Functional imaging equivalence and proof of concept for image-guided adaptive radiotherapy with fixed gantry and rotating couch.
ABSTRACT: The purpose of this article is to present the first imaging experiments to demonstrate the functional equivalence between a conventional rotational gantry and a fixed-beam imaging geometry, and the feasibility of an iterative image-reconstruction technique under gravitational deformation.Experiments were performed using an Elekta Axesse with Agility MLC and XVI, a custom-built rotating phantom stage, a Catphan QA phantom, and a porcine heart. For the imaging equivalence, a conventional cone beam computed tomography (CBCT) of the Catphan was acquired, as well as a set of 660 x-ray projections with a static gantry and rotating Catphan. Both datasets were reconstructed with the Feldkamp-Davis-Kress (FDK) algorithm, and the resultant volumetric images were compared using standard metrics. For imaging under gravitational deformation, a conventional CBCT of the Catphan and a set of 660 x-ray projections with a static gantry and rotating Catphan were also acquired with a porcine heart. The conventional CBCT was reconstructed using FDK. The projections that were acquired with the heart rotating were sorted into angular bins and reconstructed with prior image constrained compressed sensing using a deformation-blurred FDK prior. Deformation was quantified with B-spline transformation-based deformable image registration.For imaging equivalence, the difference between the two Catphan images was consistent with Poisson noise. For imaging under gravitational deformation, the conventional CBCT porcine heart image (ground truth at 0 degrees) matched the static gantry, rotating heart reconstruction with a mean magnitude of <3 mm and maximum magnitude of <5 mm of the deformation vector field. The mean deformation of the rotating heart was 3.0 to 8.9 mm, up to 16.1 mm maximum deformation. Deformation was mainly observed in the direction of gravity.We have demonstrated imaging equivalence in cone beam CT reconstructions between rigid phantom images acquired with a conventional rotating gantry and with a fixed-gantry and rotating phantom. We have presented a method for image reconstruction under a fixed-beam imaging geometry using a deformable phantom.
Project description:Four-dimensional cone beam computed tomography (4D-CBCT) has been developed to provide respiratory phase-resolved volumetric imaging in image guided radiation therapy. Conventionally, it is reconstructed by first sorting the x-ray projections into multiple respiratory phase bins according to a breathing signal extracted either from the projection images or some external surrogates, and then reconstructing a 3D CBCT image in each phase bin independently using FDK algorithm. This method requires adequate number of projections for each phase, which can be achieved using a low gantry rotation or multiple gantry rotations. Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. 4D-CBCT images at different breathing phases share a lot of redundant information, because they represent the same anatomy captured at slightly different temporal points. Taking this redundancy along the temporal dimension into account can in principle facilitate the reconstruction in the situation of inadequate number of projection images. In this work, the authors propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method.The authors define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms implementation on GPU is designed to avoid redundant and uncoalesced memory access, in order to ensure a high computational efficiency. Our algorithms have been tested on a digital NURBS-based cardiac-torso phantom and a clinical patient case.The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56-3.13 and our enhancement method increases the CNR by 2.75-3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is 509-683 s for the reconstruction algorithm and 524-540 s for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card.By innovatively taking the temporal redundancy among 4D-CBCT images into consideration, the proposed algorithms can produce high quality 4D-CBCT images with much less streak artifacts than the FDK results, in the situation of inadequate number of projections.
Project description:<h4>Background and purpose</h4>Scatter correction of cone-beam computed tomography (CBCT) projections may enable accurate online dose-delivery estimations in photon and proton-based radiotherapy. This study aimed to evaluate the impact of scatter correction in CBCT-based proton range/dose calculations, in scans acquired in both proton and photon gantries.<h4>Material and methods</h4>CBCT projections of a Catphan and an Alderson phantom were acquired on both a proton and a photon gantry. The scatter corrected CBCTs (corrCBCTs) and the clinical reconstructions (stdCBCTs) were compared against CTs rigidly registered to the CBCTs (rigidCTs). The CBCTs of the Catphan phantom were segmented by materials for CT number analysis. Water equivalent path length (WEPL) maps were calculated through the Alderson phantom while proton plans optimized on the rigidCT and recalculated on all CBCTs were compared in a gamma analysis.<h4>Results</h4>In medium and high-density materials, the corrCBCT CT numbers were much closer to those of the rigidCT than the stdCBCTs. E.g. in the 50% bone segmentations the differences were reduced from above 300 HU (with stdCBCT) to around 60-70 HU (with corrCBCT). Differences in WEPL from the rigidCT were typically well below 5 mm for the corrCBCTs, compared to well above 10 mm for the stdCBCTs with the largest deviations in the head and thorax regions. Gamma pass rates (2%/2mm) when comparing CBCT-based dose re-calculations to rigidCT calculations were improved from around 80% (with stdCBCT) to mostly above 90% (with corrCBCT).<h4>Conclusion</h4>Scatter correction leads to substantial artefact reductions, improving accuracy of CBCT-based proton range/dose calculations.
Project description:To evaluate a moving blocker-based approach in estimating and correcting megavoltage (MV) and kilovoltage (kV) scatter contamination in kV cone-beam computed tomography (CBCT) acquired during volumetric modulated arc therapy (VMAT).During the concurrent CBCT/VMAT acquisition, a physical attenuator (i.e., "blocker") consisting of equally spaced lead strips was mounted and moved constantly between the CBCT source and patient. Both kV and MV scatter signals were estimated from the blocked region of the imaging panel, and interpolated into the unblocked region. A scatter corrected CBCT was then reconstructed from the unblocked projections after scatter subtraction using an iterative image reconstruction algorithm based on constraint optimization. Experimental studies were performed on a Catphan® phantom and an anthropomorphic pelvis phantom to demonstrate the feasibility of using a moving blocker for kV-MV scatter correction.Scatter induced cupping artifacts were substantially reduced in the moving blocker corrected CBCT images. Quantitatively, the root mean square error of Hounsfield units (HU) in seven density inserts of the Catphan phantom was reduced from 395 to 40.The proposed moving blocker strategy greatly improves the image quality of CBCT acquired with concurrent VMAT by reducing the kV-MV scatter induced HU inaccuracy and cupping artifacts.
Project description:Mechanical instabilities that occur during gantry rotation of on-board cone-beam computed tomography (CBCT) imaging systems limit the efficacy of image-guided radiotherapy. Various methods for calibrating the CBCT geometry and correcting errors have been proposed, including some that utilize dedicated fiducial phantoms. The purpose of this work was to investigate the role of phantom fabrication imprecision on the accuracy of a particular CT cone-beam geometry estimate and to test a new method to mitigate errors in beam geometry arising from imperfectly fabricated phantoms.The authors implemented a fiducial phantom-based beam geometry estimation following the one described by Cho et al. [Med Phys 32(4), 968-983 (2005)]. The algorithm utilizes as input projection images of the phantom at various gantry angles and provides a full nine parameter beam geometry characterization of the source and detector position and detector orientation versus gantry angle. A method was developed for recalculating the beam geometry in a coordinate system with origin at the source trajectory center and aligned with the axis of gantry rotation, thus making the beam geometry estimation independent of the placement of the phantom. A second CBCT scan with the phantom rotated 180 degrees about its long axis was averaged with the first scan to mitigate errors from phantom imprecision. Computer simulations were performed to assess the effect of 2D fiducial marker positional error on the projections due to image discretization, as well as 3D fiducial marker position error due to phantom fabrication imprecision. Experimental CBCT images of a fiducial phantom were obtained and the algorithm used to measure beam geometry for a Varian Trilogy with an on-board CBCT.Both simulations and experimental results reveal large sinusoidal oscillations in the calculated beam geometry parameters with gantry angle due to displacement of the phantom from CBCT isocenter and misalignment with the gantry axis, which are eliminated by recalculating the beam geometry in the source coordinate system. Simulations and experiments also reveal an additional source of oscillations arising from fiducial marker position error due to phantom fabrication imprecision that are mitigated by averaging the results with those of a second CBCT scan with phantom rotated. With a typical fiducial marker position error of 0.020 mm (0.001 in.), source and detector position are found in simulations to be within 250 microm of the true values, and detector and gantry angles less than 0.2 degrees. Detector offsets are within 100 microm of the known value. Experimental results verify the efficacy of the second scan in mitigating beam geometry errors, as well as large apparent source/detector isocenter offsets arising from phantom fabrication imprecision.The authors have developed and validated a novel fiducial phantom-based CBCT beam geometry estimation algorithm that does not require precise positioning of the phantom at machine isocenter and is insensitive to positional imprecision of fiducial markers within the phantom due to fabrication errors. The method can accurately locate source and detector isocenters even when using an imprecise phantom, which is very important for measurement of isocenter coincidence of the therapy and on-board imaging systems.
Project description:PURPOSE:The goal of this study was to investigate the effectiveness of monitoring relevant variations during treatments for electronic portal imaging device (EPID)-based 3D in vivo verification performed using planning CTs. METHODS:Experiments on two simple phantoms (uniform and nonuniform phantoms) and a thoracic phantom were analyzed in this study, and six relevant variations including the machine output, planning target volume (PTV) deformation, multileaf collimator (MLC) and Phantom shift (set-up errors), and gantry and couch angle shifts were evaluated. 3D gamma and dose-volume histogram (DVH) methods were used to evaluate the detection sensitivity of the EPID-based 3D in vivo dosimetry and the dose accuracy of the EPID reconstruction, respectively, as affected by the variations, and the results were validated by determining the consistency with TPS simulated results. RESULTS:The results of the simple phantoms showed that the gamma failure rates and DVH trend of EPID reconstructions were consistent with the results of TPS simulations for machine output and MLC shifts and inconsistent for phantom shift, gantry/couch angle shift and PTV deformation variations. The results of the thoracic phantom showed that CBCT-guided EPID reconstruction sensitively detected 3-mm Phantom shift in thoracic phantom and its gamma failure rates and DVH trend were consistent with the results of TPS simulations. CONCLUSION:The variations, such as machine output and MLC shift, that are phantom unrelated and cause changes in the beam of the linear accelerator can be sensitively detected by EPID-based 3D in vivo dosimetry and do not affect the accuracy of the EPID reconstruction dose. Planning CT will limit the detection sensitivity and the accuracy of the reconstruction dose of the EPID-based 3D in vivo dosimetry for phantom-related variations (such as Phantom shift and gantry/couch angle shift). EPID reconstruction combined with IGRT technology is a more effective method to monitor phantom shift variations.
Project description:Localization prior to delivery of SBRT to free-breathing patients is performed by aligning the planning internal target volume (ITV) from 4DCT with an on-board free-breathing cone-beam CT (FB-CBCT) image. The FB-CBCT image is assumed to also generate an ITV that captures the full range of motion, due to the acquisition spanning multiple respiratory cycles. However, the ITV could potentially be underestimated when the ratio of time spent in inspiration versus time spent in expiration (I/E ratio) deviates from unity. Therefore, the aim of this study was to investigate the effect of variable I/E ratios on the FB ITV generated from a FB-CBCT scan.This study employed both phantom and patient imaging data. For the phantom study, five periodic respiratory cycles were simulated with different I/E ratios. Six patient respiratory cycles with variable I/E ratios were also selected. All profiles were then programmed into a motion phantom for imaging and modified to exhibit three peak-to-peak motion amplitudes (0.5, 1.0, and 2.0 cm). Each profile was imaged using two spherical targets with 1.0 and 3.0 cm diameters. 2D projections were acquired with full gantry rotation of a kiloVoltage (kV) imager mounted onto the gantry of a modem linear accelerator. CBCT images were reconstructed from 2D projections using a standard filtered back-projection reconstruction algorithm. Quantitative analyses for the phantom study included computing the change in contrast along the direction of target motion as well as determining the area (which is proportional to the target volume) inside of the contour extracted using a Canny edge detector. For the patient study, projection data that were previously acquired under an investigational 4D CBCT slow-gantry imaging protocol were used to generate both FB-CBCT and 4D CBCT images. Volumes were then manually contoured from both datasets (using the same window and level) for quantitative comparison.The phantom study indicated a reduction in contrast at the inferior edge of the ITV (corresponding to inspiration) as the ratio decreased, for both simulated and patient respiratory cycles. For the simulated phantom respiratory cycles, the contrast reduction of the smallest I/E ratio was 27.6% for the largest target with the smallest amplitude and 89.7% for the smallest target with the largest amplitude. For patient respiratory cycles, these numbers were 22.3% and 94.0%, respectively. The extracted area from inside of the target contours showed a decreasing trend as the I/E ratio decreased. In the patient study, the FB-CBCT ITVs of both lung tumors studied were underestimated when compared with their corresponding 4D CBCT ITV. The underestimations found were 40.1% for the smaller tumor and 24.2% for the larger tumor.The ITV may be underestimated in a FB-CBCT image when a patient's respiratory pattern is characterized by a disparate length of time spent in inspiration versus expiration. Missing the full target motion information during on-board verification imaging may result in localization errors.
Project description:To obtain on-treatment volumetric patient anatomy during respiratory gated volumetric modulated arc therapy (VMAT).On-board imaging device integrated with Linacs offers a viable tool for obtaining patient anatomy during radiation treatment delivery. In this study, the authors acquired beam-level kV images during gated VMAT treatments using a Varian TrueBeam™STx Linac. These kV projection images are triggered by a respiratory gating signal and can be acquired immediately before treatment MV beam on at every breathing cycle during delivery. Because the kV images are acquired with an on-board imaging device during a rotational arc therapy, they provide the patient anatomical information from many different angles or projection views (typically 20-40). To reconstruct the volumetric image representing patient anatomy during the VMAT treatment, the authors used a compressed sensing method with a fast first-order optimization algorithm. The conventional FDK reconstruction was also used for comparison purposes. The method was tested on a dynamic anthropomorphic physical phantom as well as a lung patient.The reconstructed volumetric images for a dynamic anthropomorphic physical phantom and a lung patient showed clearly visible soft-tissue target as well as other anatomical structures, with the proposed compressed sensing-based image reconstruction method. Compared with FDK, the compressed sensing method leads to a ≈ two and threefold increase in contrast-to-noise ratio around the target area in the phantom and patient case, respectively.The proposed technique provides on-treatment volumetric patient anatomy, with only a fraction (<10%) of the imaging dose used in conventional CBCT procedures. This anatomical information may be valuable for geometric verification and treatment guidance, and useful for verification of treatment dose delivery, accumulation, and adaptation in the future.
Project description:Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT∕CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment.In this work, we accelerated the Feldcamp-Davis-Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT∕CT reconstruction algorithm.Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10(-7). Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process.An ultrafast, reliable and scalable 4D CBCT∕CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment.
Project description:To investigate the cause of a bow-tie wobble artifact (BWA) discovered on Varian OBI CBCT images and to develop practical correction strategies.The dependence of the BWA on phantom geometry, phantom position, specific system, and reconstruction algorithm was investigated. Simulations were conducted to study the dependence of the BWA on scatter and beam hardening corrections. Geometric calibration was performed to rule out other gantry-angle dependent mechanical non-idealities as BWA causes. Air scans were acquired with ball-bearing markers to study the motions of the x-ray head assembly as functions of gantry angle. Based on measurements, we developed hypothesis regarding the BWA cause. Simulations were performed to validate our hypothesis. Two correction strategies were implemented: a measurement-based method, which acquires gantry-dependent normalization projections (NPs); and a model-based method that involves numerically shifting the single-angle NP to compensate for the previously-measured bow-tie-filter (BTF) motion.The BWA has a diameter of approximately 15 cm, is centered at the isocenter, and is reproducible independent of phantom, position, system, reconstruction, and standard corrections, but only when the BTF is used. Measurements identified a 2D sinusoidal gantry-angle-dependent motion of the x-ray head assembly, and it was the BTF motion (>3 mm amplitude projected onto the detector) resulting an intensity mismatch between the all-angle CBCT projections and a single-angle NP that caused the BWA. Both correction strategies were demonstrated effective.A geometric mismatch between the BTF modulation patterns on CBCT projections and on the NP causes the BWA. The BTF wobble requires additional degrees of freedom in CBCT geometric calibration to characterize.
Project description:Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy.