17,486 research outputs found
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a
low or even ultra-low level while maintaining the high quality of reconstructed
images. We propose a new method for CT reconstruction that combines penalized
weighted-least squares reconstruction (PWLS) with regularization based on a
sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images.
We adopt an alternating algorithm to optimize the PWLS-ST cost function that
alternates between a CT image update step and a sparse coding step. We adopt a
relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed
OS-LALM) to accelerate the CT image update step by reducing the number of
forward and backward projections. Numerical experiments on the XCAT phantom
show that for low dose levels, the proposed PWLS-ST method dramatically
improves the quality of reconstructed images compared to PWLS reconstruction
with a nonadaptive edge-preserving regularizer (PWLS-EP).Comment: This is a revised and corrected version of the IEEE IVMSP Workshop
paper DOI: 10.1109/IVMSPW.2016.752821
State of the art: iterative CT reconstruction techniques
Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms over established analytical methods, such as filtered back projection. IR improves image quality through cyclic image processing. Although all available solutions share the common mechanism of artifact reduction and/or potential for radiation dose savings, chiefly due to image noise suppression, the magnitude of these effects depends on the specific IR algorithm. In the first section of this contribution, the technical bases of IR are briefly reviewed and the currently available algorithms released by the major CT manufacturers are described. In the second part, the current status of their clinical implementation is surveyed. Regardless of the applied IR algorithm, the available evidence attests to the substantial potential of IR algorithms for overcoming traditional limitations in CT imaging
Image Reconstruction Using a Mixture Score Function (MSF)
Computed tomography (CT) reconstructs volumetric images using X-ray
projection data acquired from multiple angles around an object. For low-dose or
sparse-view CT scans, the classic image reconstruction algorithms often produce
severe noise and artifacts. To address this issue, we develop a novel iterative
image reconstruction method based on maximum a posteriori (MAP) estimation. In
the MAP framework, the score function, i.e., the gradient of the logarithmic
probability density distribution, plays a crucial role as an image prior in the
iterative image reconstruction process. By leveraging the Gaussian mixture
model, we derive a novel score matching formula to establish an advanced score
function (ADSF) through deep learning. Integrating the new ADSF into the image
reconstruction process, a new ADSF iterative reconstruction method is developed
to improve image reconstruction quality. The convergence of the ADSF iterative
reconstruction algorithm is proven through mathematical analysis. The
performance of the ADSF reconstruction method is also evaluated on both public
medical image datasets and clinical raw CT datasets. Our results show that the
ADSF reconstruction method can achieve better denoising and deblurring effects
than the state-of-the-art reconstruction methods, showing excellent
generalizability and stability
- …