On-board Cone-beam Computed Tomography Research Articles

PET/SPECT/spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part I: Quad-modal imaging.

In spite of the tremendous potential of game-changing biological image- and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent need for a highly integrated small-animal radiation therapy (SART) platform that can seamlessly combine therapeutic and diagnostic capabilities to comprehensively enhance RT for cancer treatment. We investigated a highly integrated quad-modal on-board imaging configuration combining positron emission tomography (PET), single-photon emission computed tomography (SPECT), photon-counting spectral CT, and cone-beam computed tomography (CBCT) in a SART platform using a Monte Carlo model as a proof-of-concept. The quad-modal on-board imaging configuration of the SART platform was designed and evaluated by using the GATE Monte Carlo code. A partial-ring on-board PET imaging subsystem, utilizing advanced semiconductor thallium bromide detector technology, was designed to achieve high sensitivity and spatial resolution. On-board SPECT, photon-counting spectral-CT, and CBCT imaging were performed using a single cadmium zinc telluride flat detector panel. The absolute peak sensitivity and scatter fraction of the PET subsystem were estimated by using simulated phantoms described in the NEMA NU-4 standard. The spatial resolution of the PET image of the platform was evaluated by imaging a simulated micro-Derenzo hot-rod phantom. To evaluate the quantitative imaging capability of the system's spectral CT, the Bayesian eigentissue decomposition (ETD) method was utilized to quantitatively decompose the virtual noncontrast (VNC) electron densities and iodine contrast agent fractions in the Kidney1 inserts mixed with the iodine contrast agent within the simulated phantoms. The performance of the proposed quad-model imaging in the platform was validated by imaging a simulated phantom with multiple imaging probes, including an iodine contrast agent and radioisotopes of 18F and 99mTc. The PET subsystem demonstrated an absolute peak sensitivity of 18.5% at the scanner center, with an energy window of 175-560KeV, and a scatter fraction of only 3.5% for the mouse phantom, with a default energy window of 480-540KeV. The spatial resolution of PET on-board imaging exceeded 1.2mm. All imaging probes were identified clearly within the phantom. The PET and SPECT images agreed well with the actual spatial distributions of the tracers within the phantom. Average relative errors on electron density and iodine contrast agent fraction in the Kidney1 inserts were less than 3%. High-quality PET images, SPECT images, spectral-CT images (including iodine contrast agent fraction images and VNC electron density images), and CBCT images of the simulated phantom demonstrated the comprehensive multimodal imaging capability of the system. The results demonstrated the feasibility of the proposed quad-modal imaging configuration in a SART platform. The design incorporates anatomical, molecular, and functional information about tumors, thereby facilitating successful translation of preclinical studies into clinical practices.

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calculation using deep learning. Approach. We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and small patient numbers. We introduced to the networks the concept of global residuals only learning and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view (abdomen) to our imaging dataset. This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics. Main results. We found improved performance for our proposed method, compared to a baseline cycleGAN implementation, on image-similarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0 ± 16.6 HU proposed versus 58.9 ± 16.8 HU baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured using the dice similarity coefficient (0.872 ± 0.053 proposed versus 0.846 ± 0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3 ± 2.4% proposed versus 3.7 ± 2.8% baseline). Significance. Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated.

The Feasibility of Quad-Modal PET/SPECT/Spectral-CT/CBCT On-Board Imaging in a Small-Animal Radiation Therapy Platform

<h3>Purpose/Objective(s)</h3> While radiation therapy (RT) paradigms have evolved rapidly, preclinical RT techniques, especially molecular/functional imaging guidance techniques, are still far behind, which hampers successful translation of novel RT techniques to bedside practices. In this study, we firstly investigated the feasibility of a highly integrated quad-modal on-board imaging configuration, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), spectral CT, and cone-beam computed tomography (CBCT), in a small animal radiation therapy (SART) platform, which presents a near ideal one-stop comprehensive platform for preclinical RT studies. <h3>Materials/Methods</h3> The quad-modal imaging configuration in a SART platform was modeled by using the GATE Monte Carlo code as a proof-of-concept. A partial-ring on-board PET imaging subsystem was designed using a semiconductor thallium bromide detector. A single cadmium zinc telluride flat panel detector was utilized to obtain on-board SPECT, spectral-CT, and CBCT images of small animals. A preclinical SART workflow based on the quad-modal imaging guidance was designed, with image-guided RT and emission-guided radiation therapy (EGRT) incorporated as biologically guided RT modalities. The spatial resolution, sensitivity, and scatter fraction of the PET subsystem were measured using the simulated phantoms. To demonstrate the quad-model imaging performance of the platform, a simulated phantom with multiple imaging probes, including iodine and radioisotopes of ¹⁸F and ^99mTc, was imaged. In spectral CT imaging of the phantom, the virtual non-contrast (VNC) electron density and iodine uptake fractions in Kidney1 inserts, mixed with an iodine contrast agent at electron fractions of 0.02 and 0.03, were decomposed and imaged quantitatively by using the Bayesian eigentissue decomposition (ETD) method. <h3>Results</h3> For the PET subsystem, the spatial resolution was better than 1.2 mm, the absolute peak sensitivity at the platform center with an energy window of 175–560 KeV was 18.5%, and the measured scatter fraction was 3.5% for the mouse phantom described in the NEMA NU-4 standard with a default energy window of 480–540 KeV. In quad-model imaging of the phantom, all imaging probes, including iodine uptake fractions, were imaged clearly within the phantom. The PET and SPECT images of the phantom agreed well with the actual spatial distributions of the tracers within the phantom. The quantitative spectral-CT images of iodine uptake fraction and VNC electron density matched well with the ground truth values within a reasonable error range. <h3>Conclusion</h3> The results demonstrated the feasibility of the proposed quad-modal imaging configuration in a SART platform. With the anatomical, molecular, and functional information of tumors being accounted for, this novel design can improve the accuracy of preclinical RT studies, thereby facilitating the successful translation of preclinical studies to clinical practices.

Evaluating the Quality of Patient-Specific Deformable Image Registration in Adaptive Radiotherapy Using a Digitally Enhanced Head and Neck Phantom

Despite the availability of national and international guidelines, an accurate and efficient, patient-specific, deformable image registration (DIR) validation methodology is not yet established, and several groups have found an incompatibility of the various digital phantoms with the commercial systems. To evaluate the quality of the computed tomography (CT) and on-board cone-beam CT (CBCT) DIRs, a novel methodology was developed and tested on 10 head and neck (HN) patients, using CT and CBCT anthropomorphic HN phantom images, digitally reprocessed to include the common organs at risk. Reference DVFs (refDVFs) were generated from the clinical patient CT-CBCT fused images using an independent registration software. The phantom CT images were artificially deformed, using the refDVFs, and registered with the phantom CBCT images, using the clinical registration software, generating a test DVF (testDVF) dataset. The clinical plans were recalculated on the daily patient ‘deformed’ CTs, and the dose maps transferred to the patient-planning CT, using both the refDVF and testDVF. The spatial and dosimetric errors were quantified and the DIR performance evaluated using an established operative tolerance level. The method showed the ability to quantify the DIR spatial errors and assess their dose impact at the voxel level and could be applied to patient-specific DIR evaluation during adaptive HN radiotherapy in routine practice.

CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation.

Cone-beam computed tomography (CBCT) plays a key role in image-guided radiotherapy (IGRT), however its poor image quality limited its clinical application. In this study, we developed a deep-learning based approach to translate CBCT image to synthetic CT (sCT) image that preserves both CT image quality and CBCT anatomical structures. A novel synthetic CT generative adversarial network (sCTGAN) was proposed for CBCT-to-CT translation via disentangled representation. The approach of disentangled representation was employed to extract the anatomical information shared by CBCT and CT image domains. Both on-board CBCT and planning CT of 40 patients were used for network learning and those of another 12 patients were used for testing. Accuracy of our network was quantitatively evaluated using a series of statistical metrics, including the peak signal-to-noise ratio (PSNR), mean structural similarity index (SSIM), mean absolute error (MAE), and root-mean-square error (RMSE). Effectiveness of our network was compared against three state-of-the-art CycleGAN-based methods. The PSNR, SSIM, MAE, and RMSE between sCT generated by sCTGAN and deformed planning CT (dpCT) were 34.12 dB, 0.86, 32.70 HU, and 60.53 HU, while the corresponding values between original CBCT and dpCT were 28.67 dB, 0.64, 70.56 HU, and 112.13 HU. The RMSE (60.53±14.38 HU) of sCT generated by sCTGAN was less than that of sCT generated by all the three comparing methods (72.40±16.03 HU by CycleGAN, 71.60±15.09 HU by CycleGAN-Unet512, 64.93±14.33 HU by CycleGAN-AG). The sCT generated by our sCTGAN network was closer to the ground truth (dpCT), in comparison to all the three comparing CycleGAN-based methods. It provides an effective way to generate high-quality sCT which has a wide application in IGRT and adaptive radiotherapy.

End of treatment cone-beam computed tomography (CBCT) is predictive of radiation response and overall survival in oropharyngeal squamous cell carcinoma

BackgroundImage guidance in radiation oncology has resulted in significant improvements in the accuracy and precision of radiation therapy (RT). Recently, the resolution and quality of cone beam computed tomography (CBCT) for image guidance has increased so that tumor masses and lymph nodes are readily detectable and measurable. During treatment of head and neck squamous cell carcinoma (HNSCC), on-board CBCT setup imaging is routinely obtained; however, this CBCT imaging data is not utilized to predict patient outcomes. Here, we analyzed whether changes in CBCT measurements obtained during a course of radiation therapy correlate with responses on routine 3-month follow-up diagnostic imaging and overall survival (OS).Materials/methodsPatients with oropharyngeal primary tumors who received radiation therapy between 2015 and 2018 were included. Anatomical measurements were collected of largest nodal conglomerate (LNC) at CT simulation, end of radiation treatment (EOT CBCT), and routine 3-month post-RT imaging. At each timepoint anteroposterior (AP), mediolateral (ML) and craniocaudal (CC) measurements were obtained and used to create a 2-dimensional (2D) maximum.ResultsCBCT data from 64 node positive patients were analyzed. The largest nodal 2D maximum and CC measurements on EOT CBCT showed a statistically significant correlation with complete response on 3-month post-RT imaging (r = 0.313, p = 0.02 and r = 0.318, p = 0.02, respectively). Furthermore, patients who experienced a 30% or greater reduction in the CC dimension had improved OS (Binary Chi-Square HR 4.85, p = 0.028).ConclusionDecreased size of pathologic lymph nodes measured using CBCT setup imaging during a radiation course correlates with long term therapeutic response and overall survival of HNSCC patients. These results indicate that CBCT setup imaging may have utility as an early predictor of treatment response in oropharyngeal HNSCC.

Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy

This study aimed to investigate if a commercial, knowledge-based tool for radiotherapy planning could be used to estimate the amount of sparing in organs at risk (OARs) in the re-planning strategy for adaptive radiotherapy (ART). Eighty head and neck (HN) VMAT Pareto plans from our institute’s database were used to train a knowledge-based planning (KBP) model. An evaluation set of another 20 HN patients was randomly selected. For each patient in the evaluation set, the planning computed tomography (CT) and 2 sets of on-board cone-beam CT, corresponding to the middle and second half of the radiotherapy treatment course, were extracted. The original plan was re-calculated on a daily deformed CT (delivered dose‐volume histogram (DVH)) and compared with the KBP DVH predictions and with the final KBP DVH after optimisation of the plan, which was performed on the same image sets. To evaluate the feasibility of this method, the range of KBP DVH uncertainties was compared with the gains obtained from re-planning. DVH differences and receiver operating characteristic (ROC) curve analysis were used for this purpose. On average, final KBP uncertainties were smaller than the gain in re-planning. Statistical tests confirmed significant differences between the two groups. ROC analysis showed KBP performance in terms of area under the curve values higher than 0.7, which confirmed a good accuracy in predicted values. Overall, for 48% of cases, KBP predicted a desirable outcome from re-planning, and the final dose confirmed an effective gain in 47% of cases. We have established a systematic workflow to identify effective OAR sparing in re-planning based on KBP predictions that can be implemented in an on-line, ART process.

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.

Imaging prior to radiotherapy impacts in-vitro survival.

Background and purposeCone Beam Computed Tomography (CBCT) is routinely used in radiotherapy to identify the position of the target volume. The aim of this study was to determine whether the CBCT dose, when followed by the treatment, influences the therapeutic outcomes as determined by in-vitro clonogenic cell survival in a radiobiological experiment.Materials and methodsHuman cell lines, four cancer and one normal, were exposed to a 6 MV photon beam, produced by a linear accelerator. For half of each sample, a prior imaging dose was delivered using the on-board CBCT. A sample size of n = 103 was used to achieve statistical power.ResultsThe experimental group of cell lines exposed to CBCT imaging prior to treatment exhibited a reduction in mean cancer cell survival of ~17 times (p = 0.02) greater than predicted from the average dose response and equivalent to more than 5% of the therapeutic dose, compared to 11 times greater than predicted for normal cells (n.s.).ConclusionThe greater than predicted reduction in survival resulting from the additional CBCT dose is consistent with radiation-induced bystander effects.

IMPACT OF THREE DIFFERENT MATCHING METHODS ON PATIENT SET-UP ERROR IN X-RAY VOLUMETRIC IMAGING FOR HEAD AND NECK CANCER.

Impact of three different matching methods for delivery of Volumetric Modulated Arc Therapy (VMAT) in Cone-beam computed tomography (CBCT) on patient set-up error. As per institutional imaging protocol, 300 CBCT scans of 20 VMAT head and neck cancer patients treated with 60 Gy/30 fractions were chosen for the present study. Approved CT images of the plan were registered as a reference with the CBCT images on board. Grey-scale matching (GM), manual matching (MM), and bone matching (BM) between on-board CBCT and reference CT images were used to assess patient translation errors. Patient positioning verification was evaluated using the Clip-box registration in all three matching methods. Using the GM approach as a reference point, two additional matchings were rendered in offline mode using BM and MM. For analysis, random error (σ), systematic error (∑), maximum error (E) mean set-up error (M), mean displacement vector (R), matching time (Mt), and multiple comparisons using Post hoc Tukey's HSD test were performed. In MM, less random and systematic errors were found than in GM and BM with an insignificant difference (p > 0.05) Compared to BM and GM, the maximum error, mean set-up error, and displacement vector were marginally less in MM (p > 0.05). In MM, an increased Mt relative to BM and GM was observed (p > 0.05). Furthermore, an insignificant difference in set-up error was revealed in a multiple comparison test (p > 0.05). Any of the three matching methods can be used during CBCT to check patient translation errors for the delivery of the VMAT head and neck patients.

A Monte Carlo study to investigate the feasibility of an on-board SPECT/spectral-CT/CBCT imager for medical linear accelerator.

The on-board flat-panel cone-beam computed tomography (CBCT) lacks molecular/functional information for current online image-guided radiation therapy (IGRT). It might not be adequate for adaptive radiation therapy (ART), particularly for biologically guided tumor delineation and targeting which might be shifted and/or distorted during the course of RT. A linear accelerator (Linac) gantry-mounted on-board imager (OBI) was proposed using a single photon counting detector (PCD) panel to achieve single photon emission computed tomography (SPECT), energy-resolved spectral CT, and conventional CBCT triple on-board imaging, which might facilitate online ART with an addition of volumetric molecular/functional imaging information. The system was designed and evaluated in the GATE Monte Carlo platform. The OBI system including a kV-beam source and a pixelated cadmium zinc telluride (CZT) detector panel mounted on a medical Linac orthogonally to the MV beam direction was designed to obtain online CBCT, spectral CT, and SPECT tri-modal imaging of patients in the treatment room. The spatial resolutions of the OBI system were determined by imaging simulated phantoms. The CBCT imaging was evaluated by a simulated contrast phantom. A PMMA phantom containing gadolinium was imaged to demonstrate quantitative imaging of spectral-CT/CBCT of the system. The capability of tri-modal imaging of the OBI was demonstrated using three different spectral CT imaging methods to differentiate gadolinium, gold, calcium within simulated PMMA and the SPECT to image radioactive 99m Tc distribution. The dual-isotope SPECT imaging of the system was also evaluated by imaging a phantom containing 99m Tc and 123 I. The radiotherapy-related parameters of iodine contrast fraction and virtual non-contrast (VNC) tissue electron density in the Kidney1 inserts of a simulated phantom were decomposed using the Bayesian eigentissue decomposition method for contrast-enhanced CBCT/spectral-CT of the OBI in a single scan. The spatial resolutions of CBCT and SPECT of the OBI were determined to be 15.1lp/cm at 10% MTF and 4.8-12mm for radii of rotation of 10-40cm, respectively. In CBCT image of the contrast phantom, most of the soft-tissue inserts were visible with sufficient spatial structure details. As compared to the CBCT image of gadolinium, the spectral CT image provided higher image contrasts. Calcium, gadolinium, and gold were separated well by using the spectral CT material imaging methods. The reconstructed distribution of 99m Tc agreed with the spatial position within the phantom. The two isotopes were separated from each other in dual-isotope SPECT imaging of the OBI. The iodine fractions and the VNC electron densities were estimated in the iodine-enhanced Kidney1 tissue inserts with reasonable RMS errors. The main procedures of the tri-modal imaging guided online ART workflow were presented with new functional features included. Using a single photon counting CZT detector panel, an on-board SPECT, spectral CT, and CBCT tri-modal imaging could be realized in Linacs. With the added online molecular/functional imaging obtained from the new OBI for the online ART proposed, the accuracy of radiation treatment delivery could be further improved.

Male pelvic multi-organ segmentation aided by CBCT-based synthetic MRI

To develop an automated cone-beam computed tomography (CBCT) multi-organ segmentation method for potential CBCT-guided adaptive radiation therapy workflow.The proposed method combines the deep leaning-based image synthesis method, which generates magnetic resonance images (MRIs) with superior soft-tissue contrast from on-board setup CBCT images to aid CBCT segmentation, with a deep attention strategy, which focuses on learning discriminative features for differentiating organ margins. The whole segmentation method consists of 3 major steps. First, a cycle-consistent adversarial network (CycleGAN) was used to estimate a synthetic MRI (sMRI) from CBCT images. Second, a deep attention network was trained based on sMRI and its corresponding manual contours. Third, the segmented contours for a query patient was obtained by feeding the patient’s CBCT images into the trained sMRI estimation and segmentation model. In our retrospective study, we included 100 prostate cancer patients, each of whom has CBCT acquired with prostate, bladder and rectum contoured by physicians with MRI guidance as ground truth. We trained and tested our model with separate datasets among these patients. The resulting segmentations were compared with physicians’ manual contours.The Dice similarity coefficient and mean surface distance indices between our segmented and physicians’ manual contours (bladder, prostate, and rectum) were 0.95 ± 0.02, 0.44 ± 0.22 mm, 0.86 ± 0.06, 0.73 ± 0.37 mm, and 0.91 ± 0.04, 0.72 ± 0.65 mm, respectively.We have proposed a novel CBCT-only pelvic multi-organ segmentation strategy using CBCT-based sMRI and validated its accuracy against manual contours. This technique could provide accurate organ volume for treatment planning without requiring MR images acquisition, greatly facilitating routine clinical workflow.

Evaluation of the Visibility and Artifacts of 11 Common Fiducial Markers for Image Guided Stereotactic Body Radiation Therapy in the Abdomen

PurposeThe purpose of this study was to quantitatively evaluate the visibility and artifacts of commercially available fiducial markers to optimize their selection for image guided stereotactic body radiation therapy. Methods and MaterialsFrom 6 different vendors, we selected 11 fiducials commonly used in image guided radiation therapy. The fiducials varied in material composition (e.g., gold, platinum, carbon), shape (e.g., cylindrical, notched/linear, coiled, ball-like, step), and size measured in terms of diameter (0.28-1.0 mm) and length (3.0-20.0 mm). Each fiducial was centered in 4-mm bolus within a 13-cm-thick water-equivalent phantom. Fiducials were imaged with the use of a simulation computed tomography (CT) scanner, a CT-on-rails system, and an onboard cone beam CT system. Acquisition parameters were set according to clinical protocols. Visibility was assessed in terms of contrast (Δ Hounsfield unit [HU]) and the Michelson visibility metric. Artifacts were quantified in terms of relative standard deviation and relative streak artifacts level (rSAL). Twelve radiation oncologists ranked each fiducial in terms of clinical usefulness. ResultsContrast and artifacts increased with fiducial size. For CT imaging, maximum contrast (2722 HU) and artifacts (rSAL = 2.69) occurred for the largest-diameter (0.75 mm) platinum fiducial. Minimum contrast (551 HU) and reduced artifacts (rSAL = 0.65) were observed for the smallest-diameter (0.28 mm) gold fiducial. Carbon produced the least severe artifacts (rSAL = 0.29). The survey indicated that physicians preferred gold fiducials with a 0.35- to 0.43-mm diameter, 5- to 10-mm length, and coiled or cylindrical shape that balanced contrast and artifacts. ConclusionsWe evaluated 11 different fiducials in terms of visibility and artifacts. The results of this study may assist radiation oncologists who seek to maximize contrast, minimize artifacts, or balance contrast versus artifacts by fiducial selection.

Modified fast adaptive scatter kernel superposition (mfASKS) correction and its dosimetric impact on CBCT-based proton therapy dose calculation.

While cone beam computed tomography (CBCT) is able to provide patient anatomical information, its image quality is severely degraded due to scatter contamination, which degrades the accuracy of CBCT-based dose distribution estimation in proton therapy. In this work, we combined two existing scatter kernel correction methods: the point-spread function (PSF)-based scatter kernel derivation method and the fast adaptive scatter kernel superposition (fASKS) model, and evaluated the impact of the modified fASKS (mfASKS) correction on the accuracy of proton dose distribution estimation. To evaluate feasibility of the mfASKS approach using accurate scatter distributions, both Monte Carlo simulations and experiments were performed for an on-board CBCT machine integrated with a proton therapy machine. We developed a strategy to modify central intensity, constant intensity, and amplitude of the scatter kernels derived from PSFs for the fASKS model. A parameter required for the fASKS model was derived by optimizing uniformity in the mfASKS-corrected reconstructed images. Subsequently, the mfASKS model was used to remove scatter in CBCT imaging. We quantitatively compared the Hounsfield Unit (HU) and proton stopping power ratio (SPR) images for five different phantoms. To assess improvement of dose calculation accuracy, a series of proton treatment plans were produced using the CBCT images with and without the mfASKS correction. The accuracies of both HU and SPR intensity quantifications are improved as a result of the mfASKS correction. Mean absolute water-equivalent path length difference to the true value decreases from 10.3 to 0.934mm for the Gammex phantom (simulation). At the same time, mfASKS is able to offer more accurate dose distributions, especially at the distal fall-off region where noticeable dose overestimation is observed in the uncorrected scenario. Mean absolute relative error of proton range in the pelvic phantom improves from 5.03% to 2.57% (experiment). mfASKS enables more accurate CBCT-based proton dose calculation. This technique has significant implications in image-guided radiotherapy and dose verifications in adaptive proton therapy.

Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.

The incorporation of cone-beam computed tomography (CBCT) has allowed for enhanced image-guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning-based method for generating high quality corrected CBCT (CCBCT) images is proposed. The proposed method integrates a residual block concept into a cycle-consistent adversarial network (cycle-GAN) framework, called res-cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle-GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end-to-end CBCT-to-CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning-based CBCT correction method. Overall, the MAE, PSNR, NCC, and SNU were 13.0HU, 37.5dB, 0.99, and 0.05 in the brain, 16.1HU, 30.7dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning-based method. The authors have developed a novel deep learning-based method to generate high-quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.

Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).

Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge enhancement in PCTV is affected by the deformable registration errors and anatomical changes from prior to on-board images. In this study, we develop a hybrid-PCTV method to address this limitation to enhance the robustness and accuracy of the PCTV method. Planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods. EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors (REs) were 1.3%, 1.1% and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges. Hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.

Using an on-board cone-beam computed tomography scanner as an imaging modality for gel dosimetry: A feasibility study

On-board cone-beam computed tomography (CBCT) was used to scan the N-isopropylacrylamide (NIPAM) gel dosimeter. A dose-response curve from 1 to 12 Gy was created. The dose profile and depth dose curve were measured, and the dose distribution acquired from CBCT was then compared with that obtained from a treatment planning system (TPS). The linearity of the dose-response curve obtained by CBCT scanning of the NIPAM gel was 0.985. The mean percent standard deviation of various doses was 12.8%. A 12- to 24-h post-irradiation time was required to achieve stable CBCT readouts. Both dose profile and depth dose were in agreement with the results of TPS. The dose difference at the isocenter between CBCT and TPS was 3.8%. The gamma evaluation under the conditions of 5% dose difference and 5 mm distance-to-agreement was performed with the pass rate of 92.6%. These results indicate that an on-board CBCT can be used for scanning gel dosimeters in clinical radiotherapy.

Visualizing the Invisible in Prostate Radiation Therapy: Markerless Prostate Target Localization Via a Deep Learning Model and Monoscopic Kv Projection X-Ray Image

Current image guided prostate radiotherapy often relies on the use of implanted fiducial markers (FMs) or transducers for online/offline target localization. Fiducial or transducer insertion requires an invasive procedure that adds cost and risks for bleeding, infection and discomfort to the patient. We are developing a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kV X-ray images without the need for daily cone-beam computed tomography (CBCT). We trained a deep learning model to recognize prostate treatment target location from over 800 annotated projection X-ray images using data augmentation to enhance the learning process. The end point of the deep learning was a model capable of identifying the location of the prostate target for a given input X-ray projection image. To assess the accuracy of the approach, we retrospectively studied images from four patients who received volumetric modulated arc therapy (VMAT)for prostate cancer. In each case, the daily patient setup anterior-posterior (AP) and lateral images were used as the input for analysis by our deep learning model to derive the prostate target location. We quantitatively compared the results obtained from the deep learning model with the annotations. A comparison of the results with that defined by fiducial localization and with location derived from the on-board CBCT imaging was also done. Training the deep learning model took about one hour for each view angle. After training, the model identified the prostate location on a projection kV image within 200 ms. The deviations between the target positions obtained by the deep learning model and the corresponding annotations ranged from 0.7 mm to 2.21 mm for AP direction, and from 1.23 mm to 2.20 mm for lateral direction. Oblique images may be useful for online tracking. The deviations for images obtained at oblique angles ranged from 0.67 mm to 2.88 mm. Target position provided by deep learning model for the kV images acquired using OBI is found to be consistent that derived from the implanted FMs. This study demonstrates that highly accurate markerless prostate localization based on deep learning is achievable. The strategy provides a clinically valuable solution for image guided radiotherapy of the prostate and should be adaptable to other soft tissue tumor targets. The proposed method may be useful for daily patient positioning and real-time target tracking during prostate radiotherapy.

Study to Compare the Effect of Different Registration Methods on Patient Setup Uncertainties in Cone-beam Computed Tomography during Volumetric Modulated Arc Therapy for Breast Cancer Patients.

Purpose:This study compared three different methods used in registering cone-beam computed tomography (CBCT) image set with planning CT image set for determining patient setup uncertainties during volumetric modulated arc therapy (VMAT) for breast cancer patients.Materials and Methods:Seven breast cancer patients treated with 50 Gy in 25 fractions using VMAT technique were chosen for this study. A total of 105 CBCT scans were acquired by image guidance protocol for patient setup verification. Approved plans’ CT images were used as the reference image sets for registration with their corresponding CBCT image sets. Setup errors in mediolateral, craniocaudal, and anteroposterior direction were determined using gray-scale matching between the reference CT images and onboard CBCT images. Patient setup verification was performed using clip-box registration (CBR) method during online imaging. Considering the CBR method as the reference, two more registrations were performed using mask registration (MR) method and dual registration (DR) (CBR + MR) method in the offline mode. For comparison, systematic error (∑), random error (σ), mean displacement vector (R), mean setup error (M), and registration time (Rt) were analyzed. Post hoc Tukey's honest significant difference test was performed for multiple comparisons.Results:Systematic and random errors were less in CBR as compared to MR and DR (P > 0.05). The mean displacement error and mean setup errors were less in CBR as compared to MR and DR (P > 0.05). Increased Rt was observed in DR as compared to CBR and MR (P < 0.05). In addition, multiple comparisons did not show any significant difference in patient setup error (P > 0.05).Conclusion:For breast VMAT plan delivery, all three registration methods show insignificant variation in patient setup error. One can use any of the three registration methods for patient setup verification.

Small field dosimetry for the small animal radiotherapy research platform (SARRP)

BackgroundPreclinical radiation biology has become increasingly sophisticated due to the implementation of advanced small animal image guided radiation platforms into laboratory investigation. These small animal radiotherapy devices enable state-of-the-art image guided therapy (IGRT) research to be performed by combining high-resolution cone beam computed tomography (CBCT) imaging with an isocentric irradiation system. Such platforms are capable of replicating modern clinical systems similar to those that integrate a linear accelerator with on-board CBCT image guidance.MethodsIn this study, we present a dosimetric evaluation of the small animal radiotherapy research platform (SARRP, Xstrahl Inc.) focusing on small field dosimetry. Physical dosimetry was assessed using ion chamber for calibration and radiochromic film, investigating the impact of beam focus size on the dose rate output as well as beam characteristics (beam shape and penumbra). Two film analysis tools) have been used to assess the dose output using the 0.5 mm diameter aperture.ResultsGood agreement (between 1.7–3%) was found between the measured physical doses and the data provided by Xstrahl for all apertures used. Furthermore, all small field dosimetry data are in good agreement for both film reading methods and with our Monte Carlo simulations for both focal spot sizes. Furthermore, the small focal spot has been shown to produce a more homogenous beam with more stable penumbra over time.ConclusionsFilmQA Pro is a suitable tool for small field dosimetry, with a sufficiently small sampling area (0.1 mm) to ensure an accurate measurement. The electron beam focus should be chosen with care as this can potentially impact on beam stability and reproducibility.