Image quality of CT scans suffers when objects undergo motion. Respiratory motion causes artifacts, which prevents adequate visualization of anatomy. Four‐dimensional CT (4D‐CT) is a method in which image reconstruction of moving objects is retrospectively gated according to the recorded phase information of the monitored motion pattern. Although several groups have investigated the use of 4D‐CT in radiotherapy, little has been detailed with regard to the sorting method. We present a new retrospective gating technique with sorting based on the amplitude of the motion trace. This method is compared to previously developed methods that sort based on phase. A 16‐slice CT scanner (Sensation 16, Siemens Medical Solutions, Erlangen, Germany) was used to acquire images of two phantoms on a motion platform moving in two dimensions. The motion was monitored using a strain gauge inserted inside an adjustable belt. A 180° interpolation was used for reconstruction after gating. Significant improvement using the amplitude‐sorting technique was observed, particularly when testing nonperiodic motion functions.PACS numbers: 87.59.Fm, 87.53.Kn, 87.57.Ce

A methodology for 3D image reconstruction from retrospectively gated cone-beam CT projection data has been developed. A mobile x-ray cone-beam device consisting of an isocentric C-arm equipped with a flat panel detector was used to image a moving phantom. Frames for reconstruction were retrospectively selected from complete datasets based on the known rotation of the C-arm and a signal from a respiratory monitor. Different sizes of gating windows were tested. A numerical criterion for blur on the reconstructed image was suggested. The criterion is based on minimization of an Ising energy function, similar to approaches used in image segmentation or restoration. It is shown that this criterion can be used for the determination of the optimal gating window size. Images reconstructed from the retrospectively gated projection sequences using the optimal gating window data showed a significant improvement compared to images reconstructed from the complete projection datasets.

Image quality of CT scans suffers when objects undergo motion. Respiratory motion causes artifacts, which prevents adequate visualization of anatomy. Four‐dimensional CT (4D‐CT) is a method in which image reconstruction of moving objects is retrospectively gated according to the recorded phase information of the monitored motion pattern. Although several groups have investigated the use of 4D‐CT in radiotherapy, little has been detailed with regard to the sorting method. We present a new retrospective gating technique with sorting based on the amplitude of the motion trace. This method is compared to previously developed methods that sort based on phase. A 16‐slice CT scanner (Sensation 16, Siemens Medical Solutions, Erlangen, Germany) was used to acquire images of two phantoms on a motion platform moving in two dimensions. The motion was monitored using a strain gauge inserted inside an adjustable belt. A 180° interpolation was used for reconstruction after gating. Significant improvement using the amplitude‐sorting technique was observed, particularly when testing nonperiodic motion functions.PACS numbers: 87.59.Fm, 87.53.Kn, 87.57.Ce

The purpose of this work is to relate the gating window and displacement of a moving tumor target and develop a systematic method to individualize the gating window for respiration-gated radiation therapy (RT). As the relationship between patient anatomy and respiration phase is contained in 4D images, we aim to quantify this information and utilize the data to guide gated treatment planning. After 4D image acquisition, the target and organs at risk were delineated manually on the selected gating phase. The contours were propagated automatically onto every phase-specific image set using a control volume-based contour mapping technique. The mean and maximum distances between the contours in the gating phase and each of other phases were evaluated in three dimensions. The gating window was determined in such a way that the residual movement of the target within the window is smaller or equal to the patient's setup error. The proposed method was applied to plan the gated treatments of 12 lung cancer patients. As a result of this work, a method to calculate patient-specific gating windows has been developed. The general reference drawn from this study is that, with the aide of 4D images and automated 4D contour propagation, it is feasible to individualize the gating widow selection. As compared with the current practice, the proposed technique has a potential to eliminate the guesswork involved in choosing a gating window and avoid dosimetric error in planning gated RT. In conclusion, individualization of gating windows reduces the subjectivity in respiration-gated RT and improves the treatment of moving targets.

The purpose of this work is to develop a novel strategy to automatically map organ contours from one phase of respiration to all other phases on a four-dimensional computed tomography (4D CT). A region of interest (ROI) was manually delineated by a physician on one phase specific image set of a 4D CT. A number of cubic control volumes of the size of approximately 1 cm were automatically placed along the contours. The control volumes were then collectively mapped to the next phase using a rigid transformation. To accommodate organ deformation, a model-based adaptation of the control volume positions was followed after the rigid mapping procedure. This further adjustment of control volume positions was performed by minimizing an energy function which balances the tendency for the control volumes to move to their correspondences with the desire to maintain similar image features and shape integrity of the contour. The mapped ROI surface was then constructed based on the central positions of the control volumes using a triangulated surface construction technique. The proposed technique was assessed using a digital phantom and 4D CT images of three lung patients. Our digital phantom study data indicated that a spatial accuracy better than 2.5 mm is achievable using the proposed technique. The patient study showed a similar level of accuracy. In addition, the computational speed of our algorithm was significantly improved as compared with a conventional deformable registration-based contour mapping technique. The robustness and accuracy of this approach make it a valuable tool for the efficient use of the available spatial-tempo information for 4D simulation and treatment.