Review of bio-optical imaging systems with a high space-bandwidth product

Park, Jong Chan; Brady, David J.; Zheng, Guoan; Tian, Lei; Gao, Liang

doi:10.1117/1.ap.3.4.044001

Cited by 75 publications

(46 citation statements)

References 179 publications

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“…Deep learning has been increasingly used in various mi-croscopy methods to perform tasks such as denoising, image reconstruction, and classification [29,39,42]. Despite the significant performance boost in such methods, the limitations inherited from the hardware of the microscope set the upper performance bound [7].…”

Section: Introductionmentioning

confidence: 99%

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

Herath¹,

Haputhanthri²,

Hettiarachchi³

et al. 2022

Preprint

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Ever since the first microscope by Zacharias Janssen in the late 16th century, scientists have been inventing new types of microscopes for various tasks. Inventing a novel architecture demands years, if not decades, worth of scientific experience and creativity. In this work, we introduce Differentiable Microscopy (∂µ), a deep learning-based design paradigm, to aid scientists design new interpretable microscope architectures. Differentiable microscopy first models a common physics-based optical system however with trainable optical elements at key locations on the optical path. Using pre-acquired data, we then train the model end-to-end for a task of interest. The learnt design proposal can then be simplified by interpreting the learnt optical elements. As a first demonstration, based on the optical 4-f system, we present an all-optical quantitative phase microscope (QPM) design that requires no computational post-reconstruction. A follow-up literature survey suggested that the learnt architecture is similar to the generalized phase concept developed two decades ago. We then incorporate the generalized phase contrast concept to simplify the learning procedure. Furthermore, this physical optical setup is miniaturized using a diffractive deep neural network (D2NN). We outperform the existing benchmark for all-optical phase-to-intensity conversion on multiple datasets, and ours is the first demonstration of its kind on D2NNs. The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.

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Section: Introductionmentioning

confidence: 99%

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

Herath¹,

Haputhanthri²,

Hettiarachchi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The ever-increasing demand for information throughput toward biomedical and other engineering applications has strongly promoted recent developments in imaging techniques with a high space-bandwidth product [1][2][3]. Among these techniques, lensless on-chip microscopy has become an emerging solution by leveraging recent advances in sensor technology and computational power to overcome the inherent tradeoff between spatial resolution and field of view of the conventional point to-point imaging modality [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Gao

Yang

Cao

2022

Cells

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Empowered by pixel super-resolution (PSR) and phase retrieval techniques, lensless on-chip microscopy opens up new possibilities for high-throughput biomedical imaging. However, the current PSR phase retrieval approaches are time consuming in terms of both the measurement and reconstruction procedures. In this work, we present a novel computational framework for PSR phase retrieval to address these concerns. Specifically, a sparsity-promoting regularizer is introduced to enhance the well posedness of the nonconvex problem under limited measurements, and Nesterov’s momentum is used to accelerate the iterations. The resulting algorithm, termed accelerated Wirtinger flow (AWF), achieves at least an order of magnitude faster rate of convergence and allows a twofold reduction in the measurement number while maintaining competitive reconstruction quality. Furthermore, we provide general guidance for step size selection based on theoretical analyses, facilitating simple implementation without the need for complicated parameter tuning. The proposed AWF algorithm is compatible with most of the existing lensless on-chip microscopes and could help achieve label-free rapid whole slide imaging of dynamic biological activities at subpixel resolution.

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“…Deep learning has been increasingly used in various microscopy methods to perform tasks such as denoising, image reconstruction, and classification [2][3][4]. Despite the significant performance boost in such methods, the limitations inherited from the hardware of the microscope set the upper performance bound [5].…”

Section: Introductionmentioning

confidence: 99%

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

Herath

Haputhanthri

Hettiarachchi

et al. 2022

Preprint

View full text Add to dashboard Cite

Ever since the first microscope by Zacharias Janssen in the late 16th century, scientists have been inventing new types of microscopes for various tasks. Inventing a novel architecture demands years, if not decades, worth of scientific experience and creativity. In this work, we introduce Differentiable Microscopy (∂μ), a deep learning-based design paradigm, to aid scientists design new interpretable microscope architectures. Differentiable microscopy first models a common physics-based optical system however with trainable optical elements at key locations on the optical path. Using pre-acquired data, we then train the model end-to-end for a task of interest. The learnt design proposal can then be simplified by interpreting the learnt optical elements. As a first demonstration, based on the optical 4-f system, we present an all-optical quantitative phase microscope (QPM) design that requires no computational post-reconstruction. A follow-up literature survey suggested that the learnt architecture is similar to the generalized phase concept developed two decades ago. We then incorporate the generalized phase contrast concept to simplify the learning procedure. Furthermore, we also demonstrate that similar results can be achieved by an uninterpretable learning based method, namely diffractive deep neural networks (D2NN). We outperform the existing benchmark for all-optical phase-to-intensity conversion on multiple datasets, and ours is the first demonstration of its kind on D2NNs. The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.

show abstract

Review of bio-optical imaging systems with a high space-bandwidth product

Cited by 75 publications

References 179 publications

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope

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