Motion-resolved quantitative phase imaging

Kellman, Michael; Chen, Michael; Phillips, Zachary F.; Lustig, Michael; Waller, Laura

doi:10.1364/boe.9.005456

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…The DPC images were obtained from [12] using a commercial inverted microscope (Nikon TE300) with 10 × 0.25NA objective (Nikon) and an effective pixel size of 0.454μm. A LED-array [22] (SCI Microscopy) was attached to the microscope in place of the Köhler illumination unit.…”

Section: Methodsmentioning

confidence: 99%

Neural space-time model for dynamic scene recovery in multi-shot computational imaging systems

Cao,

Divekar,

Nuñez

et al. 2024

Preprint

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Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics. Hence, we can both remove motion artifacts and resolve sample dynamics. We demonstrate NSTM in three computational imaging systems: differential phase contrast microscopy, 3D structured illumination microscopy, and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

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Section: Methodsmentioning

confidence: 99%

Neural space-time model for dynamic scene recovery in multi-shot computational imaging systems

Cao,

Divekar,

Nuñez

et al. 2024

Preprint

Self Cite

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“…However, sDPC requires three image acquisitions to obtain phase images at multiple colors, and thus the cells may be positioned at different locations in the FoV during the DPC image acquisition. These motion-related issues have recently been addressed by Kellman et al 70 The implementation of such a method or other motion-correction algorithms may allow phase imaging of the flowing RBCs and enable the highthroughput detection of the RBC indices for a large number of cells. The integration of deep-learning-based methods can also be considered.…”

Section: ■ Conclusion and Future Perspectivesmentioning

confidence: 99%

Quantitative Measurements of Red Blood Cell Indices Using Spectroscopic Differential Phase-Contrast Microscopy

Moon,

Yang,

Song

et al. 2023

Chemical & Biomedical Imaging

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Red blood cell (RBC) indices serve as clinically important parameters for diagnosing various blood-related diseases. Conventional hematology analyzers provide the highly accurate detection of RBC indices but require large blood volumes (>1 mL), and the results are bulk mean values averaged over a large number of RBCs. Moreover, they do not provide quantitative information related to the morphological and chemical alteration of RBCs at the single-cell level. Recently, quantitative phase imaging (QPI) methods have been introduced as viable detection platforms for RBC indices. However, coherent QPI methods are built on complex optical setups and suffer from coherent speckle noise, which limits their detection accuracy and precision. Here, we present spectroscopic differential phase-contrast (sDPC) microscopy as a platform for measuring RBC indices. sDPC is a computational microscope that produces color-dependent phase images with higher spatial resolution and reduced speckle noise compared to coherent QPIs. Using these spectroscopic phase images and computational algorithms, RBC indices can be extracted with high accuracy. We experimentally demonstrate that sDPC enables the high-accuracy measurement of the mean corpuscular hemoglobin concentration, mean corpuscular volume, mean corpuscular hemoglobin, red cell distribution width, hematocrit, hemoglobin concentration, and RBC count with errors smaller than 7% as compared to a clinical hematology analyzer based on flow cytometry (XN-2000; Sysmex, Kobe, Japan). We further validate the clinical utility of the sDPC method by measuring and comparing the RBC indices of the control and anemic groups against those obtained using the clinical hematology analyzer.

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“…In practice, dozens of measurements are used and the slow speed of capture prevents imaging of live cell dynamics. Several works have improved upon the temporal resolution of the single-LED design, by turning on multiple LEDs simultaneously [2], [6], spatially multiplexing the measurements across the sensor [7], colormultiplexing [8], only acquiring the most important measurements [9], or motion correction [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Design for Fourier Ptychographic Microscopy

Kellman

Bostan

Chen

et al. 2019

2019 IEEE International Conference on Computational Photography (ICCP)

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Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This is accomplished by using synthetic aperture and phase retrieval algorithms to combine many measurements captured by an LED array microscope with programmable source patterns. FPM provides simultaneous large field-of-view and high resolution imaging, but at the cost of reduced temporal resolution, thereby limiting live cell applications. In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution. This is accomplished by recasting the super-resolution reconstruction as a Physics-based Neural Network and learning the experimental design to optimize the network's overall performance. Specifically, we learn LED patterns for different applications (e.g. amplitude contrast and quantitative phase imaging) and show that the designs we learn through simulation generalize well in the experimental setting. Further, we discuss a context-specific loss function, practical memory limitations, and interpretability of our learned designs.

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Motion-resolved quantitative phase imaging

Cited by 12 publications

References 37 publications

Neural space-time model for dynamic scene recovery in multi-shot computational imaging systems

Neural space-time model for dynamic scene recovery in multi-shot computational imaging systems

Quantitative Measurements of Red Blood Cell Indices Using Spectroscopic Differential Phase-Contrast Microscopy

Data-Driven Design for Fourier Ptychographic Microscopy

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