Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions

Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT

Three dimensional optical imaging of blood volume and oxygenation in the neonatal brain

Optical methods provide a means of monitoring cerebral oxygenation in newborn infants at risk of brain injury. A 32-channel optical imaging system has been developed with the aim of reconstructing three-dimensional images of regional blood volume and oxygenation. Full image data sets were acquired from 14 out of 24 infants studied; successful images have been reconstructed in 8 of these infants. Regional variations in cerebral blood volume and tissue oxygen saturation are present in healthy preterm infants. In an infant with a large unilateral intraventricular haemorrhage, a corresponding region of low oxygen saturation was detected. These results suggest that optical tomography may provide an appropriate technique for investigating regional cerebral haemodynamics and oxygenation at the cotside. (c) 2006 Elsevier Inc. All rights reserved

Multicascade-linked synthetic wavelength digital holography using an optical-comb-referenced frequency synthesizer

Digital holography (DH) is a promising method for non-contact surface topography because the reconstructed phase image can visualize the nanometer unevenness in a sample. However, the axial range of this method is limited to the range of the optical wavelength due to the phase wrapping ambiguity. Although the use of two different wavelengths of light and the resulting synthetic wavelength, i.e., synthetic wavelength DH, can expand the axial range up to a few tens of microns, this method is still insufficient for practical applications. In this article, a tunable external cavity laser diode phase-locked to an optical frequency comb, namely, an optical-comb-referenced frequency synthesizer, is effectively used for multiple synthetic wavelengths within the range of 32 um to 1.20 m. A multiple cascade link of the phase images among an optical wavelength (= 1.520 um) and 5 different synthetic wavelengths (= 32.39 um, 99.98 um, 400.0 um, 1003 um, and 4021 um) enables the shape measurement of a reflective millimeter-sized stepped surface with the axial resolution of 34 nm. The axial dynamic range, defined as the ratio of the maximum axial range (= 0.60 m) to the axial resolution (= 34 nm), achieves 1.7*10^8, which is much larger than that of previous synthetic wavelength DH. Such a wide axial dynamic range capability will further expand the application field of DH for large objects with meter dimensions.Comment: 19 pages, 7 figure

Whole-cell 3D STORM reveals interactions between cellular structures with nanometer-scale resolution.

The ability to directly visualize nanoscopic cellular structures and their spatial relationship in all three dimensions will greatly enhance our understanding of molecular processes in cells. Here we demonstrated multicolor three-dimensional (3D) stochastic optical reconstruction microscopy (STORM) as a tool to quantitatively probe cellular structures and their interactions. To facilitate STORM imaging, we generated photoswitchable probes in several distinct colors by covalently linking a photoswitchable cyanine reporter and an activator molecule to assist bioconjugation. We performed 3D localization in conjunction with focal plane scanning and correction for refractive index mismatch to obtain whole-cell images with a spatial resolution of 20-30 nm and 60-70 nm in the lateral and axial dimensions, respectively. Using this approach, we imaged the entire mitochondrial network in fixed monkey kidney BS-C-1 cells, and studied the spatial relationship between mitochondria and microtubules. The 3D STORM images resolved mitochondrial morphologies as well as mitochondria-microtubule contacts that were obscured in conventional fluorescence images

Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain

In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 ​mm or smaller but degrades at 2 ​mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.Published versio

Method for Assessing the Fidelity of Optical Diffraction Tomography Reconstruction Methods

We use a spatial light modulator in a diffraction tomographic system to assess the accuracy of different refractive index reconstruction algorithms. Optical phase conjugation principles through complex media, allows us to quantify the error for different refractive index reconstruction algorithms without access to the ground truth. To our knowledge, this is the first assessment technique that uses structured illumination experimentally to test the accuracy of different reconstruction schemes.Comment: 11 PAGES, 6 FIGURE

Deep learning in computational microscopy

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio

Linear Shape Deformation Models with Local Support Using Graph-based Structured Matrix Factorisation

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations.Comment: Please cite CVPR 2016 versio