Low-dose X-ray Computed Tomography Images Research Articles

Evaluation of vertebral bone strength with a finite element method using low dose computed tomography imaging

BackgroundFocusing on compression fractures of bone by finite elements, we evaluated bone strength based on the computed tomography-based finite element method. However, the exposure dose is an issue. We aimed to investigate the quantity of reduction of the radiation dose with respect to the reference dose by comparing the calculation results of compression fractures of the vertebral body using experimental data obtained from the spine of a pig. MethodsComputed tomography images of a self-made phantom that enclosed the lower lumbar vertebra of edible wild pigs were obtained under baseline-dose conditions using various lower tube currents. Images obtained under reference-dose conditions were reconstructed using the filtered back-projection method, whereas images obtained under low-dose conditions were reconstructed using both the filtered back-projection method and the iterative reconstruction method. Computer simulations involving the creation of finite element models using all images were implemented for the compression load calculation for vertebral body parts. Based on the calculated results, images of the low-dose and reference-dose conditions were compared. ResultsUsing pigs’ lower lumbar vertebrae, finite element model analysis of low-dose X-ray computed tomography images showed that equivalent results can be obtained with a dose of approximately 40% of the standard radiographic reference doses. As for the compression stress intensity, the same results as those under reference-dose conditions were obtained using the iterative reconstruction method in combination with computed tomography-based finite element method. ConclusionsThe combination of the iterative reconstruction method with the computed tomography-based finite element method is an effective image reconstruction method for achieving dose reduction.

Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network

In clinical analysis and diagnosis, high resolution (HR) computed tomography (CT) images are required for proper treatment of a patient. Developing HR medical images by X-ray CT devices require extended radiation exposure with large radiative dosages, putting the patient at potential risk of inducing cancer. So, radiation exposure should be reduced. However, photon starvation and beam hardening in low-dose X-rays will cause severe artifacts. Thus, an accurate reconstruction of low-dose X-ray CT images is required. To this end, we propose a wavelet based multi-channel and multi-scale cross connected residual-in-dense grouped convolutional neural network (WCRDGCNN) for accurate super resolution (SR) of medical images. The adopted filter groups reduce the connection weights, thereby reducing the computational complexity. Gradient vanishing problem is tackled by using residual and dense skip connections. The extensive experimentation results on benchmark datasets show that our method outperforms the state-of-the-art SR methods.

Fourth-order partial differential equations based anisotropic diffusion model for low-dose CT images

The low-dose X-ray Computed Tomography (CT) is one of the most effective and indispensable imaging tools for clinical diagnosis. The reduced number of photons in low-dose X-ray CT imaging introduces the vulnerability towards Poisson and Gaussian noise. The majority of research till date focuses on reconstructing the images by reducing the effect of either Poisson or Gaussian noise. Thus, there is a need for a reconstruction framework that can counter the effects of both types of noises simultaneously. In this paper, an approach is proposed to handle the mixed noise (i.e. Poisson and Gaussian noises). Variational framework is utilized as energy minimization function. Minimizing the log likelihood gives data-fidelity term which portrays the distribution of noise in low-dose X-ray CT images. The problem of data-fidelity term as well as mixed noise issue in the sinogram data is resolved simultaneously by proposing a novel filter. The proposed filter modifies the Anisotropic Diffusion (AD) model based on Convolution Virtual Electric Field AD called as MADC. The modification in AD is achieved by applying fourth-order partial differential equations. To evaluate the effectiveness of the proposed MADC technique, both qualitative and quantitative evaluations are performed on three simulated test phantoms and one real standard thorax phantom of size [Formula: see text]. Afterwards, the performance of the proposed technique is compared with competitive denoising techniques. The experimental results reveal that the proposed framework significantly preserves the edges of reconstructed images and introduces lesser number of gradient reversal artifacts.

Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review.

Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.

Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol.

To realize low-dose imaging in X-ray computed tomography (CT) examination, lowering milliampere-seconds (low-mAs) or reducing the required number of projection views (sparse-view) per rotation around the body has been widely studied as an easy and effective approach. In this study, we are focusing on low-dose CT image reconstruction from the sinograms acquired with a combined low-mAs and sparse-view protocol and propose a two-step image reconstruction strategy. Specifically, to suppress significant statistical noise in the noisy and insufficient sinograms, an adaptive sinogram restoration (ASR) method is first proposed with consideration of the statistical property of sinogram data, and then to further acquire a high-quality image, a total variation based projection onto convex sets (TV-POCS) method is adopted with a slight modification. For simplicity, the present reconstruction strategy was termed as "ASR-TV-POCS." To evaluate the present ASR-TV-POCS method, both qualitative and quantitative studies were performed on a physical phantom. Experimental results have demonstrated that the present ASR-TV-POCS method can achieve promising gains over other existing methods in terms of the noise reduction, contrast-to-noise ratio, and edge detail preservation.

Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction

Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and other constraints, piecewise-smooth x-ray computed tomography (CT) can be reconstructed from sparse-view projection data without introducing notable artifacts. However, due to the piecewise constant assumption for the image, a conventional TV minimization algorithm often suffers from over-smoothness on the edges of the resulting image. To mitigate this drawback, we present an adaptive-weighted TV (AwTV) minimization algorithm in this paper. The presented AwTV model is derived by considering the anisotropic edge property among neighboring image voxels, where the associated weights are expressed as an exponential function and can be adaptively adjusted by the local image-intensity gradient for the purpose of preserving the edge details. Inspired by the previously reported TV-POCS (projection onto convex sets) implementation, a similar AwTV-POCS implementation was developed to minimize the AwTV subject to data and other constraints for the purpose of sparse-view low-dose CT image reconstruction. To evaluate the presented AwTV-POCS algorithm, both qualitative and quantitative studies were performed by computer simulations and phantom experiments. The results show that the presented AwTV-POCS algorithm can yield images with several notable gains, in terms of noise-resolution tradeoff plots and full-width at half-maximum values, as compared to the corresponding conventional TV-POCS algorithm.