Dark-field images are formed by small-angle scattering of x-ray photons. The small-angle scattering signal is particularly sensitive to structural variations and density fluctuation on a length scale of several ten to hundred nanometers, offering a new contrast mechanism to reveal subtle structural variation of object. In this paper, we derive a novel physical model to describe x-ray absorption and small-angle scattering, and use the proposed model to reconstruct the volumetric small-angle scattering images. The numerical experiments and test experiments demonstrate that the reconstructed scattering images reveal unique features with a high contrast resolution. The proposed approach has great potential in biomedical imaging, nondestructive detections, and other applications.
Fluorescence molecular tomography (FMT) has potential of providing high contrast images for breast tumor detection. Computational phantom provides a convenient way to a wide variety of fluorophore distribution configurations in patients and perform comprehensive evaluation of the imaging systems and methods for FMT. In this study, a digital breast phantom was used to compare the performance of a novel sparsity-based reconstruction method and Tikhonov regularization method for resolving tumors with different amount of separation. The results showed that the proposed sparse reconstruction method…
We propose a combined reconstruction-classification method for simultaneously recovering absorption and scattering in turbid media from images of absorbed optical energy. This method exploits knowledge that optical parameters are determined by a limited number of classes to iteratively improve their estimate. Numerical experiments show that the proposed approach allows for accurate recovery of absorption and scattering in 2 and 3 dimensions, and delivers superior image quality with respect to traditional reconstruction-only approaches.
Medical imaging modalities have revolutionized health-care approaches by offering a better understanding of the human anatomy. Discovery of x-rays allowed the exploiting of the micro-scaled information of human anatomy. Computed tomography is one of the well-known imaging modalities using x-rays to create images of medical and non-medical objects. CT imaging has several variety of applications from medical diagnosis to industrial non-destructive testing. In this paper, we review computed tomography imaging modality and its applications in screening, diagnosis, and treatment and study some of t…
Interior tomography is a typical strategy for radiation dose reduction in computed tomography, where only a certain region-of-interest (ROI) is scanned. However, given the truncated projection data, ROI reconstruction by conventional analytical algorithms may suffer from severe cupping artifacts. In this paper, we proposed a new extraction-based deep learning method for the reconstruction of interior tomography. Our approach works in dual domains where a sinogram-domain network (SDNet) estimates the contribution of the exterior region to the truncated projection and an image-domain network (ID…
When the inverse problem of diffuse optical tomography (DOT) is solved with the Born or Rytov approximation, the size of the matrix of the linear inverse problem becomes large if the volume (or area) of the domain in biological tissue used for reconstruction is large. The number of unknown parameters in DOT is reduced when constraints about the shape of a target are imposed for the inverse problem. Due to such constraints, the inverse problem becomes nonlinear even when the (first) Born or Rytov approximation is employed. We solve this nonlinear inverse problem by the simulated annealing, whic…
A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammography. Based on inverse scattering technique, ultrasound tomography uses some material properties such as sound contrast or attenuation to detect small targets. The Distorted Born Iterative Method (DBIM) based on first-order Born approximation is an efficient diffraction tomography approach. Compressed Sensing (CS…
For conventional computed tomography (CT) image reconstruction tasks, the most popular method is the so-called filtered-back-projection (FBP) algorithm. In it, the acquired Radon projections are usually filtered first by a ramp kernel before back-projected to generate CT images. In this work, as a contrary, we realized the idea of image-domain backproject-filter (BPF) CT image reconstruction using the deep learning techniques for the first time. With a properly designed convolutional neural network (CNN), preliminary results demonstrate that it is feasible to reconstruct CT images with maintai…
Recently, an ultra-low-cost linear scan based tomography architecture was proposed by our team. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomography architecture was named parallel translational computed tomography (PTCT). In our previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe tru…
Electron tomography has been studied in various fields. Various methods have been developed to align projection sets to construct ideally focused reconstruction. In this paper, we present how to align the projection set to distinguish whether it has an ideal sinogram pattern or not by removing translation errors and vertical tilt errors. We also analyze some important properties for certain types of samples to identify whether the reconstruction image can be made through an ideal sinogram pattern. We provide a guideline for how to construct a better reconstruction image by scanning the sample …
ESRF（欧洲同步辐射装置）；Université de Grenoble（格勒诺布尔大学）；GIPSA-lab - Grenoble Images Parole Signal Automatique - Institut Polytechnique de Grenoble（格勒诺布尔综合理工学院GIPSA实验室——格勒诺布尔图像语音信号自动化实验室）
We propose a new method to reconstruct data acquired in a local tomography setup. This method uses an initial reconstruction and refines it by correcting the low frequency artifacts known as the cupping effect. A basis of Gaussian functions is used to correct the initial reconstruction. The coefficients of this basis are iteratively optimized under the constraint of a known subregion. Using a coarse basis reduces the degrees of freedom of the problem while actually correcting the cupping effect. Simulations show that the known region constraint yields an unbiased reconstruction, in accordance …
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
Wake Forest University Health Sciences（维克森林大学健康科学学院）；Institute of High Energy Physics, CAS（中国科学院高能物理研究所）；Virginia Tech（弗吉尼亚理工大学）；University of California, Irvine（加利福尼亚大学欧文分校）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. S…
Model-based iterative reconstruction (MBIR) techniques have demonstrated many advantages in X-ray CT image reconstruction. The MBIR approach is often modeled as a convex optimization problem including a data fitting function and a penalty function. The tuning parameter value that regulates the strength of the penalty function is critical for achieving good reconstruction results but difficult to choose. In this work, we describe two path seeking algorithms that are capable of efficiently generating a series of MBIR images with different strengths of the penalty function. The root-mean-squared-…
The newly developed Core Imaging Library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularised reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g., dynamic and spectral. We formalise different optimisation problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's …
This paper proposes the combination of SAR and microwave reflection tomography for biomedical imaging applications. The proposed method can achieve accuracies of less than 5mm and does not suffer from instability during reconstruction. Fourier based reconstruction is adopted which makes it fast and computationally efficient.
Proton computed tomography (pCT) is a novel medical imaging modality for mapping the distribution of proton relative stopping power (RSP) in medical objects of interest. Compared to conventional X-ray computed tomography, where range uncertainty margins are around 3.5%, pCT has the potential to provide more accurate measurements to within 1%. This improved efficiency will be beneficial to proton-therapy planning and pre-treatment verification. A prototype pCT imaging device has recently been developed capable of rapidly acquiring low-dose proton radiographs of head-sized objects. We have also …
We present and evaluate the application of the "Reconstructed Image from Simulations Ensemble" (RISE), a novel tomographic image reconstruction method, in infrared tomography. We demonstrate that established methods of photon emission tomography, widely used with penetrating ionizing radiation, are applicable to infrared radiation. RISE, the method of choice, employs statistical physics concepts and utilizes Monte Carlo techniques to construct the imaged object from its infrared planar projections. The validity of the InfraRed Emission Tomographic (IRET) method is demonstrated, and the efficac…
J-PET Framework is an open-source software platform for data analysis, written in C++ and based on the ROOT package. It provides a common environment for implementation of reconstruction, calibration and filtering procedures, as well as for user-level analyses of Positron Emission Tomography data. The library contains a set of building blocks that can be combined by users with even little programming experience, into chains of processing tasks through a convenient, simple and well-documented API. The generic input-output interface allows processing the data from various sources: low-level data…
Edge-Aligned Beam Placement in Scanning Probe Tomography via Reconstruction-Free Sequential Design of Experiments
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA（阿贡国家实验室数学与计算机科学部）；Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA（阿贡国家实验室计算机科学部）
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Primal-Dual Network when using only 23 projections. The presented network is not optimized wrt. memory consumption or hyperparameters but merely serves as a proof of concept and is limited to low resolution projections and volumes.
Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；School of Physics, Beihang University（北京航空航天大学物理学院）；Peking University Third Hospital（北京大学第三医院）；Tianmushan Laboratory（天目山实验室）
Time-of-flight neutron imaging offers complementary attenuation contrast to X-ray computed tomography (CT), coupled with the ability to extract additional information from the variation in attenuation as a function of neutron energy (time of flight) at every point (voxel) in the image. In particular Bragg edge positions provide crystallographic information and therefore enable the identification of crystalline phases directly. Here we demonstrate Bragg edge tomography with high spatial and spectral resolution. We propose a new iterative tomographic reconstruction method with a tailored regular…
In multi-view fluorescence microscopy, each angular acquisition needs to be aligned with care to obtain an optimal volumetric reconstruction. Here, instead, we propose a neat protocol based on auto-correlation inversion, that leads directly to the formation of inherently aligned tomographies. Our method generates sharp reconstructions, with the same accuracy reachable after sub-pixel alignment but with improved point-spread-function. The procedure can be performed simultaneously with deconvolution further increasing the reconstruction resolution.
In this work, we present the Deep Newton Reconstruction Network (DNR-Net), a hybrid data-driven reconstruction technique for emission tomography inspired by Newton's method, a well-known iterative optimization algorithm. The DNR-Net employs prior information about the tomographic problem provided by the projection operator while utilizing deep learning approaches to a) imitate Newton's method by approximating the Newton descent direction and b) provide data-driven regularisation. We demonstrate that DNR-Net is capable of providing high-quality image reconstructions using data from SPECT phanto…
