Design and implementation of three-dimensional ring-scanning equipment for optimized measurements of near-infrared diffuse optical breast imaging

Yu, Jhao-Ming; Pan, Min‐Cheng; Hsu, Ya‐Fen; Chen, Liang‐Yu; Pan, Min-Chun

doi:10.1117/1.oe.54.7.073107

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…For more details, one may refer to several previous works. 25,31,32 Dataset consists of homogenous and inhomogeneous samples with a single inclusion or two inclusions. The trained deep network reconstructs absorption and reduced scattering coefficients in circular phantoms for simulation and phantom examples.…”

Section: Resultsmentioning

confidence: 99%

Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features

Murad,

Pan,

Hsu

2023

J. Med. Imag.

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Various laboratory sources have recently achieved progress in implementing deep learning models on biomedical optical imaging of soft biological tissues. The highly scattered nature of tissues at specific optical wavelengths results in poor spatial resolution. This opens up opportunities for diffuse optical imaging to improve the spatial resolution of obtained optical properties suffering from artifacts. This study aims to investigate a dual-encoder deep learning model for successfully detecting tumors in different phantoms w.r.t tumor size on diffuse optical imaging.Approach: Our proposed dual-encoder network extends U-net by adding a parallel branch of signal data to get information directly from the base source. This allows the trained network to localize the inclusions without degrading or merging with the background. The signals from the forward model and the images from the inverse problem are combined in a single decoder, filling the gap between existing direct processing and post-processing.Results: Absorption and reduced scattering coefficients are well reconstructed in both simulation and phantom test datasets. The proposed and implemented dualencoder networks characterize better optical-property images than the signalencoder and image-encoder networks, and the contrast-and-size detail resolution of the dual-encoder networks outperforms the other two approaches. From the measures of performance evaluation, the structural similarity and peak signal-to-noise ratio of the reconstructed images obtained by the dual-encoder networks remain the highest values.Conclusions: In this study, we synthesized the advantages of boundary data direct reconstruction, namely the extracted signals and iterative methods, from the obtained images into a unified network architecture.

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Section: Resultsmentioning

confidence: 99%

Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features

Murad,

Pan,

Hsu

2023

J. Med. Imag.

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“…The datasets with

measuring points (amplitude and phase shift) of each has been employed to perform DOI from laboratory experiments at our institute. 30 – 32 By calibrating the data, it is possible to remove errors caused by model mismatches, such as detector responsivity, optical fiber differences errors, and numerical noise in the inverse computation of the heterogeneous medium. To calibrate the computed homogeneous data with the measured heterogeneous data, we performed calibrations using the computed homogeneous data.…”

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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“…Both inclusions could have diameters ranging between 4–17 mm. This range along with the phantom sized between 60–150 mm in diameter are adjusted based on measurement device in practice [ 17 ]. The range for coefficients are also adjusted based on existing known range for breast tissues from clinical cases [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…The range for coefficients are also adjusted based on existing known range for breast tissues from clinical cases [ 18 ]. We employed the phantom profile with circular shape as it was the most frequently considered in previous studies [ 17 , 19 , 20 , 21 ]. The use this shape was kept as a starting point within this study.…”

Section: Methodsmentioning

confidence: 99%

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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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Design and implementation of three-dimensional ring-scanning equipment for optimized measurements of near-infrared diffuse optical breast imaging

Cited by 4 publications

References 26 publications

Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features

Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features

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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