CBCT Projections Research Articles

Cephalogram synthesis and landmark detection in dental cone-beam CT systems.

Due to the lack of a standardized 3D cephalometric analysis methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution, which impairs cephalometric landmark identification. In addition, the increased radiation dose applied to acquire the scan for 3D reconstruction causes potential health risks. In this work, we propose a sigmoid-based intensity transform that uses the nonlinear optical property of X-ray films to increase image contrast of synthetic cephalograms from 3D volumes. To improve image resolution, super resolution deep learning techniques are investigated. For low dose purpose, the pixel-to-pixel generative adversarial network (pix2pixGAN) is proposed for 2D cephalogram synthesis directly from two cone-beam projections. For landmark detection in the synthetic cephalograms, an efficient automatic landmark detection method using the combination of LeNet-5 and ResNet50 is proposed. Our experiments demonstrate the efficacy of pix2pixGAN in 2D cephalogram synthesis, achieving an average peak signal-to-noise ratio (PSNR) value of 33.8 with reference to the cephalograms synthesized from 3D CBCT volumes. Pix2pixGAN also achieves the best performance in super resolution, achieving an average PSNR value of 32.5 without the introduction of checkerboard or jagging artifacts. Our proposed automatic landmark detection method achieves 86.7% successful detection rate in the 2mm clinical acceptable range on the ISBI Test1 data, which is comparable to the state-of-the-art methods. The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93.0% and 80.7% successful detection rate in 4mm precision range for synthetic cephalograms from 3D volumes and 2D projections, respectively.

Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm−2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Technical Note: Synthetic treatment beam imaging for motion monitoring during spine SBRT treatments - a phantom study.

One of the biggest challenges in applying megavoltage (MV) treatment beam imaging for monitoring spine motion in stereotactic body radiotherapy (SBRT) is the small beam apertures in the images due to strong beam modulations in IMRT planning. The purpose of this study is to investigate the feasibility of a markerless motion tracking method in spine SBRT delivery using a novel enhanced synthetic treatment beam (ESTB) imaging technique. Three clinical spine SBRT plans using 6XFFF beams and sliding window IMRT technique were transferred to a thorax phantom and delivered by a TrueBeam machine. Before delivery, the phantom was aligned to the plan isocenter using CBCT setup and verified with a second CBCT, and then, 2mm shifts were introduced in both the craniocaudal (CC) and the left-right (LR) directions with the couch. During beam delivery, MV images were continuously taken with an electronic portal imaging device (EPID) and automatically grabbed by Varian iTools Capture software with a frame rate of 11.6Hz. After preprocessing for scatter correction and beam intensity compensation, every 50 frames of MV images were combined to generate a series of ESTB images for each beam. The ESTB images were then registered to the projections of the verification CBCT at the matched beam angles to detect the 2mm shifts. Compared to snapshot MV images, the ESTB images had significantly enlarged fields of view (FOVs) and improved image quality. Based on two-dimensional (2D) rigid registration, the ESTB image to CBCT projection matching showed submillimeter accuracy in detecting motion. Specifically, the root mean square errors in detecting the LR/CC shifts were 0.35/0.28, 0.32/0.35, 0.63/0.44, 0.55/0.51, and 0.69/0.42mm at gantry angles 180, 160, 140, 120, and 100, respectively. Our results in the phantom study suggest that ESTB images from a sliding window IMRT plan can be used to detect spine motion, with submillimeter precision in the 2D plane perpendicular to the beam.

Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy

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

Comparison of internal target volumes defined by three-dimensional, four-dimensional, and cone-beam computed tomography images of a motion phantom.

BackgroundTo explore the variations of the gross tumor volume (GTV) from different three-dimensional computed tomography (3DCT) scan modes and the consistency of internal target volume (ITV) between different 3DCT, cone-beam computed tomography (CBCT) and four-dimensional computed tomography (4DCT) scan, a study using a motion phantom simulating sinusoidal movement was conducted.MethodsFor three 3DCT scan modes: the GTV was contoured, and ITVI was generated on the basis of GTV with a 10-mm margin while ITVII and ITVIII with a 0-mm margin on the motion direction. ITVCBCT and ITVMIP were contoured on the images of CBCT and maximum-intensity projection (MIP) reconstructed 4DCT images. The centroid position shifts of ITVs were analyzed. The volume consistency between ITVI, ITVII, ITVIII and ITVMIP were evaluated by calculating the Dice similarity coefficient (Dsc) and the value of Δ Volume (ΔV). Furthermore, the 3DCT and CBCT images from 12 NSCLC patients were retrospectively collected, then the Dsc and ΔV were calculated.ResultsThe mean ± standard deviation of centroid position of ITVI, ITVII and ITVIII were 2.3±4.7, 2.6±4.0, and 1.0±1.4 mm, respectively. The mean ± standard deviation of Dsc between ITVI, ITVII, ITVIII and ITVMIP were 0.78±0.77, 0.86±0.1, and 0.94±0.05, respectively. The ΔV of ITVI, ITVII, ITVIII were 29.67%, 17.22%, and 6.46%, respectively. The ITV from CBCT showed a deduction rate of 3.1–9.3% compared to 4DCT. For the patients, the mean Dsc andΔV between ITVI and ITVCBCT were 0.50 and 60.76%.ConclusionsThe GTV acquired from 3DCT scan mode I possessed great deviation of centroid position and target volume. ITV on the basis of this GTV was significantly larger than ITVMIP. A good similarity was showed between ITVIII and ITVMIP, 4DCT is still a golden standard for the ITV delineation, but in the absence of 4DCT, image from 3DCT scan mode III and KV-CBCT may be considered for ITV delineation with caution.

Technical Note: 4D cone-beam CT reconstruction from sparse-view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT).

PurposeThe number of pencil beam scanned proton therapy (PBS‐PT) facilities equipped with cone‐beam computed tomography (CBCT) imaging treating thoracic indications is constantly rising. To enable daily internal motion monitoring during PBS‐PT treatments of thoracic tumors, we assess the performance of Motion‐Aware RecOnstructiOn method using Spatial and Temporal Regularization (MA‐ROOSTER) four‐dimensional CBCT (4DCBCT) reconstruction for sparse‐view CBCT data and a realistic data set of patients treated with proton therapy.MethodsDaily CBCT projection data for nine non‐small cell lung cancer (NSCLC) patients and one SCLC patient were acquired at a proton gantry system (IBA Proteus® One). Four‐dimensional CBCT images were reconstructed applying the MA‐ROOSTER and the conventional phase‐correlated Feldkamp‐Davis‐Kress (PC‐FDK) method. Image quality was assessed by visual inspection, contrast‐to‐noise ratio (CNR), signal‐to‐noise ratio (SNR), and the structural similarity index measure (SSIM). Furthermore, gross tumor volume (GTV) centroid motion amplitudes were evaluated.ResultsImage quality for the 4DCBCT reconstructions using MA‐ROOSTER was superior to the PC‐FDK reconstructions and close to FDK images (median CNR: 1.23 [PC‐FDK], 1.98 [MA‐ROOSTER], and 1.98 [FDK]; median SNR: 2.56 [PC‐FDK], 4.76 [MA‐ROOSTER], and 5.02 [FDK]; median SSIM: 0.18 [PC‐FDK vs FDK], 0.31 [MA‐ROOSTER vs FDK]). The improved image quality of MA‐ROOSTER facilitated GTV contour warping and realistic motion monitoring for most of the reconstructions.ConclusionMA‐ROOSTER based 4DCBCTs performed well in terms of image quality and appear to be promising for daily internal motion monitoring in PBS‐PT treatments of (N)SCLC patients.

Measurement of Prostate Inter-Fraction Movement by Tracking Implanted Fiducials in Daily CBCT Projections

To develop a method for accurately determining implanted fiducial locations in 3D using template matching and sequential stereo in CBCT projection data. This technique will be used in both retrospective studies to improve motion models and to improve fiducial tracking during treatment for motion management. During planning each fiducial is contoured individually. Using non-clinical research software, a separate template is generated from the planning CT for each fiducial. In offline mode each fiducial is tracked in the CBCT projections by template matching followed by sequential stereo. In the offline mode the full scan data is available so the sequential stereo is applied in retrospective analysis mode (as opposed to real-time tracking), and by iteration outliers are removed. The match score threshold used for accepting 2D matches is initially set to a high value and reduced incrementally until a minimum of 100 trajectory points (out of > 900 projections) are produced with a range of motion below a threshold. The estimated 3D location is taken as the average of results from these trajectory points. Horn’s method for absolute orientation using unit quaternions is used to determine the 3D translation and rotation of the fiducials from the planning CT scan to the CBCT. Results from a prostate patient, with 3 implanted fiducials and 9 treatment CBCTs are shown here. The tracked fiducial locations were overlaid on the CBCT scan and evaluated by an observer. No failed detections were identified in this preliminary dataset. The recorded prostate motion was random over the course of treatment, with no clear temporal trends. The largest translations were in the Z (P-A) direction and rotations about the X (L-R) axis. Amplitude of motions were within expected range. The residual mismatch in fiducial positions after rigid 3D transformation was on the order of the voxel size, or less. CBCT projection images can be used directly for 3D fiducial localization. Further analysis is being carried out, but the technique may benefit from not being limited by the CBCT voxel size, not influenced by CBCT reconstruction artifacts and more robust to motion. The technique can be used in both retrospective applications, where projections are archived, and online for motion management.Tabled 1Abstract 2732; TableDays Post SimulationTranslation IEC (mm)Rotation IEC isocenter (deg)TxTyTzRxRyRz13-0.042.17-3.1412.02.72.316-0.67-0.20-0.435.13.80.9300.322.25-8.3124.01.81.7370.600.25-0.196.82.81.7442.710.32-0.097.42.52.6510.701.86-6.2720.04.43.851-0.57-4.064.21-15.53.4-2.061-0.622.98-6.2821.24.42.864-1.642.892.248.35.40.4Mean0.090.94-2.039.933.461.57Range-1.6, 2.7-4.1, 3.0-8.3, 4.2-15.5, 241.8, 5.4-2.0, 3.8 Open table in a new tab

RealDRR – Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation

IntroductionDigitally reconstructed radiographs (DRRs) represent valuable patient-specific pre-treatment training data for tumor tracking algorithms. However, using current rendering methods, the similarity of the DRRs to real X-ray images is limited, requires time-consuming measurements and/or are computationally expensive. In this study we present RealDRR, a novel framework for highly realistic and computationally efficient DRR rendering. Materials and methodsRealDRR consists of two components applied sequentially to render a DRR. First, a raytracer is applied for forward projection from 3D CT data to a 2D image. Second, a conditional Generative Adverserial Network (cGAN) is applied to translate the 2D forward projection to a realistic 2D DRR.The planning CT and CBCT projections from a CIRS thorax phantom and 6 radiotherapy patients (3 prostate, 3 brain) were split in training and test sets for evaluating the intra-patient, inter-patient and inter-anatomical region generalization performance of the trained framework. Several image similarity metrics, as well as a verification based on template matching, were used between the rendered DRRs and respective CBCT projections in the test sets, and results were compared to those of a current state-of-the-art DRR rendering method. ResultsWhen trained on 800 CBCT projection images from two patients and tested on a third unseen patient from either anatomical region, RealDRR outperformed the current state-of-the-art with statistical significance on all metrics (two-sample t-test, p < 0.05). Once trained, the framework is able to render 100 highly realistic DRRs in under two minutes. ConclusionA novel framework for realistic and efficient DRR rendering was proposed. As the framework requires a reasonable amount of computational resources, the internal parameters can be tailored to imaging systems and protocols through on-site training on retrospective imaging data.

Quality control test of cone beam computed tomography – constancy tests

Purpose: The paper describes tests of CBCT cone beam scanners carried out to ensure projection quality. Design/methodology/approach: During the studies, phantoms were scanned with a leading brand volumetric CT scanner according to the device manufacturer's recommendations. The water phantom and phantom made of PMMA with materials of different radiological densities were used in the performed tests. The image area during the tests was determined as a cylinder with diameter of 80 mm and height of 90 mm. In turn, exposure parameters were selected on the basis of clinically applied protocols of cranial imaging. Within carried out research, tests of noise level were performed, image homogeneity was analysed and Hounsfield units constancy was determined. To this end, 18 quality control tests were analysed, which were performed at intervals of about 30 days. Images obtained during phantom scans were analysed by determining the Hounsfield value of selected areas and their changes over time. Findings: The analysis of all carried out projection quality control tests showed that they met the criteria set by the manufacturer, falling within the predefined value ranges. One of the performed tests presented results approaching the limit of acceptable values. After notifying this case, it was shown that the CT scanner was serviced during that period. The obtained results of the quality control tests of water phantom as well as of the material phantom imaging were maintained at similar levels after the service activities. No changes were observed in the obtained mean values of Hounsfield units, which would indicate a decrease in diagnostic quality of CBCT projections. Research limitations/implications: The results presented in this publication require further analysis. These should be complemented by incl. analyses of spatial resolution and image geometry. Practical implications: Carried out research has shown that cyclical quality control testing by a qualified operator is an essential activity to ensure high diagnostic quality of the device. In addition, this analysis showed that procedures of in-service tests should not be omitted and delayed. Originality/value: Originality in these tests is the possibility to improve the procedures for performing basic quality control tests.

Tumor phase recognition using cone-beam computed tomography projections and external surrogate information.

Directly extracting the respiratory phase pattern of the tumor using cone-beam computed tomography (CBCT) projections is challenging due to the poor tumor visibility caused by the obstruction of multiple anatomic structures on the beam's eye view. Predicting tumor phase information using external surrogate also has intrinsic difficulties as the phase patterns between surrogates and tumors are not necessary to be congruent. In this work, we developed an algorithm to accurately recover the primary oscillation components of tumor motion using the combined information from both CBCT projections and external surrogates. The algorithm involved two steps. First, a preliminary tumor phase pattern was acquired by applying local principal component analysis (LPCA) on the cropped Amsterdam Shroud (AS) images. In this step, only the cropped image of the tumor region was used to extract the tumor phase pattern in order to minimize the impact of pattern recognition from other anatomic structures. Second, by performing multivariate singular spectrum analysis (MSSA) on the combined information containing both external surrogate signal and the original waveform acquired in the first step, the primary component of the tumor phase oscillation was recovered. For the phantom study, a QUASAR respiratory motion phantom with a removable tumor-simulator insert was employed to acquire CBCT projection images. A comparison between LPCA only and our method was assessed by power spectrum analysis. Also, the motion pattern was simulated under the phase shift or various amplitude conditions to examine the robustness of our method. Finally, anatomic obstruction scenarios were simulated by attaching a heart model, PVC tubes, and RANDO® phantom slabs to the phantom, respectively. Each scenario was tested with five real-patient breathing patterns to mimic real clinical situations. For the patient study, eight patients with various tumor locations were selected. The performance of our method was then evaluated by comparing the reference waveform with the extracted signal for overall phase discrepancy, expiration phase discrepancy, peak, and valley accuracy. In tests of phase shifts and amplitude variations, the overall peak and valley accuracy was -0.009±0.18sec, and no time delay was found compared to the reference. In anatomical obstruction tests, the extracted signal had 1.6±1.2 % expiration phase discrepancy, -0.12±0.28sec peak accuracy, and 0.01±0.15sec valley accuracy. For patient studies, the extracted signal using our method had -1.05±3.0 % overall phase discrepancy, -1.55±1.45% expiration phase discrepancy, 0.04±0.13sec peak accuracy, and -0.01±0.15sec valley accuracy, compared to the reference waveforms. An innovative method capable of accurately recognizing tumor phase information was developed. With the aid of extra information from the external surrogate, an improvement in prediction accuracy, as compared with traditional statistical methods, was obtained. It enables us to employ it as the ground truth for 4D-CBCT reconstruction, gating treatment, and other clinic implementations that require accurate tumor phase information.

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (emax), the minimum error (emin), the 95th percentile error (e95%), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.

Quantitative Assessment of Weight-Bearing Fracture Biomechanics Using Extremity Cone-Beam CT.

We investigate an application of multisource extremity Cone-Beam CT (CBCT) with capability of weight-bearing tomographic imaging to obtain quantitative measurements of load-induced deformation of metal internal fixation hardware (e.g. tibial plate). Such measurements are desirable to improve the detection of delayed fusion or non-union of fractures, potentially facilitating earlier return to weight-bearing activities. To measure the deformation, we perform a deformable 3D-2D registration of a prior model of the implant to its CBCT projections under load-bearing. This Known-Component Registration (KC-Reg) framework avoids potential errors that emerge when the deformation is estimated directly from 3D reconstructions with metal artifacts. The 3D-2D registration involves a free-form deformable (FFD) point cloud model of the implant and a 3D cubic B-spline representation of the deformation. Gradient correlation is used as the optimization metric for the registration. The proposed approach was tested in experimental studies on the extremity CBCT system. A custom jig was designed to apply controlled axial loads to a fracture model, emulating weight-bearing imaging scenarios. Performance evaluation involved a Sawbone tibia phantom with an ~4 mm fracture gap. The model was fixed with a locking plate and imaged under five loading conditions. To investigate performance in the presence of confounding background gradients, additional experiments were performed with a pre-deformed femoral plate placed in a water bath with Ca bone mineral density inserts. Errors were measured using eight reference BBs for the tibial plate, and surface point distances for the femoral plate, where a prior model of deformed implant was available for comparison. Both in the loaded tibial plate case and for the femoral plate with confounding background gradients, the proposed KC-Reg framework estimated implant deformations with errors of <0.2 mm for the majority of the investigated deformation magnitudes (error range 0.14 - 0.25 mm). The accuracy was comparable between 3D-2D registrations performed from 12 x-ray views and registrations obtained from as few as 3 views. This was likely enabled by the unique three-source x-ray unit on the extremity CBCT scanner, which implements two off-central-plane focal spots that provided oblique views of the field-of-view to aid implant pose estimation. Accurate measurements of fracture hardware deformations under physiological weight-bearing are feasible using an extremity CBCT scanner and FFD 3D-2D registration. The resulting deformed implant models can be incorporated into tomographic reconstructions to reduce metal artifacts and improve quantification of the mineral content of fracture callus in CBCT volumes.

Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy.

Purpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT projection with the non-local total variation (NLTV) method.Methods: We modeled the low-dose CBCT reconstruction as recovering high quality, high-dose CBCT x-ray projections (100 kVp, 1.6 mAs) from low-dose, noisy CBCT x-ray projections (100 kVp, 0.1 mAs). The restoration of CBCT projections was performed using the NLTV regularization method. In NLTV, the x-ray image is optimized by minimizing an energy function that penalizes gray-level difference between pair of pixels between noisy x-ray projection and denoising x-ray projection. After the noisy projection is restored by NLTV regularization, the standard FDK method was applied to generate the final reconstruction output.Results: Significant noise reduction was achieved comparing to original, noisy inputs while maintaining the image quality comparable to the high-dose CBCT projections. The experimental validations show the proposed NLTV algorithm can robustly restore the noise level of x-ray projection images while significantly improving the overall image quality. The improvement in normalized mean square error (NMSE) and peak signal-to-noise ratio (PSNR) measured from the non-local total variation-gradient projection (NLTV-GPSR) algorithm is noticeable compared to that of uncorrected low-dose CBCT images. Moreover, the difference of CNRs from the gains from the proposed algorithm is noticeable and comparable to high-dose CBCT.Conclusion: The proposed method successfully restores noise degraded, low-dose CBCT projections to high-dose projection quality. Such an outcome is a considerable improvement to the reconstruction result compared to the FDK-based method. In addition, a significant reduction in reconstruction time makes the proposed algorithm more attractive. This demonstrates the potential use of the proposed algorithm for clinical practice in radiotherapy.

Scattering Correction of CBCT Projections for the Quality Enhanced CT Reconstruction Using an Efficient Monte Carlo Photon Transport Model

In industrial computed tomography, cone beam computed tomography (CBCT) setups are state-of-art because of its capabilities and speed. However, this technique highly suffers from the effect of scatter in comparison to fan beam computed tomography due to the large scanning 2D field of view. CBCT is more preferable to be used since it is considerably faster. In this work a fast Monte Carlo forward projection model based on photon transport has been developed to subtract the scatter from the measured X-ray projections. The ability of this forward model to compute any complex object from CAD models based on meshes polyhedron validate the proposed scatter subtraction algorithm. Depending on the number of photons used, the complexity and the size of the object each X-ray projection is obtained within two minute or less with this model. In addition to the use of the variance reduction techniques for a fast calculation of the scatter, the proposed scatter subtraction algorithm has been further reduced in computation time by the use of smoothing filters, mainly by the use of Savitzky-Golay filter which is a type of a squares polynomial approximation filter, this kind of smoothing plays significant role in the reduction of the computations time.

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network.

To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D-CBCT images. Under an IRB-approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free-breathing. Concurrently, an electromagnetic signal-guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross-correlation-based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave-one-out strategy using data from 11 lung patients, including 5500kV images. The root-mean-square error between the CRNN and motion traces was calculated to evaluate the localization accuracy. Three-dimensional displacement around the simulation position shown in the Calypso traces was 3.4±1.7mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3±1.4mm. CRNN had a success rate of 86±8% in determining whether the motion was within a 3D displacement window of 2mm. The latency was 20ms when CRNN ran on a high-performance computer cluster. CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross-correlation-based registration approach, and has the potential to remove reliance on the implanted fiducials.

A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data

Various imaging parameters in X-ray computed tomography (CT) should be examined and optimized by task-based assessment of human observer performance. Recently, convolutional neural networks (CNNs) have been introduced as anthropomorphic model observers. However, when human-labeled data are not available or limited, CNNs with an existing training strategy do not produce good performance agreement with human observers. The purpose of this study is to propose new training strategies for a CNN-based anthropomorphic model observer without human-labeled data for signal-known-exactly and background-known-statistically detection tasks. We acquired cone-beam CT projection data of breast background volume and reconstructed the projection data using the Feldkamp-Davis-Kress algorithm with 12 different imaging conditions including viewing image planes. Training data for the CNN were labeled utilizing conventional model observers. We employed an early stopping rule to reflect internal noise during the CNN training. To examine the CNN performance, we used three different training-testing schemes. The performance agreement between the human and model observers was measured via a Bland-Altman plot, the root-mean-squared error (RMSE), and the Pearson's correlation coefficient ($r$ ) of their proportion correct values. Throughout the three different training-testing schemes, CNNs with the proposed training strategies yielded narrower limits of agreements (with a bias lower than 0.03) and higher scores in both RMSE and $r$ than the conventional model observers. This indicates the proposed training strategies enable the CNN-based anthropomorphic model observer to have good performance agreement with human observers and generalize better to different imaging conditions than conventional anthropomorphic model observers.

First clinical real-time motion-including tumor dose reconstruction during radiotherapy delivery

PurposeTo clinically implement and characterize real-time motion-including tumor dose reconstruction during radiotherapy delivery. MethodsSeven patients with 2–3 fiducial markers implanted near liver tumors received stereotactic body radiotherapy on a conventional linear accelerator. The 3D marker motion during a setup CBCT scan was determined online from the CBCT projections and used to generate a correlation model between tumor and external marker block motion. During treatment, the correlation model was updated by kV imaging every three seconds and used for real-time tumor localization. Using streamed accelerator parameters and tumor positions, in-house developed software, DoseTracker, calculated the dose to the moving tumor in real-time assuming water density in the patient. Post-treatment, the real-time tumor localization was validated by comparison with independent marker segmentations and 3D motion estimations. Dose reconstruction was validated by comparison with treatment planning system (TPS) calculations that modeled motion as isocenter shifts and used both actual CT densities and water densities. ResultsThe real-time estimated tumor position had a mean 3D root-mean-square error of 1.7 mm (range: 0.9–2.6 mm). The motion-induced reduction in the minimum dose to 95% of the clinical target volume (CTV D95) per fraction was up to 12.3%-points. It was estimated in real-time by DoseTracker during patient treatment with a root-mean-square difference relative to the TPS of 1.3%-points (TPS CT) and 1.0%-points (TPS water). ConclusionsThe world’s first clinical real-time motion-including tumor dose reconstruction during radiotherapy was demonstrated. This marks an important milestone for real-time in-treatment quality assurance and paves the way for real-time dose-guided treatment adaptation.

SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan.

Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms. A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D-Lung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-mean-squared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) - patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV. Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion-compensation, and deformation of the prior 4D-CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm. The RMS of the three-dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA-ROOSTER method, which utilizes a 4D-CT motion model for temporal regularization, had the best and most consistent image quality and accuracy. The SPARE Challenge represents the first framework for systematically evaluating state-of-the-art algorithms for 4D-CBCT reconstruction from a 1-min scan. Results suggest the potential for reducing scan time and dose for 4D-CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

Dual-energy material decomposition for cone-beam computed tomography in image-guided radiotherapy

Background: Dual-energy (DE) diagnostic computed tomography (CT) combines two scans of different photon energy spectra which can provide additional image information as compared to standard CT. We developed a DE material decomposition scan protocol for daily cone-beam CT (CBCT) of head-and-neck patients receiving radiotherapy and tested it in a clinical trial.Material and methods: Our DE CBCT protocol consisted of an 80 and 140 kVp scan. The material decomposition algorithm split the low and high energy scan into components of two basis materials, aluminum and acrylic. Scans of different thicknesses and overlap of the basis materials were acquired to calibrate the model which decomposed the CBCT projections into thicknesses of aluminum and acrylic on a per-pixel basis. Pseudo monochromatic projections were created from these thicknesses and the known energy dependence of the attenuation coefficient of the basis materials. A frequency selective de-noising method was further applied to the basis material projections. The DE CBCT protocol was tested on seven patients. Two DE images were chosen, one at low (50–60) keV to evaluate soft tissue image quality and one at 150 keV to assess metal artifact reduction as compared to standard CBCT.Results: The de-noising algorithm reduced noise by 41% and 69% in the 60 and 150 keV images, respectively, compared to images without the de-noising. The low keV image showed an increase in soft tissue contrast-to-noise ratio of 7–43% compared to the standard clinical CBCT for six of the seven patients. The 150 keV DE CBCT image reduced metal artifacts. Enhanced streaking from metal artifacts were observed in some of the DE CBCT images.Conclusion: Monochromatic DE images from material decomposition can improve soft tissue contrast-to-noise ratio and metal artifact reduction. Improvements are limited, however, and new artifacts were also introduced by the DE algorithm.

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

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