We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source–detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.
Our earlier work has demonstrated that the data consistency condition can be used as a criterion for scatter kernel optimization in deconvolution methods in a full-fan mode cone-beam CT . However, this scheme cannot be directly applied to CBCT system with an offset detector (half-fan mode) because of transverse data truncation in projections. In this study, we proposed a modified scheme of the scatter kernel optimization method that can be used in a half-fan mode cone-beam CT, and have successfully shown its feasibility. Using the first-reconstructed volume image from half-fan projection data, we acquired full-fan projection data by forward projection synthesis. The synthesized full-fan projections were partly used to fill the truncated regions in the half-fan data. By doing so, we were able to utilize the existing data consistency-driven scatter kernel optimization method. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by an experimental study using the ACS head phantom.
This work proposed a motion detection method for cone-beam computed tomography (CBCT) that utilizes a calibration
phantom of known geometry as the motion detector and an established geometric calibration protocol to provide the
motion information. An initial numerical study regarding the consequences of motion and its correction was conducted
with a Shepp-Logan and an XCAT phantom. Motion artifacts were induced by acquiring the projections in a simple
saddle trajectory scan. Since the scanning trajectory is set, the magnitude of motion for each projection view is already
known, the correction of motion can then be efficiently implemented. Motion correction was done prior to the
backprojection process of the filtered backprojection (FBP) image reconstruction algorithm. Results showed that motion
correction improved the image quality of the reconstructed images. For a known or unknown scanning trajectory, the
geometric calibration method can define the geometric information of a scanning system. In the current work,
projections of a calibration phantom of known geometry were acquired from a saddle trajectory scan, and geometric
parameters for selected projection views were successfully computed from the projection matrix provided by the
geometric calibration method. Further studies will involve an experimental investigation wherein a calibration phantom
is attached to a randomly moving object and scanned in a circular trajectory. Utilizing the parameters extracted from the
geometric calibration, an accurate description of the object motion can be used and adapted for motion correction.