Reconstruction algorithms for multispectral diffraction imaging

Thesis (Ph.D.)--Boston UniversityIn conventional Computed Tomography (CT) systems, a single X-ray source spectrum is used to radiate an object and the total transmitted intensity is measured to construct the spatial linear attenuation coefficient (LAC) distribution. Such scalar information is adequate for visualization of interior physical structures, but additional dimensions would be useful to characterize the nature of the structures. By imaging using broadband radiation and collecting energy-sensitive measurement information, one can generate images of additional energy-dependent properties that can be used to characterize the nature of specific areas in the object of interest. In this thesis, we explore novel imaging modalities that use broadband sources and energy-sensitive detection to generate images of energy-dependent properties of a region, with the objective of providing high quality information for material component identification. We explore two classes of imaging problems: 1) excitation using broad spectrum sub-millimeter radiation in the Terahertz regime and measure- ment of the diffracted Terahertz (THz) field to construct the spatial distribution of complex refractive index at multiple frequencies; 2) excitation using broad spectrum X-ray sources and measurement of coherent scatter radiation to image the spatial distribution of coherent-scatter form factors. For these modalities, we extend approaches developed for multimodal imaging and propose new reconstruction algorithms that impose regularization structure such as common object boundaries across reconstructed regions at different frequencies. We also explore reconstruction techniques that incorporate prior knowledge in the form of spectral parametrization, sparse representations over redundant dictionaries and explore the advantage and disadvantages of these techniques in terms of image quality and potential for accurate material characterization. We use the proposed reconstruction techniques to explore alternative architectures with reduced scanning time and increased signal-to-noise ratio, including THz diffraction tomography, limited angle X-ray diffraction tomography and the use of coded aperture masks. Numerical experiments and Monte Carlo simulations were conducted to compare performances of the developed methods, and validate the studied architectures as viable options for imaging of energy-dependent properties

Development of a radiative transport based, fluorescence-enhanced, frequency-domain small animal imaging system

Herein we present the development of a fluorescence-enhanced, frequency-domain radiative transport reconstruction system designed for small animal optical tomography. The system includes a time-dependent data acquisition instrument, a radiative transport based forward model for prediction of time-dependent propagation of photons in small, non-diffuse volumes, and an algorithm which utilizes the forward model to reconstruct fluorescent yields from air/tissue boundary measurements. The major components of the instrumentation include a charge coupled device camera, an image intensifier, signal generators, and an optical switch. Time-dependent data were obtained in the frequency-domain using homodyne techniques on phantoms with 0.2% to 3% intralipid solutions. Through collaboration with Transpire, Inc., a fluorescence-enhanced, frequency-domain, radiative transport equation (RTE) solver was developed. This solver incorporates the discrete ordinates, source iteration with diffusion synthetic acceleration, and linear discontinuous finite element differencing schemes, to predict accurately the fluence of excitation and emission photons in diffuse and transport limited systems. Additional techniques such as the first scattered distributed source method and integral transport theory are used to model the numerical apertures of fiber optic sources and detectors. The accuracy of the RTE solver was validated against diffusion and Monte Carlo predictions and experimental data. The comparisons were favorable in both the diffusion and transport limits, with average errors of the RTE predictions, as compared to experimental data, typically being less than 8% in amplitude and 7% in phase. These average errors are similar to those of the Monte Carlo and diffusion predictions. Synthetic data from a virtual mouse were used to demonstrate the feasibility of using the RTE solver for reconstructing fluorescent heterogeneities in small, non-diffuse volumes. The current version of the RTE solver limits the reconstruction to one iteration and the reconstruction of marginally diffuse, frequency-domain experimental data using RTE was not successful. Multiple iterations using a diffusion solver successfully reconstructed the fluorescent heterogeneities, indicating that, when available, multiple iterations of the RTE based solver should also reconstruct the heterogeneities

Computational models for functional near-infrared spectroscopy and imaging

Functional near-infrared spectroscopy (fNIRS) is a neuro-monitoring tool that is non-invasive, non-ionising, cost efficient, and portable. Its application for the traumatic brain injury patients is a well suggested approach due to its role in being able to continuously monitor key biomarkers such as the tissue oxygenation and blood haemoglobin level to understand the flow of blood supply to the tissue in the brain to assess injury in patients. In light of the great potential that fNIRS has to offer in neuro-monitoring in critical care, it is hindered by the inconsistency seldom seen in multiple research works that can be attributed to the assumptions made on tissue scattering properties to decouple their dependency along with absorption properties that can provide information about the key biomarkers useful in neuro-monitoring. These inconsistencies can also be attributed to the application of an inaccurate model to represent photon migration in underlying the biological tissue, or it can also be attributed to the unavoidable contamination of the measured fNIRS data by the superficial (skin and scalp) tissue, which is intended to probe the brain tissue, due to the typical placing of measurement probes on the head. The possibility to overcome these challenges in fNIRS methodology is examined in this thesis, and the proposed methods to overcome these are derived theoretically and validated on numerical simulation and experimental data to demonstrate better performance as compared to existing methods. A spectrally constrained approach is designed to efficiently circumvent the coupling of absorption and scattering properties to directly yield more accurate estimates of oxygenation levels for the cerebral tissue showing an average improvement of 6.6% as compared to a conventional and widely used approach of spatially resolved spectroscopy, in estimating the tissue oxygenation level. The uncertainty factor in the knowledge of scattering coefficient of the tissue, which is a key limitation in the conventional approach, is shown to be removed in the proposed spectrally constrained approach, therefore maintaining the methodology of subject and tissue-type independence. With the demonstration of better performance on spectral constrained approach, the role of more spectral information i.e., broadband intensity data, to allow recovery of more information is also explored and is demonstrated that when the data is measured on a complex tissue such as the human head, an often used simple semi-infinite model based layered recovery can lead to uncertain results, whereas, by using an appropriate model accounting for the tissue-boundary structure and geometry, the tissue oxygenation levels are recovered with an error of 4.2%, and brain depth with an error of 11.8%. The algorithm is finally used together with human subject data, to demonstrate the robustness in application and repeatability in the recovered parameters that adhere well to expected published parameters. Finally, the signal regression of fNIRS data to reduce superficial signal contamination which is well defined for a continuous wave (CW) fNIRS system is expanded to another data-types, namely phase data as used in frequency-domain (FD) fNIRS systems, by proposing a new approach for FD fNIRS that utilizes a short-separation intensity signal directly to regress both intensity and phase measurements. This is shown to provide a better regression of superficial signal contamination from both intensity and phase data-types. Intensity-based phase regression is shown to achieve a better suppression of superficial signal contamination by 68% whereas for phase-based phase regression the suppression is only by 13%. Phase-based phase regression is also shown to generate false-positives in the image reconstruction of haemodynamic activations from the cortex, which is not desirable and therefore this work provides a better methodology for minimizing the superficial signal contamination for FD fNIRS. All the parameter recovery models and signal processing methods presented in this work, in addition to their better performance that is shown, carry an additional and most prominent advantage of being able to be applied to all existing NIRS systems without any additional instrumentation or measurement for the purpose of providing a more accurate and robust neuro-monitoring tool

Summaries of the Sixth Annual JPL Airborne Earth Science Workshop

This publication contains the summaries for the Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996. The main workshop is divided into two smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on March 4-6. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on March 6-8. The summaries for this workshop appear in Volume 2

Computational phase imaging based on intensity transport

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-150).Light is a wave, having both an amplitude and a phase. However, optical frequencies are too high to allow direct detection of phase; thus, our eyes and cameras see only real values - intensity. Phase carries important information about a wavefront and is often used for visualization of biological samples, density distributions and surface profiles. This thesis develops new methods for imaging phase and amplitude from multi-dimensional intensity measurements. Tomographic phase imaging of diffusion distributions is described for the application of water content measurement in an operating fuel cell. Only two projection angles are used to detect and localize large changes in membrane humidity. Next, several extensions of the Transport of Intensity technique are presented. Higher order axial derivatives are suggested as a method for correcting nonlinearity, thus improving range and accuracy. To deal with noisy images, complex Kalman filtering theory is proposed as a versatile tool for complex-field estimation. These two methods use many defocused images to recover phase and amplitude. The next technique presented is a single-shot quantitative phase imaging method which uses chromatic aberration as the contrast mechanism. Finally, a novel single-shot complex-field technique is presented in the context of a Volume Holographic Microscopy (VHM). All of these techniques are in the realm of computational imaging, whereby the imaging system and post-processing are designed in parallel.by Laura A. Waller.Ph.D

Efficient Computing for Three-Dimensional Quantitative Phase Imaging

Quantitative Phase Imaging (QPI) is a powerful imaging technique for measuring the refractive index distribution of transparent objects such as biological cells and optical fibers. The quantitative, non-invasive approach of QPI provides preeminent advantages in biomedical applications and the characterization of optical fibers. Tomographic Deconvolution Phase Microscopy (TDPM) is a promising 3D QPI method that combines diffraction tomography, deconvolution, and through-focal scanning with object rotation to achieve isotropic spatial resolution. However, due to the large data size, 3D TDPM has a drawback in that it requires extensive computation power and time. In order to overcome this shortcoming, CPU/GPU parallel computing and application-specific embedded systems can be utilized. In this research, OpenMP Tasking and CUDA Streaming with Unified Memory (TSUM) is proposed to speed up the tomographic angle computations in 3D TDPM. TSUM leverages CPU multithreading and GPU computing on a System on a Chip (SoC) with unified memory. Unified memory eliminates data transfer between CPU and GPU memories, which is a major bottleneck in GPU computing. This research presents a speedup of 3D TDPM with TSUM for a large dataset and demonstrates the potential of TSUM in realizing real-time 3D TDPM.M.S