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Funder: This work has been supported by ACMIT—Austrian Center for Medical Innovation and Technology, which is funded within the scope of the COMET program and funded by Austrian BMVIT and BMWFW and the governments of Lower Austria and Tyrol., Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.

Objective To investigate the relationship of the effective dose and dose area product (DAP) in dental cone beam computed tomography (CBCT) examinations and to propose conversion factors for estimation of effective doses of the patients using DAP. Dependence of organ doses on DAP was also investigated. Materials and Methods Different exposure geometries in Cranex3Dx CBCT device were simulated using Monte Carlo simulation and computational anthropomorphic phantom. Then organ doses and effective dose for every exposure geometry was compared to DAP and analysed. Results The effective dose in all simulated CBCT protocols and positions with 180-degrees tube rotation ranged from 5 µSv for 50 x 50 mm2 FOV localized on one tooth using lowest resolution to 265 µSv for the largest FOV and highest resolution. In case of 360-degrees tube rotation the effective dose ranges from 6 µSv to 332 µSv for the same FOV sizes and positions as well as resolutions as in 180-degree tube rotation. Conclusions Though the DAP introduces a large uncertainty in the risk measure in dental CBCT, it represents the dose and field of view (FOV) size which are the most important scanning parameters affecting the dose. To decrease uncertainty in the risk measure, the effective dose has to be estimated for usual clinical positions of the FOV.

Cone-beam CT (CBCT) scans utilized for image guided radiation therapy (IGRT) procedures have become an essential part of radiotherapy. The aim of this study was to assess organ and effective doses resulting from new CBCT scan protocols (head, thorax, and pelvis) released with a software upgrade of the kV on-board-imager (OBI) system. Influence of the scan parameters that were changed in the new protocols on the patient dose was also investigated. Organ and effective doses for protocols of the new software (V2.5) and a previous version (V1.6) were assessed using Monte Carlo (MC) simulations for the International Commission on Radiological Protection (ICRP) adult male and female reference computational phantoms. The number of projections and the mAs values were increased and the size of the scan field was extended in the new protocols. Influence of these changes on organ and effective doses of the scans was investigated. The OBI system was modelled in EGSnrc/BEAMnrc, and organ doses were estimated using EGSnrc/DOSXYZnrc. The MC model was benchmarked against experimental measurements. Organ doses resulting from the V2.5 protocols were higher than those of V1.6 for organs that were partially or fully inside the scans fields, and increased by (3 to 13)%, (10 to 77)%, and (13 to 21)% for the head, thorax, and pelvis protocols for both phantoms, respectively. As a result, effective doses rose by 14%, 17%, and 16% for the male phantom, and 13%, 18%, and 17% for the female phantom for the three scan protocols, respectively. The scan field extension for the V2.5 protocols contributed significantly in the dose increases, especially for organs that were partially irradiated such as the thyroid in head and thorax scans and colon in the pelvic scan. The contribution of the mAs values and projection numbers was minimal in the dose increases, up to 2.5%. The field size extension plays a major role in improving the treatment output by including more markers in the field of view to match between CBCT and CT images and hence setting up the patient precisely. Therefore, a trade-off between the risk and benefits of CBCT scans should be considered, and the dose increases should be monitored. Several recommendations have been made for optimization of the patient dose involved for IGRT procedures.

We investigated the feasibility of patient dose reduction based on six noise suppression filters for cone-beam computed tomography (CBCT) in an image-guided patient positioning (IGPP) system. A midpoint dose was employed as a patient dose index. First, a reference dose (RD) and low-dose (LD)-CBCT images were acquired with a reference dose and various low doses. Second, an automated rigid registration was performed for three axis translations to estimate patient setup errors between a planning CT image and the LD-CBCT images processed by six noise suppression filters (averaging filter, median filter, Gaussian filter, edge-preserving smoothing filter, bilateral filter, and adaptive partial median filter (AMF)). Third, residual errors representing the patient positioning accuracy were calculated as Euclidean distances between the setup error vectors estimated using the LD-CBCT and RD-CBCT images. Finally, the residual errors as a function of the patient dose index were estimated for LD-CBCT images processed by six noise suppression filters, and then the patient dose indices for the filtered LD-CBCT images were obtained at the same residual error as the RD-CBCT image. This approach was applied to an anthropomorphic phantom and four cancer patients. The patient dose for the LD-CBCT images was reduced to 19% of that for the RD-CBCT image for the phantom by using AMF, while keeping a same residual error of 0.47 mm as the RD-CBCT image by applying the noise suppression filters to the LD-CBCT images. The average patient dose was reduced to 31.1% for prostate cancer patients, and it was reduced to 82.5% for a lung cancer patient by applying the AMF. These preliminary results suggested that the proposed approach based on noise suppression filters could decrease the patient dose in IGPP systems.

Intensive computation time is required to simulate images of electronic portal imaging device (EPID) using Monte Carlo (MC) technique, limiting the development of applications associated with EPID, such as mega-voltage cone-beam computed tomography (MV-CBCT). In this study, a fast, accurate simulation strategy for MV-CBCT utilizing the FastEPID technique has been developed and validated. During FastEPID simulation, photon detection was determined by pre-calculated photon energy deposition efficiency (η) and particle transport within the EPID was replaced with a pre-calculated optical photon spread function. This method is capable of reducing the time required for EPID image simulation by a factor of 90–140, without compromising image quality. MV-CBCT images reconstructed from the FastEPID simulated projections have been validated against measurement in terms of mean Hounsfield unit (HU), noise, and cupping artifact. These images were obtained with both a Catphan 604 phantom and an anthropomorphic pelvis phantom, under treatment beam energies of 2.5 MV, 6 MV, and 6 MV flattening filter free. The agreement between measurement and simulation was excellent in all cases. This novel strategy was capable of reducing the run time of a full scan simulation of MV-CBCT performed on a CPU cluster to a matter of hours, rather than weeks or months required by a conventional approach. Multiple applications associated with MV-CBCT (e.g. imager design optimization) are anticipated to gain from the implementation of this novel simulation strategy.

The purpose of this study is to define a simplified method to accurately predict and characterize kV cone beam computed tomography (kV CBCT) and computed tomography (CT) image contrast enhancement from gold nanoparticles (GNPs). Parameters of the kV CBCT of a Varian Novalis Tx linear accelerator and of a GE LightSpeed 4 Big Bore CT machine were modeled using the MCNP 6.2 Monte Carlo code. A 0.25 × 0.25 cm(2) source, defined with a 100 kVp energy spectrum with appropriate filtration, was implemented in the MCNP6.2 model for kV CBCT, which also contained x- and y-blades and a full bowtie filter. A 1 cm(3) cube of GNP solution (modeled as a mass percentage of gold in water) was placed 100 cm below the source. For the CT-simulator model, a source was defined with energy spectra for 80 and 140 kVp x-rays with appropriate filtration and angular spectrum. A 1 cm(3) GNP solution was modeled as before and a detector was placed 40 cm below that. Attenuation coefficients of four GNP solutions were computed and Hounsfield unit (HU) values were calculated. The computed HU values were compared against experimentally measured values obtained by scanning batches of GNPs of various sizes and concentrations using a GE LightSpeed 4 Big Bore CT scanner at 80 kVp and 140 kVp energies, as well as the kV CBCT capability of a Varian Novalis Tx linear accelerator. HU analysis was carried out using Velocity Medical Solutions clinical CT image analysis software. The MCNP calculated HU values matched the measured values to within ± 5%. Image contrast enhancement analysis showed a total increase in HU of up to 223. The sample having the highest gold mass percentage tested showed the greatest increase in HU number compared to water.

Four dimensional cone beam computed tomography (4DCBCT) images suffer from angular under sampling and bunching of projections due to a lack of feedback between the respiratory signal and the acquisition system. To address this problem, respiratory motion guided 4DCBCT (RMG-4DCBCT) regulates the gantry velocity and projection time interval, in response to the patient's respiratory signal, with the aim of acquiring evenly spaced projections in a number of phase or displacement bins during the respiratory cycle. Our previous study of RMG-4DCBCT was limited to sinusoidal breathing traces. Here we expand on that work to provide a practical algorithm for the case of real patient breathing data. We give a complete description of RMG-4DCBCT including full details on how to implement the algorithms to determine when to move the gantry and when to acquire projections in response to the patient's respiratory signal. We simulate a realistic working RMG-4DCBCT system using 112 breathing traces from 24 lung cancer patients. Acquisition used phase-based binning and parameter settings typically used on commercial 4DCBCT systems (4 min acquisition time, 1200 projections across 10 respiratory bins), with the acceleration and velocity constraints of current generation linear accelerators. We quantified streaking artefacts and image noise for conventional and RMG-4DCBCT methods by reconstructing projection data selected from an oversampled set of Catphan phantom projections. RMG-4DCBCT allows us to optimally trade-off image quality, acquisition time and image dose. For example, for the same image quality and acquisition time as conventional 4DCBCT approximately half the imaging dose is needed. Alternatively, for the same imaging dose, the image quality as measured by the signal to noise ratio, is improved by 63% on average. C-arm cone beam computed tomography systems, with an acceleration up to 200°/s(2), a velocity up to 100°/s and the acquisition of 80 projections per second, allow the image acquisition time to be reduced to below 60 s. We have made considerable progress towards realizing a system to reduce projection clustering in conventional 4DCBCT imaging and hence reduce the imaging dose to the patient.

Robotic C-arm cone beam computed tomography (CBCT) systems are playing an increasingly pivotal role in interventional cardiac procedures and high precision radiotherapy treatments. One of the main challenges in any form of cardiac imaging is mitigating the intrinsic motion of the heart, which causes blurring and artefacts in the 3D reconstructed image. Most conventional 3D cardiac CBCT acquisition techniques attempt to combat heart motion through retrospective gating techniques, whereby acquired projections are sorted into the desired cardiac phase after the completion of the scan. However, this results in streaking artefacts and unnecessary radiation exposure to the patient. Here, we present our Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) acquisition protocol that uses the patient's electrocardiogram (ECG) signal to adaptively regulate the gantry velocity and projection time interval in real-time. It enables prospectively gated patient connected imaging in a single sweep of the gantry. The XCAT digital software phantom was used to complete a simulation study to compare ACROBEAT to a conventional multi-sweep retrospective ECG gated acquisition, under a variety of different acquisition conditions. The effect of location and length of the acquisition window and total number of projections acquired on image quality and total scan time were examined. Overall, ACROBEAT enables up to a 5 times average improvement in the contrast-to-noise ratio, a 40% reduction in edge response width and an 80% reduction in total projections acquired compared to conventional multi-sweep retrospective ECG gated acquisition.

Cone beam computed tomography (CBCT) images can be used for dose calculation in adaptive radiation therapy (ART). The main challenges are the large artefacts and inaccurate Hounsfield unit (HU) values. Currently, deformed planning CT images are often used for this purpose, although anatomical accuracy might be a concern. Ideally, we would like to convert CBCT images to CT images with artifacts removed or greatly reduced and HU values corrected while keeping the anatomical accuracy. Recently, deep learning has achieved great success in image-to-image translation tasks. It is very difficult to acquire paired CT and CBCT images with exactly matching anatomy for supervised training. To overcome this limitation, we developed and tested a cycle generative adversarial network (CycleGAN) which is an unsupervised learning method and does not require paired training datasets to synthesize CT images from CBCT images. The synthesized CT (sCT) images have been compared with the deformed planning CT (dpCT) showing visual and quantitative similarity with artifacts being removed and HU value errors being reduced from 71.78 HU to 27.98 HU. Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95.4% to 97.4% for 1 mm/1% criteria. A deformable phantom study has been conducted and demonstrated better anatomical accuracy for sCT over dpCT., 14 pages, 10 figures, 3 tables

Measurement of the dose gradients from the entrance surface to depth is a standard task for characterizing an ionizing radiation beam. Most gel dosimeters provide spurious results near an air interface, limiting their value for this geometry. In this study, a 3D dosimeter system consisting of a low-diffusion, radiochromic hydrogel cast in a custom polyethylene terephthalate (PETE) vessel and imaged with a modified commercial optical cone-beam computed tomography (CBCT) scanner was employed. The cylindrical vessel wall and flat ends were constructed from a 0.025 cm thick PETE sheet. The optical CBCT scanner was modified to place the entire vessel in the centre of the field of view or to have the vessel base at the optical axis. Pre-irradiation and post-irradiation scans were acquired with the sample mounted in the standard and elevated positions. The sample was irradiated with a 2x2 cm square, 6 MV x-ray beam. Normalized attenuation coefficients from the central quarter of the reconstructed beam images were compared to a Monte Carlo depth dose calculation. Placing the vessel base at the optical axis allowed accurate dose measurements to within 0.2 cm of the entrance face and for the standard position to within 0.4 cm. These measurements validated the Monte Carlo calculation and provide an alternative to parallel plate ion chambers for dose measurement in the build-up region.

X-ray scatter is a significant problem in cone-beam computed tomography when thicker objects and larger cone angles are used, as scattered radiation can lead to reduced contrast and CT number inaccuracy. Advances have been made in x-ray computed tomography (CT) by incorporating a high quality prior image into the image reconstruction process. In this paper, we extend this idea to correct scatter-induced shading artifacts in cone-beam CT image-guided radiation therapy. Specifically, this paper presents a new scatter correction algorithm which uses a prior image with low scatter artifacts to reduce shading artifacts in cone-beam CT images acquired under conditions of high scatter. The proposed correction algorithm begins with an empirical hypothesis that the target image can be written as a weighted summation of a series of basis images that are generated by raising the raw cone-beam projection data to different powers, and then, reconstructing using the standard filtered backprojection algorithm. The weight for each basis image is calculated by minimizing the difference between the target image and the prior image. The performance of the scatter correction algorithm is qualitatively and quantitatively evaluated through phantom studies using a Varian 2100 EX System with an on-board imager. Results show that the proposed scatter correction algorithm using a prior image with low scatter artifacts can substantially mitigate scatter-induced shading artifacts in both full-fan and half-fan modes.

Four-dimensional (4D) x-ray cone-beam computed tomography (CBCT) is important for a precise radiation therapy for lung cancer. Due to the repeated use and 4D acquisition over a course of radiotherapy, the radiation dose becomes a concern. Meanwhile, the scatter contamination in CBCT deteriorates image quality for treatment tasks. In this work, we propose the use of a moving blocker (MB) during the 4D CBCT acquisition ('4D MB') and to combine motion-compensated reconstruction to address these two issues simultaneously. In 4D MB CBCT, the moving blocker reduces the x-ray flux passing through the patient and collects the scatter information in the blocked region at the same time. The scatter signal is estimated from the blocked region for correction. Even though the number of projection views and projection data in each view are not complete for conventional reconstruction, 4D reconstruction with a total-variation (TV) constraint and a motion-compensated temporal constraint can utilize both spatial gradient sparsity and temporal correlations among different phases to overcome the missing data problem. The feasibility simulation studies using the 4D NCAT phantom showed that 4D MB with motion-compensated reconstruction with 1/3 imaging dose reduction could produce satisfactory images and achieve 37% improvement on structural similarity (SSIM) index and 55% improvement on root mean square error (RMSE), compared to 4D reconstruction at the regular imaging dose without scatter correction. For the same 4D MB data, 4D reconstruction outperformed 3D TV reconstruction by 28% on SSIM and 34% on RMSE. A study of synthetic patient data also demonstrated the potential of 4D MB to reduce the radiation dose by 1/3 without compromising the image quality. This work paves the way for more comprehensive studies to investigate the dose reduction limit offered by this novel 4D MB method using physical phantom experiments and real patient data based on clinical relevant metrics.

While megavoltage (MV) cone-beam computed tomography (CBCT) using an electronic portal imaging device (EPID) provides many advantages over kilovoltage (kV) CBCT, clinical adoption is limited by its high doses. Multi-layer imager (MLI) EPIDs increase DQE(0) while maintaining high resolution. However, even well-designed, high-performance MLIs suffer from increased electronic noise from each readout, degrading low-dose image quality. To improve low-dose performance, shift-and-bin addition (ShiBA) imaging is proposed, leveraging the unique architecture of the MLI. ShiBA combines hardware readout-binning and super-resolution concepts, reducing electronic noise while maintaining native image sampling. The imaging performance of full-resolution (FR); standard, aligned binned (BIN); and ShiBA images in terms of noise power spectrum (NPS), electronic NPS, modulation transfer function (MTF), and the ideal observer signal-to-noise ratio (SNR) - the detectability index (d′) – are compared. The FR 4-layer readout of the prototype MLI exhibits an electronic NPS magnitude 6-times higher than a state-of-the-art single layer (SLI) EPID. Although the MLI is built on the same readout platform as the SLI, with each layer exhibiting equivalent electronic noise, the multi-stage readout of the MLI results in electronic noise 50% higher than simple summation. Electronic noise is mitigated in both BIN and ShiBA imaging, reducing its total by ~12 times. ShiBA further reduces the NPS, effectively upsampling the image, resulting in a multiplication by a sinc2 function. Normalized NPS (NNPS) show that neither ShiBA or BIN otherwise affects image noise. The LSF shows that ShiBA removes the pixilation artifact of BIN images and mitigates the effect of detector shift, but does not quantifiably improve the MTF. ShiBA provides a pre-sampled representation of the images, mitigating phase dependence. Hardware binning strategies lower the quantum noise floor, with 2×2 implementation reducing the dose at which DQE(0) degrades by 10% from 0.01 MU to 0.004 MU, representing 20% improvement in d′.

Measured trajectories of markers on vessel wall and beads on a plumb are compared with calculated trajectories for transmission images obtained with Vista16™ optical cone beam scanner. Effective pixel size was 0.11 mm. An algorithm was developed to measure trajectories for the plumb line phantom and a semi-automatic version was required for the gel filled vessels due to overlap of markers and seam projections at maximum lateral positions. It was found that for 15 cm diameter vessels, the vessel edge was not visible due to divergence in cone beam geometry. Accounting for this effect was necessary in order to remove artefacts from calculated vessel trajectory. Displacement differences were found to be less than 0.1 mm for the plumb line, 0.3 mm for a beads and 1 mm for markers on gel filled vessel. Minimization of the vessel marker trajectory deviation will provide a more accurate approach to refractive index optimization. No optical aberrations were detected from the trajectory analysis and manufacturer scanner alignment and geometrical calibrations were independently verified.

Cone-beam computed tomography (CBCT) has been clinically used to verify patient position and to localize the target of treatment in image-guided radiation therapy (IGRT). However, when the chest and the upper abdomen are scanned, respiratory-induced motion blurring limits the utility of CBCT. In order to mitigate this blurring, respiratory-gated CBCT, i.e. 4D CBCT, was introduced. In 4D CBCT, the cone-beam projection data sets acquired during a gantry rotation are sorted into several respiratory phases. In these gated reconstructions, the number of projections for each respiratory phase is significantly reduced. Consequently, undersampling streaking artifacts are present in the reconstructed images, and the image contrast resolution is also significantly compromised. In this paper, we present a new method to simultaneously achieve both high temporal resolution ( approximately 100 ms) and streaking artifact-free image volumes in 4D CBCT. The enabling technique is a newly proposed image reconstruction method, i.e. prior image constrained compressed sensing (PICCS), which enables accurate image reconstruction using vastly undersampled cone-beam projections and a fully sampled prior image. Using PICCS, a streak-free image can be reconstructed from 10-20 cone-beam projections while the signal-to-noise ratio is determined by a denoising feature of the selected objective function and by the prior image, which is reconstructed using all of the acquired cone-beam projections. This feature of PICCS breaks the connection between the temporal resolution and streaking artifacts' level in 4D CBCT. Numerical simulations and experimental phantom studies have been conducted to validate the method.

In a recently published paper by Li et al (2018 Phys. Med. Biol. 63 075001), reconstruction parameters were optimized for the so-called synchronized multiArtifact reduction with tomographic reconstruction (SMART-RECON) method to enable time-resolved Cone beam computed tomography angiography (TR-CBCTA) from a single short-scan CBCT data set. However, the paper did not quantitatively address how much temporal resolution can be improved by using the SMART-RECON algorithm in TR-CBCTA. The purpose of this note was to present a method to quantify this temporal resolution improvement factor and to use that method to quantify the improvement in temporal resolution using SMART-RECON and optimized reconstruction parameters. In the method proposed in this note, the potential temporal blurring caused by SMART-RECON was modeled as a convolution between a temporal Gaussian blurring kernel and the ground truth temporal enhancement curve. The width parameter of the resulting Gaussian blurring kernel was used as a surrogate metric for temporal resolution to quantify the achievable temporal resolution for the conventional filtered backprojection (FBP) and SMART-RECON methods. The ratio of the surrogate temporal resolutions for the two reconstruction methods was calculated to quantify the factor of temporal resolution improvement. The quantitative results show that the temporal resolution is improved by a factor of 4.5 using SMART-RECON compared with FBP.

Mandibular ramus thickness is one of the most important factors that must be considered while performing bilateral sagittal split osteotomy (BSSO). Unfavourable fracture or bad split could occur while performing BSSO if the mandibular ramus is thin. To the best of our knowledge, there are only a few published reports with anthropometric data about the thickness of the mandibular ramus. The objective of this study was to measure the thickness of the mandibular ramus based on cone beam computed tomography (CBCT) to be used as a reference while performing BSSO. The study subjects comprised 61 data samples of CBCT-based DICOM images which reoriented in three planes, and we measured the thickness of the mandibular ramus. The mean thickness was 8.049 + 1.205 mm for males and 8.463 + 1.358 mm for females. For 18–30-, 31–40- and 41–50-year-old patients, the mean thickness of the mandibular ramus was 8.087 + 1.29 mm, 8.176 + 1.49 mm and 8.742 + 1.04 mm, respectively. Based on the CBCT images, there were no statistically significant differences between the thicknesses of the mandibular ramus in terms of sex and age.

Three alternative methodologies to the Computed-Tomography Dose Index for the evaluation of Cone-Beam Computed Tomography dose are compared, the Cone-Beam Dose Index, IAEA Human Health Report No. 5 recommended methodology and the AAPM Task Group 111 recommended methodology. The protocols were evaluated for Pelvis and Thorax scan modes on Varian® On-Board Imager and Truebeam kV XI imaging systems. The weighted planar average dose was highest for the AAPM methodology across all scans, with the CBDI being the second highest overall. A 17.96% and 1.14% decrease from the TG-111 protocol to the IAEA and CBDI protocols for the Pelvis mode and 18.15% and 13.10% decrease for the Thorax mode were observed for the XI system. For the OBI system, the variation was 16.46% and 7.14% for Pelvis mode and 15.93% to the CBDI protocol in Thorax mode respectively.

Cone-beam computed tomography (CBCT) technique has been widely used in dental imaging as it offers different image views such as panoramic, cephalometric, tomographic, and three dimensions (3D). This study aimed to assess image distortion due to beam misalignment for CBCT system using phantom study. A ball phantom was scanned with default exposure setting for standard panoramic imaging (68 kV, 8 mA and patient size of M) using Planmeca ProMax CBCT unit. Three different measurements were performed on the phantom images such as distance measurement between the centre of the middle ball and rear middle ball, distance measured from the centre of middle ball to tenth ball (left and right), and measurement of both vertical and horizontal distance of any four balls. From the findings, most of measured distance between middle ball and rear middle ball were below the acceptable level, but two measurements exceeded the reference value by 6% and 75%. Similar with the measured distances of the tenth balls, two measurements exceeded the acceptable level by 3% and 28%. The vertical and horizontal distance of 4 balls were slightly differ from standard value with overall magnification of 1.06 and percentage error of 6%. The beam alignment and patient positioning should be accurately evaluated to avoid image distortion and misdiagnosis in dental CBCT images.

A new scanner geometry for fast optical cone-beam computed tomography is reported. The system consists of a low power laser beam, raster scanned, under computer control, through a transparent object in a refractive index matching aquarium. The transmitted beam is scattered from a diffuser screen and detected by a photomultiplier tube. Modest stray light is present in the projection images since only a single ray is present in the object during measurement and there is no imaging optics to introduce further stray light in the form of glare. A scan time of 30 minutes was required for 512 projections with a field of view of 12 × 18 cm. Initial performance from scanning a 15 cm diameter jar with black solutions is presented. Averaged reconstruction coefficients are within 2% along the height of the jar and within the central 85% of diameter, due to the index mismatch of the jar. Agreement with spectrometer measurements was better than 0.5% for a minimum transmission of 4% and within 4% for a dark, 0.1% transmission sample. This geometry's advantages include high dynamic range and low cost of scaling to larger (>15 cm) fields of view.

Both x-ray and optical cone-beam CT (CBCT) scanners are limited by scattered photons that contaminate the primary images. Transparent radiochromic gels and plastics are anticipated to produce less scattering than polymerization gels and hence result in reconstructed dose distributions with greater contrast and dynamic range. Four methods of scatter measurement were investigated to quantify scatter fractions using an in-house optical CBCT scanner. The methods consisted of generating either "coin" shadows or primary beamlets in transmission images. The four methods generated similar results of 10% scatter fractions in gel transmission images for this particular scanner configuration with both leuco crystal violet and carbon black micelle gels. The scatter contributions were distributed as 5% from micelle gel, 2% from Teflon wall, 2% from refractive index matching liquid and 1% other sources. Applying a uniform background subtraction of 2% of open field intensity resulted in 0.2% difference between laser CT and cone-beam CT reconstructed attenuation coefficients for a black micelle gel finger phantom.

Objective. Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning. However, the performance of existing metal artifact reduction (MAR) methods in handling the loss of the morphological structures of teeth in reconstructed CT images remains relatively limited. In this study, we developed an innovative MAR method to achieve optimal restoration of anatomical details. Approach. The proposed MAR approach is based on a two-stage deep learning-based method. In the first stage, we employ a deep learning network that utilizes intra-oral scan data as side-inputs and performs multi-task learning of auxiliary tooth segmentation. The network is designed to improve the learning ability of capturing teeth-related features effectively while mitigating metal artifacts. In the second stage, a 3D bone-teeth-jaw model is constructed with weighted thresholding, where the weighting region is determined depending on the geometry of the intra-oral scan data. Main results. The results of numerical simulations and clinical experiments are presented to demonstrate the feasibility of the proposed approach. Significance. We propose for the first time a MAR method using radiation-free intra-oral scan data as supplemental information on the tooth morphological structures of teeth, which is designed to perform accurate 3D bone-teeth-jaw modeling in low-dose CBCT environments.

Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy.

In this study, the performance of a new iterative reconstruction algorithm, the pre-clinical AcurosXB iCBCT algorithm, has been characterized on Varian Halcyon linear accelerators with respect to the potential of radiotherapy dose calculations on CBCT images. The study utilized various phantom setups to verify the accuracy of the pre-clinical algorithm under different scatter conditions and compared dose calculations performed on CBCT images reconstructed with the pre-clinical algorithm to those performed on typical planning CT images. The results indicated that despite showing improvements compared to the existing iCBCT protocol, certain restrictions should be introduced when the pre-clinical AcurosXB iCBCT algorithm was used for dose calculations. Changes in the scatter condition exhibited a larger effect on CBCTs than on planning CTs. Therefore, users should be careful in offsetting the patient and positioning the patient’s arms if the resultant images will be used for dose calculations. In addition, protocols with different kV settings should be approached with caution, where 100 kV protocols should only be used to scan the head and neck area, while the rest of the body should be scanned with the 125 kV and 140 kV protocols. When the patient is set up properly and the appropriate energy is selected for the anatomical area, the uncertainty of using the novel AcurosXB iCBCT algorithm for treatment planning dose calculation is within ±2.0%.

Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT. Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric. Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σ DL-VIF = 0.008 versus σ gradient entropy = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images. Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.

Objective. Modern preclinical small animal radiation platforms utilize cone beam computerized tomography (CBCT) for image guidance and experiment planning purposes. The resolution of CBCT images is of particular importance for visualizing fine animal anatomical structures. One major cause of spatial resolution reduction is the finite size of the x-ray focal spot. In this work, we proposed a simple method to measure x-ray focal spot intensity map and a CBCT image domain deblurring model to mitigate the effect of focal spot-induced image blurring. Approach. We measured a projection image of a tungsten ball bearing using the flat panel detector of the CBCT platform. We built a forward blurring model of the projection image and derived the spot intensity map by deconvolving the measured projection image. Based on the measured spot intensity map, we derived a CBCT image domain blurring model for images reconstructed by the filtered backprojection algorithm. Based on this model, we computed image domain blurring kernel and improved the CBCT image resolution by deconvolving the CBCT image. Main results. We successfully measured the x-ray focal spot intensity map. The spot size characterized by full width at half maximum was ∼0.75 × 0.55 mm2 at 40 kVp. We computed image domain convolution kernels caused by the x-ray focal spot. A simulation study on noiseless projections was performed to evaluate the spatial resolution improvement exclusively by the focal spot kernel, and the modulation transfer function (MTF) at 50% was increased from 1.40 to 1.65 mm−1 for in-plane images and 1.05–1.32 mm−1 for cross-plane images. Experimental studies on a CT insert phantom and a plastinated mouse phantom demonstrated improved spatial resolution after image domain deconvolution, as indicated by visually improved resolution of fine structures. MTF at 50% was improved from 1.00 to 1.12 mm−1 for in-plane direction and from 0.72 to 0.84 mm−1 for cross-plane direction. Significance. The proposed method to mitigate blurring caused by finite x-ray spot size and improve CBCT image resolution is simple and effective.

Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds.

Objective. In this study we developed an automatic method to predict tumour volume and shape in weeks 3 and 4 of radiotherapy (RT), using cone-beam computed tomography (CBCT) scans acquired up to week 2, allowing identification of large tumour changes. Approach. 240 non-small cell lung cancer (NSCLC) patients, treated with 55 Gy in 20 fractions, were collected. CBCTs were rigidly registered to the planning CT. Intensity values were extracted in each voxel of the planning target volume across all CBCT images from days 1, 2, 3, 7 and 14. For each patient and in each voxel, four regression models were fitted to voxel intensity; applying linear, Gaussian, quadratic and cubic methods. These models predicted the intensity value for each voxel in weeks 3 and 4, and the tumour volume found by thresholding. Each model was evaluated by computing the root mean square error in pixel value and structural similarity index metric (SSIM) for all patients. Finally, the sensitivity and specificity to predict a 30% change in volume were calculated for each model. Main results. The linear, Gaussian, quadratic and cubic models achieved a comparable similarity score, the average SSIM for all patients was 0.94, 0.94, 0.90, 0.83 in week 3, respectively. At week 3, a sensitivity of 84%, 53%, 90% and 88%, and specificity of 99%, 100%, 91% and 42% were observed for the linear, Gaussian, quadratic and cubic models respectively. Overall, the linear model performed best at predicting those patients that will benefit from RT adaptation. The linear model identified 21% and 23% of patients in our cohort with more than 30% tumour volume reduction to benefit from treatment adaptation in weeks 3 and 4 respectively. Significance. We have shown that it is feasible to predict the shape and volume of NSCLC tumours from routine CBCTs and effectively identify patients who will respond to treatment early.

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

Visualization of anatomy in real time is of critical importance for motion management in lung cancer radiotherapy. To achieve real-time, and high-contrast in-treatment imaging, we propose a novel scheme based on the measurement of Compton scatter photons. In our method, a slit x-ray beam along the superior-inferior direction is directed to the patient, (intersecting the lung region at a 2D plane) containing most of the tumor motion trajectory. X-ray photons are scattered off this plane primarily due to the Compton interaction. An imager with a pinhole or a slat collimator is placed at one side of the plane to capture the scattered photons. The resulting image, after correcting for incoming fluence inhomogeneity, x-ray attenuation, scatter angle variation, and outgoing beam geometry, represents the linear attenuation coefficient of Compton scattering. This allows the visualization of the anatomy on this plane. We performed Monte Carlo simulation studies both on a phantom and a patient for proof-of-principle purposes. In the phantom case, a small tumor-like structure could be clearly visualized. The contrast-resolution calculated using tumor/lung as foreground/background for kV fluoroscopy, cone beam computed tomography (CBCT), and scattering image were 0.037, 0.70, and 0.54, respectively. In the patient case, tumor motion could be clearly observed in the scatter images. Imaging dose to the voxels directly exposed by the slit beam was ~0.4 times of that under a single CBCT projection. These studies demonstrated the potential feasibility of the proposed imaging scheme to capture the instantaneous anatomy of a patient on a 2D plane with a high image contrast. Clear visualization of the tumor motion may facilitate marker-less tumor tracking.

The purpose of this study was to develop metrics to evaluate uncertainties in deformable dose accumulation for patients with non-small cell lung cancer (NSCLC). Initial treatment plans (primary) and cone-beam CT (CBCT) images were retrospectively processed for seven NSCLC patients, who showed significant tumor regression during the course of treatment. Each plan was developed with IMRT for 2 Gy × 33 fractions. A B-spline-based DIR algorithm was used to register weekly CBCT images to a reference image acquired at fraction 21 and the resultant displacement vector fields (DVFs) were then modified using a finite element method (FEM). The doses were calculated on each of these CBCT images and mapped to the reference image using a tri-linear dose interpolation method, based on the B-spline and FEM-generated DVFs. Contours propagated from the planning image were adjusted to the residual tumor and OARs on the reference image to develop a secondary plan. For iso-prescription adaptive plans (relative to initial plans), mean lung dose (MLD) was reduced, on average from 17.3 Gy (initial plan) to 15.2, 14.5 and 14.8 Gy for the plans adapted using the rigid, B-Spline and FEM-based registrations. Similarly, for iso-toxic adaptive plans (considering MLD relative to initial plans) using the rigid, B-Spline and FEM-based registrations, the average doses were 69.9 ± 6.8, 65.7 ± 5.1 and 67.2 ± 5.6 Gy in the initial volume (PTV1), and 81.5 ± 25.8, 77.7 ± 21.6, and 78.9 ± 22.5 Gy in the residual volume (PTV21), respectively. Tumor volume reduction was correlated with dose escalation (for isotoxic plans, correlation coefficient = 0.92), and with MLD reduction (for iso-fractional plans, correlation coefficient = 0.85). For the case of the iso-toxic dose escalation, plans adapted with the B-Spline and FEM DVFs differed from the primary plan adapted with rigid registration by 2.8 ± 1.0 Gy and 1.8 ± 0.9 Gy in PTV1, and the mean difference between doses accumulated using the B-spline and FEM DVF's was 1.1 ± 0.6 Gy. As a dose mapping-induced energy change, energy defect in the tumor volume was 20.8 ± 13.4% and 4.5 ± 2.4% for the B-spline and FEM-based dose accumulations, respectively. The energy defect of the B-Spline-based dose accumulation is significant in the tumor volume and highly correlated to the difference between the B-Spline and FEM-accumulated doses with their correlation coefficient equal to 0.79. Adaptive planning helps escalate target dose and spare normal tissue for patients with NSCLC, but deformable dose accumulation may have a significant loss of energy in regressed tumor volumes when using image intensity-based DIR algorithms. The metric of energy defect is a useful tool for evaluation of adaptive planning accuracy for lung cancer patients.

Tumor regression during the course of fractionated radiotherapy confounds the ability to accurately estimate the total dose delivered to tumor targets. Here we present a new criterion to improve the accuracy of image intensity-based dose mapping operations for adaptive radiotherapy for patients with non-small cell lung cancer (NSCLC). Six NSCLC patients were retrospectively investigated in this study. An image intensity-based B-spline registration algorithm was used for deformable image registration (DIR) of weekly CBCT images to a reference image. The resultant displacement vector fields were employed to map the doses calculated on weekly images to the reference image. The concept of energy conservation was introduced as a criterion to evaluate the accuracy of the dose mapping operations. A finite element method (FEM)-based mechanical model was implemented to improve the performance of the B-Spline-based registration algorithm in regions involving tumor regression. For the six patients, deformed tumor volumes changed by 21.2 ± 15.0% and 4.1 ± 3.7% on average for the B-Spline and the FEM-based registrations performed from fraction 1 to fraction 21, respectively. The energy deposited in the gross tumor volume (GTV) was 0.66 Joules (J) per fraction on average. The energy derived from the fractional dose reconstructed by the B-spline and FEM-based DIR algorithms in the deformed GTV's was 0.51 J and 0.64 J, respectively. Based on landmark comparisons for the 6 patients, mean error for the FEM-based DIR algorithm was 2.5 ± 1.9 mm. The cross-correlation coefficient between the landmark-measured displacement error and the loss of radiation energy was -0.16 for the FEM-based algorithm. To avoid uncertainties in measuring distorted landmarks, the B-Spline-based registrations were compared to the FEM registrations, and their displacement differences equal 4.2 ± 4.7 mm on average. The displacement differences were correlated to their relative loss of radiation energy with a cross-correlation coefficient equal to 0.68. Based on the principle of energy conservation, the FEM-based mechanical model has a better performance than the B-Spline-based DIR algorithm. It is recommended that the principle of energy conservation be incorporated into a comprehensive QA protocol for adaptive radiotherapy.

A prototype cone-beam CT (CBCT) head scanner featuring model-based iterative reconstruction (MBIR) has been recently developed and demonstrated the potential for reliable detection of acute intracranial hemorrhage (ICH), which is vital to diagnosis of traumatic brain injury and hemorrhagic stroke. However, data truncation (e.g., due to the head holder) can result in artifacts that reduce image uniformity and challenge ICH detection. We propose a multi-resolution MBIR method with an extended reconstruction field of view (RFOV) to mitigate truncation effects in CBCT of the head. The image volume includes a fine voxel size in the (inner) nontruncated region and a coarse voxel size in the (outer) truncated region. This multi-resolution scheme allows extension of the RFOV to mitigate truncation effects while introducing minimal increase in computational complexity. The multi-resolution method was incorporated in a penalized weighted least-squares (PWLS) reconstruction framework previously developed for CBCT of the head. Experiments involving an anthropomorphic head phantom with truncation due to a carbon-fiber holder were shown to result in severe artifacts in conventional single-resolution PWLS, whereas extending the RFOV within the multi-resolution framework strongly reduced truncation artifacts. For the same extended RFOV, the multi-resolution approach reduced computation time compared to the single-resolution approach (viz. time reduced by 40.7%, 83.0%, and over 95% for an image volume of 6003, 8003, 10003 voxels). Algorithm parameters (e.g., regularization strength, the ratio of the fine and coarse voxel size, and RFOV size) were investigated to guide reliable parameter selection. The findings provide a promising method for truncation artifact reduction in CBCT and may be useful for other MBIR methods and applications for which truncation is a challenge.

The aim of this work is to investigate in-room proton radiographies to compensate realistic rigid and non-rigid transformations in clinical-like scenarios based on 2D–3D deformable image registration (DIR) framework towards future clinical implementation of adaptive radiation therapy (ART). Monte Carlo simulations of proton radiographies (pRads) based on clinical x-ray CT of a head and neck, and a brain tumor patients are simulated for two different detector configurations (i.e. integration-mode and list-mode detectors) including high and low proton statistics. A realistic deformation, derived from cone beam CT of the patient, is applied to the treatment planning CT. Rigid inaccuracies in patient positioning are also applied and the effect of small, medium and large fields of view (FOVs) is investigated. A stopping criterion, as desirable in realistic scenarios devoid of ground truth proton CT (pCT), is proposed and investigated. Results show that rigid and non-rigid transformations can be compensated based on a limited number of low dose pRads. The root mean square error with respect to the pCT shows that the 2D–3D DIR of the treatment planning CT based on 10 pRads from integration-mode data and 2 pRads from list-mode data is capable of achieving comparable accuracy (∼90% and >90%, respectively) to conventional 3D–3D DIR. The dice similarity coefficient over the segmented regions of interest also verifies the improvement in accuracy prior to and after 2D–3D DIR. No relevant changes in accuracy are found between high and low proton statistics except for 2 pRads from integration-mode data. The impact of FOV size is negligible. The convergence of the metric adopted for the stopping criterion indicates the optimal convergence of the 2D–3D DIR. This work represents a further step towards the potential implementation of ART in proton therapy. Further computational optimization is however required to enable extensive clinical validation.

Objective. Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (μCBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies. Approach. A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of the μCBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD95p), and center of mass displacement (ΔCoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed - 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout uncertainty was performed for delineation of the heart. Main results. The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD 95p Significance. A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low.

Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size 100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution penalized-weighted least squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10× can be used without introducing artifacts, yielding a ~50× speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view.

Characterization of anatomical change and other differences is important in sequential computed tomography (CT) imaging, where a high-fidelity patient-specific prior image is typically present, but is not used, in the reconstruction of subsequent anatomical states. Here, we introduce a penalized likelihood (PL) method called reconstruction of difference (RoD) to directly reconstruct a difference image volume using both the current projection data and the (unregistered) prior image integrated into the forward model for the measurement data. The algorithm utilizes an alternating minimization to find both the registration and reconstruction estimates. This formulation allows direct control over the image properties of the difference image, permitting regularization strategies that inhibit noise and structural differences due to inconsistencies between the prior image and the current data. Additionally, if the change is known to be local, RoD allows local acquisition and reconstruction, as opposed to traditional model-based approaches that require a full support field of view (or other modifications). We compared the performance of RoD to a standard PL algorithm, in simulation studies and using test-bench cone-beam CT data. The performances of local and global RoD approaches were similar, with local RoD providing a significant computational speedup. In comparison across a range of data with differing fidelity, the local RoD approach consistently showed lower error (with respect to a truth image) than PL in both noisy data and sparsely sampled projection scenarios. In a study of the prior image registration performance of RoD, a clinically reasonable capture ranges were demonstrated. Lastly, the registration algorithm had a broad capture range and the error for reconstruction of CT data was 35% and 20% less than filtered back-projection for RoD and PL, respectively. The RoD has potential for delivering high-quality difference images in a range of sequential clinical scenarios including image-guided surgeries and treatments where accurate and quantitative assessments of anatomical change is desired.

A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

To suppress scatter in cone beam computed tomography (CBCT), two-dimensional antiscatter grids (2D grid) have been recently proposed. In this work, we developed several grid prototypes with higher grid ratios and smaller grid pitches than previous designs, and quantified their primary and scatter transmission properties in the context of CBCT for radiation therapy. Three focused 2D grid prototypes were developed with grid ratios at 12 and 16, and grid pitches at 2 and 3 mm. Their scatter transmission properties were measured between 80-140 kVp, and benchmarked against a high performance radiographic grid (1D grid) using a Varian TrueBeam CBCT system. The effect of source-grid misalignment on the primary transmission and the improvement in contrast-to-noise ratio (CNR) were also evaluated. Changing the grid pitch from two to three mm increased the average primary transmission from 84% to 89%. Maximum scatter-to-primary ratio (SPR) with grid ratio of 12 was 0.3, and increasing the grid ratio to 16 reduced SPR by 30%. A 10 mm misalignment in 2D grid position led to a 6%-8% reduction in average primary transmission, and reduction was more pronounced for the higher grid ratio. 2D grids provided up to factor of seven lower SPR and 21% better primary transmission than the 1D grid, and their scatter transmission exhibited lower energy dependence. While 2D grids provided up to factor of 2.3 higher CNR improvement, a significant variation in CNR improvement was not observed among different grid pitch and ratios. In summary, grid ratio of 16 and grid pitch of 2 mm can keep SPRs below 0.2 even in high scatter conditions, while keeping primary transmission fractions above 80%, key benefits of the investigated 2D grids in improving image quality of CBCT. However, such grids require precise alignment in source-grid geometry during CBCT acquisitions. This study also implies that 2D grids can provide substantially better scatter suppression and primary transmission than high-performance 1D grids currently available.

In this study, we propose a novel approach to investigate changes in the visible tumour and surrounding tissues with the aim of identifying patterns of tumour change during radiotherapy (RT) without segmentation on the follow-up images. On-treatment cone-beam computed tomography (CBCT) images of 240 non-small cell lung cancer (NSCLC) patients who received 55 Gy of RT were included. CBCTs were automatically aligned onto planning computed tomography (planning CT) scan using a two-step rigid registration process. To explore density changes across the lung-tumour boundary, eight shells confined to the shape of the gross tumour volume (GTV) were created. The shells extended 6 mm inside and outside of the GTV border, and each shell is 1.5 mm thick. After applying intensity correction on CBCTs, the mean intensity was extracted from each shell across all CBCTs. Thereafter, linear fits were created, indicating density change over time in each shell during treatment. The slopes of all eight shells were clustered to explore patterns in the slopes that show how tumours change. Seven clusters were obtained, 97% of the patients were clustered into three groups. After visual inspection, we found that these clusters represented patients with little or no density change, progression and regression. For the three groups, the survival curves were not significantly different between the groups, p-value = 0.51. However, the results show that definite patterns of tumour change exist, suggesting that it may be possible to identify patterns of tumour changes from on-treatment CBCT images.