Geometric calibration is a major step in computed tomography (CT) where it provides values for geometrical parameters that later define the system matrix in reconstructing CT images. A standard calibration process usually involves the illumination of an accurate calibration phantom with known coordinates of ball markers using the imaging system, followed by calculation of geometrical parameters by minimizing the errors between reprojected projection of ball markers and its acquired projection image. Although many attempts have been made to estimate the geometrical parameters, little attention has been paid to the optimal structure of calibration phantom. Inspired by the assumption that the larger the regularity of ball markers in the calibration phantom is, the more the stable is, and the better accuracy of estimated geometric parameters is, we propose a method to design phantom that maximize the accuracy of calibration process and mitigate the contribution of errors in indicating the ball centers. The method aims to maximize the regularity of ball markers in the calibration phantom and also in its projection image. The proposed method is applied to different phantom designs with the standard cylindrical holder and is proven to provide more accurate results than the traditional designs. The method can be applied to design scanner-dependent calibration phantoms and potentially free manufacturers and practitioners from manually searching work.
Geometric parameters that define the geometry of imaging systems are crucial for image reconstruction and image quality in x-ray computed tomography (CT). The problem of determining geometric parameters for an offset flat-panel cone beam CT (CBCT) system, a recently introduced modality with a large field of view, with the assumption of an unstable mechanism and geometric parameters that vary in each view, is considered. To accurately and rapidly find the geometric parameters for each projection view, we use the projection matrix method and design a dedicated phantom that is partially visible in all projection views. The phantom consists of balls distributed symmetrically in a cylinder to ensure the inclusion of the phantom in all views, and a large portion of the phantom is covered in the projection image. To efficiently use calibrated geometric information in the reconstruction process and get rid of approximation errors, instead of decomposing the projection matrix into actual geometric parameters that are manually corrected before being used in reconstruction, as in conventional methods, we directly use the projection matrix and its pseudo-inverse in projection and backprojection operations of reconstruction algorithms. The experiments illustrate the efficacy of the proposed method with a real offset flat-panel CBCT system in dental imaging.
We propose a new method to acquire three-dimensional tomographic images of a large object from a dental panoramic
X-ray scanner which was originally designed to produce a panoramic image of the teeth and jaws on a single frame. The
method consists of two processes; (i) a new acquisition scheme to acquire the tomographic projection data using a
narrow detector, and (ii) a dedicated model-based iterative technique to reconstruct images from the acquired projection
data. In conventional panoramic X-ray scanners, the suspension arm that holds the X-ray source and the narrow detector
has two moving axes of the angular movement and the linear movement. To acquire the projection data of a large object,
we develop a new data acquisition scheme that can emulate an acquisition of the projectional view in a large detector by
stitching narrow projection images, each of which is formed by a narrow detector, and design a trajectory to move the
suspension arm accordingly. To reconstruct images from the acquired projection data, an accelerated model-based
iterative reconstruction method derived from the ordered subset convex maximum-likelihood expectation-maximization
algorithm is used. In this method each subset of the projection data is constructed by collecting narrow projection images
to form emulated tomographic projectional views in a large detector. To validate the performance of the proposed
method, we tested with a real dental panoramic X-ray system. The experimental results demonstrate that the new method
has great potential to enable existing panoramic X-ray scanners to have an additional CT’s function of providing useful
tomographic images.
We propose a new nonlocal regularization method for PET image reconstruction with the aid of high-resolution
anatomical images. Unlike conventional reconstruction methods using prior anatomical information, our method using
nonlocal regularization does not require additional processes to extract anatomical boundaries or segmented regions. The
nonlocal regularization method applied to anatomy-based PET image reconstruction is expected to effectively reduce the
error that often occurs due to signal mismatch between the PET image and the anatomical image. We also show that our
method can be useful for enhancing the image resolution. To reconstruct the high-resolution image that represents the
original underlying source distribution effectively sampled at a higher spatial sampling rate, we model the underlying
PET image on a higher-resolution grid and perform our nonlocal regularization method with the aid of the side
information obtained from high-resolution anatomical images. Our experimental results demonstrate that, compared to
the conventional method based on local smoothing, our nonlocal regularization method enhances the resolution as well
as the reconstruction accuracy even with the imperfect prior anatomical information or in the presence of signal
mismatch between the PET image and the anatomical image.
This paper describes the development of rapid 3-D regularized EM (expectation maximization) reconstruction methods
for Compton cameras using commodity graphics hardware. Since the size of the system matrix for a typical Compton
camera is extremely large, it is impractical to use a caching scheme that reads pre-stored values of the elements of the
system matrix instead of repeatedly calculating conical projection and backprojection which are the most time
consuming operations. In this paper we propose GPU (graphics processing unit) accelerated methods that can rapidly
perform conical projection and backprojection on the fly. Since the conventional ray-based backprojection method is
inefficient for GPU, we develop fully voxel-based conical backprojection methods using two different approaches. In the
first approach, we approximate the intersecting chord length of the ray passing through a voxel with the normal distance
from the center of the voxel to the ray. In the second approach, each voxel is regarded as a dimensionless point, and the
backprojection is performed without the need for calculating intersecting chord lengths. Our experimental studies with
the M-BSREM (modified block sequential regularized EM) algorithm show that GPU-based methods significantly
outperforms the conventional CPU-based method in computation time without a considerable loss of reconstruction
accuracy.
We investigate performance of a convex nonquadratic (CNQ) spline regularization method applied to limited-angle tomography reconstruction. Since limited-angle data lack projections over a certain range of view angles, they produce poor reconstructions with streak artifacts and geometric distortions. To obtain a good solution, a feasible prior that can eliminate or reduce artifacts and distortions is necessary. The CNQ prior used in this paper is expressed as a linear combination of the first- and the second-order spatial derivatives and applied to a CNQ penalty function. To determine a solution efficiently, we use the fast globally convergent block sequential regularized expectation maximization algorithm. Our experimental results demonstrate that the hybrid CNQ spline prior outperforms conventional nonquadratic priors in eliminating limited-angle artifacts.
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