nanoRIP

• Qin, Yingying; Butola, Ankit & Agarwal, Krishna (2023). 3D refractive index reconstruction from phaseless coherent optical microscopy data using multiple scattering-based inverse solvers—a study. Inverse Problems. ISSN 0266-5611. 40(1). doi: 10.1088/1361-6420/ad0c29. Full text in Research Archive Show summary

  Reconstructing 3D refractive index profile of scatterers using optical microscopy measurements presents several challenges over the conventional microwave and RF domain measurement scenario. These include phaseless and polarization-insensitive measurements, small numerical aperture, as well as a Green's function where spatial frequencies are integrated in a weighted manner such that far-field angular spectrum cannot be probed and high spatial frequencies that permit better resolution are weighed down. As a result of these factors, the non-linearity and the ill-posedness of the inverse problem are quite severe. These limitations have imposed that inverse scattering problems in the microscopy domain largely consider scalar wave approximations and neglect multiple scattering. Here, we present first inverse scattering results for optical microscopy setup where full-wave vectorial formulation and multiple scattering is incorporated. We present (a) how three popular inverse scattering solvers from microwave domain can be adapted for the present inverse problem, (b) the opportunities and challenges presented by each of these solvers, (c) a comparative insight into these solvers and contrast with the simpler Born approximation approach, and (d) potential routes to improve the performance of these solvers for the hard inverse problem of optical microscopy.

• Qin, Yingying; Butola, Ankit & Agarwal, Krishna (2023). 3D full-wave multi-scattering forward solver for coherent microscopes. Optics Express. 31(9), p. 15015–15034. doi: 10.1364/OE.480578. Full text in Research Archive Show summary

  A rigorous forward model solver for conventional coherent microscope is presented. The forward model is derived from Maxwell’s equations and models the wave behaviour of light matter interaction. Vectorial waves and multiple-scattering effect are considered in this model. Scattered field can be calculated with given distribution of the refractive index of the biological sample. Bright field images can be obtained by combining the scattered field and reflected illumination, and experimental validation is included. Insights into the utility of the full-wave multi-scattering (FWMS) solver and comparison with the conventional Born approximation based solver are presented. The model is also generalizable to the other forms of label-free coherent microscopes, such as quantitative phase microscope and dark-field microscope.

• Liu, Zicheng; Roy, Mayank; Prasad, Dilip K. & Agarwal, Krishna (2022). Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering. IEEE Transactions on Computational Imaging. 8, p. 236–245. doi: 10.1109/TCI.2022.3158865. Full text in Research Archive Show summary

  Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost. Recently, deep neural network (DNN) techniques have been successfully applied on ISPs and shown potential of superior imaging over conventional methods. In this paper, we discuss techniques for effective incorporation of important physical phenomena in the training process.We show the importance of including near-field priors in the learning process of DNNs. To this end, we propose new designs of loss functions which incorporate multiple-scattering based near-field quantities (such as scattered fields or induced currents within domain of interest). Effects of physics-guided loss functions are studied using a variety of numerical experiments. Pros and cons of the investigated ISP solvers with different loss functions are summarized.

• Do, Quan; Maldonado, Sebastian Andres Acuña; Kristiansen, Jon Ivar; Agarwal, Krishna & Ha, Hoai Phuong (2021). Highly Efficient and Scalable Framework for High-Speed Super-Resolution Microscopy. IEEE Access. 9, p. 97053–97067. doi: 10.1109/ACCESS.2021.3094840. Full text in Research Archive Show summary

  The multiple signal classification algorithm (MUSICAL) is a statistical super-resolution technique for wide-field fluorescence microscopy. Although MUSICAL has several advantages, such as its high resolution, its low computational performance has limited its exploitation. This paper aims to analyze the performance and scalability of MUSICAL for improving its low computational performance. We first optimize MUSICAL for performance analysis by using the latest high-performance computing libraries and parallel programming techniques. Thereafter, we provide insights into MUSICAL’s performance bottlenecks. Based on the insights, we develop a new parallel MUSICAL in C++ using Intel Threading Building Blocks and the Intel Math Kernel Library. Our experimental results show that our new parallel MUSICAL achieves a speed-up of up to 30.36x on a commodity machine with 32 cores with an efficiency of 94.88%. The experimental results also show that our new parallel MUSICAL outperforms the previous versions of MUSICAL in Matlab, Java, and Python by 30.43x, 2.63x, and 1.69x, respectively, on commodity machines.

• Samanta, Krishnendu; Sarkar, Swagato; Maldonado, Sebastian Andres Acuna; Joseph, Joby; Ahluwalia, Balpreet Singh & Agarwal, Krishna (2021). Blind Super-Resolution Approach for Exploiting Illumination Variety in Optical-Lattice Illumination Microscopy. ACS Photonics. 8, p. 2626–2634. doi: 10.1021/acsphotonics.1c00503. Full text in Research Archive Show summary

  Optical-lattice illumination patterns help in pushing high spatial frequency components of the sample into the optical transfer function of a collection microscope. However, exploiting these high-frequency components require precise knowledge of illumination if reconstruction approaches similar to structured illumination microscopy are employed. Here, we present an alternate blind reconstruction approach that can provide super-resolution without the requirement of extra frames. For this, the property of exploiting temporal fluctuations in the sample emissions using “multiple signal classification algorithm” is extended aptly toward using spatial fluctuation of phase-modulated lattice illuminations for super-resolution. The super-resolution ability is shown for sinusoidal and multiperiodic lattice with approximately 3- and 6-fold resolution enhancements, respectively, over the diffraction limit.

• Maldonado, Sebastian Andres Acuna; ROY, MAYANK; Villegas, Luis; Dubey, Vishesh Kumar; Ahluwalia, Balpreet Singh & Agarwal, Krishna (2021). Deriving high contrast fluorescence microscopy images through low contrast noisy image stacks. Biomedical Optics Express. 12(9), p. 5529–5543. doi: 10.1364/BOE.422747. Full text in Research Archive Show summary

  Contrast in fluorescence microscopy images allows for the differentiation between different structures by their difference in intensities. However, factors such as point-spread function and noise may reduce it, affecting its interpretability. We identified that fluctuation of emitters in a stack of images can be exploited to achieve increased contrast when compared to the average and Richardson-Lucy deconvolution. We tested our methods on four increasingly challenging samples including tissue, in which case results were comparable to the ones obtained by structured illumination microscopy in terms of contrast.

• Maldonado, Sebastian Andres Acuña; Opstad, Ida Sundvor; Godtliebsen, Fred; Ahluwalia, Balpreet Singh & Agarwal, Krishna (2020). Soft thresholding schemes for multiple signal classification algorithm. Optics Express. 28(23), p. 34434–34449. doi: 10.1364/OE.409363. Full text in Research Archive Show summary

  Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super-resolving indicator function across a fine sample grid. A key step in the algorithm is the separation of the measurements into signal and noise subspaces, based on a single user-specified parameter called the threshold. The resulting image is strongly sensitive to this parameter and the subjectivity arising from multiple practical factors makes it difficult to determine the right rule of selection. We address this issue by proposing soft thresholding schemes derived from a new generalized framework for indicator function design. We show that the new schemes significantly alleviate the subjectivity and sensitivity of hard thresholding while retaining the super-resolution ability. We also evaluate the trade-off between resolution and contrast and the out-of-focus light rejection using the various indicator functions. Through this, we create significant new insights into the use and further optimization of MUSICAL for a wide range of practical scenarios.

• Liu, Zicheng & Agarwal, Krishna (2020). Silicon substrate significantly alters dipole-dipole resolution in coherent microscope. Optics Express. 28(26), p. 39713–39726. doi: 10.1364/OE.409629. Full text in Research Archive Show summary

  Considering a coherent microscopy setup, influences of the substrate below the sample in the imaging performances are studied, with a focus on high refractive index substrate such as silicon. Analytical expression of 3D full-wave vectorial point spread function, i.e. the dyadic Green's function is derived for the optical setup together with the substrate. Numerical analysis are performed in order to understand and compare magnification, depth of field, and resolution when using silicon substrate versus the conventional glass substrate or usually modelled condition of no substrate. Novel insights are generated about the scope of resolution improvement due to near field effect of the silicon substrate. Importantly, we show that the expected resolution varies greatly with the position of the sources and the substrate interface relative to the focal plane. Both better and worse resolution as compared to glass substrate may be expected with small changes in their positions. Therefore, our studies show that deriving a single indicative number of expected resolution is neither possible nor judicious for the case of silicon substrate.

• Zhang, Lu; Xu, Kuiwen; Zhong, Yu & Agarwal, Krishna (2020). Solving Phaseless Highly Nonlinear Inverse Scattering Problems With Contraction Integral Equation for Inversion. IEEE Transactions on Computational Imaging. 6, p. 1106–1116. doi: 10.1109/TCI.2020.3006728. Full text in Research Archive

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