Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information.

Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs).

Generalized-equiangular geometry CT: Concept and shift-invariant FBP algorithms.

Background: With advanced x-ray source and detector technologies being continuously developed, non-traditional CT geometries have been widely explored. Generalized-Equiangular Geometry CT (GEGCT) architecture, in which an x-ray source might be positioned radially far away from the focus of arced detector array that is equiangularly spaced, is of importance in many novel CT systems and designs.

Purpose: GEGCT, unfortunately, has no theoretically exact and shift-invariant analytical image reconstruction algorithm in general.

Joint Reconstruction and Spectrum Refinement for Photon-Counting-Detector Spectral CT.

Photon-counting detector CT (PCD-CT) is a revolutionary technology in decades in the field of CT. Its potential benefits in lowering noise, dose reduction, and material-specific imaging enable completely new clinical applications. Spectral reconstruction of basis material maps requires knowledge of the x-ray spectrum and the spectral response calibration of the detector.

Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images.

CT metal artefact reduction (MAR) methods based on supervised deep learning are often troubled by domain gap between simulated training dataset and real-application dataset, i.e., methods trained on simulation cannot generalize well to practical data.

Method of sparse-view coded-aperture x-ray diffraction tomography.

X-ray diffraction (XRD) has been considered as a valuable diagnostic technology providing material specific 'finger-print' information i.e. XRD pattern to distinguish different biological tissues.

Sam's Net: A Self-Augmented Multistage Deep-Learning Network for End-to-End Reconstruction of Limited Angle CT.

Limited angle reconstruction is a typical ill-posed problem in computed tomography (CT). Given incomplete projection data, images reconstructed by conventional analytical algorithms and iterative methods suffer from severe structural distortions and artifacts. In this paper, we proposed a self-augmented multi-stage deep-learning network (Sam's Net) for end-to-end reconstruction of limited angle CT.

Reciprocal-FDK reconstruction for x-ray diffraction computed tomography.

X-ray diffraction (XRD) technology uses x-ray small-angle scattering signal for material analysis, which is highly sensitive to material inter-molecular structure. To meet the high spatial resolution requirement in applications such as medical imaging, XRD computed tomography (XRDCT) has been proposed to provide XRD intensity with improved spatial resolution from point-wise XRD scan. In XRDCT, 2D spatial tomography corresponds to a 3D reconstruction problem with the third dimension being the XRD spectrum dimension, i.

Analytical covariance estimation for iterative CT reconstruction methods.

Covariance of reconstruction images are useful to analyze the magnitude and correlation of noise in the evaluation of systems and reconstruction algorithms. The covariance estimation requires a big number of image samples that are hard to acquire in reality. A covariance propagation method from projection by a few noisy realizations is studied in this work.

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learning methods in CT image restoration, different loss functions will lead to different image qualities which may affect clinical diagnosis. In this paper, to compare commonly used loss functions and give a better alternative, we studied a widely generalizable framework for loss functions which are defined in the feature space extracted by neural networks.

Fluence adaptation for contrast-based dose optimization in x-ray phase-contrast imaging.

Purpose: X-ray phase-contrast imaging (XPCI) can provide multiple contrasts with great potentials for clinical and industrial applications, including conventional attenuation, phase contrast, and dark field. Grating-based imaging (GBI) and edge-illumination (EI) are two promising types of XPCI as the conventional x-ray sources can be directly utilized. For the GBI and EI systems, the phase-stepping acquisition with multiple exposures at a constant fluence is usually adopted in the literature.

Fourier-based interpretation and noise analysis of the moments of small-angle x-ray scattering in grating-based x-ray phase contrast imaging.

In grating-based x-ray phase contrast imaging, Fourier component analysis (FCA) is usually recognized as a gold standard to retrieve the contrasts including attenuation, phase and dark-field, since it is well-established on wave optics and is of high computational efficiency. Meanwhile, an alternative approach basing on the particle scattering theory is being developed and can provide similar contrasts with FCA by calculating multi-order moments of deconvolved small-angle x-ray scattering, so called as multi-order moment analysis (MMA). Although originated from quite different physics theories, the high consistency between the contrasts retrieved by FCA and MMA implies us that there may be some intrinsic connections between them, which has not been fully revealed to the best of our knowledge.

Fourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction With Neural Network.

In this work, we investigate the Fourier properties of a symmetric-geometry computed tomography (SGCT) with linearly distributed source and detector in a stationary configuration. A linkage between the 1D Fourier Transform of a weighted projection from SGCT and the 2D Fourier Transform of a deformed object is established in a simple mathematical form (i.e.

A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT.

Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator (MSC) in the axis direction. This type of system can not only lower radiation dose, and suppress scattering by MSC, but also cuts down the manufacturing cost of the detector.

Stationary computed tomography with source and detector in linear symmetric geometry: Direct filtered backprojection reconstruction.

Purpose: Inverse-geometry computed tomography (IGCT) could have great potential in medical applications and security inspections, and has been actively investigated in recent years. In this work, we explore a special architecture of IGCT in a stationary configuration: symmetric-geometry computed tomography (SGCT), where the x-ray source and detector are linearly distributed in a symmetric design. A direct filtered backprojection (FBP)-type algorithm is developed to analytically reconstruct images from the SGCT projections.

Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning.

Purpose: Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods.

A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks.

Compared with conventional gastroscopy which is invasive and painful, wireless capsule endoscopy (WCE) can provide noninvasive examination of gastrointestinal (GI) tract. The WCE video can effectively support physicians to reach a diagnostic decision while a huge number of images need to be analyzed (more than 50 000 frames per patient). In this paper, we propose a computer-aided diagnosis method called second glance (secG) detection framework for automatic detection of ulcers based on deep convolutional neural networks that provides both classification confidence and bounding box of lesion area.

Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization.

Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks.

Fully connected neural network for virtual monochromatic imaging in spectral computed tomography.

Spectral computed tomography (SCT) has advantages in multienergy material decomposition for material discrimination and quantitative image reconstruction. However, due to the nonideal physical effects of photon counting detectors, including charge sharing, pulse pileup and -escape, it is difficult to obtain precise system models in practical SCT systems. Serious spectral distortion is unavoidable, which introduces error into the decomposition model and affects material decomposition accuracy.

Experimental study to optimize configurations of PCD Spectral CT.

Background: High dose efficiency of photon counting detector based spectral CT (PCD-SCT) and its value in some clinical diagnosis have been well acknowledged. However, it has not been widely adopted in practical use for medical diagnosis and security inspection.

Objective: To evaluate the influence on PCD-SCT from multiple aspects including the number of energy channels, k-edge materials, energy thresholding, basis functions in spectral information decomposition, and the combined optimal setting for these parameters and configurations.

Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database.

The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This paper aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm, for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively.

Geometry calibration method for a cone-beam CT system.

Purpose: The positioning accuracy of each component is important to ensure the image quality of cone-beam CT. However, accurate positioning is not easy and requires experience and time. The option is to calibrate the geometric parameters and then plug them into a reconstruction algorithm which is the preferred solution in practice.

Multienergy CT acquisition and reconstruction with a stepped tube potential scan.

Purpose: Based on an energy-dependent property of matter, one may obtain a pseudomonochromatic attenuation map, a material composition image, an electron-density distribution, and an atomic number image using a dual- or multienergy computed tomography (CT) scan. Dual- and multienergy CT scans broaden the potential of x-ray CT imaging. The development of such systems is very useful in both medical and industrial investigations.

Prior image guided undersampled dual energy reconstruction with piecewise polynomial function constraint.

Dual energy CT has the ability to give more information about the test object by reconstructing the attenuation factors under different energies. These images under different energies share identical structures but different attenuation factors. By referring to the fully sampled low-energy image, we show that it is possible to greatly reduce the sampling rate of the high-energy image in order to lower dose.