Eric Lawrence Miller

Tufts University | Tufts · Department of Electrical and Computer Engineering

This paper is concerned with the problem of recovering third-order tensor data from limited samples. A recently proposed tensor decomposition (BMD) method has been shown to efficiently compress third-order spatiotemporal data. Using the BMD, we formulate a slicewise nuclear norm penalized algorithm to recover a third-order tensor from limited obser...

Significance Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information. Aim We aim to employ deep n...

Objective We challenge the paradigm that a simplistic approach evaluating anatomic regions (e.g., medial femur or tibia) is ideal for assessing articular cartilage loss on magnetic resonance (MR) imaging. We used a data‐driven approach to explore whether specific topographical locations of knee cartilage loss may identify novel patterns of cartilag...

Given tensors $\boldsymbol{\mathscr{A}}, \boldsymbol{\mathscr{B}}, \boldsymbol{\mathscr{C}}$ of size $m \times 1 \times n$, $m \times p \times 1$, and $1\times p \times n$, respectively, their Bhattacharya-Mesner (BM) product will result in a third order tensor of dimension $m \times p \times n$ and BM-rank of 1 (Mesner and Bhattacharya, 1990). Thu...

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Rec...

We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such as human activity analysis where observed accelerometer time series contains segments representing distinct activities, which we call pure states , as well as periods ch...

Measured intensity in high-energy monochromatic X-ray diffraction (HEXD) experiments provides information regarding the microstructure of the crystalline material under study. The location of intensity on an areal detector is determined by the lattice spacing and orientation of crystals so that changes in the heterogeneity of these quantities are r...

We consider probabilistic time-series models for systems that gradually transition among a finite number of states, in contrast to the more commonly considered case where such transitions are abrupt or instantaneous. We are particularly motivated by applications such as human activity analysis where the observed time-series contains segments repres...

We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non-conjugacy or strong posterior dependencies when using conjugate auxiliary variable methods. We define a new class of GLMs for categorical data called categorical-from-bi...

In this paper, we consider the restoration and reconstruction of piecewise constant objects in two and three dimensions using PaLEnTIR, a significantly enhanced Parametric level set (PaLS) model relative to the current state-of-the-art. The primary contribution of this paper is a new PaLS formulation which requires only a single level set function...

This paper shows that a popular approach to the supervised embedding of documents for classification, namely, contrastive Word Mover's Embedding, can be significantly enhanced by adding interpretability. This interpretability is achieved by incorporating a clustering promoting mechanism into the contrastive loss. On several public datasets, we show...

Many time series can be modeled as a sequence of segments representing high-level discrete states, such as running and walking in a human activity application. Flexible models should describe the system state and observations in stationary ``pure-state'' periods as well as transition periods between adjacent segments, such as a gradual slowdown bet...

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improv...

Here we introduce a new reconstruction technique for two-dimensional Bragg scattering tomography (BST), based on the Radon transform models of Webber and Miller [Inverse Probl. Imaging 15, 683 (2021).10.3934/ipi.2021010]. Our method uses a combination of ideas from multibang control and microlocal analysis to construct an objective function which c...

Markov chain Monte Carlo (MCMC) approaches are traditionally used for uncertainty quantification in inverse problems where the physics of the underlying sensor modality is described by a partial differential equation (PDE). However, the use of MCMC algorithms is prohibitively expensive in applications where each log-likelihood evaluation may requir...

Here we present a new non-parametric approach to density estimation and classification derived from theory in Radon transforms and image reconstruction. We start by constructing a “forward problem” in which the unknown density is mapped to a set of one dimensional empirical distribution functions computed from the raw input data. Interpreting this...

Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A...

Here we introduce a new reconstruction technique for two-dimensional Bragg Scattering Tomography (BST), based on the Radon transform models of [arXiv preprint, arXiv:2004.10961 (2020)]. Our method uses a combination of ideas from multibang control and microlocal analysis to construct an objective function which can regularize the BST artifacts; spe...

Economical sensing and recording of temperature are important in the transportation and management of sensitive goods such as medicine. Existing approaches measure the entire temperature profile using electronic devices running on a continuous power source. This paper presents a simple, intelligent, and battery-free solution for capturing informati...

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms. However, randomness in the test statistic as a function of time makes them susceptible to false positives and localization ambiguity. We address these issues by deriving and applying filters matched to the expected temporal sign...

We present a new inner-outer iterative algorithm for edge enhancement in imaging problems. At each outer iteration, we formulate a Tikhonov-regularized problem where the penalization is expressed in the 2-norm and involves a regularization operator designed to improve edge resolution as the outer iterations progress, through an adaptive process. An...

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms.However, noise in the data make these tests susceptible to false positives and localization ambiguity. We address these issues by deriving asymptotically matched filters under standard IID assumptions on the data for various slid...

We present new joint reconstruction and regularization techniques inspired by ideas in microlocal analysis and lambda tomography, for the simultaneous reconstruction of the attenuation coefficient and electron density from x-ray transmission (i.e., x-ray CT) and backscattered data (assumed to be primarily Compton scattered). To demonstrate our theo...

Here we introduce a new forward model and imaging modality for Bragg Scattering Tomography (BST). The model we propose is based on an X-ray portal scanner with linear detector collimation, currently being developed for use in airport baggage screening. The geometry under consideration leads us to a novel two-dimensional inverse problem, where we ai...

A novel statistical approach is developed and implemented for the stochastic reconstruction of nonaqueous phase liquid (NAPL) source zone realizations and the quantification of source zone metrics and associated uncertainty. The approach employs discriminative random field (DRF) models, to simulate the spatial distributions and relationships among...

Here we present new joint reconstruction and regularization techniques inspired by ideas in microlocal analysis and lambda tomography, for the simultaneous reconstruction of the attenuation coefficient and electron density from X-ray transmission (i.e., X-ray CT) and backscattered data (assumed to be primarily Compton scattered). To demonstrate our...

Abstract. Here we introduce a new forward model and imaging modality for Bragg Scattering Tomography (BST). The model we propose is based on an Xray portal scanner with linear detector collimation, currently being developed for use in airport baggage screening. The geometry under consideration leads us to a novel two-dimensional inverse problem, wh...

We present a new inner-outer iterative algorithm for edge enhancement in imaging problems. At each outer iteration, we formulate a Tikhonov-regularized problem where the penalization is expressed in the 2-norm and involves a regularization operator designed to improve edge resolution as the outer iterations progress, through an adaptive process. An...

Motivated by the use of X-rays for security screening, we demonstrate the utility of fusing energy-resolved observations of scattered photons with traditional attenuation data for the joint recovery of electron density and photoelectric coefficient in the context of limited-view tomographic imaging scenarios. We begin by developing a physical and a...

X-ray inspection systems are critical in medical, non-destructive testing, and security applications, with systems typically measuring attenuation along straight-line paths connecting sources and detectors. Computed tomography (CT) systems can provide higher-quality images than single-or dualview systems, but the need to measure many projections le...

Two common problems in time series analysis are the decomposition of the data stream into disjoint segments, each of which is in some sense 'homogeneous' - a problem that is also referred to as Change Point Detection (CPD) - and the grouping of similar nonadjacent segments, or Time Series Segment Clustering (TSSC). Building upon recent theoretical...

Here we present new L2 injectivity results for 2-D and 3-D Compton scattering tomography (CST) problems in translational geometries. The results are proven through the explicit inversion of a new toric section and apple Radon transform, which describe novel 2-D and 3-D acquisition geometries in CST. The geometry considered has potential application...

Here we present new $L^2$ injectivity results for 2-D and 3-D Compton scattering tomography (CST) problems in translational geometries. The results are proven through the explicit inversion of a new toric section and apple Radon transform, which describe novel 2-D and 3-D acquisition geometries in CST. The geometry considered has potential applicat...

Here we present a new non-parametric approach to density estimation and classification derived from theory in Radon transforms and image reconstruction. We start by constructing a "forward problem" in which the unknown density is mapped to a set of one dimensional empirical distribution functions computed from the raw input data. Interpreting this...

High energy X-ray diffraction data collected in situ during loading experiments permits probing of the crystal structure of a plastically deforming material sample. An elastoplastic deformation is associated with heterogeneity in both crystal orientation and lattice spacing—each manifesting as azimuthal broadening and radial broadening of diffracti...

Significance Focused ultrasound is currently the only method of reversible blood–brain barrier disruption for targeted drug delivery without incision or radiation. A significant challenge for its clinical translation is a lack of reliable real-time treatment control. Here a closed-loop, real-time control paradigm is shown capable of sustaining stab...

Sub-megahertz transmission is not usually adopted in pre-clinical small animal experiments for focused ultrasound (FUS) brain therapy due to the large focal size. However, low frequency FUS is vital for preclinical evaluations due to the frequency-dependence of cavitation behavior. To maximize clinical relevance, a dual-aperture FUS system was desi...

Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently. In this paper, we investigate this problem in the context of Gaussian Processes (GPs) and propose a new model which learns a mixture of latent processes by decomposing the covariance matrix into a sum of...

In this paper we demonstrate the utility of fusing energy-resolved observations of Compton scattered photons with traditional attenuation data for the joint recovery of mass density and photoelectric absorption in the context of limited view tomographic imaging applications. We begin with the development of a physical and associated numerical model...

There is a need for an accurate end-of-life indicator for packaged food (meat, seafood, dairy food etc.) beyond a simple “best use by” date on the food package. In this work, we propose a low cost solution by repurposing the food’s barcode as a colorimetric sensor array to monitor food condition. A smart phone camera is used to read color informati...

The papers in this special section focused on computational imaging for the earth sciences market. From the core of the earth to the farthest reaches of our planets magnetic fields, the earth sciences are concerned with all aspects of monitoring, exploring, explaining, and exploiting of natural events and resources in the geosphere. Revolutions in c...

Microbubble-mediated focused ultrasound (FUS) therapies harness mechanical and/or thermal effects to deliver drugs or ablate tissues. Passive acoustic mapping (PAM) enables the spatio-temporal monitoring of cavitation activity, which is critical for the clinical translation of this technique. Traditional PAM is based on delay-and-sum (DAS) beamform...

With the recent development of CubeSats, several ultracompact, low cost, and rapidly deployable satellites have been developed for earth observation missions. Because of the geometry of the acquisition process, measurements are irregularly sampled, whereas in meteorological applications, data are preferred on a regular grid. This problem is further...

Ultrasound computed tomography (USCT) is a non-invasive imaging technique that provides information about the acoustic properties of soft tissues in the body, such as the speed of sound (SS) and acoustic attenuation (AA). Knowledge of these properties can improve the discrimination between benign and malignant masses, especially in breast cancer st...

http://www.fusfoundation.org/symposium/2016/docs/FUSF_Symposium_2016_Abstracts_web.pdf

With their greatly reduced sizes, low development cost, and rapid construction time, CubeSats have merged as a platform of considerable interest for a wide range of applications, including remote sensing. Many applications require the interpolation of sensor data into a regularly spaced grid for the development of downstream scientific products. Th...

There is growing interest in developing X-ray computed tomography (CT) imaging systems with improved ability to discriminate material types, going beyond the attenuation imaging provided by most current systems. Dual- energy CT (DECT) systems can partially address this problem by estimating Compton and photoelectric (PE) coefficients of the materia...

On page 711 S. Sonkusale, A. Khademhosseini, and co-workers present pH-responsive hydrogel fibers that can be used for long-term monitoring of epidermal wound conditions. pH-responsive dyes are loaded into mesoporous microparticles, which are then embedded into hydrogel fibers developed through microfluidic spinning. The fabricated pH-responsive mi...

Epidermal pH is an indication of the skin's physiological condition. For example, pH of wound can be correlated to angiogenesis, protease activity, bacterial infection, etc. Chronic nonhealing wounds are known to have an elevated alkaline environment, while healing process occurs more readily in an acidic environment. Thus, dermal patches capable o...

The purpose of this study was to expand and validate the cartilage damage index (CDI) to detect cartilage damage in the lateral tibiofemoral compartment. We used an iterative 3-step process to develop and validate the lateral CDI: development (100 knees), testing (80 knees), and validation (100 knees). The validation set included 100 knees from the...

Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studie...

This paper presents strategies for spectral de- noising of hyperspectral images and 3-D data cube reconstruction from a limited number of tomographic measurements, arising in single snapshot imaging systems. For de-noising the main idea is to exploit the incoherency between the algebraic complexity measure, namely the low rank of the noise-free hyp...

The imaging of shape perturbation and chromophore concentration using Diffuse Optical Tomography (DOT) data can be mathematically described as an ill-posed and non-linear inverse problem. The reconstruction algorithm for hyperspectral data using a linearized Born model is prohibitively expensive, both in terms of computation and memory. We model th...

In this paper, we develop machine learning approaches for estimating quantitative features (or metrics) characterizing subsurface zones of chemical contamination, focusing on problems involving dense nonaqueous-phase liquid (DNAPL). Source zone characterization, a necessary first step in the development of a remediation strategy, is challenging due...

Supplemental Figure 1: Plots for lateral CDI and JSW using ranks. Supplemental Figure 2: Plots for lateral CDI and static alignment (HKA) using ranks.

A novel approach to determine the global topological structure of a microvasculature network from noisy and low-resolution fluorescence microscopy data that does not require the detailed segmentation of the vessel structure is proposed here. The method is most appropriate for problems where the tortuosity of the network is relatively low and procee...

Cellular hypertrophy of adipose tissue underlies many of the proposed pro-inflammatory mechanisms for obesity-related diseases. Adipose hypertrophy results from an accumulation of esterified lipids (triglycerides) into membrane-enclosed intracellular lipid droplets (LDs). The coupling between adipocyte metabolism and LD morphology could be exploite...

Background The disruption of neuron arrangement is associated with several pathologies. In contrast to action potentials, the role of resting potential (Vmem) in regulating connectivity remains unknown. Methods Neuron assemblies were quantified when their Vmem was depolarized using ivermectin (Ivm), a drug that opens chloride channels, for 24 h in...

The image reconstruction of chromophore concentrations using Diffuse Optical Tomography (DOT) data can be described mathematically as an ill-posed inverse problem. Recent work has shown that the use of hyperspectral DOT data, as opposed to data sets comprising of a single or, at most, a dozen wavelengths, has the potential for improving the quality...

There is an ongoing need to develop image denoising approaches that suppress noise while maintaining edge information. The non-local means (NLM) algorithm, a widely used patch-based method, is a highly effective edge-preserving technique but is sensitive to parameter tuning. We use a variational approach to combine multiple NLM estimates, seeking a...

While recent years have seen considerable progress in image denoising, the leading techniques have been developed for digital photographs or other images that can have very different characteristics than those encountered in X-ray applications. In particular here we examine X-ray backscatter (XBS) images collected by airport security systems, where...

Background Cartilage morphometry based on magnetic resonance images (MRIs) is an emerging outcome measure for clinical trials among patients with knee osteoarthritis (KOA). However, current methods for cartilage morphometry take many hours per knee and require extensive training on the use of the associated software. In this study we tested the fea...

An approach to automatically detecting, classifying and/or highlighting abnormal structures such as brain aneurysms is based on three-dimensional studies of the brain vessels. The approach is applicable to effectively all currently available modalities of acquisition of the cerebral vessels, including magnetic resonance angiography (MRA), computed...

In this paper we develop three manifold regression approaches for estimating quantitative metrics characterizing subsurface zones contaminated by Dense Non-Aqueous Phase Liquids (DNAPLs) based on sparse down-gradient concentration data. We are particularly interested in estimating source zone characteristics related to the distribution of contamina...

We present a reduced complexity algorithm for time-lapse Electrical Resistivity Tomography (ERT) based on an extended Kalman filter. The key idea of the fast algorithm is an efficient representation of state covariance matrix at each step as a weighted combination of the system noise covariance matrix and a low-rank perturbation term. We propose an...

Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually the training data consists of whole shapes of certain or...

We develop a fast algorithm for Kalman Filter applied to the random walk forecast model. The key idea is an efficient representation of the estimate covariance matrix at each time-step as a weighted sum of two contributions - the process noise covariance matrix and a low rank term computed from a generalized eigenvalue problem, which combines infor...

The development of energy selective, photon counting X-ray detectors allows for a wide range of new possibilities in the area of computed tomographic image formation. Under the assumption of perfect energy resolution, here we propose a tensor-based iterative algorithm that simultaneously reconstructs the X-ray attenuation distribution for each ener...

Recent years have seen growing interest in exploiting dual- and multi-energy measurements in computed tomography (CT) in order to characterize material properties as well as object shape. Material characterization is performed by decomposing the scene into constitutive basis functions, such as Compton scatter and photoelectric absorption functions....

Characterization of dense non-aqueous phase liquid (DNAPL) source zones is a critical component for successful remediation of sites contaminated by chlorinated solvents. Although Push-Pull Tracer Tests (PPTTs) offer a promising approach for local in situ source zone characterization, non-equilibrium mass transfer effects and the spatial variability...

Automated Image Analysis of Intracellular Lipid Droplets: Toward High-Throughput Screening of Anti-Obesity Agents Introduction and Motivation - Cellular hypertrophy of body fat, or white adipose tissue (AT), underlies many of the proposed mechanisms for obesity-related illnesses. In hypertrophic adipocytes, the lipid volume accounts for >90% of c...

We evaluated the associations between bone marrow lesion (BML) volume change and changes in periarticular bone mineral density (paBMD) as well as subchondral sclerosis to determine whether BML change is associated with other local bone changes. The convenience sample comprised participants in the Osteoarthritis Initiative (OAI) with weight-bearing...

Bone marrow lesion (BML) size may be an important imaging biomarker for osteoarthritis-related clinical trials and reducing BML size may be an important therapeutic goal. However, data on the inter-relationships between BML size, pain, and structural progression are inconsistent and rarely examined in the same cohort. Therefore, we evaluated the cr...

Recent years have seen growing interest in exploiting dual- and multi-energy measurements in computed tomog­ raphy (CT) in order to characterize material properties as well as object geometry. Materials characterization is performed by decomposing the scene into constitutive basis functions, such as Compton and photoelectric scattering functions us...

This paper presents several strategies for spectral de-noising of hyperspectral images and hypercube reconstruction from a limited number of tomographic measurements. In particular we show that the non-noisy spectral data, when stacked across the spectral dimension, exhibits low-rank. On the other hand, under the same representation, the spectral n...

Segmentation of noisy and low-resolution images of microvasculature from 3-D fluorescence microscopy has been proven a challenging task. In this paper, we propose an approach to identify the global connectivity structure in a microvasculature network, which can be of use in obtaining more detailed segmentation results and for comparing and validati...