Motion correction for improved target localization with on-board cone-beam computed tomography

Abstract

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.

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

“…Respiratory motion creates artefacts in CBCT images, such as blurring, doubling, streaking and distortions, which greatly degrade the image quality and hence affect the target localization accuracy. [17][18][19] Nowadays, the four-dimensional CT (4DCT) scan is increasingly used in clinics for radiotherapy planning of thoracic and upperabdomen sites 20,21 owing to its superiority in eliminating the respiration-induced artefacts, which also enables a more accurate description of tumour motion. For a true four-dimensional (4D) treatment, ideally the verification imaging should also include a fourth dimension.…”

Iterative sorting for four‐dimensional CT images based on internal anatomy motion

“…Those scanners have large flat-panel detectors, leading to more consistent volume sampling, but they usually rotate very slowly (1 min per rotation) because they are mounted on linac systems. Methods have been proposed to use such scanners to image the thorax under free breathing conditions [9][10][11][12]. In this paper we mainly focus on 4D multi-slice CT imaging.…”

Multiatlas segmentation of thoracic and abdominal anatomy with level set‐based local search

“…The method can also be extended to 4D CT datasets, where a higher level of noise is present, but possibly with a different approach to eliminate the noise by accounting for patient motion during acquisition, (49) or with customized refinement settings to account for the increased level of noise in the 4D CT scans. Such extensions to the current algorithm to allow automated segmentation of multimodality images may be especially important in clinics implementing IGRT, where large datasets have to be segmented to track tumor changes or trajectory.…”