On-board imager (OBI) based cone-beam computed tomography (CBCT) has become available in radiotherapy clinics to accurately identify the target in the treatment position. However, due to the relatively slow gantry rotation (typically about 60 s for a full 360 degrees scan) in acquiring the CBCT projection data, the patient's respiratory motion causes serious problems such as blurring, doubling, streaking and distortion in the reconstructed images, which heavily degrade the image quality and the target localization. In this work, we present a motion compensation method for slow-rotating CBCT scans by incorporating into image reconstruction a patient-specific motion model, which is derived from previously obtained four-dimensional (4D) treatment planning CT images of the same patient via deformable registration. The registration of the 4D CT phases results in transformations representing a temporal sequence of three-dimensional (3D) deformation fields, or in other words, a 4D model of organ motion. The algorithm was developed heuristically in two-dimensional (2D) parallel-beam geometry and extended to 3D cone-beam geometry. By simulations with digital phantoms capable of translational motion and other complex motion, we demonstrated that the algorithm can reduce the motion artefacts locally, and restore the tumour size and shape, which may thereby improve the accuracy of target localization and patient positioning when CBCT is used as the treatment guidance.

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Purpose: 4D CT is useful clinically for detailed abdominal and thoracic imaging over the course of the respiratory cycle. However, it usually delivers 10∼15 times more radiation dose to the patient as compared to the standard 3D CT, since multiple scans at each couch position are required to obtain the temporal information. In this work we propose a method to obtain high quality 4D CT with low tube current, hence reducing the radiation exposure of patients. Method and Materials: The improvement of the signal‐to‐noise ratio (SNR) of the CT image at a given phase was achieved by superposing the imaging information from other phases with the use of a deformable image registration model. To further reduce the statistical noise caused by low tube current, we developed a novel 4D penalized weighted least square (4D‐PWLS) method to smooth the data spatially and temporally. The method was validated by motion‐phantom and patient studies using a GE Discovery‐ST PET/CT scanner. A Varian RPM respiratory gating system was used to track the motion and to facilitate the phase binning of the 4D CT data. Results: We calculated the SNRs for both studies. The average SNR of 10 mA phantom images increased by more than three‐fold from 0.051 to 0.165 after the proposed 4D‐PWLS processing, without noticeable resolution loss. The patient images acquired at 90mA showed an increase from 2.204 to 4.558 for the end‐inspiration phase, and from 1.741 to 3.862 for the end‐expiration phase, respectively. By examining the subtraction images before and after processing, good edge preservation was also observed in the patient study. Conclusion: By appropriately utilizing the temporal information in 4D‐CT, the proposed method effectively suppresses the noise while preserving the resolution. The technique provides a useful way to reduce the patient dose during 4D CT and is thus valuable for 4D‐radiotherapy.

Purpose: In four‐dimensional (4D) cone‐beam computed tomography (CBCT), there is a spatio‐temporal tradeoff. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from low‐dose projections. Methods: The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under‐sampled projection data, a compressed sensing method is employed for solving each subproblem. We formulate an unconstrained lasso (least absolute shrinkage and selection operator) problem based on least‐square criterion regularized by total‐variation. The least‐square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained lasso problem is updated and solved repeatedly by Bregman iterations. Results: The performance of the proposed algorithm is demonstrated through a series of phantom experiments, and the results are compared to those of conventional filtered back‐projection (FBP). The simulation studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction without slowing down the gantry rotation. With such small number of projections, the conventional FBP failed to yield meaningful 4D CBCT images. Conclusions: The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the conventional 4D CBCT imaging based on FBP technique.

Purpose: In this work we develop a strategy of automatic contouring to relieve the effort of organ segmentation in 4D radiation therapy. The method adopts a novel technique of control volumes to achieve robust contour mapping among a series of 4D CT images. Methods and Materials: For a given patient, segmentation of tumor and sensitive structures was manually performed for one of the breathing phases by a physician. Along the segmented contours a number of small control volumes (∼ 1cm) were selected. To obtain contours on another CT phase we mapped the control volumes collectively to this phase using rigid transformation, which served as a good starting contour for further adjustment. The final positions of mapped control volumes were determined by minimizing the energy function consisting of two terms: intensity similarity between the mapped volumes and the original volumes in the selected phase; elastic potential energy preventing control volumes from movement. The approach was tested with the 4D CT images of 5 lung cancer patients. Results: For the patients the knowledge‐based approach of automatic contouring worked well even for CT images with significant deformations. In the lung case the contours have the average error of less than 2mm and a maximum error of 5mm for noisy anatomical structures. A significant reduction of time compared with manual contouring was achieved. Conclusions: The auto‐mapping of contours in 4D radiation therapy was implemented with control volumes. The method provides an efficient way for 4D segmentation with high accuracy.