demonstrated an SV movement distance of >6 mm. In the presence of rectal gas, the SVs require greater PTV margins than the prostate. Rectal gas removal should be considered if the movement distance on prostate matching is greater than the minimum PTV margin at treating institution.

Evaluation of Dose Calculation Based on Cone-Beam CT Using Different Measuring Correction Methods for Head and Neck Cancer Patients

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCT_HD and CBCT_HM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the D_mean, D_max and D_5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D_2%, in parallel organs such as the parotid glands, the deviations from the CBCT_HM plan dose were less than those of the CBCT_HD plan dose. The differences were statistically significant (P < .05). Meanwhile, the V₃₀ value based on the HM method was better than that based on the HD method in the oral cavity region (P = .016). In addition, we also compared the γ passing rates of kVCT plan doses with the 2 CBCT plan doses, and negligible differences were found. Conclusion: The HM method was more suitable for head and neck cancer patients than the HD one. Furthermore, with the CBCT_HM-based method, the dose calculation result better matches the kVCT-based dose calculation.

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

Background and purpose

Currently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity.

Materials and methods

Algorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting.

Results

ARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions.

Conclusion

The high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.

3D-printed large-area focused grid for scatter reduction in cone-beam CT

BACKGROUND

Cone-beam computed tomography (CBCT) systems acquire volumetric data more efficiently than fan-beam or multislice CT, particularly when the anatomy of interest resides within the axial field-of-view of the detector and data can be acquired in one rotation. For such systems, scattered radiation remains a source of image quality degradation leading to increased noise, image artifacts, and CT number inaccuracies.

PURPOSE

Recent advances in metal additive manufacturing allow the production of highly focused antiscatter grids (2D-ASGs) that can be used to reduce scatter intensity, while preserving primary radiation transmission. We present the first implementation of a large-area, 2D-ASG for flat-panel CBCT, including grid-line artifact removal and related improvements in image quality.

METHODS

A 245 × 194 × 10 mm 2D-ASG was manufactured from chrome-cobalt alloy using laser powder-bed fusion (LPBF) (AM-400; Renishaw plc, New Mills Wotton-under-Edge, UK). The 2D-ASG had a square profile with a pitch of 9.09 lines/cm and 10:1 grid-ratio. The nominal 0.1 mm grid septa were focused to a 732 mm x-ray source to optimize primary x-ray transmission and reduce grid-line shadowing at the detector. Powder-bed fusion ensured the structural stability of the ASG with no need for additional interseptal support. The 2D-ASG was coupled to a 0.139-mm element pitch flat-panel detector (DRX 3543, Carestream Health) and proper alignment was confirmed by consistent grid-line shadow thickness across the whole detector array. A 154-mm diameter CBCT image-quality-assurance phantom was imaged using a rotary stage and a ceiling-mounted, x-ray unit (Proteus XR/a, GE Medical Systems, 80kVp, 0.5mAs). Grid-line artifacts were removed using a combination of exposure-dependent gain correction and spatial-frequency, Fourier filtering. Projections were reconstructed using a Parker-weighted, FDK algorithm and voxels were spatially averaged to 357 × 357 × 595 µm to improve the signal-to-noise characteristics of the CBCT reconstruction. Finally, in order to compare image quality with and without scatter, the phantom was scanned again under the same CBCT conditions but with no 2D-ASG. No additional antiscatter (i.e., air-gap, bowtie filtration) strategies were used to evaluate the effects in image quality caused by the 2D-ASG alone.

RESULTS

The large-area, 2D-ASG prototype was successfully designed and manufactured using LPBF. CBCT image-quality improvements using the 2D-ASG included: an overall 14.5% CNR increase across the volume; up to 48.8% CNR increase for low-contrast inserts inside the contrast plate of the QA phantom; and a 65% reduction of cupping artifact in axial profiles of water-filled cross sections of the phantom. Advanced image processing strategies to remove grid line artifacts did not affect the spatial resolution or geometric accuracy of the system.

CONCLUSIONS

LPBF can be used to manufacture highly efficient, 2D-focused ASGs that can be easily coupled to clinical, flat-panel detectors. The implementation of ASGs in CBCT leads to reduced scatter-related artifacts, improved CT number accuracy, and enhanced CNR with no increased equivalent dose to the patient. Further improvements to image quality might be achieved with a combination of scatter-correction algorithms and iterative-reconstruction strategies. Finally, clinical applications where other scatter removal strategies are unfeasible might now achieve superior soft-tissue visualization and quantitative capabilities.

Investigation of Computed Tomography Numbers on Multiple Imaging Systems using Single and Multislice Methods

Aim

The aim of this study is to determine the variation in Hounsfield values with single and multi-slice methods using in-house software on fan-beam computed tomography (FCT), linear accelerator (linac) cone-beam computed tomography (CBCT), and Icon-CBCT datasets acquired using Gammex and advanced electron density (AED) phantoms.

Materials and Methods

The AED phantom was scanned on a Toshiba computed tomography (CT) scanner, five linac-based CBCT X-ray volumetric imaging systems, and Leksell Gamma Knife Icon. The variation between single and multi-slice methods was assessed by comparing scans acquired using Gammex and AED phantoms. The variation in Hounsfield units (HUs) between seven different clinical protocols was assessed using the AED phantom. A CIRS Model 605 Radiosurgery Head Phantom (TED) phantom was scanned on all three imaging systems to assess the target dosimetric changes due to HU variation. An in-house software was developed in MATLAB to assess the HU statistics and the trend along the longitudinal axis.

Results

The FCT dataset showed a minimal variation (central slice ± 3 HU) in HU values along the long axis. A similar trend was also observed between the studied clinical protocols acquired on FCT. Variation among multiple linac CBCTs was insignificant. In the case of the water insert, a maximum HU variation of -7.23 ± 68.67 was observed for Linac 1 towards the inferior end of the phantom. All five linacs appeared to have a similar trend in terms of HU variation from the proximal to the distal end of the phantom, with a few outliers for Linac 5. Among three imaging modalities, the maximum variation was observed in gamma knife CBCTs, whereas FCT showed no appreciable deviation from the central value. In terms of dosimetric comparison, the mean dose in CT and Linac CBCT scans differed by <0.5 Gy, whereas at least a 1 Gy difference was observed between CT and gamma knife CBCT.

Conclusion

This study shows a minimal variation with FCT between single, volume-based, and multislice methods, and hence the current approach of determining the CT-electron density curve based on a single-slice method would be sufficient for producing a HU calibrations curve for treatment planning. However, CBCTs acquired on linac, and in particular, gamma knife systems, show noticeable variations along the long axis, which is likely to affect the dose calculations performed on CBCTs. It is highly recommended to assess the Hounsfield values on multiple slices before using the HU curve for dose calculations.

Evaluation of the dosimetric influence of interfractional 6D setup error in hypofractionated prostate cancer treated with IMRT and VMAT using daily kV-CBCT

INTRODUCTION

Prostate cancer is one of the most common malignant tumors in men and is usually treated with advanced intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). Significant uncorrected interfractional 6-Dimensional setup errors could impact the delivered dose. The aim of this study was to assess the dosimetric impact of 6D interfractional setup errors in hypofractionated prostate cancer using daily kilovoltage cone-beam computed tomography (kV-CBCT).

METHODS

This retrospective study comprised twenty prostate cancer patients treated with hypofractionated IMRT (8) and VMAT (12) with daily kV-CBCT image guidance. Interfraction 6D setup errors along lateral, longitudinal, vertical, pitch, roll, and yaw axes were evaluated for 400 CBCTs. For targets and organs at risk (OARs), the dosimetric impact of rotational error (R_Error), translational error (T_Error), and translational plus rotational error (T+R_Error) were evaluated on kV-CBCT images.

RESULTS

The single fraction maximum T_Error ranged from 12-20 mm, and the R_Error ranged from 2.8⁰-3.0⁰. The maximum mean absolute dose variation ΔD in D_98% (dose to 98% volume) of CTV-55 and PTV-55 was -0.66±0.82 and -5.94±3.8 Gy, respectively, in the T+R_Error. The maximum ΔD (%) for D_98% and D_0.035cc in CTV-55 was -4.29% and 2.49%, respectively, while in PTV-55 it was -24.9% and 2.36%. The mean dose reduction for D_98% in CTV-55 and D_98% and D_95% in PTV-55 was statistically significant (p<0.05) for T_Error and T+R_Error. The mean dose variation for D_mean and D_50% in the rectum was statistically significant (p<0.05) for T_Error and T+R_Error.

CONCLUSION

The uncorrected interfractional 6D setup error results in significant target underdosing and OAR overdosing in prostate cancer. This emphasizes the need to correct interfractional 6D setup errors daily in IMRT and VMAT.

Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?

Background

The prostate demonstrates inter- and intra- fractional changes and thus adaptive radiotherapy would be required to ensure optimal coverage. Daily adaptive radiotherapy for MRI-guided radiotherapy can be both time and resource intensive when structure delineation is completed manually. Contours can be auto-generated on the MR-Linac via a deformable image registration (DIR) based mapping process from the reference image. This study evaluates the performance of automatically generated target structure contours against manually delineated contours by radiation oncologists for prostate radiotherapy on the Elekta Unity MR-Linac.

Methods

Plans were generated from prostate contours propagated by DIR and rigid image registration (RIR) for forty fractions from ten patients. A two-dose level SIB (simultaneous integrated boost) IMRT plan is used to treat localised prostate cancer; 6000 cGy to the prostate and 4860 cGy to the seminal vesicles. The dose coverage of the PTV 6000 and PTV 4860 created from the manually drawn target structures was evaluated with each plan. If the dose objectives were met, the plan was considered successful in covering the gold standard (clinician-delineated) volume.

Results

The mandatory PTV 6000 dose objective (D98% > 5580 cGy) was met in 81 % of DIR plans and 45 % of RIR plans. The SV were mapped by DIR only and for all the plans, the PTV 4860 dose objective met the optimal target (D98% > 4617 cGy). The plans created by RIR led to under-coverage of the clinician-delineated prostate, predominantly at the apex or the bladder-prostate interface.

Conclusion

Plans created from DIR propagation of prostate contours outperform those created from RIR propagation. In approximately 1 in 5 DIR plans, dosimetric coverage of the gold standard PTV was not clinically acceptable. Thus, at our institution, we use a combination of DIR propagation of contours alongside manual editing of contours where deemed necessary for online treatments.

TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

We presented TIGRE-VarianCBCT, an open-source toolkit Matlab-GPU for Varian on-board cone-beam CT with particular emphasis to address challenges in raw data preprocessing, artifacts correction, tomographic reconstruction and image post-processing. The aim of this project is to provide not only a tool to bridge the gap between clinical usage of CBCT scan data and research algorithms but also a framework that breaks down the imaging chain into individual processes so that research effort can be focused on a specific part. The entire imaging chain, module-based architecture, data flow and techniques used in the creation of the toolkit are presented. Raw scan data are first decoded to extract X-ray fluoro image series and set up the imaging geometry. Data conditioning operations including scatter correction, normalization, beam-hardening correction, ring removal are performed sequentially. Reconstruction is supported by TIGRE with FDK as well as a variety of iterative algorithms. Pixel-to-HU mapping is calibrated by a Catphan^TM 504 phantom. Imaging dose in CTDIw is calculated in an empirical formula. The performance was validated on real patient scans with good agreement with respect to vendor-designed program. Case studies in scan protocol optimization, low dose imaging and iterative algorithm comparison demonstrated its substantial potential in performing scan data based clinical studies. The toolkit is released under the BSD license, imposing minimal restrictions on its use and distribution. The toolkit is accessible as a module at https://github.com/CERN/TIGRE.

A Proof-of-Concept Study of Artificial Intelligence-assisted Contour Editing

Purpose

To present a concept called artificial intelligence-assisted contour editing (AIACE) and demonstrate its feasibility.

Materials and Methods

The conceptual workflow of AIACE is as follows: Given an initial contour that requires clinician editing, the clinician indicates where large editing is needed, and a trained deep learning model uses this input to update the contour. This process repeats until a clinically acceptable contour is achieved. In this retrospective, proof-of-concept study, the authors demonstrated the concept on two-dimensional (2D) axial CT images from three head-and-neck cancer datasets by simulating the interaction with the AIACE model to mimic the clinical environment. The input at each iteration was one mouse click on the desired location of the contour segment. Model performance is quantified with the Dice similarity coefficient (DSC) and 95th percentile of Hausdorff distance (HD95) based on three datasets with sample sizes of 10, 28, and 20 patients.

Results

The average DSCs and HD95 values of the automatically generated initial contours were 0.82 and 4.3 mm, 0.73 and 5.6 mm, and 0.67 and 11.4 mm for the three datasets, which were improved to 0.91 and 2.1 mm, 0.86 and 2.5 mm, and 0.86 and 3.3 mm, respectively, with three mouse clicks. Each deep learning-based contour update required about 20 msec.

Conclusion

The authors proposed the newly developed AIACE concept, which uses deep learning models to assist clinicians in editing contours efficiently and effectively, and demonstrated its feasibility by using 2D axial CT images from three head-and-neck cancer datasets.Keywords: Segmentation, Convolutional Neural Network (CNN), CT, Deep Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.

Image guidance for FLASH radiotherapy

FLASH radiotherapy (FLASH-RT) is an emerging ultra-high dose (>40 Gy/s) delivery that promises to improve the therapeutic potential by limiting toxicities compared to conventional RT while maintaining similar tumor eradication efficacy. Image guidance is an essential component of modern RT that should be harnessed to meet the special emerging needs of FLASH-RT and its associated high risks in planning and delivering of such ultra-high doses in short period of times. Hence, this contribution will elaborate on the imaging requirements and possible solutions in the entire chain of FLASH-RT treatment, from the planning, through the setup and delivery with online in vivo imaging and dosimetry, up to the assessment of biological mechanisms and treatment response. In patient setup and delivery, higher temporal sampling than in conventional RT should ensure that the short treatment is delivered precisely to the targeted region. Additionally, conventional imaging tools such as cone-beam computed tomography will continue to play an important role in improving patient setup prior to delivery, while techniques based on magnetic resonance imaging or positron emission tomography may be extremely valuable for either linear accelerator (Linac) or particle FLASH therapy, to monitor and track anatomical changes during delivery. In either planning or assessing outcomes, quantitative functional imaging could supplement conventional imaging for more accurate utilization of the biological window of the FLASH effect, selecting for or verifying things such as tissue oxygen and existing or transient hypoxia on the relevant timescales of FLASH-RT delivery. Perhaps most importantly at this time, these tools might help improve the understanding of the biological mechanisms of FLASH-RT response in tumor and normal tissues. The high dose deposition of FLASH provides an opportunity to utilize pulse-to-pulse imaging tools such as Cherenkov or radiation acoustic emission imaging. These could provide individual pulse mapping or assessing the 3D dose delivery superficially or at tissue depth, respectively. In summary, the most promising components of modern RT should be used for safer application of FLASH-RT, and new promising developments could be advanced to cope with its novel demands but also exploit new opportunities in connection with the unique nature of pulsed delivery at unprecedented dose rates, opening a new era of biological image guidance and ultrafast, pulse-based in vivo dosimetry.

Proton Therapy for Prostate Cancer: Challenges and Opportunities

Simple Summary

Reported clinical outcomes of proton therapy (PT) for localized prostate cancer are similar to photon-based external beam radiotherapy. Apparently, the dosimetric advantages of PT have yet to be translated to clinical benefits. The suboptimal clinical outcomes of PT might be attributable to inadequate dose prescription, as indicated by the ASCENDE-RT trial. Moreover, uncertainties involved in the treatment planning and delivery processes, as well as technological limitations in PT treatment systems, may lead to discrepancies between planned doses and actual doses delivered to patients. In this article, we reviewed the current status of PT for prostate cancer and discussed different clinical implementations that could potentially improve the clinical outcome of PT for prostate cancer. Various technological advancements under which uncertainties in dose calculations can be minimized, including MRI-guided PT, dual-energy photon-counting CT and high-resolution Monte Carlo-based treatment planning systems, are highlighted.

Abstract

The dosimetric advantages of proton therapy (PT) treatment plans are demonstrably superior to photon-based external beam radiotherapy (EBRT) for localized prostate cancer, but the reported clinical outcomes are similar. This may be due to inadequate dose prescription, especially in high-risk disease, as indicated by the ASCENDE-RT trial. Alternatively, the lack of clinical benefits with PT may be attributable to improper dose delivery, mainly due to geometric and dosimetric uncertainties during treatment planning, as well as delivery procedures that compromise the dose conformity of treatments. Advanced high-precision PT technologies, and treatment planning and beam delivery techniques are being developed to address these uncertainties. For instance, external magnetic resonance imaging (MRI)-guided patient setup rooms are being developed to improve the accuracy of patient positioning for treatment. In-room MRI-guided patient positioning systems are also being investigated to improve the geometric accuracy of PT. Soon, high-dose rate beam delivery systems will shorten beam delivery time to within one breath hold, minimizing the effects of organ motion and patient movements. Dual-energy photon-counting computed tomography and high-resolution Monte Carlo-based treatment planning systems are available to minimize uncertainties in dose planning calculations. Advanced in-room treatment verification tools such as prompt gamma detector systems will be used to verify the depth of PT. Clinical implementation of these new technologies is expected to improve the accuracy and dose conformity of PT in the treatment of localized prostate cancers, and lead to better clinical outcomes. Improvement in dose conformity may also facilitate dose escalation, improving local control and implementation of hypofractionation treatment schemes to improve patient throughput and make PT more cost effective.

Utility of deformable image registration for adaptive prostate cancer treatment. Analysis and comparison of two commercially available algorithms

INTRODUCTION

This study aimed to assess and compare the capabilities of two commercially available deformable image registration algorithms implemented in Raystation 9A (A₁) and Velocity AI (A₂) for possible usage in adaptive prostate radiotherapy based on the propagation of anatomical contours from computed tomography (CT) images to cone-beam CT (CBCT) images.

MATERIAL AND METHODS

Ten patients were retrospectively selected from a group treated for localized prostate cancer. The propagation of rectum contours was analyzed in a set of CT-CBCT pairs. Two independent observers carried out qualitative analysis using the two-level descriptive scale (meet/fail). Quantitative analysis was done using landmark points distances based on implanted markers as navigation points and differently obtained contours (manually and automatically using DIR algorithms). Quantitative analysis was taken on sets preselected by qualitative analysis.

RESULTS

Qualitative analysis shows that 83.7% of the rectum contours were scored identically (meet or fail) for both algorithms, from which 53.5% and 55.8% are failed results for A₁ and A₂, respectively. For the rectum size (RWD parameter), differences between referenced and deformation-based values were 5.5 and 5.8mm, and for the rectum wall, the prostate marker distance (WMD parameter) was 4.5 and 5.5mm for A₁ and A₂, respectively. The differences between the WMD parameters were statistically significant (p=0.007).

CONCLUSIONS

In both tested algorithms, neither effectiveness nor measured uncertainties in the propagation of rectum contour process in prostate patient cases were satisfactory. Careful selection of input images followed by case/set-based verification of every deformable registration is a substantial step to avoid inappropriate results.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Difference in VMAT dose distribution for prostate cancer with/without rectal gas removal and/or adaptive replanning

We investigated differences in the volumetric-modulated arc therapy (VMAT) dose distribution in prostate cancer patients treated by rectal gas removal and/or adaptive replanning. Cone-beam computed tomography (CBCT) scans were performed daily for 22 treatments in eight prostate cancer patients with excessive rectal gas, and the CBCT images were analyzed. Rectal gas removal was performed, and irradiation was delivered after prostate matching. We compared dose-volume histograms for the daily CBCT images before and after rectal gas removal. Plan A was the original plan on CBCT images before rectal gas removal. Plan B was a single reoptimized plan on CBCT images before rectal gas removal. Plan C was the original plan on CBCT images after rectal gas removal. Plan D was a single reoptimized plan on CBCT images after rectal gas removal. D₉₅ of the planning target volume (PTV) minus the rectum of Plan C (94.7% ± 6.6%) was significantly higher than that of Plan A (88.5% ± 10.4%). All dosimetric parameters of Plan C were improved by rectal gas removal compared with Plan A, regardless of the initial rectal gas volume. Dosimetric parameters of PTV minus the rectum of Plan B were significantly improved compared with Plan C. Additionally, the V₇₈ of the rectal wall of Plan B (0.2% ± 0.5%) was significantly improved compared with Plan C (3.9% ± 6.3%, p = 0.003). The dosimetric parameters of Plan D were not significantly different from Plan B. The dose distribution of prostate VMAT was improved by rectal gas removal and/or adaptive replanning. An adaptive replanning on daily CBCT images might be a better method than rectal gas removal for prostate cancer patients with excessive rectal gas.

Impact of Acquisition Protocols on Accuracy of Dose Calculation Based on XVI Cone Beam Computed Tomography

Purpose:

The objective of this work is to study the impact of acquisition protocols on the accuracy of cone beam computed tomography (CBCT)-based dose calculation and to determinate its limits from image characteristics such as image quality, Hounsfield numbers consistency, and restrictive sizes of volume acquisition, compared to the CT imaging for the different anatomy localizations: head and neck (H&N), thorax, and pelvis.

Materials and Methods:

In this work, we used a routine on-board imaging CBCT of the XVI system (Elekta, Stockholm, Sweden). Dosimetric calculations performed on CT images require the knowledge of the Hounsfield unit-relative electron density (HU-ReD) calibration curve, which is determined for each imaging technology and must be adapted to the imaging acquisition parameters (filter/field of view). The accuracy of the dose calculation from CBCT images strongly depends on the quality of these images and also on the appropriate correspondence to the electronic densities, which will be used by the treatment planning system to simulate the dose distribution. In this study, we evaluated the accuracy of the dose calculation for each protocol, as already pointed in many studies.

Results:

As a result, the protocols that give better results in terms of dose calculation are F0S20 for the H&N region and F1M20 for the thoracic and pelvic regions, with an error <2% compared to results obtained with CT images. In addition, the dose distributions obtained with CT and CBCT imaging modalities were compared by two different methods. The first comparison was done by gamma index in three planes (sagittal, coronal, and transverse) with 2%; 2 mm criteria. The results showed good correspondence, with more than 95% of points passed the criteria. We also compared the target volume, the organs at risk (OARs), and the maximum and minimum doses for the three localizations (H&N, thorax, and pelvis) in CT and CBCT imaging modalities using a Rando phantom.

Conclusions:

The choice of the adequate CBCT acquisition protocol and the appropriate phantom to determine the HU-ReD calibration curve provides a better precision in the calculation of dose on CBCT images. This allows improving the results obtained when using the HU-ReD calibration method for dose calculation in adaptive radiotherapy.

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.

Analysis of the Influence of Peripheral Anatomical Changes for CBCT-Guided Prostate Cancer Radiotherapy

Purpose:

To analyze the influence of the bladder and rectum filling and the body contour changes on the prostate target dose.

Methods:

A total of 190 cone-beam CT (CBCT) image data sets from 16 patients with prostate cancer were used in this study. Dose reconstruction was performed on the virtual CT generated by the deformable planning CT. Then, the effects of the bladder filling, rectal filling, and the patient’s body contour changes of the PCTV1 (the prostate area, B1) and PCTV2 (the seminal vesicle area, B2) on the target dose were analyzed. Correlation analysis was performed for the ratio of bladder and rectal volume variation and the variation of the bladder and rectal dose.

Results:

The mean Dice coefficients of B1, B2, bladder, and rectum were 0.979, 0.975, 0.888 and 0.827, respectively, and the mean Hausdorff distances were 0.633, 1.505, 2.075, and 1.533, respectively. With the maximum volume variations of 142.04 ml for the bladder and 40.50 ml for the rectum, the changes of V₁₀₀, V₉₅, D₂, and D₉₈ were 1.739 ± 1.762 (%), 0.066 ± 0.169 (%), 0.562 ± 0.442 (%), and 0.496 ± 0.479 (%) in PCTV1 and 1.686 ± 1.051 (%), 0.240 ± 0.215 (%), 1.123 ± 0.925 (%), and 0.924 ± 0.662 (%) in PCTV2, respectively. With a 10% increase in the volume of the bladder and rectum, the V₇₅, V₇₀, and V₆₅ of rectum increased at 0.73 (%), 0.71 (%), and 1.18 (%), and the V₇₅, V₇₀, and V₆₅ of bladder changed at −0.21 (%), −0.32 (%), and −0.39 (%), respectively.

Conclusion:

Significant correlations were observed between the volume variation and the dose variation of the bladder and rectum. However, when a bladder and rectal filling protocol was adopted, the target dose coverage can be effectively ensured based on CBCT guidance to correct the prostate target position.

Determination of the CTV-PTV margin for prostate cancer radiotherapy depending on the prostate gland positioning control method

Objective: The objective of the study was to determine the correct CTV-PTV margin, depending on the method used to verify the PG position. In the study, 3 methods of CBCT image superimposition were assessed as based on the location of the prostate gland (CBCT images), a single gold marker, and pubic symphysis respectively.

Materials and methods: The study group consisted of 30 patients undergoing irradiation therapy at the University Hospital in Zielona Góra. The therapy was delivered using the VMAT (Volumetric Modulated Arc Therapy) protocol. CBCT image-based superimposition (prostate-based alignment) was chosen as the reference method. The uncertainty of the PG positioning method was determined and the margin to be used was determined for the CBCT-based reference method. Then, changes in the position of the prostate gland relative to these determined using the single marker and pubic symphysis-based methods were determined. The CTV-PTV margin was calculated at the root of the sum of the squares for the doubled value of method uncertainty for the CBCT image-based alignment method and the value of the difference between the locations of planned and actual isocenters as determined using the method of interest and the CBCT-based alignment method for which the total number of differences accounted for 95% of all differences.

Results: The CTV-PTV margins to be used when the prostate gland is positioned using the CBCT imaging, single marker, and pubic symphysis-based methods were determined. For the CBCT-based method, the following values were obtained for the Vrt, Lng, and Lat directions respectively: 0.43 cm, 0.48 cm, 0.29 cm. For the single marker-based method, the respective values were 0.7 cm, 0.88 cm, and 0.44 cm whereas for the pubic symphysis-based method these were 0.65 cm, 0.76 cm, and 0.46 cm.

Conclusions: Regardless of the method, the smallest margin values were obtained for the lateral direction, with the CBCT-based method facilitating the smallest margins to be used. The largest margins were obtained using the single marker-based alignment method.

International survey; current practice in On-line adaptive radiotherapy (ART) delivered using Magnetic Resonance Image (MRI) guidance

BACKGROUND AND PURPOSE

The uptake of new technologies has varied internationally and there have often been barriers to implementation. On-line adaptive radiotherapy (ART) promises to improve patient outcome. This survey focuses on the implementation phase of delivering ART and professional roles and responsibilities currently involved in the workflow and changes which may be expected in the future.

MATERIALS AND METHODS

A 38 question survey included aspects on current practice; professional responsibilities; benefits and barriers; and decision making and responsibilities. For the purposes of the questionnaire and paper, ART was considered where tumour and /or organs at risk were contoured and re-planning was performed on-line. The questionnaire was electronically distributed via radiotherapy networks.

RESULTS

Nineteen international responses were received. Europe (n = 11), United States of America (n = 4); Canada (n = 2), Australia (n = 1) and Hong Kong (n = 1). The majority of centres started using ART in either 2018 (n = 7) or 2019 (n = 6). Four centres started treating with ART between 2015 and 2017, and the first was in 2014. Centres initially treated prostate and oligometastases patients, expanding to treat prostate, oligometastases, pancreas and rectum. The majority of centres were working in conventional roles, however moving towards radiographers taking more responsibility in contouring organs at risk (OAR), target and dosimetry. The three most important criteria chosen by medical doctors to determine if ART should be used were overall gross anatomy changes of target and OAR, target not covered by planning target volume (PTV) and OAR close to the high dose area. There was no clear consensus on the minimum improvement in dose to target or reduction in dose to OAR to warrant adaption.

CONCLUSION

On-line ART has been implemented successfully internationally. Initial practice maintains conventional professional roles and responsibilities, however there is trend to changing roles for the future. There is little consensus regarding the triggers of adaption.

Four-Dimensional Cone-Beam Computed Tomography Image Compression Using Video Encoder for Radiotherapy

Four dimensional cone-beam computed tomography (4D-CBCT) images were widely used for patient positing and target localization in radiotherapy. As consisting of multiple CBCT sets, it needs more time and space for data transferring and storage. In this study the feasibility of applying video coding algorithms for 4D-CBCT image compression was investigated. Prior to compression 4D-CBCT images were arranged in an order based on breathing phase or slice location for input sequence of video encoder. Median filtering was applied to suppress noise and artifact of 4D-CBCT for improved image quality. Three popular video coding algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG-4) were tested and their performances were evaluated on a publicly available 4D-CBCT database. The average compression ratio of MPEG-4 was 135, while the values of Motion JPEG AVI and Motion JPEG 2000 were 16 and 7, respectively. The compression rate of two ordering methods was comparable and the location-based ordering method was slightly higher. With pre-processing of median filtering, the inter-frame similarity of input sequence was improved and the resulting compression rate was increased. MPEG-4 provided extremely higher compression rate for 4D-CBCT images. The ordering method based on slice location resulted in higher compression rate than the ordering method based on breathing phase. The median filtering was effective in improving inter-frame similarity and resulted in higher compression rate. The video coding algorithms are not only applicable for 4D image modalities but also feasible for serial 3D image modalities.

Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy

BACKGROUND AND PURPOSE

The purpose of this study is to develop a deep learning-based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone-beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning.

MATERIALS AND METHODS

We propose to utilize both CBCT and CBCT-based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle-consistent adversarial networks (CycleGAN), which was trained using paired CBCT-MR images. To combine the advantages of both CBCT and sMRI, we developed a cross-modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT-specific and sMRI-specific features prior to combining them in a late-fusion network for final segmentation. The network was trained and tested using 100 patients' datasets, with each dataset including the CBCT and manual physician contours. For comparison, we trained another two networks with different network inputs and architectures. The segmentation results were compared to manual contours for evaluations.

RESULTS

For the proposed method, dice similarity coefficients and mean surface distances between the segmentation results and the ground truth were 0.96 ± 0.03, 0.65 ± 0.67 mm; 0.91 ± 0.08, 0.93 ± 0.96 mm; 0.93 ± 0.04, 0.72 ± 0.61 mm; 0.95 ± 0.05, 1.05 ± 1.40 mm; and 0.95 ± 0.05, 1.08 ± 1.48 mm for bladder, prostate, rectum, left and right femoral heads, respectively. As compared to the other two competing methods, our method has shown superior performance in terms of the segmentation accuracy.

CONCLUSION

We developed a deep learning-based segmentation method to rapidly and accurately segment five pelvic organs simultaneously from daily CBCTs. The proposed method could be used in the clinic to support rapid target and organs-at-risk contouring for prostate adaptive radiation therapy.

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.

[Hybrid radiotherapy machines: Evolution or revolution?]

The arrival of new hybrid radiotherapy machines with MRI or PET is announced as a milestone in radiotherapy management. Based on recent literature, we will describe the contribution of each of these modalities and the technological challenges that have already been or are still to be addressed.