In this paper, we develop an improved auto-focusing capability of a panoramic dental tomosynthesis imager. We
propose an auto-focusing algorithm with an efficient sharpness indicator based on exponential polynomials which
provides better quantitation of steep gradients than the conventional one based on algebraic polynomials. With its
accurate estimation of the sharpness of the reconstructed slices, the proposed method resulted in a better performance of
automatically extracting in-focus slices in the dental panoramic tomosynthesis.
In computed tomography (CT) imaging, radiation dose delivered to the patient is one of the major concerns. Many CT
developers and researchers have been making efforts to reduce radiation dose. Sparse-view CT takes projections at
sparser view-angles and provides a viable option to reducing radiation dose. However, a fast power switching of an x-ray
tube, which is needed for the sparse-view sampling, can be challenging in many CT systems. We have recently proposed
a novel alternative approach to sparse-view circular CT that can be readily incorporated in the existing CT systems.
Instead of switching the x-ray tube power, we proposed to use a multi-slit collimator placed between the x-ray source
and the patient to partially block the x-ray beam thereby reducing the radiation. In this study, we performed a simulation
study based on numerically acquired projection data to demonstrate a feasibility of using a multi-slit collimator in a
helical CT. The XCAT phantom was used and a numerical collimator has been made to apply on the projection data.
Numerical multi-slit collimator was designed to have equal size of slit-openings and radio-opaque rectangular areas, and
the length dimension of the slits is perpendicular to the rotation axis. For image reconstruction, we used a total-variation
minimization (TV) algorithm which has shown its out-performance in many sparse-view CT applications. We
demonstrated that the proposed multiple fan-beam helical CT can provide a useful low-dose scanning option.
We proposed a novel scanning method for low-dose computed tomography (CT) that uses an oscillating multi-slit collimator between the x-ray source and the patient. It can be thought as a realization of sparse data sampling that does not require a fast x-ray power switching. A simulation study was performed based on experimentally acquired microCT data of a mouse to demonstrate the feasibility of the proposed method. A numerical collimation was designed to leave only one-fourth of each projection data for use in image reconstruction. A total-variation minimization algorithm was implemented for image reconstruction from the sparely sampled data. We have successfully shown that the proposed method provides a viable option to low-dose CT.
Dual-energy cone-beam CT is an important imaging modality in diagnostic applications, and may also find its use
in other applications such as therapeutic image guidance. Despite of its clinical values, relatively high radiation dose of
dual-energy scan may pose a challenge to its wide use. In this work, we investigated a low-dose, pre-reconstruction type of
dual-energy cone-beam CT (CBCT) using a total-variation minimization algorithm for image reconstruction. An empirical
dual-energy calibration method was used to prepare material-specific projection data. Raw data acquired at high and low
tube voltages are converted into a set of basis functions which can be linearly combined to produce material-specific data
using the coefficients obtained through the calibration process. From much fewer views than are conventionally used,
material specific images are reconstructed by use of the total-variation minimization algorithm. An experimental study
was performed to demonstrate the feasibility of the proposed method using a micro-CT system. We have reconstructed
images of the phantoms from only 90 projections acquired at tube voltages of 40 kVp and 90 kVp each. Aluminum-only
and acryl-only images were successfully decomposed. A low-dose dual-energy CBCT can be realized via the proposed
method by greatly reducing the number of projections.