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Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course. Our algorithm captures the response patterns of the esophagus to radiation on a patch level, using a convolutional neural network. A recurrence neural network then parses the evolutionary patterns of the selected features in the time series, and produces a predicted esophagus-or-not label for each individual patch over future weeks. Finally, the esophagus is reconstructed, using all the predicted labels. The algorithm is trained and validated by means of ∼ 250 000 patches taken from MRI scans acquired weekly from a variety of patients, and tested using both weekly MRI and CBCT scans under a leave-one-patient-out scheme. In addition, our approach is externally validated using a publicly available dataset (Hugo 2017). Using the first three weekly scans, the algorithm can predict the condition of the esophagus over the succeeding 3 weeks with a Dice coefficient of 0.83 ± 0.04, estimate esophagus volume highly (0.98), correlated with the actual volume, using our institutional MRI/CBCT data. When evaluated using the external weekly CBCT data, the averaged Dice coefficient is 0.89 ± 0.03. Our novel algorithm may prove useful in enabling radiation oncologists to monitor and detect AE in its early stages, and could potentially play an important role in the ART decision-making process. [ABSTRACT FROM AUTHOR]

During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1–3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 0.07/0.81 0.04 (week 4), 0.79 0.06/0.81 0.05 (week 5) and 0.78 0.06/0.82 (week 6). The DICE with the Demons model were 0.78 0.08/0.82 0.03 (week 4), 0.77 0.07/0.82 0.04 (week 5) and 0.75 0.07/0.82 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow. [ABSTRACT FROM AUTHOR]

X-ray CT polymer gel dosimetry (PGD) remains a promising tool for three dimensional verification of high-dose treatment deliveries such as non-coplanar stereotactic irradiations. Recent demonstrations have shown a proof-of-principle application of linac-integrated cone beam CT-imaged (LI-CBCT) PGDs for 3D dose verification. LI-CBCT offers advantages over previous CT based PGD, including close to real-time imaging of the irradiated dosimeter, as well as the ability to maintain the dosimeter in the same physical location for irradiation and imaging, thereby eliminating spatial errors due to dosimeter re-positioning for read-out that may occur for other systems. However the dosimetric characteristics of a LI-CBCT PGD system remain to be established. The work herein determines the dosimetric properties and critical parameters needed to perform cone beam PGD. In particular, we show that imaging the dosimeter 20–30 min post irradiation offers excellent recovery of maximum polymerization yield (90%), averaging with as few as 10 image averages can provide ∼90% gamma pass rates (3%, 3 mm) as compared to treatment planning, and that eliminating outlier averaging points can improve the precision and signal to noise ratio of resultant images. In summary, with appropriate methodology LI-CBCT PGD can provide dosimetric data capable of verification of complex high dose radiation deliveries in three dimensions and may find use in commissioning and validation of novel complex treatments. [ABSTRACT FROM AUTHOR]

The purpose of this study is implementation of an anthropomorphic model observer using a convolutional neural network (CNN) for signal-known-statistically (SKS) and background-known-statistically (BKS) detection tasks. We conduct SKS/BKS detection tasks on simulated cone beam computed tomography (CBCT) images with eight types of signal and randomly varied breast anatomical backgrounds. To predict human observer performance, we use conventional anthropomorphic model observers (i.e. the non-prewhitening observer with an eye-filter, the dense difference-of-Gaussian channelized Hotelling observer (CHO), and the Gabor CHO) and implement CNN-based model observer. We propose an effective data labeling strategy for CNN training reflecting the inefficiency of human observer decision-making on detection and investigate various CNN architectures (from single-layer to four-layer). We compare the abilities of CNN-based and conventional model observers to predict human observer performance for different background noise structures. The three-layer CNN trained with labeled data generated by our proposed labeling strategy predicts human observer performance better than conventional model observers for different noise structures in CBCT images. This network also shows good correlation with human observer performance for general tasks when training and testing images have different noise structures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Three-dimensional cone-beam imaging has become valuable in interventional radiology. Currently, this tool, referred to as C-arm CT, employs a circular short-scan for data acquisition, which limits the axial volume coverage and yields unavoidable cone-beam artifacts. To improve flexibility in axial coverage and image quality, there is a critical need for novel data acquisition geometries and related image reconstruction algorithms. For this purpose, we previously introduced the extended line-ellipse-line trajectory, which allows complete scanning of arbitrary volume lengths in the axial direction together with adjustable axial beam collimation, from narrow to wide depending on the targeted application. A first implementation of this trajectory on a state-of-the-art robotic angiography system is reported here. More specifically, an assessment of the quality of this first implementation is presented. The assessment is in terms of geometric fidelity and repeatability, complemented with a first visual inspection of how well the implementation enables imaging an anthropomorphic head phantom. The geometric fidelity analysis shows that the ideal trajectory is closely emulated, with only minor deviations that have no impact on data completeness and clinical practicality. Also, mean backprojection errors over short-term repetitions are shown to be below the detector pixel size at field-of-view center for most views, which indicates repeatability is satisfactory for clinical utilization. These repeatability observations are further supported by values of the Structural Similarity Index Metric above 94% for reconstructions of the FORBILD head phantom from computer-simulated data based on repeated data acquisition geometries. Last, the real data experiment with the anthropomorphic head phantom shows that the high contrast features of the phantom are well reconstructed without distortions as well as without breaks or other disturbing transition zones, which was not obvious given the complexity of the data acquisition geometry and the major variations in axial coverage that occur over the scan. [ABSTRACT FROM AUTHOR]

Four-dimensional (4D) cone-beam CT (CBCT) reconstructs temporally-resolved phases of 3D volumes often with the same amount of projection data that are meant for reconstructing a single 3D volume. 4D CBCT is a sparse-data problem that is very challenging for high-quality 4D CBCT image reconstruction. Here we develop a new method, namely 4D-AirNet, that synergizes analytical and iterative method with deep learning for high-quality temporally-resolved CBCT slice reconstruction. 4D-AirNet is an unrolling method using the optimization framework of fused analytical and iterative reconstruction (AIR), which is based on proximal forward-backward splitting (PFBS). Three different strategies are developed for 4D-AirNet: random-phase (RP), prior-guided (PG), and all-phase (AP). RP-AirNet and PG-AirNet utilize phase-by-phase training and reconstruction, while PG-AirNet also uses a prior image reconstructed with all-phase projection data. Dense connectivity is built into 4D-AirNet networks for improved reconstruction quality. In contrast, AP-AirNet trains and reconstructs all phases simultaneously. In addition, the joint regularization method of DL and conventional spatiotemporal total variation (TV) is investigated. 4D-AirNet methods were evaluated in comparison with conventional iterative (TV) and deep learning (LEARN) methods, using simulated 2D-t CBCT scans from a lung dataset with various sparse-data levels. The reconstruction results suggest 4D-AirNet methods outperform TV and LEARN, and AP-AirNet provides the best reconstruction quality overall. [ABSTRACT FROM AUTHOR]

Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy—often in the very region of interest pertinent to the imaging/surgical tasks. We present a method to reduce the influence of metal artifacts by prospectively defining an image acquisition protocol—viz., the C-arm source-detector orbit—that mitigates metal-induced biases in the projection data. The metal artifact avoidance (MAA) method is compatible with simple mobile C-arms, does not require exact prior information on the patient or metal implants, and is consistent with 3D filtered backprojection (FBP), more advanced (e.g. polyenergetic) model-based image reconstruction (MBIR), and metal artifact reduction (MAR) post-processing methods. The MAA method consists of: (i) coarse localization of metal objects in the field-of-view (FOV) via two or more low-dose scout projection views and segmentation (e.g. a simple U-Net) in coarse backprojection; (ii) model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices accessible by the imaging system (e.g. gantry rotation and tilt angles); and (iii) identification of a circular or non-circular orbit that reduces the variation in spectral shift. The method was developed, tested, and evaluated in a series of studies presenting increasing levels of complexity and realism, including digital simulations, phantom experiment, and cadaver experiment in the context of image-guided spine surgery (pedicle screw implants). The MAA method accurately predicted tilted circular and non-circular orbits that reduced the magnitude of metal artifacts in CBCT reconstructions. Realistic distributions of metal instrumentation were successfully localized (0.71 median Dice coefficient) from 2–6 low-dose scout views even in complex anatomical scenes. The MAA-predicted tilted circular orbits reduced root-mean-square error (RMSE) in 3D image reconstructions by 46%–70% and 'blooming' artifacts (apparent width of the screw shaft) by 20–45%. Non-circular orbits defined by MAA achieved a further ∼46% reduction in RMSE compared to the best (tilted) circular orbit. The MAA method presents a practical means to predict C-arm orbits that minimize spectral bias from metal instrumentation. Resulting orbits—either simple tilted circular orbits or more complex non-circular orbits that can be executed with a motorized multi-axis C-arm—exhibited substantial reduction of metal artifacts in raw CBCT reconstructions by virtue of higher fidelity projection data, which are in turn compatible with subsequent MAR post-processing and/or polyenergetic MBIR to further reduce artifacts. [ABSTRACT FROM AUTHOR]

The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep convolutional autoencoder (DCAE) and projection-based scatter removal algorithm were constructed for the ImagingRingTM system on rails (IRr), which allows for non-isocentric acquisitions around virtual rotation centers with its independently rotatable source and detector arms. A Monte Carlo model was developed to simulate (i) a non-isocentric training dataset of ≈1200 projection pairs (primary + scatter) from 27 digital head-and-neck cancer patients around five different virtual rotation centers (DCAENONISO), and (ii) an isocentric dataset existing of ≈1200 projection pairs around the physical rotation center (DCAEISO). The scatter removal performance of both DCAE networks was investigated in two digital anthropomorphic phantom simulations and due to superior performance only the DCAENONISO was applied on eight real patient acquisitions. Measures for the quantitative error, the signal-to-noise ratio, and the similarity were evaluated for two simulated digital head-and-neck patients, and the contrast-to-noise ratio (CNR) was investigated between muscle and adipose tissue in the real patient image reconstructions. Image quality metrics were compared between the uncorrected data, the currently implemented heuristic scatter correction data, and the DCAE corrected image reconstruction. The DCAENONISO corrected image reconstructions of two digital patient simulations showed superior image quality metrics compared to the uncorrected and corrected image reconstructions using a heuristic scatter removal. The proposed DCAENONISO scatter correction in this study was successfully demonstrated in real non-isocentric patient CBCT acquisitions and achieved statistically significant higher CNRs compared to the uncorrected or the heuristic corrected image data. This paper presents for the first time a projection-based scatter removal algorithm for isocentric and non-isocentric CBCT imaging using a deep convolutional autoencoder trained on Monte Carlo composed datasets. The algorithm was successfully applied to real patient data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status. This work proposes to resolve such challenge by generating 'semi-synthetic' CT images from rigidly aligned CT and MR image pairs. Contrasts within skeletal elements of the 'semi-synthetic' CT images were determined from CT images, while contrasts of soft tissue and air volumes were determined from voxel-wise intensity classification results on MR images. The resulting 'semi-synthetic' CT images were paired with their corresponding MR images and used to train a simple U-net model without adversarial components. MR and CT scans of 46 patients were investigated and the proposed method was evaluated for 31 patients with clinical radiotherapy plans, using 3-fold cross validation. The averaged mean absolute errors between synthetic CT and CT images across patients were 24.10 HU for liver, 28.62 HU for spleen, 47.05 HU for kidneys, 29.79 HU for spinal cord, 105.68 HU for lungs and 110.09 HU for vertebral bodies. VMAT and IMRT plans were optimized using CT-derived electron densities, and doses were recalculated using corresponding synthetic CT-derived density grids. Resulting dose differences to planning target volumes and various organs at risk were small, with the average difference less than 0.15 Gy for all dose metrics evaluated. The similarities in both image intensity and radiation dose distributions between CT and synthetic CT volumes demonstrate the accuracy of the method and its potential in supporting MRI-only radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

The dose length product (DLP) provides a measurement related to energy imparted from a computed tomography (CT) scan. The DLP is based on the volume-averaged CT dose index (CTDIvol), which is designed for fan beams. The aims of this study were to investigate the use of DLP for scans with wide beams used in cone beam CT (DLPCBCT) in radiotherapy that would be analogous to the DLP of fan beam scans (DLPCT), and to compare the efficiencies of DLPCT and DLPCBCT in reporting the total energy imparted in patients. A validated Monte Carlo model of a kV imaging system integrated into a Varian TrueBeam linac was employed. The DLPCT was assessed by multiplying the CTDIvol for a 20 mm fan beam by scan length, and the DLPCBCT determined through multiplying the CTDIvol, estimated for wide beams using a correction factor based on free-in-air measurements, by the beam width. Two scan protocols for head and body were investigated for tube potentials between 80 and 140 kV and a range of scan lengths/widths. Efficiency values were estimated by normalising the DLPCT and DLPCBCT with respect to the corresponding dose profile integrals (DPIs), which were evaluated within 900 mm long phantoms. The results show that the DLPCBCT values were within 1% of those for DLPCT of similar length performed on the same system, and the efficiencies decrease with tube potential. However, whereas DLP values for fan beams are approximately proportional to scan length, those for wide beams decrease by ∼2% between beam widths of 20 and 320 mm. As a result, while the DLPCT efficiency is similar over all scan lengths, that for DLPCBCT increases slightly with beam width. The DLPCT and DLPCBCT underestimated the total energy imparted by comparable amounts with efficiencies within the range of 80–81% and 80–83% for the head scans, and 71–76% and 70–77% for the body scans, respectively. The results indicate that the DLPCBCT can be considered as an analogous dose index to the DLPCT. It could, therefore, be used for quantification of doses from imaging in radiotherapy and provide a valuable tool to aid optimisation. [ABSTRACT FROM AUTHOR]

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

Purpose: To develop size-specific institutional diagnostic reference levels (DRLs) for computed tomography (CT) protocols used in neck CT imaging (cervical spine CT, cervical CT angiography (CTA) and cervical staging CT) and to compare institutional to national DRLs. Materials and methods: Cervical CT examinations (spine, n = 609; CTA, n = 505 and staging CT, n = 184) performed between 01/2016 and 06/2017 were included in this retrospective study. For each region and examination, the volumetric CT dose index (CTDIvol) and dose-length product (DLP) were determined and binned into size bins according to patient water-equivalent diameter (dw). Linear regression analysis was performed to calculate size-specific institutional DRLs for CTDIvol and DLP, applying the 75th percentile as the upper limit for institutional DRLs. The mean institutional CTDIvol and DLP were compared to national DRLs (CTDIvol 20 mGy for cervical spine CT (DLP 300 mGycm) and cervical CTA (DLP 600 mGycm), and CTDIvol 15 mGy for cervical staging CT (DLP 330 mGycm)). Results: The mean CTDIvol and DLP (±standard deviation) were 15.2 ± 4.1 mGy and 181.5 ± 88.3 mGycm for cervical spine CT; 8.1 ± 4.3 mGy and 280.2 ± 164.3 mGycm for cervical CTA; 8.6 ± 1.9 mGy and 162.8 ± 85.0 mGycm for cervical staging CT. For all CT protocols, there was a linear increase in CTDIvol and DLP with increasing dw. For the CTDIvol, size-specific institutional DRLs increased with dw from 14 to 29 mGy for cervical spine CT, from 5 to 17 mGy for cervical CTA and from 8 to 13 mGy for cervical staging CT. For the DLP, size-specific institutional DRLs increased with dw from 130 to 510 mGycm for cervical spine CT, from 140 to 640 mGycm for cervical CTA and from 140 to 320 mGycm for cervical staging CT. Institutional DRLs were lower than national DRLs by 81% and 67% for cervical spine CT (dw = 17.8 cm), 43% and 51% for cervical CTA (dw = 19.5 cm) and 59% and 53% for cervical staging CT (dw = 18.8 cm) for CTDIvol and DLP, respectively. Conclusion: Size-specific institutional DRLs were generated for neck CT examinations. The mean institutional CTDIvol and DLP values were well below national DRLs. [ABSTRACT FROM AUTHOR]

X-ray tubes for medical applications typically generate x-rays by accelerating electrons, emitted from a cathode, with an interelectrode electric field, towards an anode target. X-rays are not emitted from one point, but from an irregularly shaped area on the anode, the focal spot. Focal spot intensity distributions and off-focal radiation negatively affect the imaging spatial resolution and broadens the beam penumbra. In this study, a Monte Carlo simulation model of an x-ray tube was developed to evaluate the spectral and spatial characteristics of off-focal radiation for multiple photon energies. Slit camera measurements were used to determine the horizontal and vertical intensity profiles of the small and the large focal spot of a diagnostic x-ray tube. First, electron beamlet weighting factors were obtained via an iterative optimization method to represent both focal spot sizes. These weighting factors were then used to extract off-focal spot radiation characteristics for the small and large focal spot sizes at 80, 100, and 120 kV. Finally, 120 kV simulations of a steel sphere (d = 4 mm) were performed to investigate image blurring with a point source, the small focal spot, and the large focal spot. The magnitude of off-focal radiation strongly depends on the anode size and the electric field coverage, and only minimally on the tube potential and the primary focal spot size. In conclusion, an x-ray tube Monte Carlo simulation model was developed to simulate focal spot intensity distributions and to evaluate off-focal radiation characteristics at several energies. This model can be further employed to investigate focal spot correction methods and to improve cone-beam CT image quality. [ABSTRACT FROM AUTHOR]

To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images. Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp–Davis–Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values. The results after VM-CBCT production showed good material decomposition and consistent HUVM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. RED values measured for our method resulted in a mean percentage error of 0.0% ± 1.8%. This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments. [ABSTRACT FROM AUTHOR]

Cone-beam computed tomography (CBCT) images acquired during radiotherapy may allow early response assessment. Previous studies have reported inconsistent findings on an association of CBCT-measured tumor volume changes with clinical outcomes. The purpose of this pilot study was twofold: (1) to characterize changes in CBCT-based radiomics features during treatment; and (2) to quantify the potential association of CBCT-based delta-radiomics features with overall survival in locally advanced lung cancer. We retrospectively identified 23 patients and calculated 658 radiomics features from each of 11 CBCT images per patient. Feature selection was performed based on repeatability, robustness against contouring uncertainties, and non-redundancy. We calculated the coefficient of determination (R2) for the relationship between the actual feature value at the end of treatment and predicted value based on linear models fitted using features between the first and kth fractions. We also quantified the predictive ability for survival with two methods by: (1) comparing delta-radiomics features (defined as the mean change between the first and kth fractions) between two groups of patients divided by a cutoff survival time of 18 months using the t-test or Wilcoxon rank-sum test; and (2) quantifying univariate discrimination of two groups divided by the median of delta-radiomics feature. All selected seven radiomics features during treatment (as early as the 10th fraction) were predictive of those at the end of treatment (R2 > 0.64). Three delta-radiomics features demonstrated significant differences (q < 0.05, as early as the 10th fraction) between the two groups of patients divided by the cutoff survival time. Two of those three features were also predictive of survival according to the log-rank statistics. We provided the first demonstration of a potential association of CBCT-based delta-radiomics features early during treatment with overall survival in locally advanced lung cancer. Our preliminary findings should be validated for a larger cohort of patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In presence of inter-fractional anatomical changes, clinical benefits are anticipated from image-guided adaptive radiotherapy. Nowadays, cone-beam CT (CBCT) imaging is mostly utilized during pre-treatment imaging for position verification. Due to various artifacts, image quality is typically not sufficient for photon or proton dose calculation, thus demanding accurate CBCT correction, as potentially provided by deep learning techniques. This work aimed at investigating the feasibility of utilizing a cycle-consistent generative adversarial network (cycleGAN) for prostate CBCT correction using unpaired training. Thirty-three patients were included. The network was trained to translate uncorrected, original CBCT images (CBCTorg) into planning CT equivalent images (CBCTcycleGAN). HU accuracy was determined by comparison to a previously validated CBCT correction technique (CBCTcor). Dosimetric accuracy was inferred for volumetric-modulated arc photon therapy (VMAT) and opposing single-field uniform dose (OSFUD) proton plans, optimized on CBCTcor and recalculated on CBCTcycleGAN. Single-sided SFUD proton plans were utilized to assess proton range accuracy. The mean HU error of CBCTcycleGAN with respect to CBCTcor decreased from 24 HU for CBCTorg to −6 HU. Dose calculation accuracy was high for VMAT, with average pass-rates of 100%/89% for a 2%/1% dose difference criterion. For proton OSFUD plans, the average pass-rate for a 2% dose difference criterion was 80%. Using a (2%, 2 mm) gamma criterion, the pass-rate was 96%. 93% of all analyzed SFUD profiles had a range agreement better than 3 mm. CBCT correction time was reduced from 6–10 min for CBCTcor to 10 s for CBCTcycleGAN. Our study demonstrated the feasibility of utilizing a cycleGAN for CBCT correction, achieving high dose calculation accuracy for VMAT. For proton therapy, further improvements may be required. Due to unpaired training, the approach does not rely on anatomically consistent training data or potentially inaccurate deformable image registration. The substantial speed-up for CBCT correction renders the method particularly interesting for adaptive radiotherapy. [ABSTRACT FROM AUTHOR]

Intraoperative cone-beam CT (CBCT) is increasingly used for surgical navigation and validation of device placement. In spinal deformity correction, CBCT provides visualization of pedicle screws and fixation rods in relation to adjacent anatomy. This work reports and evaluates a method that uses prior information regarding such surgical instrumentation for improved metal artifact reduction (MAR). The known-component MAR (KC-MAR) approach achieves precise localization of instrumentation in projection images using rigid or deformable 3D–2D registration of component models, thereby overcoming residual errors associated with segmentation-based methods. Projection data containing metal components are processed via 2D inpainting of the detector signal, followed by 3D filtered back-projection (FBP). Phantom studies were performed to identify nominal algorithm parameters and quantitatively investigate performance over a range of component material composition and size. A cadaver study emulating screw and rod placement in spinal deformity correction was conducted to evaluate performance under realistic clinical imaging conditions. KC-MAR demonstrated reduction in artifacts (standard deviation in voxel values) across a range of component types and dose levels, reducing the artifact to 5–10 HU. Accurate component delineation was demonstrated for rigid (screw) and deformable (rod) models with sub-mm registration errors, and a single-pixel dilation of the projected components was found to compensate for partial-volume effects. Artifacts associated with spine screws and rods were reduced by 40%–80% in cadaver studies, and the resulting images demonstrated markedly improved visualization of instrumentation (e.g. screw threads) within cortical margins. The KC-MAR algorithm combines knowledge of surgical instrumentation with 3D image reconstruction in a manner that overcomes potential pitfalls of segmentation. The approach is compatible with FBP—thereby maintaining simplicity in a manner that is consistent with surgical workflow—or more sophisticated model-based reconstruction methods that could further improve image quality and/or help reduce radiation dose. [ABSTRACT FROM AUTHOR]

An algorithm for automatic spinal pedicle screw planning is reported and evaluated in simulation and first clinical studies. A statistical atlas of the lumbar spine (N = 40 members) was constructed for active shape model (ASM) registration of target vertebrae to an unsegmented patient CT. The atlas was augmented to include 'reference' trajectories through the pedicles as defined by a spinal neurosurgeon. Following ASM registration, the trajectories are transformed to the patient CT and accumulated to define a patient-specific screw trajectory, diameter, and length. The algorithm was evaluated in leave-one-out analysis (N = 40 members) and for the first time in a clinical study (N = 5 patients undergoing cone-beam CT (CBCT) guided spine surgery), and in simulated low-dose CBCT images. ASM registration achieved (2.0 ± 0.5) mm root-mean-square-error (RMSE) in surface registration in 96% of cases, with outliers owing to limitations in CT image quality (high noise/slice thickness). Trajectory centerlines were conformant to the pedicle in 95% of cases. For all non-breaching trajectories, automatically defined screw diameter and length were similarly conformant to the pedicle and vertebral body (98.7%, Grade A/B). The algorithm performed similarly in CBCT clinical studies (93% centerline and screw conformance) and was consistent at the lowest dose levels tested. Average runtime in planning five-level (lumbar) bilateral screws (ten trajectories) was (312.1 ± 104.0) s. The runtime per level for ASM registration was (41.2 ± 39.9) s, and the runtime per trajectory was (4.1 ± 0.8) s, suggesting a runtime of ~(45.3 ± 39.9) s with a more fully parallelized implementation. The algorithm demonstrated accurate, automatic definition of pedicle screw trajectories, diameter, and length in CT images of the spine without segmentation. The studies support translation to clinical studies in free-hand or robot-assisted spine surgery, quality assurance, and data analytics in which fast trajectory definition is a benefit to workflow. [ABSTRACT FROM AUTHOR]

Tumor motion caused by respiration is an important issue in image-guided radiotherapy. A 2D/4D matching method between 4D volumes derived from cone beam computed tomography (CBCT) and 2D fluoroscopic images was implemented to track the tumor motion without the use of implanted markers. In this method, firstly, 3DCBCT and phase-rebinned 4DCBCT are reconstructed from cone beam acquisition. Secondly, 4DCBCT volumes and a streak-free 3DCBCT volume are combined to improve the image quality of the digitally reconstructed radiographs (DRRs). Finally, the 2D/4D matching problem is converted into a 2D/2D matching between incoming projections and DRR images from each phase of the 4DCBCT. The diaphragm is used as a target surrogate for matching instead of using the tumor position directly. This relies on the assumption that if a patient has the same breathing phase and diaphragm position as the reference 4DCBCT, then the tumor position is the same. From the matching results, the phase information, diaphragm position and tumor position at the time of each incoming projection acquisition can be derived. The accuracy of this method was verified using 16 candidate datasets, representing lung and liver applications and one-minute and two-minute acquisitions. The criteria for the eligibility of datasets were described: 11 eligible datasets were selected to verify the accuracy of diaphragm tracking, and one eligible dataset was chosen to verify the accuracy of tumor tracking. The diaphragm matching accuracy was 1.88 ± 1.35 mm in the isocenter plane and the 2D tumor tracking accuracy was 2.13 ± 1.26 mm in the isocenter plane. These features make this method feasible for real-time marker-free tumor motion tracking purposes. [ABSTRACT FROM AUTHOR]

A new model-based dose calculation algorithm is presented for kilovoltage x-rays and is tested for the cases of calculating the radiation dose from kilovoltage cone-beam CT (kV-CBCT) and 2D planar projected radiographs. This algorithm calculates the radiation dose to water-like media as the sum of primary and scattered dose components. The scatter dose is calculated by convolution of a newly introduced, empirically parameterized scatter dose kernel with the primary photon fluence. Several approximations are introduced to increase the scatter dose calculation efficiency: (1) the photon energy spectrum is approximated as monoenergetic; (2) density inhomogeneities are accounted for by implementing a global distance scaling factor in the scatter kernel; (3) kernel tilting is ignored. These approximations allow for efficient calculation of the scatter dose convolution with the fast Fourier transform. Monte Carlo simulations were used to obtain the model parameters. The accuracy of using this model-based algorithm was validated by comparing with the Monte Carlo method for calculating dose distributions for real patients resulting from radiotherapy image guidance procedures including volumetric kV-CBCT scans and 2D planar projected radiographs. For all patients studied, mean dose-to-water errors for kV-CBCT are within 0.3% with a maximum standard deviation error of 4.1%. Using a medium-dependent correction method to account for the effects of photoabsorption in bone on the dose distribution, mean dose-to-medium errors for kV-CBCT are within 3.6% for bone and 2.4% for soft tissues. This algorithm offers acceptable accuracy and has the potential to extend the applicability of model-based dose calculation algorithms from megavoltage to kilovoltage photon beams. [ABSTRACT FROM AUTHOR]

The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼40–80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4–2.2× over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼1.7 mGy and benefits from 50% sparsity at dose below ∼1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose. [ABSTRACT FROM AUTHOR]

Cone-beam computed tomography (CBCT) is an important online imaging modality for image guided radiotherapy. But suboptimal image quality and the lack of a real-time stereoscopic imaging function limit its implementation in advanced treatment techniques, such as online adaptive and 4D radiotherapy. Tetrahedron beam computed tomography (TBCT) is a novel online imaging modality designed to improve on the image quality provided by CBCT. TBCT geometry is flexible, and multiple detector and source arrays can be used for different applications. In this paper, we describe a novel dual source–dual detector TBCT system that is specially designed for LINAC radiation treatment machines. The imaging system is positioned in-line with the MV beam and is composed of two linear array x-ray sources mounted aside the electrical portal imaging device and two linear arrays of x-ray detectors mounted below the machine head. The detector and x-ray source arrays are orthogonal to each other, and each pair of source and detector arrays forms a tetrahedral volume. Four planer images can be obtained from different view angles at each gantry position at a frame rate as high as 20 frames per second. The overlapped regions provide a stereoscopic field of view of approximately 10–15 cm. With a half gantry rotation, a volumetric CT image can be reconstructed having a 45 cm field of view. Due to the scatter rejecting design of the TBCT geometry, the system can potentially produce high quality 2D and 3D images with less radiation exposure. The design of the dual source–dual detector system is described, and preliminary results of studies performed on numerical phantoms and simulated patient data are presented. [ABSTRACT FROM AUTHOR]

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°/s2, 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. [ABSTRACT FROM AUTHOR]

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The per cent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson's r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation. [ABSTRACT FROM AUTHOR]

Cone-beam consistency conditions (also known as range conditions) are mathematical relationships between different cone-beam projections, and they therefore describe the redundancy or overlap of information between projections. These redundancies have often been exploited for applications in image reconstruction. In this work we describe new consistency conditions for cone-beam projections whose source positions lie on a plane. A further restriction is that the target object must not intersect this plane. The conditions require that moments of the cone-beam projections be polynomial functions of the source positions, with some additional constraints on the coefficients of the polynomials. A precise description of the consistency conditions is that the four parameters of the cone-beam projections (two for the detector, two for the source position) can be expressed with just three variables, using a certain formulation involving homogeneous polynomials. The main contribution of this work is our demonstration that these conditions are not only necessary, but also sufficient. Thus the consistency conditions completely characterize all redundancies, so no other independent conditions are possible and in this sense the conditions are full. The idea of the proof is to use the known consistency conditions for 3D parallel projections, and to then apply a 1996 theorem of Edholm and Danielsson that links parallel to cone-beam projections. The consistency conditions are illustrated with a simulation example. [ABSTRACT FROM AUTHOR]

An inflatable endorectal balloon (ERB) is often used during stereotactic body radiation therapy (SBRT) for treatment of prostate cancer in order to reduce both intrafraction motion of the target and risk of rectal toxicity. However, the ERB can exert significant force on the prostate, and this work assessed the impact of ERB position errors on deformation of the prostate and treatment dose metrics. Seventy-one cone-beam computed tomography (CBCT) image datasets of nine patients with clinical stage T1cN0M0 prostate cancer were studied. An ERB (Flexi-Cuff, EZ-EM, Westbury, NY) inflated with 60 cm3 of air was used during simulation and treatment, and daily kilovoltage (kV) CBCT imaging was performed to localize the prostate. The shape of the ERB in each CBCT was analyzed to determine errors in position, size, and shape. A deformable registration algorithm was used to track the dose received by (and deformation of) the prostate, and dosimetric values such as D95, PTV coverage, and Dice coefficient for the prostate were calculated. The average balloon position error was 0.5 cm in the inferior direction, with errors ranging from 2 cm inferiorly to 1 cm superiorly. The prostate was deformed primarily in the AP direction, and tilted primarily in the anterior–posterior/superior–inferior plane. A significant correlation was seen between errors in depth of ERB insertion (DOI) and mean voxel-wise deformation, prostate tilt, Dice coefficient, and planning-to-treatment prostate inter-surface distance (p < 0.001). Dosimetrically, DOI is negatively correlated with prostate D95 and PTV coverage (p < 0.001). For the model of ERB studied, error in ERB position can cause deformations in the prostate that negatively affect treatment, and this additional aspect of setup error should be considered when ERBs are used for prostate SBRT. Before treatment, the ERB position should be verified, and the ERB should be adjusted if the error is observed to exceed tolerable values. [ABSTRACT FROM AUTHOR]

Truncation of a cone-beam computed tomography (CBCT) image, mainly caused by the limited field of view (FOV) of CBCT imaging, poses challenges to the problem of deformable image registration (DIR) between computed tomography (CT) and CBCT images in adaptive radiation therapy (ART). The missing information outside the CBCT FOV usually causes incorrect deformations when a conventional DIR algorithm is utilized, which may introduce significant errors in subsequent operations such as dose calculation. In this paper, based on the observation that the missing information in the CBCT image domain does exist in the projection image domain, we propose to solve this problem by developing a hybrid deformation/reconstruction algorithm. As opposed to deforming the CT image to match the truncated CBCT image, the CT image is deformed such that its projections match all the corresponding projection images for the CBCT image. An iterative forward–backward projection algorithm is developed. Six head-and-neck cancer patient cases are used to evaluate our algorithm, five with simulated truncation and one with real truncation. It is found that our method can accurately register the CT image to the truncated CBCT image and is robust against image truncation when the portion of the truncated image is less than 40% of the total image. [ABSTRACT FROM AUTHOR]

Deformable image registration (DIR) is an integral component for adaptive radiation therapy. However, accurate registration between daily cone-beam computed tomography (CBCT) and treatment planning CT is challenging, due to significant daily variations in rectal and bladder fillings as well as the increased noise levels in CBCT images. Another significant challenge is the lack of ‘ground-truth’ registrations in the clinical setting, which is necessary for quantitative evaluation of various registration algorithms. The aim of this study is to establish benchmark registrations of clinical patient data. Three pairs of CT/CBCT datasets were chosen for this institutional review board approved retrospective study. On each image, in order to reduce the contouring uncertainty, ten independent sets of organs were manually delineated by five physicians. The mean contour set for each image was derived from the ten contours. A set of distinctive points (round natural calcifications and three implanted prostate fiducial markers) were also manually identified. The mean contours and point features were then incorporated as constraints into a B-spline based DIR algorithm. Further, a rigidity penalty was imposed on the femurs and pelvic bones to preserve their rigidity. A piecewise-rigid registration approach was adapted to account for the differences in femur pose and the sliding motion between bones. For each registration, the magnitude of the spatial Jacobian (|JAC|) was calculated to quantify the tissue compression and expansion. Deformation grids and finite-element-model-based unbalanced energy maps were also reviewed visually to evaluate the physical soundness of the resultant deformations. Organ DICE indices (indicating the degree of overlap between registered organs) and residual misalignments of the fiducial landmarks were quantified. Manual organ delineation on CBCT images varied significantly among physicians with overall mean DICE index of only 0.7 among redundant contours. Seminal vesicle contours were found to have the lowest correlation amongst physicians (DICE = 0.5). After DIR, the organ surfaces between CBCT and planning CT were in good alignment with mean DICE indices of 0.9 for prostate, rectum, and bladder, and 0.8 for seminal vesicles. The Jacobian magnitudes |JAC| in the prostate, rectum, and seminal vesicles were in the range of 0.4–1.5, indicating mild compression/expansion. The bladder volume differences were larger between CBCT and CT images with mean |JAC| values of 2.2, 0.7, and 1.0 for three respective patients. Bone deformation was negligible (|JAC| = ∼ 1.0). The difference between corresponding landmark points between CBCT and CT was less than 1.0 mm after DIR. We have presented a novel method of establishing benchmark DIR accuracy between CT and CBCT images in the pelvic region. The method incorporates manually delineated organ surfaces and landmark points as well as pixel similarity in the optimization, while ensuring bone rigidity and avoiding excessive deformation in soft tissue organs. Redundant contouring is necessary to reduce the overall registration uncertainty. [ABSTRACT FROM AUTHOR]

Non-coplanar beams are important for treatment of both cranial and noncranial tumors. Treatment verification of such beams with couch rotation/kicks, however, is challenging, particularly for the application of cone beam CT (CBCT). In this situation, only limited and unconventional imaging angles are feasible to avoid collision between the gantry, couch, patient, and on-board imaging system. The purpose of this work is to develop a CBCT verification strategy for patients undergoing non-coplanar radiation therapy. We propose an image reconstruction scheme that integrates a prior image constrained compressed sensing (PICCS) technique with image registration. Planning CT or CBCT acquired at the neutral position is rotated and translated according to the nominal couch rotation/translation to serve as the initial prior image. Here, the nominal couch movement is chosen to have a rotational error of 5° and translational error of 8 mm from the ground truth in one or more axes or directions. The proposed reconstruction scheme alternates between two major steps. First, an image is reconstructed using the PICCS technique implemented with total-variation minimization and simultaneous algebraic reconstruction. Second, the rotational/translational setup errors are corrected and the prior image is updated by applying rigid image registration between the reconstructed image and the previous prior image. The PICCS algorithm and rigid image registration are alternated iteratively until the registration results fall below a predetermined threshold. The proposed reconstruction algorithm is evaluated with an anthropomorphic digital phantom and physical head phantom. The proposed algorithm provides useful volumetric images for patient setup using projections with an angular range as small as 60°. It reduced the translational setup errors from 8 mm to generally <1 mm and the rotational setup errors from 5° to <1°. Compared with the PICCS algorithm alone, the integration of rigid registration significantly improved the reconstructed image quality, with a reduction of mostly 2–3 folds (up to 100) in root mean square image error. The proposed algorithm provides a remedy for solving the problem of non-coplanar CBCT reconstruction from limited angle of projections by combining the PICCS technique and rigid image registration in an iterative framework. In this proof of concept study, non-coplanar beams with couch rotations of 45° can be effectively verified with the CBCT technique. [ABSTRACT FROM AUTHOR]

The mechanical motion of the gantry in conventional cone beam CT scanners restricts the speed of data acquisition in applications with near real time requirements. A possible resolution of this problem is to replace the moving source detector assembly with static parts that are electronically activated. An example of such a system is the Rapiscan Systems RTT80 real time tomography scanner, with a static ring of sources and axially offset static cylinder of detectors. A consequence of such a design is asymmetrical axial truncation of the cone beam projections resulting, in the sense of integral geometry, in severely incomplete data. In particular we collect data only in a fraction of the Tam–Danielsson window, hence the standard cone beam reconstruction techniques do not apply. In this work we propose a family of multi-sheet surface rebinning methods for reconstruction from such truncated projections. The proposed methods combine analytical and numerical ideas utilizing linearity of the ray transform to reconstruct data on multi-sheet surfaces, from which the volumetric image is obtained through deconvolution. In this first paper in the series, we discuss the rebinning to multi-sheet surfaces. In particular we concentrate on the underlying transforms on multi-sheet surfaces and their approximation with data collected by offset multi-source scanning geometries like the RTT. The optimal multi-sheet surface and the corresponding rebinning function are found as a solution of a variational problem. In the case of the quadratic objective, the variational problem for the optimal rebinning pair can be solved by a globally convergent iteration. Examples of optimal rebinning pairs are computed for different trajectories. We formulate the axial deconvolution problem for the recovery of the volumetric image from the reconstructions on multi-sheet surfaces. Efficient and stable solution of the deconvolution problem is the subject of the second paper in this series (Betcke and Lionheart 2013 Inverse Problems29 115004). [ABSTRACT FROM AUTHOR]

In airport baggage scanning it is desirable to have a system that can scan baggage moving at standard conveyor belt speeds. One way to achieve this is to use multiple electronically switched sources rather than a single source on a mechanically rotated gantry. In such a system placing the detectors opposite the sources would obstruct the beam, so they have to be offset (hence offset multi-source geometry). This results in asymmetrical axial truncation of the cone beam projections. As such projections do not constitute complete data in the sense of integral geometry, the standard cone beam reconstruction algorithms do not apply. In this series of papers we introduce a new family of rebinning methods for reconstruction from axially asymmetrically truncated cone beam projections. In the first paper we discussed the approximation of the data on the multi-sheet surface with the truncated projection data obtained from offset multi-source geometries. In this second paper we focus on the recovery of the volumetric image from the reconstruction of data rebinned to multi-sheet surfaces. Multi-sheet rebinning effects an implicit relation between the fan beam transforms on the individual sheets and the rebinned data. This relation in conjunction with the linearity of the ray transform allows us to formulate the deconvolution problem for the recovery of the volume from a stack of reconstructed images on multi-sheet surfaces. We discuss the errors in the right-hand side of the deconvolution problem (reconstruction on multi-sheet surfaces) resulting from rebinning approximation. We introduce convolution matrix models based on the distribution of the distances of the rays from the multi-sheet surface, which considerably improve the data model fit and in turn lead to a superior solution. Multiple strategies for solution of the deconvolution problem are discussed and an efficient and robust implementation is presented, which makes the method capable of real time reconstruction. We conclude with some reconstruction results from simulated as well as real data collected with a Rapiscan RTT80 scanner. [ABSTRACT FROM AUTHOR]

A novel technique based on Fourier transform theory has been developed that directly extracts respiratory information from projections without the use of external surrogates. While the feasibility has been demonstrated with three patients, a more extensive validation is necessary. Therefore, the purpose of this work is to investigate the effects of a variety of respiratory and anatomical scenarios on the performance of the technique with the 4D digital extended cardiac torso phantom. FT-phase and FT-magnitude methods were each applied to identify peak-inspiration projections and quantitatively compared to the gold standard of visual identification. Both methods proved to be robust across the studied scenarios with average differences in respiratory phase <10% and percentage of projections assigned within 10% of the gold standard >90%, when incorporating minor modifications to region-of-interest (ROI) selection and/or low-frequency location for select cases of DA and lung percentage in the field of view of the projection. Nevertheless, in the instance where one method initially faltered, the other method prevailed and successfully identified peak-inspiration projections. This is promising because it suggests that the two methods provide complementary information to each other. To ensure appropriate clinical adaptation of markerless, self-sorted four-dimensional cone-beam CT (4D-CBCT), perhaps an optimal integration of the two methods can be developed. [ABSTRACT FROM AUTHOR]

Due to the higher LET of kilovoltage (kV) radiation, there is potential for an increase in relative biological effectiveness (RBE) of absorbed doses of radiation from kV cone beam computed tomography (CBCT) sources in reference to megavoltage or Co-60 doses. This work develops a method for accurately coupling a Monte Carlo (MC) radiation transport code (PENELOPE) with the damage simulation (MCDS) to predict relative numbers of DNA double strand breaks (DSBs). The MCDS accounts for slowing down of electrons and delta ray production within the cell nucleus; however, determining the spectrum of electrons incident on the cell nucleus from photons interacting in a larger region of tissue is not trivial. PENELOPE simulations were conducted with a novel tally algorithm invoked where electrons incident on a detection material were tracked and both the incident energy and the final deposited dose were recorded. The DSB yield predicted by a set of MCDS runs of monoenergetic electrons was then looked up in a table and weighted by the specific energy of the incident electron. Our results indicate that the RBE for DSB induction is 1.1 for diagnostic x-rays with energies from 80 to 125 kVp. We found no significant change in RBE with depth or filtration. The predicted absolute DSB yields are about three times lower for cells irradiated under anoxic conditions than the yield in cells irradiated under normoxic (5%) or fully aerobic (100%) conditions. However, oxygen concentration has a negligible (±0.02) effect on the RBE of kV CBCT x-rays. [ABSTRACT FROM AUTHOR]

Differential phase-contrast computed tomography (DPC-CT) reconstruction problems are usually solved by using parallel-, fan- or cone-beam algorithms. For rod-shaped objects, the x-ray beams cannot recover all the slices of the sample at the same time. Thus, if a rod-shaped sample is required to be reconstructed by the above algorithms, one should alternately perform translation and rotation on this sample, which leads to lower efficiency. The helical cone-beam CT may significantly improve scanning efficiency for rodshaped objects over other algorithms. In this paper, we propose a theoretically exact filter-backprojection algorithm for helical cone-beam DPC-CT, which can be applied to reconstruct the refractive index decrement distribution of the samples directly from two-dimensional differential phase-contrast images. Numerical simulations are conducted to verify the proposed algorithm. Our work provides a potential solution for inspecting the rod-shaped samples using DPC-CT, which may be applicable with the evolution of DPC-CT equipments. [ABSTRACT FROM AUTHOR]

Cone-beam computed tomography (CBCT) has rapidly become a clinically useful imaging modality for image-guided radiation therapy. Unfortunately, CBCT images of the thorax are susceptible to artefacts due to scattered photons, beam hardening, lag in data acquisition, and respiratory motion during a slow scan. These limitations cause dose errors when CBCT image data are used directly in dose computations for on-line, dose adaptive radiation therapy (DART). The purpose of this work is to assess the magnitude of errors in CBCT numbers (HU), and determine the resultant effects on derived tissue density and computed dose accuracy for stereotactic body radiation therapy (SBRT) of lung cancer. Planning CT (PCT) images of three lung patients were acquired using a Philips multi-slice helical CT simulator, while CBCT images were obtained with a Varian On-Board Imaging system. To account for erroneous CBCT data, three practical correction techniques were tested: (1) conversion of CBCT numbers to electron density using phantoms, (2) replacement of individual CBCT pixel values with bulk CT numbers, averaged from PCT images for tissue regions, and (3) limited replacement of CBCT lung pixels values (LCT) likely to produce artificial lateral electron disequilibrium. For each corrected CBCT data set, lung SBRT dose distributions were computed for a 6 MV volume modulated arc therapy (VMAT) technique within the Philips Pinnacle treatment planning system. The reference prescription dose was set such that 95% of the planning target volume (PTV) received at least 54 Gy (i.e. D95). Further, we used the relative depth dose factor as an a priori index to predict the effects of incorrect low tissue density on computed lung dose in regions of severe electron disequilibrium. CT number profiles from co-registered CBCT and PCT patient lung images revealed many reduced lung pixel values in CBCT data, with some pixels corresponding to vacuum (-1000 HU). Similarly, CBCT data in a plastic lung phantom were reduced by 200 HU compared with known CT number values. For the three patients, dose results using the CBCT number data registered with PCT showed a prescription dose reduction ranging from 4 to 13% (D95 = 47 Gy). Therefore, accurate determination of lung density, especially for very low lung density (<0.2 g cm-3) is essential, but difficult to achieve using the CBCT data. Applying corrective techniques (1) and (2) to CBCT patient data produced unacceptable dose differences. For one typical VMAT SBRT patient, the D95 for the corrected CBCT and BCT image-based plans differed by -4% (D95 = 52 Gy) and 9% (D95 = 59 Gy) compared to the co-registered PCT image-based plan. However, corrective technique (3) produced negligible dose differences comparing LCT and PCT imagebased plans. With regard to implementing on-line DART, dose errors must be minimized because they affect re-optimization decisions, and prevent accurate accumulation of the dose distribution. [ABSTRACT FROM AUTHOR]

In this paper, we present an exact inversion formula for the cone beam transform of vector fields. This transform is the mathematical model for vector tomography with cone beam data. The cone beam data consist of line integrals of a vector field along rays which emanate from points on a given source trajectory. It is known that an exact reconstruction of the solenoidal part of the searched for vector field is possible if the trajectory satisfies the Tuy condition of order 3. We derive an inversion formula under the assumption that the source trajectory satisfies Tuy’s condition of order 3 and the vector field is smooth and compactly supported. This formula thus represents an analogue of the known inversion formulas for the scalar cone beam transform. [ABSTRACT FROM AUTHOR]

In adaptive radiotherapy, deformable image registration is often conducted between the planning CT and treatment CT (or cone beam CT) to generate a deformation vector field (DVF) for dose accumulation and contour propagation. The auto-propagated contours on the treatment CT may contain relatively large errors, especially in low-contrast regions. A clinician's inspection and editing of the propagated contours are frequently needed. The edited contours are able to meet the clinical requirement for adaptive therapy; however, the DVF is still inaccurate and inconsistent with the edited contours. The purpose of this work is to develop a contour-guided deformable image registration (CG-DIR) algorithm to improve the accuracy and consistency of the DVF for adaptive radiotherapy. Incorporation of the edited contours into the registration algorithm is realized by regularizing the objective function of the original demons algorithm with a term of intensity matching between the delineated structures set pairs. The CG-DIR algorithm is implemented on computer graphics processing units (GPUs) by following the original GPU-based demons algorithm computation framework (Gu et al 2010 Phys Med Biol. 55 207-219). The performance of CG-DIR is evaluated on five clinical head-and-neck and one pelvic cancer patient data. It is found that compared with the original demons, CG-DIR improves the accuracy and consistency of the DVF, while retaining similar high computational efficiency. [ABSTRACT FROM AUTHOR]

Four dimensional cone beam computed tomography (4DCBCT) is an emerging clinical image guidance strategy for tumour sites affected by respiratory motion. In current generation 4DCBCT techniques, both the gantry rotation speed and imaging frequency are constant and independent of the patient's breathing which can lead to projection clustering. We present a mixed integer quadratic programming (MIQP) model for respiratory motion guided-4DCBCT (RMG- 4DCBCT) which regulates the gantry velocity and projection time interval, in response to the patient's respiratory signal, so that a full set of evenly spaced projections can be taken in a number of phase, or displacement, bins during the respiratory cycle. In each respiratory bin, an image can be reconstructed from the projections to give a 4D view of the patient's anatomy so that the motion of the lungs, and tumour, can be observed during the breathing cycle. A solution to the full MIQP model in a practical amount of time, 10 s, is not possible with the leading commercial MIQP solvers, so a heuristic method is presented. Using parameter settings typically used on current generation 4DCBCT systems (4 min image acquisition, 1200 projections, 10 respiratory bins) and a sinusoidal breathing trace with a 4 s period, we show that the root mean square (RMS) of the angular separation between projections with displacement binning is 2.7° using existing constant gantry speed systems and 0.6° using RMG-4DCBCT. For phase based binning the RMS is 2.7° using constant gantry speed systems and 2.5° using RMG-4DCBCT. The optimization algorithm presented is a critical step on the path to developing a system for RMG-4DCBCT. [ABSTRACT FROM AUTHOR]

The patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such a signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principal component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely the Amsterdam Shroud method, the intensity analysis method and the Fourier-transform-based phase analysis method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting a respiratory signal. We also identified the applicability of each existing method. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate whether quality assurance (QA) for cone-beam computed tomography (CBCT) image rotation is necessary in order to ensure the accuracy of CBCT based image-guided radiation therapy (IGRT) and adaptive radiotherapy (ART). Misregistration of angular coordinates duringCBCTacquisitionmay lead to a rotated reconstructed image. If target localization is performed based on this image, an under- or over-dosage of the target volume (TV) and organs at risk (OARs) may occur. Therefore, patient CT image sets were rotated by 1° up to 3° and the treatment plans were recalculated to quantify changes in dose-volume histograms. A computer code in C++ was written to model the TV displacement and overlap area of an ellipse shape at the target and dose prescription levels corresponding to the image rotation. We investigated clinical scenarios in IGRT and ART in order to study the implications of image rotation on dose distributions for: (1) lateral TV and isocenter (SBRT), (2) central TV and isocenter (IMRT), (3) lateral TV and isocenter (IMRT). Mathematical analysis showed the dose coverage of TV depends on its shape, size, location, and orientation relative to the isocenter. Evaluation of three first scenario for θ = 1° showed variations in TV D95 in the context of IGRT and ART when compared to the original plan were within 2.7 ± 2.6% and 7.7 ± 6.9% respectively while variations in the second and third scenarios were less significant (<0.5%) for the angular range evaluated. However a larger degree of variation was found in terms of minimum and maximum doses for target and OARs. The rotation of CBCT image data sets may have significant dosimetric consequences in IGRT and ART. The TV's location relative to isocenter and shape determine the extent of alterations in dose indicators. Our findings suggest that a CBCT QA criterion of 1? would be a reasonable action level to ensure accurate dose delivery. [ABSTRACT FROM AUTHOR]

To present a new method of estimating 3D positions of the ipsi-lateral hemidiaphragm apex (IHDA) from 2D projection images of mega-voltage cone beam CT (MVCBCT). The detection framework reconstructs a 3D volume from all the 2D projection images. An initial estimated 3D IHDA position is determined in this volume based on an imaging processing pipeline, including Otsu thresholding, connected component labeling and template matching. This initial position is then projected onto each 2D projection image to create a region of interest (ROI). To accurately detect the IHDA position in 2D projection space, two methods, dynamic Hough transform (DHT) and a tracking approach based on a joint probability density function (PDF) are developed. Both methods utilize a double-parabola model to fit the 2D diaphragm boundary. The 3D IHDA motion in the superior-inferior (SI) direction is estimated from the initial static 3D position and the detected 2D positions in projection space. The two Hough-based detectionmethods are tested on 35MVCBCT scans from 15 patients. The detection is compared to manually identified IHDA positions in 2D projection space by three clinicians. An average and standard deviation of 4.252 ± 3.354 and 2.485 ± 1.750 mm was achieved for DHT and trackingbased approaches respectively, compared with the inter-expert variance among three experts of 1.822 ± 1.106 mm. Based on the results of the scans, the PDF tracking-based approach appears more robust than the DHT. The combination of the automatic ROI localization and the tracking-based approach is a quicker and more accurate method of extracting 3D IHDA motion from 2D projection images. [ABSTRACT FROM AUTHOR]

Volumetric scintillation dosimetry has the potential to provide fast, highresolution, three-dimensional radiation dosimetry. However, scintillators exhibit a nonlinear response at the high linear energy transfer (LET) values characteristic of proton Bragg peaks. The purpose of this study was to develop a quenching correction method for volumetric scintillation dosimetry of proton beams. Scintillation light from a miniature liquid scintillator detector was measured along the central axis of a 161.6 MeV proton pencil beam. Threedimensional dose and LET distributions were calculated for 85.6, 100.9, 144.9 and 161.6 MeV beams using a validated Monte Carlo model. LET values were also calculated using an analytical formula. A least-squares fit to the data established the empirical parameters of a quenching correction model. The light distribution in a tank of liquid scintillator was measured with a CCD camera at all four beam energies. The quenching model and LET data were used to correct the measured light distribution. The calculated and measured Bragg peak heights agreed within ±3% for all energies except 85.6MeV,where the agreementwaswithin±10%. The quality of the quenching correctionwas poorer for sharp low-energy Bragg peaks because of blurring and detector size effects. The corrections performed using analytical LET values resulted in doses within 1% of those obtained using Monte Carlo LET values. The proposed method can correct for quenching with sufficient accuracy for dosimetric purposes. The required LET values may be computed effectively using Monte Carlo or analytical methods. Future detectors should improve blurring correction methods and optimize the pixel size to improve accuracy for low-energy Bragg peaks. [ABSTRACT FROM AUTHOR]

Due to a limited number of projections at each phase, severe view aliasing artifacts are present in four-dimensional cone beam computed tomography (4D-CBCT) when reconstruction is performed using conventional algorithms. In this work, we aim to obtain high-quality 4D-CBCT of lung cancer patients in radiation therapy by deforming the planning CT. The deformation vector fields (DVF) to deform the planning CT are estimated through matching the forward projection of the deformed prior image and measured on-treatment CBCT projection. The estimation of the DVF is formulated as an unconstrained optimization problem, where the objective function to be minimized is the sum of the squared difference between the forward projection of the deformed planning CT and the measured 4D-CBCT projection. A nonlinear conjugate gradient method is used to solve the DVF. As the number of the variables in the DVF is much greater than the number of measurements, the solution to such a highly ill-posed problem is very sensitive to the initials during the optimization process. To improve the estimation accuracy of DVF, we proposed a new strategy to obtain better initials for the optimization. In this strategy, 4D-CBCT is first reconstructed by total variation minimization. Demons deformable registration is performed to register the planning CT and the 4D-CBCT reconstructed by total variation minimization. The resulted DVF from demons registration is then used as the initial parameters in the optimization process.A4Dnonuniform rotational B-spline-based cardiac-torso (NCAT) phantom and a patient 4D-CBCT are used to evaluate the algorithm. Image quality of 4D-CBCT is substantially improved by using the proposed strategy in both NCAT phantom and patient studies. The proposed method has the potential to improve the temporal resolution of 4D-CBCT. Improved 4DCBCT can better characterize the motion of lung tumors and will be a valuable tool for image-guided adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

The field of view (FOV) of a cone-beam computed tomography (CBCT) unit in a single-photon emission computed tomography (SPECT)/CBCT system can be increased by offsetting the CBCT detector. Analytic-based algorithms have been developed for image reconstruction from data collected at a large number of densely sampled views in offset-detector CBCT. However, the radiation dose involved in a large number of projections can be of a health concern to the imaged subject. CBCT-imaging dose can be reduced by lowering the number of projections. As analytic-based algorithms are unlikely to reconstruct accurate images from sparse-view data, we investigate and characterize in the work optimization-based algorithms, including an adaptive steepest descentweighted projection onto convex sets (ASD-WPOCS) algorithms, for image reconstruction from sparse-view data collected in offset-detector CBCT. Using simulated data and real data collected from a physical pelvis phantom and patient, we verify and characterize properties of the algorithms under study. Results of our study suggest that optimization-based algorithms such as ASDWPOCS may be developed for yielding images of potential utility from a number of projections substantially smaller than those used currently in clinical SPECT/CBCT imaging, thus leading to a dose reduction in CBCT imaging. [ABSTRACT FROM AUTHOR]

Metal artifacts remain a challenge for computed tomography (CT) reconstruction, especially for medical CT, in which the radiation dose for patients is strictly limited. This study presents a novel method of reducing the metal artifacts for cone-beam CT by renovating the metal areas in the 2D cone-beam projection. We first calculate the 3D coordinates of all the metallic implants, which will then be projected into the projection images in each angle as the metal seed points (MSPs), increasing the precision of the segmentation of the metal areas. Different from the existing methods of calculating the metal positions with two projection images, we proposed a new method to calculate the 3D coordinates of all the metallic implants using three different projection images that are nearly orthogonal. One is the parallelbeam projection image along the Z-axial direction (i.e. along the rotation axis) calculated by the projection synthesizing method, while the other two are the cone-beam projections at two different views chosen from the scanned data. The three nearly orthogonal views will greatly help to accurately locate the metallic implants when there is more than one implant in the object. Then the MSPs can be located in each projection by geometry calculations. The region growing segmentation method and the bilinear interpolation method are used to modify the projection areas of the metallic implants. Experimental studies demonstrate that this method is accurate in locating MSPs and efficient in reducing metal artifacts. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a novel semiautomatic bladder segmentation approach for selecting the appropriate plan from the library of plans for a multiple-plan adaptive radiotherapy (ART) procedure. A population-based statistical bladder model was first built from a training data set (95 bladder contours from 8 patients). This model was then used as constraint to segment the bladder in an independent validation data set (233 CBCT scans from the remaining 22 patients). All 3D bladder contours were converted into parametric surface representations using spherical harmonic expansion. Principal component analysis (PCA) was applied in the spherical harmonicbased shape parameter space to calculate the major variation of bladder shapes. The number of dominating PCA modes was chosen such that 95% of the total shape variation of the training data set was described. The automatic segmentation started from the bladder contour of the planning CT of each patient, which was modified by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to best fit the bladder boundary in the localization CBCT image. A cost function was defined to measure the goodness of fit of the segmentation on the localization CBCT image. The segmentation was obtained by minimizing this cost function using a simplex optimizer. After automatic segmentation, a fast manual correction method was provided to correct those bladders (parts) that were poorly segmented. Volume- and distance-based metrics and the accuracy of plan selection from multiple plans were evaluated to quantify the performance of the automatic and semiautomatic segmentation methods. For the training data set, only seven PCA modes were needed to represent 95% of the bladder shape variation. The mean CI overlap and residual error (SD) of automatic bladder segmentation over all of the validation data were 70.5% and 0.39 cm, respectively. The agreement of plan selection between automatic bladder segmentation and manual delineation was 56.7%. The automatic segmentation and visual assessment took on average 7.8 and 9.7 s, respectively. In 53.4% of the cases, manual correction was performed after automatic segmentation. Themanual correction improved the mean CI overlap, mean residual error and plan selection agreement to 77.7%, 0.30 cmand 80.7%, respectively. Manual correction required on average 8.4 markers and took on average 35.5 s. The statistical shape-based segmentation approach allows automatic segmentation of the bladder on CBCT with moderate accuracy. Limited user intervention can quickly and reliably improve the bladder contours. This segmentation method is suitable to select the appropriate plan for multiple-plan ART of bladder cancer. [ABSTRACT FROM AUTHOR]

This report presents the first experimental demonstration, to our knowledge, of benchtop polychromatic cone-beam x-ray fluorescence computed tomography (XFCT) for a simultaneous determination of the spatial distribution and amount of gold nanoparticles (GNPs) within small-animal-sized objects. The current benchtop experimental setup successfully produced XFCT images accurately showing the regions containing small amount of GNPs (on the order of 0.1 mg) within a 3 cm diameter plastic phantom. In particular, the performance of the current XFCT setup was improved remarkably (e.g., at least a factor of 3 reduction in XFCT scan time) using a tin-filtered polychromatic beam in comparison with a lead-filtered beam. The results of this study strongly suggest that the current benchtop XFCT configuration can be made practical with a few modifications such as the deployment of array detectors, while meeting realistic constraints on x-ray dose, scan time and image resolution for routine pre-clinical in vivo imaging with GNPs. [ABSTRACT FROM AUTHOR]