Nontuberculous mycobacterial infection following cat scratch in the setting of topical steroid use.

Infections due to nontuberculous mycobacteria (NTM) are caused by mycobacterial species other than Mycobacterium tuberculosis, M. leprae, and M. bovis. Patients who are immunocompromised have increased susceptibility to pulmonary, lymphatic, and skin infections by these pathogens. We present a case of a 78-year-old male who presented to dermatology with a left dorsolateral hand infection after sustaining cat scratches in the setting of topical steroid therapy for suspected pyoderma gangrenosum. A shave biopsy of the lesion showed granulomatous dermatitis and associated acid-fast bacilli, while tissue culture grew Mycobacterium chelonae. This case demonstrates cat scratches as an uncommon risk factor for cutaneous NTM disease. Although an association between cat scratches and human NTM infections has only been reported in two previous cases, it must be considered in cases of unusual and persistent cutaneous lesions, especially in immunocompromised patients, even those with only local immunosuppression from topical agents.

Joint 3D localization and classification of space debris using a multispectral rotating point spread function.

We consider the problem of joint three-dimensional (3D) localization and material classification of unresolved space debris using a multispectral rotating point spread function (RPSF). The use of RPSF allows one to estimate the 3D locations of point sources from their rotated images acquired by a single 2D sensor array, since the amount of rotation of each source image about its x, y location depends on its axial distance z. Using multispectral images, with one RPSF per spectral band, we are able not only to localize the 3D positions of the space debris but also classify their material composition. We propose a three-stage method for achieving joint localization and classification. In stage 1, we adopt an optimization scheme for localization in which the spectral signature of each material is assumed to be uniform, which significantly improves efficiency and yields better localization results than possible with a single spectral band. In stage 2, we estimate the spectral signature and refine the localization result via an alternating approach. We process classification in the final stage. Both Poisson noise and Gaussian noise models are considered, and the implementation of each is discussed. Numerical tests using multispectral data from NASA show the efficiency of our three-stage approach and illustrate the improvement of point source localization and spectral classification from using multiple bands over a single band.

Image reconstruction from double random projection.

We present double random projection methods for reconstruction of imaging data. The methods draw upon recent results in the random projection literature, particularly on low-rank matrix approximations, and the reconstruction algorithm has only two simple and noniterative steps, while the reconstruction error is close to the error of the optimal low-rank approximation by the truncated singular-value decomposition. We extend the often-required symmetric distributions of entries in a random-projection matrix to asymmetric distributions, which can be more easily implementable on imaging devices. Experimental results are provided on the subsampling of natural images and hyperspectral images, and on simulated compressible matrices. Comparisons with other random projection methods are also provided.

Joint segmentation and reconstruction of hyperspectral data with compressed measurements.

This work describes numerical methods for the joint reconstruction and segmentation of spectral images taken by compressive sensing coded aperture snapshot spectral imagers (CASSI). In a snapshot, a CASSI captures a two-dimensional (2D) array of measurements that is an encoded representation of both spectral information and 2D spatial information of a scene, resulting in significant savings in acquisition time and data storage. The reconstruction process decodes the 2D measurements to render a three-dimensional spatio-spectral estimate of the scene and is therefore an indispensable component of the spectral imager. In this study, we seek a particular form of the compressed sensing solution that assumes spectrally homogeneous segments in the two spatial dimensions, and greatly reduces the number of unknowns, often turning the underdetermined reconstruction problem into one that is overdetermined. Numerical tests are reported on both simulated and real data representing compressed measurements.

Freshwater non-O1 Vibrio cholerae infection.

It is not appreciated by most physicians that vibrio infections can be acquired from freshwater exposure. A case of non-O1 Vibrio cholerae urinary tract infection associated with freshwater exposure is reported. The potential for vibrios to grow in brachish water and for summer heat to cause evaporation leading to relative increased salinity in freshwater bodies and the broad geographic range of these occurrences to include North American and both eastern and western Europe is noted. A literature review of vibrio infection acquired from freshwater exposure and the relationship to these epidemiologic and pathophysiologic events is discussed.

Line-source based x-ray tomography.

Current computed tomography (CT) scanners, including micro-CT scanners, utilize a point x-ray source. As we target higher and higher spatial resolutions, the reduced x-ray focal spot size limits the temporal and contrast resolutions achievable. To overcome this limitation, in this paper we propose to use a line-shaped x-ray source so that many more photons can be generated, given a data acquisition interval. In reference to the simultaneous algebraic reconstruction technique (SART) algorithm for image reconstruction from projection data generated by an x-ray point source, here we develop a generalized SART algorithm for image reconstruction from projection data generated by an x-ray line source. Our numerical simulation results demonstrate the feasibility of our novel line-source based x-ray CT approach and the proposed generalized SART algorithm.

Tensor methods for hyperspectral data analysis: a space object material identification study.

An important and well-studied problem in hyperspectral image data applications is to identify materials present in the object or scene being imaged and to quantify their abundance in the mixture. Due to the increasing quantity of data usually encountered in hyperspectral datasets, effective data compression is also an important consideration. In this paper, we develop novel methods based on tensor analysis that focus on all three of these goals: material identification, material abundance estimation, and data compression. Test results are reported in all three perspectives.

A computational method for the restoration of images with an unknown, spatially-varying blur.

In this paper, we present an algorithm for the restoration of images with an unknown, spatially-varying blur. Existing computational methods for image restoration require the assumption that the blur is known and/or spatially-invariant. Our algorithm uses a combination of techniques. First, we section the image, and then treat the sections as a sequence of frames whose unknown PSFs are correlated and approximately spatially-invariant. To estimate the PSFs in each section, phase diversity is used. With the PSF estimates in hand, we then use a technique by Nagy and O'Leary for the restoration of images with a known, spatially-varying blur to restore the image globally. Test results on star cluster data are presented.