Brian Grekowicz

Algorithm to extend reconstruction field-of-view

Images obtained from CT scanners are often used to estimate the radiation dose delivered to a target organ in oncology applications. Since a patient needs to be positioned in a similar manner as in a therapy machine, a portion of the patient is often positioned outside the scan field-of-view (SFOV). Projection truncation problem also occurs frequently in conventional CT scans and causes imaging artifacts that lead to suboptimal image quality. In this paper, we propose a reconstruction algorithm that enables an adequate estimation of the object outside the SFOV. We make use of the fact that the total attenuation of each ideal projection in a parallel sampling geometry remains constant over views. We use the magnitudes and slopes of the projection samples at the location of truncation to estimate water cylinders that can best fit to the projection data outside the SFOV. To improve the robustness of the algorithm, continuity constraints are placed on the fitting parameters. Extensive phantom and patient experiments were conducted to test the robustness and accuracy of the proposed algorithm.

Novel approach to extend the scanner coverage beyond detector field-of-view

In oncology applications, images obtained from CT scanners are often used to estimate the radiation dose delivered to a target organ. Since the patient needs to be positioned in a similar manner as in a therapy machine, a part of the patient is often outside the FOV defined by the CT scanner. The patient anatomy outside FOV may lie in the treatment beam path and must be considered when calculating dose delivery. In addition, truncated projections often produce image artifacts and make attenuation estimation difficult. In this paper, we propose a reconstruction algorithm that allows adequate estimation of the object outside the FOV. We make use of the fact that the total attenuation of each ideal projection in a parallel sampling geometry remains constant over all views. To overcome the small fluctuation resulting from the non-perfect calibrations and patient motion, we use projections of two neighboring non-truncated views as the basis of the attenuation estimation. We use the magnitudes and slopes of the samples at the location of truncation to estimate the water cylinders that can best fit to the projection data outside the FOV. To improve the robustness of the algorithm, continuity constraints are placed on the fitting parameters. Extensive phantom and patient experiments were conducted to test the robustness and accuracy of the proposed algorithm. Results show that CT number accuracy is fully recovered inside the scan FOV. In all cases, the shape of the truncated object outside the FOV is recovered to an accuracy of a few millimeters.

Convolution reconstruction algorithm for multi-slice helical CT

One of the most recent technological advancements in computed tomography (CT) is the introduction of multi-slice CT (MSCT). The state-of-the-art MSCT contains 16 detector rows and is capable of acquiring 16 projections simultaneously. In this paper, we propose a reconstruction algorithm that makes use of nontraditional reconstruction planes and convolution weighting. To minimize the impact of interpolation on slice-sensitivity-profile (SSP), conjugate samples are used for the projection interpolation. We use multiple convex planes as teh region of construction. This allows the generated weighting function to be smooth and differentiable. In addition, we make use of the fact that projections collected from a subset of detector rows are sufficient to perform a complete reconstruction. A convolution function is applied to the weighting function of each subset to minimize the impact of cone beam effects. The convolution function is chosen so that optimal balance is achieved between image artifact, slice-sensitivity-profile (SSP), and noise. Extensive phantom and clinical studies have been conducted to validate our approach. Our study indicates that compared to other row-interpolation based reconstruction algorithms, a 30% SSP improvement can be achieved with the proposed approach. In addition, image artifact suppression achieved with the proposed approach is on par or slightly better than the existing reconstruction algorithms. Extensive clinical studies have shown that the 16-slice scanner in conjugation with this algorithm produces nearly isotropic spatial resolution and allows much improved diagnostic image quality.