A proposed unified interphase nucleus chromosome structure: Preliminary preponderance of evidence.

Cryoelectron tomography of the cell nucleus using scanning transmission electron microscopy and deconvolution processing technology has highlighted a large-scale, 100- to 300-nm interphase chromosome structure, which is present throughout the nucleus. This study further documents and analyzes these chromosome structures. The paper is divided into four parts: 1) evidence (preliminary) for a unified interphase chromosome structure; 2) a proposed unified interphase chromosome architecture; 3) organization as chromosome territories (e.

A proposed unified mitotic chromosome architecture.

A molecular architecture is proposed for a representative mitotic chromosome, human chromosome 10. This architecture is built on an interphase chromosome structure based on cryo-electron microscopy (cryo-EM) cellular tomography [J. Sedat et al.

Entropy-regularized deconvolution of cellular cryotransmission electron tomograms.

Cryo-electron tomography (cryo-ET) allows for the high-resolution visualization of biological macromolecules. However, the technique is limited by a low signal-to-noise ratio (SNR) and variance in contrast at different frequencies, as well as reduced Z resolution. Here, we applied entropy-regularized deconvolution (ER-DC) to cryo-ET data generated from transmission electron microscopy (TEM) and reconstructed using weighted back projection (WBP).

Fast Roughness Minimizing Image Restoration Under Mixed Poisson-Gaussian Noise.

Image acquisition in many biomedical imaging modalities is corrupted by Poisson noise followed by additive Gaussian noise. While total variation and related regularization methods for solving biomedical inverse problems are known to yield high quality reconstructions in most situations, such methods mostly use log-likelihood of either Gaussian or Poisson noise models, and rarely use mixed Poisson-Gaussian (PG) noise model. There is a recent work which deals with exact PG likelihood and total variation regularization.

Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation.

Total Variation (TV) and related extensions have been popular in image restoration due to their robust performance and wide applicability. While the original formulation is still relevant after two decades of extensive research, its extensions that combine derivatives of first and second orders are now being explored for better performance, with examples being Combined Order TV (COTV) and Total Generalized Variation (TGV). As an improvement over such multi-order convex formulations, we propose a novel non-convex regularization functional which adaptively combines Hessian-Schatten (HS) norm and first order TV (TV1) functionals with spatially varying weight.

Multiresolution-based weighted regularization for denoised image interpolation from scattered samples with application to confocal microscopy.

The problem of reconstructing an image from nonuniformly spaced, spatial point measurements is frequently encountered in bioimaging and other scientific disciplines. The most successful class of methods in handling this problem uses the regularization approach involving the minimization of a derivative-based roughness functional. It has been well demonstrated, in the presence of noise, that nonquadratic roughness functionals such as ℓ measure yield better performance compared to the quadratic ones in inverse problems in general and in deconvolution in particular.

Nested Conjugate Gradient Algorithm With Nested Preconditioning for Non-Linear Image Restoration.

We develop a novel optimization algorithm, which we call nested non-linear conjugate gradient (CG) algorithm (NNCG), for image restoration based on quadratic data fitting and smooth non-quadratic regularization. The algorithm is constructed as a nesting of two conjugate gradient iterations. The outer iteration is constructed as a preconditioned non-linear CG algorithm; the preconditioning is performed by the inner CG iteration that is linear.

High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio.

Four-dimensional fluorescence microscopy--which records 3D image information as a function of time--provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations.

Effect of depth dependent spherical aberrations in 3D structured illumination microscopy.

We model the effect of depth dependent spherical aberration caused by a refractive index mismatch between the mounting and immersion mediums in a 3D structured illumination microscope (SIM). We first derive a forward model that takes into account the effect of the depth varying aberrations on both the illumination and the detection processes. From the model, we demonstrate that depth dependent spherical aberration leads to loss of signal only due to its effect on the detection response of the system, while its effect on illumination leads to phase shifts between orders that can be handled computationally in the reconstruction process.

A Parallel Product-Convolution approach for representing the depth varying Point Spread Functions in 3D widefield microscopy based on principal component analysis.

We address the problem of computational representation of image formation in 3D widefield fluorescence microscopy with depth varying spherical aberrations. We first represent 3D depth-dependent point spread functions (PSFs) as a weighted sum of basis functions that are obtained by principal component analysis (PCA) of experimental data. This representation is then used to derive an approximating structure that compactly expresses the depth variant response as a sum of few depth invariant convolutions pre-multiplied by a set of 1D depth functions, where the convolving functions are the PCA-derived basis functions.

Full motion and flow field recovery from echo Doppler data.

We present a new computational method for reconstructing a vector velocity field from scattered, pulsed-wave ultrasound Doppler data. The main difficulty is that the Doppler measurements are incomplete, for they do only capture the velocity component along the beam direction. We thus propose to combine measurements from different beam directions.

Myocardial motion analysis from B-mode echocardiograms.

The quantitative assessment of cardiac motion is a fundamental concept to evaluate ventricular malfunction. We present a new optical-flow-based method for estimating heart motion from two-dimensional echocardiographic sequences. To account for typical heart motions, such as contraction/expansion and shear, we analyze the images locally by using a local-affine model for the velocity in space and a linear model in time.

Variational image reconstruction from arbitrarily spaced samples: a fast multiresolution spline solution.

We propose a novel method for image reconstruction from nonuniform samples with no constraints on their locations. We adopt a variational approach where the reconstruction is formulated as the minimizer of a cost that is a weighted sum of two terms: (1) the sum of squared errors at the specified points and (2) a quadratic functional that penalizes the lack of smoothness. We search for a solution that is a uniform spline and show how it can be determined by solving a large, sparse system of linear equations.

Multiscale motion mapping: a novel computer vision technique for quantitative, objective echocardiographic motion measurement independent of Doppler: first clinical description and validation.

Background: Objective, quantitative, segmental noninvasive/bedside measurement of cardiac motion is highly desirable in cardiovascular medicine, but current technology suffers from significant drawbacks, such as subjectivity of conventional echocardiographic reading, angle dependence of tissue Doppler measurements, radiation exposure by computer tomography, and infrastructure requirements in MRI. We hypothesized that computer vision technology could represent a powerful new paradigm for quantification in echocardiography.

Methods And Results: We present multiscale motion mapping, a novel computer vision technology that is based on mathematical image processing and that exploits echocardiographic information in a fashion similar to the human visual system.

Multiresolution moment filters: theory and applications.

We introduce local weighted geometric moments that are computed from an image within a sliding window at multiple scales. When the window function satisfies a two-scale relation, we prove that lower order moments can be computed efficiently at dyadic scales by using a multiresolution wavelet-like algorithm. We show that B-splines are well-suited window functions because, in addition to being refinable, they are positive, symmetric, separable, and very nearly isotropic (Gaussian shape).