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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]