Journal of X-ray Science and Technology
About: Journal of X-ray Science and Technology is an academic journal published by IOS Press. The journal publishes majorly in the area(s): Medicine & Iterative reconstruction. It has an ISSN identifier of 0895-3996. Over the lifetime, 1271 publications have been published receiving 12224 citations.
Papers published on a yearly basis
TL;DR: In this paper, the authors developed and investigated an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT.
Abstract: In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.
TL;DR: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
Abstract: Background The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. Objective One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. Methods Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. Results A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. Conclusion This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
TL;DR: It is shown that the first OS-SART is a special case of the OS version of the Landweber scheme, and converges in the weighted least square sense even in the case of inconsistent data.
Abstract: In this paper, we propose two ordered-subset simultaneous algebraic reconstruction techniques (OS-SART). First, we describe the heuristics in support of the two OS-SART formulas. Then, we study the convergence in the framework of our recent work on the OS version of the Landweber scheme. It is shown that our first OS-SART is a special case of the OS version of the Landweber scheme. Hence, it converges in the weighted least square sense even in the case of inconsistent data. Both the OS-SART formulas are tested for reconstruction of CT images from practical data.
TL;DR: This paper implements Feldkamp-Davis-Kress (FDK) algorithm on commodity GPU using an acceleration scheme that saves the copy time, and the combination of z-axis symmetry and multiple render targets (MRTs) reduces the computational cost on the geometry mapping between slices to be reconstructed and projection views.
Abstract: Three dimension Computed Tomography (CT) reconstruction is computationally demanding To accelerate the speed of reconstruction, Application Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA) has been used, but they are expensive, inflexible and not easy to upgrade The modern Graphics Processing Unit (GPU) with its programmable features improves this situation and becomes one of the powerful and flexible tools for 3D CT reconstruction In this paper, we implement Feldkamp-Davis-Kress (FDK) algorithm on commodity GPU using an acceleration scheme In the scheme, two techniques are developed and combined One is cyclic render-to-texture (CRTT) which saves the copy time, and the other is the combination of z-axis symmetry and multiple render targets (MRTs), which reduces the computational cost on the geometry mapping between slices to be reconstructed and projection views Our algorithm performs reconstruction of a 5123 volume from 360 views of the size 512 x 512 about 52s on a single NVIDIA GeForce 8800GTX card
TL;DR: In this article, constrained dual energy decomposition, adaptive scatter correction, nonlinear filtering of decomposed projections, and real-time image-based correction for x-ray spectral drifts.
Abstract: Single energy computed tomography (CT) scanners use measurements of densities to detect explosives in luggage. It is desirable to apply dual energy techniques to these CT scanners to obtain atomic number measurements to reduce false alarm rates. However, the direct application of existing dual energy techniques has practical problems, such as, approximation errors and lack of boundary constraints in dual energy decomposition, image artifacts, and x-ray spectral drifts. In this paper, we present methods to reduce these problems. The methods include constrained dual energy decomposition, adaptive scatter correction, nonlinear filtering of decomposed projections, and real-time image-based correction for x-ray spectral drifts. We demonstrate the effectiveness of the methods using simulated data and real data obtained from a commercial dual energy CT scanner.