Imaging dose reduction in cone beam CT (CBCT) is an important topic for image guided radiation therapy. In contrast to conventional approaches that employ novel reconstruction techniques to retrieve a CBCT image from data acquired at a low-exposure setting, this project uses temporal correlations of CBCT images for a patient at different treatment fractions for dose reduction.
We created a progressive dose-control scheme that maximally employs prior information from existing CBCT images. It uses image-processing techniques to maintain the quality of a newly acquired CBCT image at a low dose level. With this approach, it becomes possible to use a gradually reduced exposure along a treatment course while achieving a constant image quality (see figure). This scheme allows significantly reduced overall dose in a radiotherapy course compared to a conventional approach, which uses the same exposure level at each fraction.
4D cone beam CT (4D-CBCT) provides phase-resolved volumetric imaging for lung radiotherapy. Conventional reconstruction approaches from image intensity domain are susceptible to the problem of low projection number due to phase binning. We propose to use a new reconstruction approach via motion-vector optimization to overcome this problem and achieve high image quality. This method uses a prior image acquired from treatment planning stage and optimizes a deformation vector field for each phase, such that the projections of the deformed volumetric image match the measurements. A novel algorithm is developed to solve the optimization problem. High-quality 4D-CBCT imaging based on the clinically standard 1-minute 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.
While 4D-cone beam CT provides phase-resolved volumetric images for lung region, it assumes periodic motion of the anatomy and hence neglect irregular motions. To overcome this problem, we develop novel real-time volumetric imaging approach using machine-learning approach. Due to the limited amount of measurements, it is impossible to reconstruct an instantaneous volumetric image from conventional CT reconstruction perspective. Instead, we employ machine-learning methods, such as sparse learning and manifold learning, to build a model that relates respiration related measurements (x-ray projection, surface imaging, etc) to the volumetric image. Our method is able to automatically identify the most relevant data to predict an instantaneous volumetric image with clinically acceptable accuracy.