Marc Niethammer

Crack characterization using guided circumferential waves

This paper examines the propagation of guided circumferential waves in a hollow isotropic cylinder that contains a crack, with the goal of using these guided waves to both locate and size the crack. The crack is sized using a modified Aulds formula, which relates the cracks length to a reflected energy coefficient. The crack is then located by operating on the backscattered signal with a time-frequency digital signal processing (DSP) technique, and then comparing these results to those obtained if the cylinder is perfect. The guided circumferential waves are generated with a commercial finite element method (FEM) code. One objective of this work is to demonstrate the effectiveness of using sophisticated DSP techniques to describe the effect of scattering on dispersive waves, showing it is possible to characterize cracks systematically and accurately by quantifying this scattering effect. The results show that the need for high frequency signals to detect small cracks is significantly decreased by using these techniques.

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging

We describe a new method to automatically discriminate between patients with Alzheimers disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimers disease.

Time-frequency representations of Lamb waves

The objective of this study is to establish the effectiveness of four different time-frequency representations (TFRs)--the reassigned spectrogram, the reassigned scalogram, the smoothed Wigner-Ville distribution, and the Hilbert spectrum--by comparing their ability to resolve the dispersion relationships for Lamb waves generated and detected with optical techniques. This paper illustrates the utility of using TFRs to quantitatively resolve changes in the frequency content of these nonstationary signals, as a function of time. While each technique has certain strengths and weaknesses, the reassigned spectrogram appears to be the best choice to characterize multimode Lamb waves.

A method for normalizing histology slides for quantitative analysis

Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.

Laplace-Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis

This paper proposes the use of the surface based Laplace-Beltrami and the volumetric Laplace eigenvalues and -functions as shape descriptors for the comparison and analysis of shapes. These spectral measures are isometry invariant and therefore allow for shape comparisons with minimal shape pre-processing. In particular, no registration, mapping, or remeshing is necessary. The discriminatory power of the 2D surface and 3D solid methods is demonstrated on a population of female caudate nuclei (a subcortical gray matter structure of the brain, involved in memory function, emotion processing, and learning) of normal control subjects and of subjects with schizotypal personality disorder. The behavior and properties of the Laplace-Beltrami eigenvalues and -functions are discussed extensively for both the Dirichlet and Neumann boundary condition showing advantages of the Neumann vs. the Dirichlet spectra in 3D. Furthermore, topological analyses employing the Morse-Smale complex (on the surfaces) and the Reeb graph (in the solids) are performed on selected eigenfunctions, yielding shape descriptors, that are capable of localizing geometric properties and detecting shape differences by indirectly registering topological features such as critical points, level sets and integral lines of the gradient field across subjects. The use of these topological features of the Laplace-Beltrami eigenfunctions in 2D and 3D for statistical shape analysis is novel.

Restoration of DWI Data Using a Rician LMMSE Estimator

This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images. A method to estimate the noise level based on local estimations of mean or variance is used to automatically parametrize the estimator. The restoration performance is evaluated using quality indexes and compared to alternative estimation schemes. The overall scheme is simple, robust, fast, and improves estimations. Filtering diffusion weighted magnetic resonance imaging (DW-MRI) with the proposed methodology leads to more accurate tensor estimations. Real and synthetic datasets are analyzed.

Geodesic regression for image time-series

Registration of image-time series has so far been accomplished (i) by concatenating registrations between image pairs, (ii) by solving a joint estimation problem resulting in piecewise geodesic paths between image pairs, (iii) by kernel based local averaging or (iv) by augmenting the joint estimation with additional temporal irregularity penalties. Here, we propose a generative model extending least squares linear regression to the space of images by using a second-order dynamic formulation for image registration. Unlike previous approaches, the formulation allows for a compact representation of an approximation to the full spatio-temporal trajectory through its initial values. The method also opens up possibilities to design image-based approximation algorithms. The resulting optimization problem is solved using an adjoint method.

Global medical shape analysis using the Laplace-Beltrami spectrum

This paper proposes to use the Laplace-Beltrami spectrum (LBS) as a global shape descriptor for medical shape analysis, allowing for shape comparisons using minimal shape preprocessing: no registration, mapping, or remeshing is necessary. The discriminatory power of the method is tested on a population of female caudate shapes of normal control subjects and of subjects with schizotypal personality disorder.

Time-frequency representation of Lamb waves using the reassigned spectrogram

This brief note reports on a study that applies the reassigned spectrogram (the reassigned energy density spectrum of the short-time Fourier transform [STFT]) to develop the dispersion curves for multimode Lamb waves propagating in an aluminum plate. The proposed procedure first uses the spectrogram to operate on a single, laser-generated and detected waveform to develop the dispersion relationship for this plate. Next, a reassignment procedure is used to refine the time-frequency resolution of the calculated dispersion curves. This reassignment operation clarifies the definition of the measured modes. This study demonstrates that the reassigned spectrogram is capable of distinguishing multiple, closely spaced Lamb modes in the ultrasonic frequency range.

The power of correlative microscopy: multi-modal, multi-scale, multi-dimensional

Correlative microscopy is a sophisticated approach that combines the capabilities of typically separate, but powerful microscopy platforms: often including, but not limited, to conventional light, confocal and super-resolution microscopy, atomic force microscopy, transmission and scanning electron microscopy, magnetic resonance imaging and micro/nano CT (computed tomography). When targeting rare or specific events within large populations or tissues, correlative microscopy is increasingly being recognized as the method of choice. Furthermore, this multi-modal assimilation of technologies provides complementary and often unique information, such as internal and external spatial, structural, biochemical and biophysical details from the same targeted sample. The development of a continuous stream of cutting-edge applications, probes, preparation methodologies, hardware and software developments will enable realization of the full potential of correlative microscopy.