Model based iterative reconstruction (MBIR) algorithms have shown significant improvement in CT image
quality by increasing resolution as well as reducing noise and artifacts. In diagnostic protocols, radiologists
often need the high-resolution reconstruction of a limited region of interest (ROI). This ROI reconstruction
is complicated for MBIR which should reconstruct an image in a full field of view (FOV) given full sinogram
measurements. Multi-resolution approaches are widely used for this ROI reconstruction of MBIR, in which the
image with a full FOV is reconstructed in a low-resolution and the forward projection of non-ROI is subtracted
from the original sinogram measurements for high-resolution ROI reconstruction. However, a low-resolution
reconstruction of a full FOV can be susceptible to streaking and blurring artifacts and these can be propagated
into the following high-resolution ROI reconstruction. To tackle this challenge, we use a coupled dictionary
representation model between low- and high-resolution training dataset for artifact removal and super resolution
of a low-resolution full FOV reconstruction. Experimental results on phantom data show that the restored full
FOV reconstruction via a coupled dictionary learning significantly improve the image quality of high-resolution
ROI reconstruction for MBIR.
Dose reduction in clinical X-ray computed tomography (CT) causes low signal-to-noise ratio (SNR) in photonsparse situations. Statistical iterative reconstruction algorithms have the advantage of retaining image quality while reducing input dosage, but they meet their limits of practicality when significant portions of the sinogram near photon starvation. The corruption of electronic noise leads to measured photon counts taking on negative values, posing a problem for the log() operation in preprocessing of data. In this paper, we propose two categories of projection correction methods: an adaptive denoising filter and Bayesian inference. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. Bayesian inference is a point-wise estimation based on measurements and prior information. Both approaches help improve diagnostic image quality at dramatically reduced dosage.
Model- based iterative reconstruction (MBIR) is increasingly widely applied as an improvement over conventional, deterministic methods of image reconstruction in X-ray CT. A primary advantage of MBIR is potentially dras tically reduced dosage without diagnostic quality loss. Early success of the method has naturally led to growing numbers of scans at very low dose, presenting data which does not match well the simple statistical models heretofore considered adequate. This paper addresses several issues arising in limiting cases which call for refine ment of standard data models. The emergence of electronic noise as a significant contributor to uncertainty, and bias of sinogram values in photon-starved measurements are demonstrated to be important modeling problems in this new environment. We present also possible ameliorations to several of these low-dosage estimation issues.
Compressed sensing can recover a signal that is sparse in some way from a small number of samples. For computed tomography (CT) imaging, this has the potential to obtain good reconstruction from a smaller number of projections or views, thereby reducing the amount of radiation that a patient is exposed to In this work, we applied compressed sensing to fan beam CT image reconstruction, which is a special case of an important 3-D CT problem (cone beam CT). We compared the performance of two compressed sensing algorithms, denoted as the LP and the QP, in simulation. Our results indicate that the LP generally provides smaller reconstruction error and converges faster; therefore, it is preferable.
Model-based iterative reconstruction (MBIR) methods based on maximum a posteriori (MAP) estimation have been
recently introduced to multi-slice CT scanners. The model-based approach has shown promising image quality
improvement with reduced radiation dose compared to conventional FBP methods, but the associated high computation
cost limits its widespread use in clinical environments. Among the various choices of numerical algorithms to optimize
the MAP cost function, simultaneous update methods such as the conjugate gradient (CG) method have a relatively high
level of parallelism to take full advantage of a new generation of many-core computing hardware. With proper
preconditioning techniques, fast convergence speeds of CG algorithms have been demonstrated in 3D emission and 2D
transmission reconstruction. However, 3D transmission reconstruction using preconditioned conjugate gradient (PCG)
has not been reported. Additional challenges in applying PCG in 3D CT reconstruction include the large size of clinical
CT data, shift-variant and incomplete sampling, and complex regularization schemes to meet the diagnostic standard of
image quality. In this paper, we present a ramp-filter based PCG algorithm for 3D CT MBIR. Convergence speeds of
algorithms with and without using the preconditioner are compared.
Today lowering patient radiation dose while maintaining image quality in Computed Tomography has become a very
active research field. Various iterative reconstruction algorithms have been designed to improve/maintain image quality
for low dose patient scans. Typically radiation dose variation will result in detectability variation for low contrast
objects. This paper assesses the low contrast detectability performance of the images acquired at different dose levels
and obtained using different image generation algorithms via two-alterative forced choice human observer method.
Filtered backprojection and iterative reconstruction algorithms were used in the study. Results showed that for the
objects and scan protocol used, the iterative algorithm employed in this study has similar low contrast detectability
performance compared to filtered backprojection algorithm at a 4 times lower dose level. It also demonstrated that well
controlled human observer study is feasible to assess the image quality of a CT system.
In this paper, we describe a prediction based compressed-sensing approach for multi-slice
(same time, different locations) or multi-frame (same location, different time) CT image reconstruction.
In this approach, the second slice/frame of a pair of consecutive slices/frames is
reconstructed through reconstructing the prediction error image between the first and second
slice/frame, using compressed-sensing. This approach exploits the inter-slice/inter-frame correlation
and the higher degree of sparsity of the prediction error image to achieve more efficient
image reconstruction, i.e., fewer projections for the same image quality or higher image quality
for the same number of projections. The efficacy of our approach is demonstrated through
Bayesian estimation is a statistical approach for incorporating prior information through the choice of an a
priori distribution for a random field. A priori image models in Bayesian image estimation are typically low-order
Markov random fields (MRFs), effectively penalizing only differences among immediately neighboring
voxels. This limits spectral description to a crude low-pass model. For applications where more flexibility in
spectral response is desired, potential benefit exists in models which accord higher a priori probability to content
in higher frequencies. Our research explores the potential of larger neighborhoods in MRFs to raise the number
of degrees of freedom in spectral description. Similarly to classical filter design, the MRF coefficients may be
chosen to yield a desired pass-band/stop-band characteristic shape in the a priori model of the images. In this
paper, we present an alternative design method, where high-quality sample images are used to estimate the MRF
coefficients by fitting them into the spatial correlation of the given ensemble. This method allows us to choose
weights that increase the probability of occurrence of strong components at particular spatial frequencies. This
allows direct adaptation of the MRFs for different tissue types based on sample images with different frequency
content. In this paper, we consider particularly the preservation of detail in bone structure in X-ray CT. Our
results show that MRF design can be used to obtain bone emphasis similar to that of conventional filtered
back-projection (FBP) with a bone kernel.
Compressed sensing can recover a signal that is sparse in some way from a small number of
samples. For CT imaging, this has the potential to obtain good reconstruction from a smaller
number of projections or views, thereby reducing the amount of patient radiation. In this
work, we applied compressed sensing to fan beam CT image reconstruction , which is a special
case of an important 3D CT problem (cone beam CT). We compared the performance of
two compressed sensing algorithms, denoted as the LP and the QP, in simulation. Our results
indicate that the LP generally provides smaller reconstruction error and converges faster, hence
is more preferable.
We introduce a post-processing approach to improve the quality of CT reconstructed images. The scheme is
adapted from the resolution-synthesis (RS)1 interpolation algorithm. In this approach, we consider the input
image, scanned at a particular dose level, as a degraded version of a high quality image scanned at a high
dose level. Image enhancement is achieved by predicting the high quality image by classification based linear
regression. To improve the robustness of our scheme, we also apply the minimum description length principle
to determine the optimal number of predictors to use in the scheme, and the ridge regression to regularize the
design of the predictors. Experimental results show that our scheme is effective in reducing the noise in images
reconstructed from filtered back projection without significant loss of image details. Alternatively, our scheme
can also be applied to reduce dose while maintaining image quality at an acceptable level.
Model based iterative reconstruction (MBIR) algorithms have recently been applied to computed tomography and
demonstrated superior image quality. This algorithmic framework also provides us the flexibility to incorporate
more sophisticated models of the data acquisition process. In this paper, we present the kinetic parameter
iterative reconstruction (KPIR) algorithm which estimates voxel values as a function of time in the MBIR
framework. We introduce a parametric kinetic model for each voxel, and estimate the kinetic parameters directly
from the data. Results on phantom study and clinical data show that the proposed method can significantly
reduce motion artifacts in the reconstruction.
Medical imaging typically requires the reconstruction of a limited region of interest (ROI) to obtain a high
resolution image of the anatomy of interest. Although targeted reconstruction is straightforward for analytical
reconstruction methods, it is more complicated for statistical iterative techniques, which must reconstruct all
objects in the field of view (FOV) to account for all sources of attenuation along the ray paths from x-ray
source to detector. A brute force approach would require the reconstruction of the full field of view in high-resolution,
but with prohibitive computational cost. In this paper, we propose a multi-resolution approach to
accelerate targeted iterative reconstruction using the non-homogeneous ICD (NH-ICD) algorithm. NH-ICD aims
at speeding up convergence of the coordinate descent algorithm by selecting preferentially those voxels most in
need of updating. To further optimize ROI reconstruction, we use a multi-resolution approach which combines
three separate improvements. First, we introduce the modified weighted NH-ICD algorithm, which weights the
pixel selection criteria according to the position of the voxel relative to the ROI to speed up convergence within
the ROI. Second, we propose a simple correction to the error sinogram to correct for inconsistencies between
resolutions when the forward model is not scale invariant. Finally, we leverage the flexibility of the ICD algorithm
to add selected edge pixels outside the ROI to the ROI reconstruction in order to minimize transition artifacts
at the ROI boundary. Experiments on clinical data illustrate how each component of the method improves
convergence speed and image quality.
Backprojection is an essential step of the cone-beam reconstruction algorithm for computed tomography. Conventional backprojection maps a reconstructed voxel to the projection space by interpolating across adjacent detector channels and rows of a single projection. Although this approach is computationally efficient, both theoretical analysis and phantom experiments have shown that a significant degradation in z resolution can result. To overcome this shortcoming, we propose a conjugate cone-beam backprojection approach in which two projections that are 180 deg apart are backprojected simultaneously. By carefully selecting the helical pitch and designing the interpolation function, the z resolution of the reconstructed images can be significantly improved. The proposed conjugate backprojection algorithm has the potential to reduce data extrapolation artifacts due to limited detector size in step-and-shoot data acquisition. Extensive computer simulations and phantom experiments are used to demonstrate the efficacy of our approach.
Statistical reconstruction methods show great promise for improving resolution, and reducing noise and artifacts
in helical X-ray CT. In fact, statistical reconstruction seems to be particularly valuable in maintaining reconstructed
image quality when the dosage is low and the noise is therefore high. However, high computational
cost and long reconstruction times remain as a barrier to the use of statistical reconstruction in practical applications.
Among the various iterative methods that have been studied for statistical reconstruction, iterative
coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its
This paper presents a novel method for further speeding the convergence of the ICD algorithm, and therefore
reducing the overall reconstruction time for statistical reconstruction. The method, which we call nonhomogeneous
iterative coordinate descent (NH-ICD) uses spatially non-homogeneous updates to speed convergence
by focusing computation where it is most needed. Experimental results with real data indicate that the
method speeds reconstruction by roughly a factor of two for typical 3D multi-slice geometries.
Computed Tomography (CT) screening and pediatric imaging, among other applications, demand the development of more efficient reconstruction techniques to diminish radiation dose to the patient. While many methods are proposed to limit or modulate patient exposure to x-ray at scan time, the resulting data is excessively noisy, and generates image artifacts unless properly corrected. Statistical iterative reconstruction (IR) techniques have recently been introduced for reconstruction of low-dose CT data, and rely on the accurate modeling of the distribution of noise in the acquired data. After conversion from detector counts to attenuation measurements, however, noisy data usually deviate from simple Gaussian or Poisson representation, which limits the ability of IR to generate artifact-free images. This paper introduces a recursive filter for IR, which conserves the statistical
properties of the measured data while pre-processing attenuation measurements. A basic framework for inclusion of detector electronic noise into the statistical model for IR is also presented. The results are shown to successfully eliminate streaking artifacts in photon-starved situations.
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.
Emission Computed Tomography (ECT) is widely applied in medical diagnostic imaging, especially to determine physiological function. The available set of measurements is,however, often incomplete and corrupted, and the quality of image reconstruction is enhanced by the computation of a statistically optimal estimate. We present here a numerical method of ECT image reconstruction based on a Taylor series quadratic approximation to the usual Poison log-likelihood function. The quadratic approximation yields simplification in understanding and manipulating Poisson models. We introduce an algorithm similar to global Newton methods which updates the point of expansion a limited number of time sand we give quantitative measures of the accuracy of the reconstruction. The result show little difference in quality from those obtained with the exact Poisson model.