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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 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]

Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an effective way for us to enhance the image quality at the matched regions between the prior and current images compared to the existing PICCS algorithm. Compared to the current CBCT imaging protocols, the APICCS algorithm allows an imaging dose reduction of 10-40 times due to the greatly reduced number of projections and lower x-ray tube current level coming from the low-dose protocol. [ABSTRACT FROM AUTHOR]

Four-dimensional cone-beam computed tomography (4D-CBCT) of the free-breathing thorax is a valuable tool in image-guided radiation therapy of the thorax and the upper abdomen. It allows the determination of the position of a tumor throughout the breathing cycle, while only its mean position can be extracted from three-dimensional CBCT. The classical approaches are not fully satisfactory: respiration-correlated methods allow one to accurately locate high-contrast structures in any frame, but contain strong streak artifacts unless the acquisition is significantly slowed down. Motion-compensated methods can yield streak-free, but static, reconstructions. This work proposes a 4D-CBCT method that can be seen as a trade-off between respiration-correlated and motion-compensated reconstruction. It builds upon the existing reconstruction using spatial and temporal regularization (ROOSTER) and is called motion-aware ROOSTER (MA-ROOSTER). It performs temporal regularization along curved trajectories, following the motion estimated on a prior 4D CT scan. MA-ROOSTER does not involve motion-compensated forward and back projections: the input motion is used only during temporal regularization. MA-ROOSTER is compared to ROOSTER, motion-compensated Feldkamp–Davis–Kress (MC-FDK), and two respiration-correlated methods, on CBCT acquisitions of one physical phantom and two patients. It yields streak-free reconstructions, visually similar to MC-FDK, and robust information on tumor location throughout the breathing cycle. MA-ROOSTER also allows a variation of the lung tissue density during the breathing cycle, similar to that of planning CT, which is required for quantitative post-processing. [ABSTRACT FROM AUTHOR]

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

Kilo-voltage (KV) cone-beam computed tomography (CBCT) unit mounted onto a linear accelerator treatment system, often referred to as on-board imager (OBI), plays an increasingly important role in image-guided radiation therapy. While the FDK algorithm is currently used for reconstructing images from clinical OBI data, optimization-based reconstruction has also been investigated for OBI CBCT. An optimization-based reconstruction involves numerous parameters, which can significantly impact reconstruction properties (or utility). The success of an optimization-based reconstruction for a particular class of practical applications thus relies strongly on appropriate selection of parameter values. In the work, we focus on tailoring the constrained-TV-minimization-based reconstruction, an optimization-based reconstruction previously shown of some potential for CBCT imaging conditions of practical interest, to OBI imaging through appropriate selection of parameter values. In particular, for given real data of phantoms and patient collected with OBI CBCT, we first devise utility metrics specific to OBI-quality-assurance tasks and then apply them to guiding the selection of parameter values in constrained-TV-minimization-based reconstruction. The study results show that the reconstructions are with improvement, relative to clinical FDK reconstruction, in both visualization and quantitative assessments in terms of the devised utility metrics. [ABSTRACT FROM AUTHOR]

Onboard cone-beam CT (CBCT) has been widely used in image guided radiation therapy. However, the longitudinal coverage is only 15.5 cm in the pelvis scan mode. As a result, a single CBCT scan cannot cover the planning target volume in the longitudinal direction for over 80% of the patients. The common approach is to use double- or multiple-circular scans and then combine multiple CBCT volumes after reconstruction. However it raises concerns regarding doubled imaging dose at the imaging beam junctions due to beam divergence. In this work, we present a new method, DSCS (Dual Scan with Complementary Shifts), to address the CBCT coverage problem using a pair of complementary circular scans. In DSCS, two circular scans were performed at 39.5 cm apart longitudinally. In the superior scan, the detector panel was offset by 16 cm to the left, 15 cm to the inferior. In the inferior scan, the detector panel was shifted 16 cm to the right and 15 cm to the superior. The effective imaging volume is 39.5 cm longitudinally with a 45 cm lateral field-of-view (FOV). Half beam blocks were used to confine the imaging radiation inside the volume of interest. A new image reconstruction algorithm was developed, based on the Feldkamp–Davis–Kress cone-beam CT reconstruction algorithm, to support the DSCS scanning geometry. Digital phantom simulations were performed to demonstrate the feasibility of DSCS. Physical phantom studies were performed using an anthropomorphic phantom on a commercial onboard CBCT system. With basic scattering corrections, the reconstruction results were acceptable. Other issues, including the discrepancy in couch vertical at different couch longitudinal positions, and the inaccuracy in couch table longitudinal movement, were manually corrected during the reconstruction process. In conclusion, the phantom studies showed that, using DSCS, a 39.5 cm longitudinal coverage with a 45 cm FOV was accomplished. The efficiency of imaging dose usage was near 100%. This proposed method could be potentially useful for image guidance and subsequent treatment plan adaptation. [ABSTRACT FROM AUTHOR]