This PDF file contains the front matter associated with SPIE Proceedings Volume 9600, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Serial femtosecond nanocrystallography (SFX) is a form of x-ray coherent diffraction imaging that utilises a stream of tiny nanocrystals of the biological assembly under study, in contrast to the larger crystals used in conventional x-ray crystallography using conventional x-ray synchrotron x-ray sources. Nanocrystallography utilises the extremely brief and intense x-ray pulses that are obtained from an x-ray free-electron laser (XFEL). A key advantage is that some biological macromolecules, such as membrane proteins for example, do not easily form large crystals, but spontaneously form nanocrystals. There is therefore an opportunity for structure determination for biological molecules that are inaccessible using conventional x-ray crystallography. Nanocrystallography introduces a number of interesting image reconstruction problems. Weak diffraction patterns are recorded from hundreds of thousands of nancocrystals in unknown orientations, and these data have to be assembled and merged into a 3D intensity dataset. The diffracted intensities can also be affected by the surface structure of the crystals that can contain incomplete unit cells. Furthermore, the small crystal size means that there is potentially access to diffraction information between the crystalline Bragg peaks. With this information, phase retrieval is possible without resorting to the collection of additional experimental data as is necessary in conventional protein crystallography. We report recent work on the diffraction characteristics of nanocrystals and the resulting reconstruction algorithms.

Optically multiplexed imagers overcome the tradeoff between field of view and resolution by superimposing images from multiple fields of view onto a single focal plane. In this paper, we consider the implications of independently shifting each field of view at a rate exceeding the frame rate of the focal plane array and with a precision that can exceed the pixel pitch. A sequence of shifts enables the reconstruction of the underlying scene, with the number of frames required growing inversely with the number of multiplexed images. As a result, measurements from a sufficiently fast sampling sensor can be processed to yield a low distortion image with more pixels than the original focal plane array, a wider field of view than the original optical design, and an aspect ratio different than the original lens. This technique can also enable the collection of low-distortion, wide field of view videos. A sequence of sub-pixel spatial shifts extends this capability to allow the recovery of a wide field of view scene at sub-pixel resolution. To realize this sensor concept, a novel and compact divided aperture multiplexed sensor, capable of rapidly and precisely shifting its fields of view, was prototyped. Using this sensor, we recover twenty-four megapixel images from a four-megapixel focal plane and show the feasibility of simultaneous de-multiplexing and super-resolution.

In this work, the concept of quasi-point source (qps) is used for the restoration of defocused images of extended objects detected with incoherent light. The method consists of the characterization of the optical system by a qps illuminated with coherent light, its defocused image is then introduced in the restoration process, which is a deconvolution performed with a Wiener filter. The results of the restored images are shown and compared with those images restored when the qps is illuminated with incoherent light. Finally, a numerical evaluation using the RMSD (root mean square deviation method) about the quality of the restoration was made for both cases.

The method for estimating the space-varying kernel for rotation motion which can’t be solved by a single kernel is proposed. After layering the image by multiplex difference of Gaussian model, the method estimate the rotary direction and the movement for each block in Fourier domain. An improved optimization for both direction and scale of different parts around the rotation center estimated with the same radius, through a constraint of United Least square Filter is taken in our algorithm to structure the blur path accurately. Aiming at the different position of the rotary region, combining the blur distribution estimated with an operator which created related to the spatial location, character and degree of rotation motion, here build a model to estimate a space-varying kernel for the rotation motion which is replaced by such pixels on the motion-blur-path with uniform and separate influence during the exposure in the original algorithm, it also could be used for space-variant de-blurring. Experimental results for synthetic and real images demonstrate the effectiveness of this algorithm.

Magnetic resonance imaging (MRI) has the potential to be the best technique for assessing complications in patients with metal orthopedic implants. The presence of fat can obscure definition of the other soft tissues in MRI images, so fat suppression is often required. However, the performance of existing fat suppression techniques is inadequate near implants, due to very significant magnetic field perturbations induced by the metal. The three-point Dixon technique is potentially a method of choice as it is able to suppress fat in the presence of inhomogeneities, but the success of this technique depends on being able to accurately calculate the phase shift. This is generally done using phase unwrapping and/or iterative reconstruction algorithms. Most current phase unwrapping techniques assume that the phase function is slowly varying and phase differences between adjacent points are limited to less than π radians in magnitude. Much greater phase differences can be present near metal implants. We present our experience with two phase unwrapping techniques which have been adapted to use prior knowledge of the implant. The first method identifies phase discontinuities before recovering the phase along paths through the image. The second method employs a transform to find the least squares solution to the unwrapped phase. Simulation results indicate that the methods show promise.

The problem of reconstructing multiple objects from the average of their diffracted intensities is considered. Three cases of technical interest are studied. The first is where the incoherent average is measured over a single object that adopts a number of positions described by a symmetry group. The second is where the average is over a small number of distinct objects. The third is where the average is over a set of unit cells that can occur in an ensemble of nanocrystals as a result of different edge terminations. As a result of some redundancy in the multi-dimensional phase problem, a unique solution can be obtained for these problems under some circumstances. Uniqueness is characterised using the constraint ratio. Iterative projection algorithms can be adapted to accommodate these cases and example simulated reconstructions are presented.

The optical instrument function is used as the basis to develop optical system theory for imaging applications. The detection of optical signals is conveniently described as the overlap integral of the Wigner distribution functions of instrument and optical signal. Based on this framework various optical imaging systems, including plenoptic cameras, phase-retrieval algorithms, and Shack-Hartman sensors are shown to acquire information about a domain in phase-space, with finite extension and finite resolution. It is demonstrated how phase space optics can be used both to analyze imaging systems, as well as for designing methods for image reconstruction.

In optical interferometry are several optimization methods applied to estimate the phase of a closed fringe pattern. There are several optimization techniques which play an important role in finding the phase interferogram parameters. In this work, a Genetic Algorithm in combination with Frequency guided Sequential Demodulation are implemented to estimation the phase of synthetic fringe pattern. The method gives good results in demodulation of closed fringe patterns. Results in processing time are similar to traditional optimization techniques, but the method presents computer simplifications to others algorithms.

High resolution images have been obtained by “coding” evanescent waves from high spatial frequencies into propagating waves using a random medium. Time reversal of the scattered wave propagated back into the same random medium recovers the image. We investigate the scattering properties of a (remote) reciprocal scattering medium to accomplish the same image recovery but without the need for wave detection and followed by time reversal. We present examples of such (metamaterial) scattering media and their reciprocal structures and suggest how they might best be used for remote high resolution imaging. A metamaterial structure offers the possibility of low reflection losses with an index, |n| ~ 1.

In this paper, we review joint sparse recovery based reconstruction approach for inverse scattering problems that can solve the nonlnear inverse scattering probleme without linearization or iterative Green's function update. The main idea is to exploit the common support conditions of anomalies during multiple illumination or current injections, after which unknown potential or field can be estimated using recursive integral equation relationship. Explicit derivation for electric impedance tomography and diffuse optical tomography are discussed.

Inverse scattering algorithms typically rely on weak scattering approximations and the inversion of far field data on an Ewald sphere. This, in turn, fixes the resolution of the computed image. However, it has long been observed that when multiple scattering occurs in a strongly interacting object, and a nonlinear inversion method is employed to image it, the resulting image can reveal subwavelength resolution. We have observed this phenomenon using a cepstral filtering approach and characterize it more fully here.

Objects in a 3D space can be located and segmented using information obtained by computational integral imaging. This paper implements an approach for objects isolation based on detecting the sharp edges of the focused regions in the reconstructed confocal images. Several edge feature detection methods are employed and examined. Results show that while the ability to detect the correct object depth locations does not depend on the edge detection method, the resulting quality of the detected object features may be significantly affected by the method used.

In single-particle cryo electron microscopy, many electron microscope images each of a single instance of a biological particle such as a virus or a ribosome are measured and the 3-D electron scattering intensity of the particle is reconstructed by computation. Because each instance of the particle is imaged separately, it should be possible to characterize the heterogeneity of the different instances of the particle as well as a nominal reconstruction of the particle. In this paper, such an algorithm is described and demonstrated on the bacteriophage Hong Kong 97. The algorithm is a statistical maximum likelihood estimator computed by an expectation maximization algorithm implemented in Matlab software.

Spectrally encoded endoscopy (SEE) is a minimally invasive optical imaging modality capable of fast confocal imaging of internal tissue structures. Modern SEE systems use coherent sources to image deep within the tissue and data are processed similar to optical coherence tomography (OCT); however, standard processing of SEE data via the Fast Fourier Transform (FFT) leads to degradation of the axial resolution as the bandwidth of the source shrinks, resulting in a well-known trade-off between speed and axial resolution. Recognizing the limitation of FFT as a general spectral estimation algorithm to only take into account samples collected by the detector, in this work we investigate alternative high-resolution spectral estimation algorithms that exploit information such as sparsity and the general region position of the bulk sample to improve the axial resolution of processed SEE data. We validate the performance of these algorithms using bothMATLAB simulations and analysis of experimental results generated from a home-built OCT system to simulate an SEE system with variable scan rates. Our results open a new door towards using non-FFT algorithms to generate higher quality (i.e., higher resolution) SEE images at correspondingly fast scan rates, resulting in systems that are more accurate and more comfortable for patients due to the reduced image time.

X-ray computed tomography (CT) is an important technique for noninvasive clinical diagnosis and nondestructive testing. In many applications a number of image processing steps are needed before the image features are available. One of these processing steps is image segmentation, which generates the edge and the structural features of the regions of interest. The conventional flow is to first reconstruct images and then apply image segmentation methods on reconstructed images. In contrast, an emerging technique obtains the tomographic image and segmentation simultaneously, which is especially useful in the case of limited data. An iterative method for simultaneous reconstruction and segmentation (SRS) with Mumford-Shah model has been proposed, which not only regularizes the ill-posed tomographic reconstruction problem, but also produces the image segmentation at the same time. The Mumford-Shah model is both mathematically and computationally challenging. In this paper, we propose an asynchronous ray-parallel algorithm of the SRS method and accelerate it using field-programmable gate array (FPGA) devices, which drastically improves the energy efficiency. Experimental results show that the FPGA implementation achieves a 1:2× speedup with an energy efficiency as great as 58×, over the GPU implementation.

In this paper, submillimeter three dimensional tomography imaging of paramagnetic contaminants flow rate in multiphase flow pipelines is presented. The device, which is based on Magnetic Particle Imaging (MPI), consists of an array of twelve coils and a pair of permanent magnets and is not influenced with the other phases that constitute the crude oil (e.g. oil, water, sand, and gas) and which are mainly diamagnetic materials. The concentration of the paramagnetic particles can be measured in a three dimensional volumetric space with high spatial and temporal sensitivities which are proportional to the strength of the applied magnetic field. This is also influenced by the size and distribution of the particles and the anisotropy of the permanent magnet. To increase the sensitivity and improve the spatioencoding field, a two dimensional Linear Field Scanning (LFS) technique coupled with a two dimensional excitation field is proposed. The results demonstrate that the technique would constitute a breakthrough in the area of solid flow measurements and imaging.

We describe intensity correlations as a function of object position as a means to determine the field incident on a randomly scattering medium and to obtain information about objects within a scattering medium. The approach requires robust phase retrieval, presenting the central challenge to key applications. The experimental method is described and example results presented.

Neuro-scientific studies are often aimed at imaging brain activity, which is time-locked to external stimuli. This provides the possibility to use statistical methods to extract even weak signal components, which occur with each stimulus. For electroencephalographic recordings this concept is limited by inevitable time jitter, which cannot be controlled in all cases. Our study is based on a cross-correlation analysis of trials to alignment trials based on the recorded data. This is demonstrated both with simulated signals and with clinical EEG data, which were recorded intracranially. Special attention is given to the evaluation of the time-frequency resolved phase-locking across multiple trails.

The detection of epileptiform activity, such as interictal spikes, in electrical brain recordings has important clinical and research applications. Identification of interictal spikes is often carried out manually by trained neurologists. It is a time-consuming process and can exhibit variability between experts. In this work, we develop and evaluate an automated spike detector. We implement a template-matching approach and improve its accuracy on one set of recordings using a supervised machine-learning algorithm. Evaluation with two independent datasets shows the template-matching detector to perform comparably with experts and the version augmented with a classifier. In one test dataset, variations of the detection threshold partially explain discrepancies between experts. In the other, the detector demonstrates similar behavior to an existing algorithm developed with this dataset.