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eScholarship
Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2020)

In computational imaging systems (e.g. tomographic systems, computational optics, magnetic resonance imaging) the acquisition of data and reconstruction of images are co-designed to retrieve information which is not traditionally accessible. The performance of such systems is characterized by how information is encoded to (forward process) and decoded from (inverse problem) the measurements. Recently, critical aspects of these systems, such as their signal prior, have been optimized using deep neural networks formed from unrolling the iterations of a physics-based image reconstruction.

In this dissertation, I will detail my work, physics-based learned design, to optimize the performance of the entire computational imaging system by jointly learning aspects of its experimental design and computational reconstruction. As an application, I introduce how the LED-array microscope performs super-resolved quantitative phase imaging and demonstrate how physics-based learning can optimize a reduced set of measurements without sacrificing performance to enable the imaging of live fast moving biology.

In this dissertation’s latter half, I will discuss how to overcome some of the computational challenges encountered in applying physics-based learning concepts to large-scale computational imaging systems. I will describe my work, memory-efficient learning, that makes physics-based learning for large-scale systems feasible on commercially-available graphics processing units. I demonstrate this method on two large-scale real-world systems: 3D multi-channel compressed sensing MRI and super-resolution optical microscopy.

    Cover page: Physics-based Learning for Large-scale Computational Imaging
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2022)
    3D phase imaging recovers an object's volumetric refractive index from intensity and/or holographic measurements. Partially coherent methods, such as illumination-based differential phase contrast (DPC), are particularly simple to implement in a commercial brightfield microscope. 3D DPC acquires images at multiple focus positions and with different illumination source patterns in order to reconstruct 3D refractive index. Here, we present a practical extension of the 3D DPC method that does not require a precise motion stage for scanning the focus and uses optimized illumination patterns for improved performance. The user scans the focus by hand, using the microscope's focus knob, and the algorithm self-calibrates the axial position to solve for the 3D refractive index of the sample through a computational inverse problem. We further show that the illumination patterns can be optimized by an end-to-end learning procedure. Combining these two, we demonstrate improved 3D DPC with a commercial microscope whose only hardware modification is LED array illumination.
      Cover page: Self-calibrated 3D differential phase contrast microscopy with optimized illumination.
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2017)
      A resistive pulse sensing device is able to extract quantities such as concentration and size distribution of particles, e.g. cells or microspheres, as they flow through the device's sensor region, i.e. channel, in an electrolyte solution. The dynamic range of detectable particle sizes is limited by the channel dimensions. In addition, signal interference from multiple particles transiting the channel simultaneously, i.e. coincidence event, further hinder the dynamic range. Coincidence data is often considered unusable and is discarded, reducing the throughput and introducing possible biases and errors into the distributions. Here, we propose a two-step solution. We code the channel such that the system response results in a Manchester encoded Barker-Code sequence, allowing us to take advantage of the code's pulse compression properties. We pose the parameter estimation problem as a sparse inverse problem, which enables estimation of particle sizes and velocities while resolving coincidences, and solve it with a successive interference cancellation algorithm. We introduce modifications to the algorithm to account for device fabrication variations and natural stochastic variations in flow. We demonstrate the ability to resolve coincidences and possible increases in the device's dynamic range by screening particles of different size through a Barker encoded device.
        Cover page: Barker-Coded Node-Pore Resistive Pulse Sensing with Built-In Coincidence Correction
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2018)
        The temporal resolution of quantitative phase imaging with Differential Phase Contrast (DPC) is limited by the requirement for multiple illumination-encoded measurements. This inhibits imaging of fast-moving samples. We present a computational approach to model and correct for non-rigid sample motion during the DPC acquisition in order to improve temporal resolution to that of a single-shot method and enable imaging of motion dynamics at the framerate of the sensor. Our method relies on the addition of a simultaneously-acquired color-multiplexed reference signal to enable non-rigid registration of measurements prior to phase retrieval. We show experimental results where we reduce motion blur from fast-moving live biological samples.
          Cover page: Motion-resolved quantitative phase imaging.
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2018)
          We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.
            Cover page: Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution
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