Fourier optics approaches to enhanced depth-of-field applications in millimetre-wave imaging and microscopy

In the first part of this thesis millimetre-wave interferometric imagers are considered for short-range applications such as concealed weapons detection. Compared to real aperture systems, synthetic aperture imagers at these wavelengths can provide improvements in terms of size, cost, depth-of-field (DoF) and imaging flexibility via digitalrefocusing. Mechanical scanning between the scene and the array is investigated to reduce the number of antennas and correlators which drive the cost of such imagers. The tradeoffs associated with this hardware reduction are assessed before to jointly optimise the array configuration and scanning motion. To that end, a novel metric is proposed to quantify the uniformity of the Fourier domain coverage of the array and is maximised with a genetic algorithm. The resulting array demonstrates clear improvements in imaging performances compared to a conventional power-law Y-shaped array. The DoF of antenna arrays, analysed via the Strehl ratio, is shown to be limited even for infinitely small antennas, with the exception of circular arrays. In the second part of this thesis increased DoF in optical systems with Wavefront Coding (WC) is studied. Images obtained with WC are shown to exhibit artifacts that limit the benefits of this technique. An image restoration procedure employing a metric of defocus is proposed to remove these artifacts and therefore extend the DoF beyond the limit of conventional WC systems. A transmission optical microscope was designed and implemented to operate with WC. After suppression of partial coherence effects, the proposed image restoration method was successfully applied and extended DoF images are presented

Jacobi–Fourier phase mask for wavefront coding

In this work we propose Jacobi–Fourier phase masks for wavefront coding-based imaging systems. The optical properties of the phase mask is study in detail and numerical simulation are shown. Pixel size and noise are taken into account for the deconvolution of images. Numerical simulations indicate that overall performance is better than of the well-known and commonly used trefoil phaseThis work was supported by the Spanish Ministry of Economía y Competitividad FIS2016-77319-C2-1-R, and FEDER, Xunta de Galicia/FEDER ED431E 2018/08. E. González Amador thanks to Consejo Nacional de Ciencia y Tecnología (CONACyT); with CVU no. 714742. Also, we thank by the support to PADES program; Award no. 2018-13-011-047S

A review of snapshot multidimensional optical imaging: Measuring photon tags in parallel

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition–also dubbed snapshot imaging–has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications

Programmable 3D snapshot microscopy with Fourier convolutional networks

3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image, and has found a variety of biological applications such as whole brain imaging of fast neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Highly programmable optical elements create new possibilities for sample-specific computational optimization of microscope parameters, e.g. tuning the collection of light for a given sample structure. We perform such optimization with deep learning, using a differentiable wave-optics simulation of light propagation through a programmable microscope and a neural network to reconstruct volumes from the microscope image. We introduce a class of global kernel Fourier convolutional neural networks which can efficiently decode information from multiple depths in the volume, globally encoded across a 3D snapshot image. We show that our proposed networks succeed in large field of view volume reconstruction and microscope parameter optimization where traditional networks fail. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for lensless computational photography.Comment: Make zebrafish Types A,B,C,D more clea

Principles and applications of wavefront coding

Abstract unavailable please refer to PD

A review of snapshot multidimensional optical imaging: Measuring photon tags in parallel

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition–also dubbed snapshot imaging–has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications

Limited Angle Ultrasound Tomography of the Compressed Breast

X-ray mammography is widely accepted as the clinical standard for breast cancer screening and diagnosis. However, reflection mode ultrasound has been known to outperform x-ray in screening performance in dense breasts. With newer modes of ultrasound, acoustic properties of breast tissue, such as the speed of sound and attenuation coefficient distributions, can be extracted from captured ultrasound signals and used to characterize breast tissue types and contribute to detection and diagnosis of malignancy. The same is possibly true for optical absorption via photoacoustic imaging. Recently, we have developed a dual-sided ultrasound scanner that can be integrated with existing x-ray mammographic systems and acquire images in the mammographic view and compression. Transmission imaging for speed of sound and attenuation coefficient in this geometry is termed limited angle tomography, as the beams at frequencies yielding high resolution cannot transit the long axis of the compressed breast. This approach, ideally, should facilitate the co-registration and comparisons between images from three modalities discussed here (x-ray, ultrasound and photoacoustic) and increase diagnostic detection confidence. However, potential limitations inherent in limited angle tomography have received minimal exploration up to this study, and existing imaging techniques developed for this approach are based on overly optimistic assumptions that hinder achievement of the desired image quality. This investigation of these problems should contribute valuable information to the validation and translation of the mammographically-configured, dual-sided ultrasound, or ultrasound and photoacoustic, scanner to the clinic. This dissertation first aims to extensively identify possible sources of error resulting from imaging in the limited angle tomography approach. Simulation findings mapping parametric conditions reveal that image artifacts arising in reflection mode (B-mode) can be modulated or mitigated by ultrasound gels with adequate acoustic properties. In addition, sound speed imaging was performed determining the level of significance for several key sources of error. Results suggest that imaging in transmission mode is the most sensitive to transducer misplacement in the signal propagation direction. This misplacement, however, could be minimized easily by routinely calibrating transducer positions. Next, this dissertation aims to advance speed of sound, attenuation, and photoacoustic image reconstruction algorithms for the limited angle tomography approach. This was done by utilizing both structural information of the imaged objects/tissues by means of the corresponding reflection mode images taken from the same imaging location, and a full acoustic modeling framework to account for complex acoustic interactions within the field of view. We have shown through simulations that both a priori information from reflection mode images and full acoustic modeling contribute to a noticeable improvement in the reconstructed images. Work done throughout the course of this dissertation should provide a foundation and insight necessary for improvements upon the existing dual-sided ultrasound scanner towards breast imaging in the clinic.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143944/1/rungroj_1.pd

Doctor of Philosophy

dissertationDiffusion tensor MRI (DT-MRI or DTI) has been proven useful for characterizing biological tissue microstructure, with the majority of DTI studies having been performed previously in the brain. Other studies have shown that changes in DTI parameters are detectable in the presence of cardiac pathology, recovery, and development, and provide insight into the microstructural mechanisms of these processes. However, the technical challenges of implementing cardiac DTI in vivo, including prohibitive scan times inherent to DTI and measuring small-scale diffusion in the beating heart, have limited its widespread usage. This research aims to address these technical challenges by: (1) formulating a model-based reconstruction algorithm to accurately estimate DTI parameters directly from fewer MRI measurements and (2) designing novel diffusion encoding MRI pulse sequences that compensate for the higher-order motion of the beating heart. The model-based reconstruction method was tested on undersampled DTI data and its performance was compared against other state-of-the-art reconstruction algorithms. Model-based reconstruction was shown to produce DTI parameter maps with less blurring and noise and to estimate global DTI parameters more accurately than alternative methods. Through numerical simulations and experimental demonstrations in live rats, higher-order motion compensated diffusion-encoding was shown to successfully eliminate signal loss due to motion, which in turn produced data of sufficient quality to accurately estimate DTI parameters, such as fiber helix angle. Ultimately, the model-based reconstruction and higher-order motion compensation methods were combined to characterize changes in the cardiac microstructure in a rat model with inducible arterial hypertension in order to demonstrate the ability of cardiac DTI to detect pathological changes in living myocardium

Self-evolving ghost imaging

Ghost imaging captures 2D images with a point detector instead of an array sensor. It could therefore solve the challenge of building cameras in wave bands where sensors are difficult and expensive to produce and could open up more routine THz, near-infrared, lifetime, and hyperspectral imaging simply by using single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline by correlating measured light intensities with pre-designed illuminating patterns. Here we present a "self-evolving"ghost imaging (SEGI) strategy for imaging objects bypassing offline postprocessing. It also offers the capability to image objects in turbid media. By inspecting the optical feedback, we evaluate the illumination patterns by a cost function and generate offspring illumination patterns that mimic the object's image, bypassing the reconstruction process. At the initial evolving state, the object's "genetic information"is stored in the patterns. At the following imaging stage, the object's image (48×48 pixels) can be updated at a 40 Hz imaging rate. We numerically and experimentally demonstrate this concept for static and moving objects. The frame-memory effect between the self-evolving illumination patterns provided by the genetic algorithm enables SEGI imaging through turbid media.We further demonstrate this capability by imaging an object placed in a container filled with water and sand. SEGI shows robust and superior imaging power compared with traditional computational ghost imaging. This strategy could enhance ghost imaging in applications such as remote sensing, imaging through scattering media, and low-irradiative biological imaging