Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization

Chen, Liang Yu; Pan, Min Cheng; Pan, Min

doi:10.1364/ao.52.001173

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…Metalenses are the most promising optical elements that can replace or improve bulk optical systems. − The recent research on broadband or tunable metalenses has led to increased interest of the metalenses. − The advantages of their ultracompact properties have already been discussed in numerous applications, including mobile phone cameras, endoscopes, and ultrathin microscope objectives. − In addition, their capability to manipulate a wavefront provides additional functionalities for various applications that cannot be realized in a traditional optical system or require a complex setup. In particular, metalenses making two focal spots in the longitudinal or transverse direction, called bifocal or dual-focus metalens, are promising building blocks for various applications, including optical communications, − multi-imaging systems, , tomography technique, − data storage, , and optical tweezers. − In these applications, relative movement between the light beam and specimen is often required. For instance, one-dimensional nanoparticles, such as nanowires, which act as probes for high-resolution photonic force microscopy, should be stably trapped by dual focusing and displaced to measure a single-molecule force or scan a biological specimen. − Fundamentally, this movement can be achieved by employing a specimen translation stage with a stationary light beam (stage scanning) or a scanned light beam with a stationary stage (beam scanning).…”

Section: Resultsmentioning

confidence: 99%

Scannable Dual-Focus Metalens with Hybrid Phase

Park

Woo

et al. 2023

Nano Lett.

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Metalenses with two foci in the longitudinal or transverse direction, called bifocal or dual-focus metalenses, are promising building blocks in tomography techniques, data storage, and optical tweezers. For practical applications, relative movement between the beam and specimen is required, and beam scanning is highly desirable for high-speed operation without vibration. However, dual-focus metalenses employ a hyperbolic phase that experiences off-axis aberrations, which is not suitable for beam scanning. Here, we demonstrated a scannable dual-focus metalens by employing a new phase called “hybrid phase”. The hybrid phase consists of a hyperbolic phase inside and a quadratic phase outside to reduce off-axis aberrations while maintaining a high numerical aperture. We show that the two foci of the scannable dual-focus metalens move together without severe distortion for incident angles of up to 2.5°. Our design easily extends to the case of multifocusing, which is essential for various applications ranging from imaging to manipulation.

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Section: Resultsmentioning

confidence: 99%

Scannable Dual-Focus Metalens with Hybrid Phase

Park

Woo

et al. 2023

Nano Lett.

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“…Within this study, all 10,000 simulation samples for three boundary data types were prepared by our in-house computation code NIR•FD_PC 27 , 33 – 35 In addition, the code was also applied to reconstruct optical-property images (μa and μs′) using the TR method. A total of 4400 samples were formed with one inclusion and 5500 samples with two inclusions 36 .…”

Section: Methodsmentioning

confidence: 99%

Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

2023

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. Significance The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently. Aim This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages. Approach The proposed Periodic-net is a nondestructive deep learning (DL) algorithm for the reconstruction and evaluation of inhomogeneities in an inverse model with high accuracy, while boundary measurements are calculated by solving a forward problem with sources/detectors arranged uniformly around a circular domain in various combinations, including , , and boundary measurement setups. Results The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors. Conclusions The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

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“…Training and validation datasets obtained from the inhouse MATLAB ® -coded TR computation scheme, NIR•FD_PC [ 29 , 30 , 31 ], were employed to create image-reconstruction deep-learning models. It is noted all the simulation samples were prepared by our inhouse computation code NIR•FD_PC [ 29 , 30 , 31 ], and the system calibration for both experimentation and computation scheme was performed on ring scanning rotating test bench [ 32 , 33 , 34 ], as shown in Figure 7 . The datasets with 16 × 15 measuring points (amplitude and phase shift) of each has been employed to perform DOI from laboratory experiments at our institute.…”

Section: Methodsmentioning

confidence: 99%

Sensor-to-Image Based Neural Networks: A Reliable Reconstruction Method for Diffuse Optical Imaging of High-Scattering Media

Yuliansyah

Pan

Hsu

2022

Sensors

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Imaging tasks today are being increasingly shifted toward deep learning-based solutions. Biomedical imaging problems are no exception toward this tendency. It is appealing to consider deep learning as an alternative to such a complex imaging task. Although research of deep learning-based solutions continues to thrive, challenges still remain that limits the availability of these solutions in clinical practice. Diffuse optical tomography is a particularly challenging field since the problem is both ill-posed and ill-conditioned. To get a reconstructed image, various regularization-based models and procedures have been developed in the last three decades. In this study, a sensor-to-image based neural network for diffuse optical imaging has been developed as an alternative to the existing Tikhonov regularization (TR) method. It also provides a different structure compared to previous neural network approaches. We focus on realizing a complete image reconstruction function approximation (from sensor to image) by combining multiple deep learning architectures known in imaging fields that gives more capability to learn than the fully connected neural networks (FCNN) and/or convolutional neural networks (CNN) architectures. We use the idea of transformation from sensor- to image-domain similarly with AUTOMAP, and use the concept of an encoder, which is to learn a compressed representation of the inputs. Further, a U-net with skip connections to extract features and obtain the contrast image, is proposed and implemented. We designed a branching-like structure of the network that fully supports the ring-scanning measurement system, which means it can deal with various types of experimental data. The output images are obtained by multiplying the contrast images with the background coefficients. Our network is capable of producing attainable performance in both simulation and experiment cases, and is proven to be reliable to reconstruct non-synthesized data. Its apparent superior performance was compared with the results of the TR method and FCNN models. The proposed and implemented model is feasible to localize the inclusions with various conditions. The strategy created in this paper can be a promising alternative solution for clinical breast tumor imaging applications.

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Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization

Cited by 18 publications

References 25 publications

Scannable Dual-Focus Metalens with Hybrid Phase

Scannable Dual-Focus Metalens with Hybrid Phase

Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

Sensor-to-Image Based Neural Networks: A Reliable Reconstruction Method for Diffuse Optical Imaging of High-Scattering Media

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