Regularized Newton Methods for X-ray Phase Contrast and General Imaging Problems

Like many other advanced imaging methods, x-ray phase contrast imaging and tomography require mathematical inversion of the observed data to obtain real-space information. While an accurate forward model describing the generally nonlinear image formation from a given object to the observations is often available, explicit inversion formulas are typically not known. Moreover, the measured data might be insufficient for stable image reconstruction, in which case it has to be complemented by suitable a priori information. In this work, regularized Newton methods are presented as a general framework for the solution of such ill-posed nonlinear imaging problems. For a proof of principle, the approach is applied to x-ray phase contrast imaging in the near-field propagation regime. Simultaneous recovery of the phase- and amplitude from a single near-field diffraction pattern without homogeneity constraints is demonstrated for the first time. The presented methods further permit all-at-once phase contrast tomography, i.e. simultaneous phase retrieval and tomographic inversion. We demonstrate the potential of this approach by three-dimensional imaging of a colloidal crystal at 95 nm isotropic resolution.Comment: (C)2016 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibite

On Algorithms Based on Joint Estimation of Currents and Contrast in Microwave Tomography

This paper deals with improvements to the contrast source inversion method which is widely used in microwave tomography. First, the method is reviewed and weaknesses of both the criterion form and the optimization strategy are underlined. Then, two new algorithms are proposed. Both of them are based on the same criterion, similar but more robust than the one used in contrast source inversion. The first technique keeps the main characteristics of the contrast source inversion optimization scheme but is based on a better exploitation of the conjugate gradient algorithm. The second technique is based on a preconditioned conjugate gradient algorithm and performs simultaneous updates of sets of unknowns that are normally processed sequentially. Both techniques are shown to be more efficient than original contrast source inversion.Comment: 12 pages, 12 figures, 5 table

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

Visualizing Escherichia coli Sub-Cellular Structure Using Sparse Deconvolution Spatial Light Interference Tomography

Studying the 3D sub-cellular structure of living cells is essential to our understanding of biological function. However, tomographic imaging of live cells is challenging mainly because they are transparent, i.e., weakly scattering structures. Therefore, this type of imaging has been implemented largely using fluorescence techniques. While confocal fluorescence imaging is a common approach to achieve sectioning, it requires fluorescence probes that are often harmful to the living specimen. On the other hand, by using the intrinsic contrast of the structures it is possible to study living cells in a non-invasive manner. One method that provides high-resolution quantitative information about nanoscale structures is a broadband interferometric technique known as Spatial Light Interference Microscopy (SLIM). In addition to rendering quantitative phase information, when combined with a high numerical aperture objective, SLIM also provides excellent depth sectioning capabilities. However, like in all linear optical systems, SLIM's resolution is limited by diffraction. Here we present a novel 3D field deconvolution algorithm that exploits the sparsity of phase images and renders images with resolution beyond the diffraction limit. We employ this label-free method, called deconvolution Spatial Light Interference Tomography (dSLIT), to visualize coiled sub-cellular structures in E. coli cells which are most likely the cytoskeletal MreB protein and the division site regulating MinCDE proteins. Previously these structures have only been observed using specialized strains and plasmids and fluorescence techniques. Our results indicate that dSLIT can be employed to study such structures in a practical and non-invasive manner

CALIBRATION OF AN ULTRASONIC TRANSMISSIVE COMPUTED TOMOGRAPHY SYSTEM

Tato dizertace je zaměřena na medicínskou zobrazovací modalitu – ultrazvukovou počítačovou tomografii – a algoritmy zlepšující kvalitu zobrazení, zejména kalibraci USCT přístroje. USCT je novou modalitou kombinující ultrazvukový přenos signálů a principy tomografické rekonstrukce obrazů vyvíjených pro jiné tomografické systémy. V principu lze vytvořit kvantitativní 3D obrazové objemy s vysokým rozlišením a kontrastem. USCT je primárně určeno pro diagnózu rakoviny prsu. Autor spolupracoval na projektu Institutu Zpracování dat a Elektroniky, Forschungszentrum Karlsruhe, kde je USCT systém vyvíjen. Jeden ze zásadních problémů prototypu USCT v Karlsruhe byla absence kalibrace. Tisíce ultrazvukových měničů se liší v citlivosti, směrovosti a frekvenční odezvě. Tyto parametry jsou navíc proměnné v čase. Další a mnohem závažnější problém byl v pozičních odchylkách jednotlivých měničů. Všechny tyto aspekty mají vliv na konečnou kvalitu rekonstruovaných obrazů. Problém kalibrace si autor zvolil jako hlavní téma dizertace. Tato dizertace popisuje nové metody v oblastech rekonstrukce útlumových obrazů, kalibrace citlivosti měničů a zejména geometrická kalibrace pozic měničů. Tyto metody byly implementovány a otestovány na reálných datech pocházejících z prototypu USCT z Karlsruhe.This dissertation is centered on a medical imaging modality – the ultrasonic computed tomography (USCT) – and algorithms which improve the resulting image quality, namely the calibration of a USCT device. The USCT is a novel imaging modality which combines the phenomenon of ultrasound and image reconstruction principles developed for other tomographic systems. It is capable of producing quantitative 3D image volumes with high resolution and tissue contrast and is primarily aimed at breast cancer diagnosis. The author was involved in a joint research project at the Institute of Data Processing and Electronics, Forschungszentrum Karlsruhe (German National Research Center), where a USCT system is being developed. One of the main problems in the Karlsruhe USCT prototype was the absence of any calibration. The thousands of transducers used in the system have deviations in sensitivity, directivity, and frequency response. These parameters change over time as the transducers age. Also the mechanical positioning of the transducer elements is not precise. All these aspects greatly affect the overall quality of the reconstructed images. The problem of calibration of a USCT system was chosen as the main topic for this dissertation. The dissertation thesis presents novel methods in the area of reconstruction of attenuation images, sensitivity calibration, and mainly geometrical calibration. The methods were implemented and tested on real data generated by the Karlsruhe USCT device.

Optimizing multi-LGS WFS AO performance in the context of sodium profile evolution and non-common path aberration

For Extremely Large Telescope (ELT) adaptive optics (AO) systems, multiple Sodium Laser Guide Star (LGS) wavefront sensors (WFSs) are required to achieve high sky coverage and diffraction limited performance. However, temporal and spatial variation of the sodium profile causes measurement biases that appear at all time scales and vary between LGS WFSs. To make things worse, optical design residuals, polishing and alignment errors also create non-common-path aberrations (NCPA) that vary between sub-apertures and different WFS, causing LGS WFS to work significantly off null with a nonlinear response. The induced aberrations are consequently non-radially symmetric, even for center launch laser beams with polar coordinate detectors. Natural guide star (NGS) based truth wavefront sensors are often suggested as a method of sensing these LGS WFS aberrations, but a single sensor will suffer strong anisoplanatism that may introduce additional errors. In this paper, we present mitigation strategies and performance estimations based on simulations for the Thirty Meter Telescope (TMT) Narrow Field Infrared AO system (NFIRAOS)