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Open Access Publications from the University of California

Scholarly Works (31 results)

  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2015)
Illumination-based differential phase contrast (DPC) is a phase imaging method that uses a pair of images with asymmetric illumination patterns. Distinct from coherent techniques, DPC relies on spatially partially coherent light, providing 2× better lateral resolution, better optical sectioning and immunity to speckle noise. In this paper, we derive the 2D weak object transfer function (WOTF) and develop a quantitative phase reconstruction method that is robust to noise. The effect of spatial coherence is studied experimentally, and multiple-angle DPC is shown to provide improved frequency coverage for more stable phase recovery. Our method uses an LED array microscope to achieve real-time (10 Hz) quantitative phase imaging with in vitro live cell samples.
    Cover page: Quantitative differential phase contrast imaging in an LED array microscope
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2015)
    Realizing high resolution across large volumes is challenging for 3D imaging techniques with high-speed acquisition. Here, we describe a new method for 3D intensity and phase recovery from 4D light field measurements, achieving enhanced resolution via Fourier ptychography. Starting from geometric optics light field refocusing, we incorporate phase retrieval and correct diffraction artifacts. Further, we incorporate dark-field images to achieve lateral resolution beyond the diffraction limit of the objective (5× larger NA) and axial resolution better than the depth of field, using a low-magnification objective with a large field of view. Our iterative reconstruction algorithm uses a multislice coherent model to estimate the 3D complex transmittance function of the sample at multiple depths, without any weak or single-scattering approximations. Data are captured by an LED array microscope with computational illumination, which enables rapid scanning of angles for fast acquisition. We demonstrate the method with thick biological samples in a modified commercial microscope, indicating the technique’s versatility for a wide range of applications.
      Cover page: 3D intensity and phase imaging from light field measurements in an LED array microscope
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2015)
      Artificial neural networks have been combined with microscopy to visualize the 3D structure of biological cells. This could lead to solutions for difficult imaging problems, such as the multiple scattering of light.
        Cover page: Machine learning for 3D microscopy
        • Thesis
        • Peer Reviewed
        UC Irvine Electronic Theses and Dissertations (2014)

        Engineered thick tissues require rapid blood perfusion upon implantation for survival. We have previously described a method to prevascularize (in vitro development of a vascular network) engineered tissues with endothelial cells and mural cells prior to implantation. That strategy has the potential to overcome the limitation of oxygen delivery by diffusion, and thus increase post-implantation survival rate. Pericytes are recruited to facilitate vessel maturation and stabilization during the formation of blood vessels. We hypothesized that the introduction of pericytes into the prevascularized tissue would enhance vessel formation and stimulate host anastomosis. Fibrin tissues were prevascularized by co-culturing human placental pericytes with endothelial colony forming cell-derived endothelial cells (ECFC-EC) from cord blood and normal human lung fibroblasts (NHLF) for 7 or 14 days in vitro. Tissues were then subcutaneously implanted to the dorsal side of SCID mice and retrieved 7 days later. Tissues with a low pericyte-fibroblast ratio developed a vessel network that was well-perfused with host circulation after 7 days in vivo culture. In contrast, pericytes alone or with a high pericyte-fibroblast ratio failed to develop significant in vitro vessel networks, and did not anastomose with the host circulation. Our results suggest that a low pericyte-fibroblast ratio can enhance the in vivo perfusion of engineered tissues.

        Additionally, we designed and constructed a model to control oxygen diffusion during both in vitro and in vivo culture using biocompatible Poly (methyl methacrylate) (PMMA) and low-density polyethylene (LDPE). Tissues were prevascularized in the devices without limiting oxygen diffusion in vitro for 7 days. Once tissues were implanted into the host, tissue access to oxygen was limited to a small opening facing the host skin side. Oxygen could diffuse through this opening, or be transported by convection following anastomosis with host vasculature. After 7 days, tissues were explanted. Blood perfusion is observed around the access point, but not the entire tissues, which is consistent with blood clotting following anastomosis.

          Cover page: Creation of Thick Prevascularized Implantable Tissues
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2014)
          We demonstrate 3D differential phase-contrast (DPC) microscopy, based on computational illumination with a programmable LED array. By capturing intensity images with various illumination angles generated by sequentially patterning an LED array source, we digitally refocus images through various depths via light field processing. The intensity differences from images taken at complementary illumination angles are then used to generate DPC images, which are related to the gradient of phase. The proposed method achieves 3D DPC with simple, inexpensive optics and no moving parts. We experimentally demonstrate our method by imaging a camel hair sample in 3D.
            Cover page: 3D differential phase-contrast microscopy with computational illumination using an LED array.
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2016)
            We demonstrate 3D phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential phase contrast (DPC) model is derived. The partially coherent transfer functions relate the samples complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution.
              Cover page: 3D differential phase contrast microscopy.
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2017)
              Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system. Generally, the reconstruction process requires prior knowledge of the illumination patterns, which implies a well-calibrated and aberration-free system. Here, we propose a new algorithmic self-calibration strategy for SIM that does not need to know the exact patterns a priori, but only their covariance. The algorithm, termed PE-SIMS, includes a pattern-estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a statistical prior (SIMS). Additionally, we perform a pixel reassignment process (SIMS-PR) to enhance the reconstruction quality. We achieve 2× better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.
                Cover page: Structured illumination microscopy with unknown patterns and a statistical prior.
                • Article
                • Peer Reviewed
                UC Davis Previously Published Works (2022)
                This guest editorial provides an introduction to the Special Section on Computational Approaches for Neuroimaging.
                  Cover page: Special Section Guest Editorial: Computational Approaches for NeuroimagingCreative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2014)
                  Fourier Ptychography is a new computational microscopy technique that achieves gigapixel images with both wide field of view and high resolution in both phase and amplitude. The hardware setup involves a simple replacement of the microscope's illumination unit with a programmable LED array, allowing one to flexibly pattern illumination angles without any moving parts. In previous work, a series of low-resolution images was taken by sequentially turning on each single LED in the array, and the data were then combined to recover a bandwidth much higher than the one allowed by the original imaging system. Here, we demonstrate a multiplexed illumination strategy in which multiple randomly selected LEDs are turned on for each image. Since each LED corresponds to a different area of Fourier space, the total number of images can be significantly reduced, without sacrificing image quality. We demonstrate this method experimentally in a modified commercial microscope. Compared to sequential scanning, our multiplexed strategy achieves similar results with approximately an order of magnitude reduction in both acquisition time and data capture requirements.
                    Cover page: Multiplexed coded illumination for Fourier Ptychography with an LED array microscope.
                    • Article
                    • Peer Reviewed
                    UC Berkeley Previously Published Works (2015)
                    Fourier Ptychographic Microscopy (FPM) is a newly proposed computational imaging method aimed at reconstructing a high-resolution wide-field image from a sequence of low-resolution images. These low-resolution images are captured under varied illumination angles and the FPM recovery routine then stitches them together in the Fourier domain iteratively. Although FPM has achieved success with static sample reconstructions, the long acquisition time inhibits real-time application. To address this problem, we propose here a self-learning based FPM which accelerates the acquisition and reconstruction procedure. We first capture a single image under normally incident illumination, and then use it to simulate the corresponding low-resolution images under other illumination angles. The simulation is based on the relationship between the illumination angles and the shift of the sample's spectrum. We analyze the importance of the simulated low-resolution images in order to devise a selection scheme which only collects the ones with higher importance. The measurements are then captured with the selection scheme and employed to perform the FPM reconstruction. Since only measurements of high importance are captured, the time requirements of data collection as well as image reconstruction can be greatly reduced. We validate the effectiveness of the proposed method with simulation and experimental results showing that the reduction ratio of data size requirements can reach over 70%, without sacrificing image reconstruction quality.
                      Cover page: Self-learning based Fourier ptychographic microscopy
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