Statistical Image Reconstruction for Polyenergetic X-Ray Computed Tomography

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85895/1/Fessler74.pd

Algorithms for enhanced artifact reduction and material recognition in computed tomography

Computed tomography (CT) imaging provides a non-destructive means to examine the interior of an object which is a valuable tool in medical and security applications. The variety of materials seen in the security applications is higher than in the medical applications. Factors such as clutter, presence of dense objects, and closely placed items in a bag or a parcel add to the difficulty of the material recognition in security applications. Metal and dense objects create image artifacts which degrade the image quality and deteriorate the recognition accuracy. Conventional CT machines scan the object using single source or dual source spectra and reconstruct the effective linear attenuation coefficient of voxels in the image which may not provide the sufficient information to identify the occupying materials. In this dissertation, we provide algorithmic solutions to enhance CT material recognition. We provide a set of algorithms to accommodate different classes of CT machines. First, we provide a metal artifact reduction algorithm for conventional CT machines which perform the measurements using single X-ray source spectrum. Compared to previous methods, our algorithm is robust to severe metal artifacts and accurately reconstructs the regions that are in proximity to metal. Second, we propose a novel joint segmentation and classification algorithm for dual-energy CT machines which extends prior work to capture spatial correlation in material X-ray attenuation properties. We show that the classification performance of our method surpasses the prior work's result. Third, we propose a new framework for reconstruction and classification using a new class of CT machines known as spectral CT which has been recently developed. Spectral CT uses multiple energy windows to scan the object, thus it captures data across higher energy dimensions per detector. Our reconstruction algorithm extracts essential features from the measured data by using spectral decomposition. We explore the effect of using different transforms in performing the measurement decomposition and we develop a new basis transform which encapsulates the sufficient information of the data and provides high classification accuracy. Furthermore, we extend our framework to perform the task of explosive detection. We show that our framework achieves high detection accuracy and it is robust to noise and variations. Lastly, we propose a combined algorithm for spectral CT, which jointly reconstructs images and labels each region in the image. We offer a tractable optimization method to solve the proposed discrete tomography problem. We show that our method outperforms the prior work in terms of both reconstruction quality and classification accuracy

Lithic Technology and Risk: Winter Houses at Bridge River Villages

The 2012 excavation of a single housepit (Housepit 54) at the Bridge River Village site (EeR14) offers the unique opportunity to look at lithic organization and techinological strategies during the Fur Trade era in the Middle Fraser Canyon. The main goal of this research is to understand how the winter occupation of Housepit 54 may have affected the lithic technological strategies carried out at Bride River Village. As a winter pithouse, lithic raw material sources would be inaccessible during the three months of occupation. The hypothesis of this thesis is structured with a theory of risk framework in order to understand what strategies may have been implemented in order to minimize the risk of exhausting raw material over the winter. This thesis will also seek to explore the ethnographic record in relation to the archaeological record in order to extrapolate a model of lithic organization. The hypothesis proposes that certain strategies such as bipolar reduction and high production intensity would be applied in order to conserve raw material over the winter. Tools size, expedient reuse and longer use-lives are also factors anticipated from the hypothesis. These factors are highly testable variables that will provide a deeper understanding of lithic technological strategies, but also, will provide insight into the activities being carried out over the winter occupation at Bridge River Village during the Fur Trade era

A Computational Model to Predict \u3cem\u3eIn Vivo\u3c/em\u3e Kinetics in Implanted and Non-Implanted Shoulders

The purpose of this study was to develop and implement a computational model designed to input in vivo kinematic and predict in vivo forces and torques for the shoulder, elbow, and wrist in normal, rotator cuff-deficient (RCD), reverse shoulder arthroplasty (RSA) and total shoulder arthroplasty (TSA) shoulder subjects. Twenty subjects, divided evenly amongst the four shoulder types, performed a box-lift activity while under fluoroscopic surveillance. Three dimensional (3D) in vivo kinematics was determined for the subjects using implant models and bone models created from CT (computed tomography) scans in a 2D-to-3D registration process. The kinematics were used as input for an inverse dynamics mathematical model, and the subject-specific kinetics were derived. Average resultant shoulder forces were 78.3N (range: 70.4N to 117N, SD: 5.213), 102N (range: 90.2N to 180.2N, SD: 12.339), 94.9N (range: 84.9N to 149N, SD: 10.02), and 92.5N (range: 87.984N to 95.370N, SD: 1.848), for normal, RCD, RSA, and TSA subjects, respectively. Average resultant shoulder torques were 23.6Nm (range: 8.32Nm to 73.7Nm, SD: 11.227), 29.6Nm (range: 22.892Nm to 71.377Nm, SD: 7.581), 27.2Nm (range: 19.961Nm to 59.352Nm, SD: 6.664), 20.3Nm (range: 11.700Nm to 31.409Nm, SD: 6.496), for normal, RCD, RSA, and TSA shoulders, respectively. This study revealed that RCD subjects exhibited a decreased ROM (range of motion) of the humeral head with respect to the glenoid, as compared to the other groups. This study also showed that subjects having a rotator cuff-deficient shoulder and/or a replaced shoulder tend to use compensatory motions to perform the task of lifting a box, and, as a result, they experience greater forces at the glenohumeral joint. Paradoxically, the RCD subjects experienced the highest joint forces and torques among the different shoulder types

Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction

In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-Lambert Law. Conventional reconstruction often involves inverting this nonlinearity as a preprocessing step and then solving a convex inverse problem. However, this nonlinear measurement preprocessing required to use the Radon transform is poorly conditioned in the vicinity of high-density materials, such as metal. This preprocessing makes CT reconstruction methods numerically sensitive and susceptible to artifacts near high-density regions. In this paper, we study a technique where the signal is directly reconstructed from raw measurements through the nonlinear forward model. Though this optimization is nonconvex, we show that gradient descent provably converges to the global optimum at a geometric rate, perfectly reconstructing the underlying signal with a near minimal number of random measurements. We also prove similar results in the under-determined setting where the number of measurements is significantly smaller than the dimension of the signal. This is achieved by enforcing prior structural information about the signal through constraints on the optimization variables. We illustrate the benefits of direct nonlinear CT reconstruction with cone-beam CT experiments on synthetic and real 3D volumes. We show that this approach reduces metal artifacts compared to a commercial reconstruction of a human skull with metal dental crowns

Grazing to Gravy: Faunal Remains and Indications of Genízaro Foodways on the Spanish Colonial Frontier of New Mexico

Understanding identity aspects of those labeled Genízaro during the late Spanish Colonial period of New Mexico benefits from finer-grained perspectives on what ranges and mixtures of practices persons bearing this casta designation may have performed while preparing cuisine. Materials from the northern frontier site of Casitas Viejas (LA 917) suggest that the closely related households of this fortified plaza may have departed from the less expansive culinary practices of colonial elites while drawing from their multiple social relationships at the various stages of production and consumption of foods. In other words, at different temporal and spatial scales, behaviors reflected in the material record refute historical notions about a creolized community that tried to diminish identity difference within the village. The goal of this work is to explore through the study of faunal remains some of the relationships between foodways and cultural identity in a manner that might assist in some disentangling of the sticky problems archaeologists face in interpreting traces of dynamic past situations of identity from a static material record recovered today

Deep Learning with Constraints and Priors for Improved Subject Clustering, Medical Imaging, and Robust Inference

Deep neural networks (DNNs) have achieved significant success in several fields including computer vision, natural language processing, and robot control. The common philosophy behind these success is the use of large amount of annotated data and end-to-end networks with task-specific constraints and priors implicitly incorporated into the trained model without the need for careful feature engineering. However, DNNs are shown to be vulnerable to distribution shifts and adversarial perturbations, which indicates that such implicit priors and constraints are not sufficient for real world applications. In this dissertation, we target three applications and design task-specific constraints and priors for improved performance of deep neural networks. We first study the problem of subject clustering, the task of grouping face images of the same person together. We propose to utilize the prior structure in the feature space of DNNs trained for face identification to design a novel clustering algorithm. Specifically, the clustering algorithm exploits the local neighborhood structure of deep representations by exemplar-based learning based on k-nearest neighbors (k-NN). Extensive experiments show promising results for grouping face images according to subject identity. As an example, we apply the proposed clustering algorithm to automatically curate a large-scale face dataset with noisy labels and show that the performance of face recognition DNNs can be significantly improved by training on the curated dataset. Furthermore, we empirically find that the k-NN rule does not capture proper local structures for deep representations when each subject has very few face images. We then propose to improve upon the exemplar-based approach by a density-aware similarity measure and theoretically show its asymptotic convergence to a density estimator. We conduct experiments on challenging face datasets that show promising results. Second, we study the problem of metal artifact reduction in computed tomography (CT). Unlike typical image restoration tasks such as super-resolution and denoising, metal artifacts in CT images are structured and non-local. Conventional DNNs do not generalize well when metal implants with unseen shapes are presented. We find that the imaging process of CT induces a data consistency prior that can be exploited for image enhancement. Based on this observation, we propose a dual-domain learning approach to CT metal artifact reduction. We design and implement a novel Radon inversion layer that allows gradients in the image domain to be backpropagated to the projection domain. Experiments conducted on both simulated datasets and clinical datasets show promising results. Compared to conventional DNN-based models, the proposed dual-domain approach leads to impressive metal artifact reduction and has improved generalization capability. Finally, we study the problem of robust classification. In the past few years, the vulnerability of DNNs to small imperceptible perturbations has been widely studied, which raises concerns about the security and robustness of DNNs against possible threat models. To defend against threat models, Samangoui et al. proposed DefenseGAN, a preprocessing approach which removes adversarial perturbations by projecting the input images onto the learned data prior. However, the projection operation in DefenseGAN is time-consuming and may not yield proper reconstruction when images have complicated textures. We propose an inversion network to constrain the initial estimates of the latent code for input images. With the proposed constraint, the number of optimization steps in DefenseGAN can be reduced while achieving improved accuracy and robustness. Furthermore, we conduct empirical studies on attack methods that have claimed to break DefenseGAN, which shows that on-manifold robustness might be the key factor for ensuring adversarial robustness