Low-dose X-ray CT Research Articles

Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks

Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks

Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners

Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 "Low-Dose CT Grand Challenge." To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research.

A Gaussian Mixture MRF for Model-Based Iterative Reconstruction With Applications to Low-Dose X-Ray CT

Markov random fields (MRFs) have been widely used as prior models in various inverse problems such as tomographic reconstruction. While MRFs provide a simple and often effective way to model the spatial dependencies in images, they suffer from the fact that parameter estimation is difficult. In practice, this means that MRFs typically have very simple structure that cannot completely capture the subtle characteristics of complex images. In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction. The GM-MRF forms a global image model by merging together individual Gaussian-mixture models (GMMs) for image patches. In addition, we present a novel analytical framework for computing MAP estimates using the GM-MRF prior model through the construction of surrogate functions that result in a sequence of quadratic optimizations. We also introduce a simple but effective method to adjust the GM-MRF so as to control the sharpness in low- and high-contrast regions of the reconstruction separately. We demonstrate the value of the model with experiments including image denoising and low-dose CT reconstruction.

Special issue on medical imaging

Medical Imaging has been playing one of the most important roles in medical diagnosis. As the technologies advance, so does medical imaging, and it gets an increasing role, not only in diagnosis, but also in treatment, such as image-guided therapies. Medical imaging also has a great impact on many research areas including neuroscience, drug development, phycology, psychiatry, cardiology, and so on. Advancement of the medical imaging technologies involves multidisciplinary research fields that encompass physics, medicine, electrical and mechanical engineering, biology and chemical engineering, etc. Considering the impact of medical imaging on clinics, research, and engineering, we select “Medical Imaging” as the theme of this Special Issue of Biomedical Engineering Letters. This special issue includes five review papers on recent advances of several medical imaging technologies and their applications. The first review paper entitled, “Model based filtered backprojection algorithm: a tutorial”, by Gengsheng L. Zeng, IEEE Fellow, USA, provides in-depth knowledge of very interesting new type of linear reconstruction algorithm with incorporation of noise models in it [1]. For many decades, the filtered backprojection (FBP) algorithm has been a popular choice in image reconstruction. However, recent developments of new medical imaging systems such as lowdose x-ray CT, nuclear medicine, and real-time MRI applications require more advanced reconstruction algorithms than the conventional FBP in order to incorporate measurement noise and system models. In recent years, many researchers have been developing various iterative algorithms to meet this demand and improve the final reconstructed images. However, the drawback of most iterative algorithms is the heavy computational cost. Therefore, it would be wonderful if we could have an FBP algorithm that performs comparable tasks to the iterative algorithms. In this first review paper, Dr. Zeng provides a way to develop such an FBP algorithm with incorporation of noise models, which may replace the state-of-the-art iterative algorithms. The second review paper, “Challenges and advances in MR imaging of the intervertebral disc could the cartilaginous endplate be a biomarker for the disc health?,” by Sung M. Moon, Ph.D., GE Healthcare, USA, provides an overview of new magnetic resonance imaging (MRI) techniques and findings on disc disease and cartilaginous endplate (CEP) [2]. A brief description on the anatomy of the CEP and intervertebral disc is followed by their roles in the disc Dosik Hwang ( ) School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea Tel : +82-2-2123-5771 E-mail : dosik.hwang@yonsei.ac.kr

Model Based Filtered Backprojection Algorithm: A Tutorial.

People have been wandering for a long time whether a filtered backprojection (FBP) algorithm is able to incorporate measurement noise in image reconstruction. The purpose of this tutorial is to develop such an FBP algorithm that is able to minimize an objective function with an embedded noise model. An objective function is first set up to model measurement noise and to enforce some constraints so that the resultant image has some pre-specified properties. An iterative algorithm is used to minimize the objective function, and then the result of the iterative algorithm is converted into the Fourier domain, which in turn leads to an FBP algorithm. The model based FBP algorithm is almost the same as the conventional FBP algorithm, except for the filtering step. The model based FBP algorithm has been applied to low-dose x-ray CT, nuclear medicine, and real-time MRI applications. Compared with the conventional FBP algorithm, the model based FBP algorithm is more effective in reducing noise. Even though an iterative algorithm can achieve the same noise-reducing performance, the model based FBP algorithm is much more computationally efficient. The model based FBP algorithm is an efficient and effective image reconstruction tool. In many applications, it can replace the state-of-the-art iterative algorithms, which usually have a heavy computational cost. The model based FBP algorithm is linear and it has advantages over a nonlinear iterative algorithm in parametric image reconstruction and noise analysis.

The adaptive sinogram restoration algorithm based on anisotropic diffusion by energy minimization for low-dose X-ray CT

Apparent streak artifacts will present in reconstructed images due to excessive quantum noise in low-dose X-ray imaging process. Estimating a noise-free sinogram to satisfy the filtered back-projection (FBP) reconstruction is an effective way to solve this problem. In this paper, we propose a novel sinogram noise reduction method by energy minimization. An adaptive smoothness parameter based on a modified anisotropic diffusion coefficient is applied for an optimal estimation. The smoothness parameter can make the method effectively adjust the degree of smoothness according to the noise level and the region feature in the sinogram. Visual effect together with quantitative analysis of the experimental result shows the developed approach has the excellent performance in protection of the edge and removal of streak artifacts in the reconstructed image.

Dictionary learning based denoising of low-dose X-ray CT image.

Dictionary learning based denoising of low-dose X-ray CT image.

A statistical iteration approach with energy minimization to sinogram noise reduction for low-dose X-ray CT

Low-dose CT imaging has been particularly used in modern medical practice for its advantage on reducing the radiation dose to patients. However, excessive quantum noise is present in low dose X-ray imaging along with the decrease of the radiation dose; thus, there are obvious streak-like artifacts in reconstructed images. The statistical iterative reconstruction approach applied to the noisy sinogram before a filtered back-projection (FBP) is a resolution to deal with the noisy problem. In this paper, the statistical property of the noise sinogram was considered to achieve a satisfactory image reconstruction and a statistical iterative method with energy minimization was proposed to address the problem of streak-like artifacts. Simulations were performed and indicated that the proposed method could suppress noise and obviously decrease streak-like artifacts in reconstructed images.

Nonlinear sinogram smoothing for low-dose X-ray CT

When excessive quantum noise is present in extremely low dose X-ray CT imaging, statistical properties of the data has to be considered to achieve a satisfactory image reconstruction. Statistical iterative reconstruction with accurate modeling of the noise, rather than a filtered back-projection (FBP) with low-pass filtering, is one way to deal with the problem. Estimating a noise-free sinogram to satisfy the FBP reconstruction for the Radon transform is another way. The benefits of the latter include a higher computation efficiency, more uniform spatial resolution in the reconstructed image, and less modification of the current machine configurations. In a clinic X-ray CT system, the acquired raw data must be calibrated, in addition to the logarithmic transform, to achieve the high diagnostic image quality. The calibrated projection data or sinogram no longer follow a compound Poisson distribution in general, but are close to a Gaussian distribution with signal-dependent variance. In this paper, we first investigated a relatively accurate statistical model for the sinogram data, based on several phantom experiments. Then we developed a penalized likelihood method to smooth the sinogram, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time. The method was applied to several experimental datasets acquired at 120 kVp, 10 mA/20 mA/50 mA protocols with a GE HiSpeed multi-slice detector CT scanner and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution.

The Influence of Photon Counting Statistics on Picture Noise and Reproducibility in Quantitative Computed Tomography

Photon noise in the projection data of CT-measurements causes picture noise in the reconstructed images. This noise limits the precision of quantitative parameters such as for example the mean CT-value within a region of interest (ROI). The precision of this parameter is usually estimated as the standard error of the mean (SEMpix) calculated from the root mean square deviation of the pixel values within the ROI. The reproducibility of the mean value (SEMrep) however is limited by the photon statistics in the projection data and can be determined from repeated measurements as the root mean square deviation of the mean ROI values. The reconstruction process correlates individual picture elements, therefore SEMpix and SEMrep are not identical. Using the linearity of the convolution backprojection reconstruction algorithm, we present a new linear vector space notation and derive the relation between photon noise in the measured projection data and reproducibility of the mean CT-value in arbitrarily shaped ROIs. An easy to implement formula enables the calculation of the reproducibility, even if the tissue distribution in the ROI is not homogeneous. CT data from a special purpose low dose X-ray CT scanner were used to document the validity of our results for actual measurements. In addition the theoretical results and those from actual measurements of a comparison between SEMpix and SEMrep for the special case of circular ROIs with homogeneous material distribution are given.