Developments of infrared-pump visible-probe phase imaging on chemical selectivity and deep-learning enhancement

2020 ◽  
Author(s):  
Weiru Fan ◽  
Tianrun Chen ◽  
Vlad V. Yakovlev ◽  
Dawei Wang ◽  
Delong Zhang
Keyword(s):  
Deep Learning ◽  
Phase Imaging ◽  
Learning Enhancement ◽  
Chemical Selectivity

Super-resolution quantitative phase imaging of out-of-focus images based on deep learning

2021 ◽  
Author(s):  
Xin Qian ◽  
Hao Ding ◽  
Fajing Li ◽  
Shouping Nie ◽  
Caojin Yuan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

scholarly journals Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning

2020 ◽  
Vol 28 (19) ◽  
pp. 28140
Author(s):  
Jiaosheng Li ◽  
Qinnan Zhang ◽  
Liyun Zhong ◽  
Jindong Tian ◽  
Giancarlo Pedrini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dual Wavelength ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

scholarly journals Quantifying the Utility of Low Resolution Brain Imaging:: Treatment of Infant Hydrocephalus

2021 ◽  
Author(s):  
Joshua Harper ◽  
Venkateswararao Cherukuri ◽  
Tom O'Riley ◽  
Mingzhao Yu ◽  
Edith Mbabazi-Kabachelor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Treatment Planning ◽  
Image Resolution ◽  
Learning Enhancement ◽  
Low Contrast ◽  
Substantial Risk ◽  
Noise Ratio ◽  
Hydrocephalus Treatment ◽  
Post Hoc

As low-field MRI technology is being disseminated into clinical settings, it is important to assess the image quality required to properly diagnose and treat a given disease. In this post-hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. Images were degraded in terms of resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in LMIC for assessment of clinical utility in treatment planning for hydrocephalus. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of a useful image for hydrocephalus treatment planning. For images with 128x128 resolution, a contrast-to-noise ratio of 2.5 has a high probability of being useful (91%, 95% CI 73% to 96%; P=2e-16). Deep learning enhancement of a 128x128 image with very low contrast-to-noise (1.5) and low probability of being useful (23%, 95% CI 14% to 36%; P=2e-16) increases CNR improving the apparent likelihood of being useful, but carries substantial risk of structural errors leading to misleading clinical interpretation (CNR after enhancement = 5; risk of misleading results = 21%, 95% CI 3% to 32%; P=7e-11). Lower quality images not customarily considered acceptable by clinicians can be useful in planning hydrocephalus treatment. We find substantial risk of misleading structural errors when using deep learning enhancement of low quality images. These findings advocate for new standards in assessing acceptable image quality for clinical use.

Review of deep learning based de-noising algorithms for phase imaging and applications to high-speed coherent imaging

2021 ◽  
Author(s):  
Silvio Montresor ◽  
Marie Tahon ◽  
Pascal Picart
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Phase Imaging ◽  
Coherent Imaging

Rapid quantitative phase imaging using deep learning for phase object with refractive index variation

2021 ◽  
Vol 68 (6) ◽  
pp. 327-338
Author(s):  
Xiaoqing Xu ◽  
Ming Xie ◽  
Ying Ji ◽  
Yawei Wang
Keyword(s):  
Refractive Index ◽  
Deep Learning ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Phase Object ◽  
Refractive Index Variation ◽  
Quantitative Phase ◽  
Index Variation

scholarly journals Reliable deep-learning-based phase imaging with uncertainty quantification: erratum

Optica ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 332
Author(s):  
Yujia Xue ◽  
Shiyi Cheng ◽  
Yunzhe Li ◽  
Lei Tian
Keyword(s):  
Deep Learning ◽  
Uncertainty Quantification ◽  
Phase Imaging

scholarly journals Reliable deep-learning-based phase imaging with uncertainty quantification

Optica ◽  
2019 ◽  
Vol 6 (5) ◽  
pp. 618 ◽  
Author(s):  
Yujia Xue ◽  
Shiyi Cheng ◽  
Yunzhe Li ◽  
Lei Tian
Keyword(s):  
Deep Learning ◽  
Uncertainty Quantification ◽  
Phase Imaging

scholarly journals Deep-Learning-Based Halo-Free White-Light Diffraction Phase Imaging

2021 ◽  
Vol 9 ◽  
Author(s):  
Kehua Zhang ◽  
Miaomiao Zhu ◽  
Lihong Ma ◽  
Jiaheng Zhang ◽  
Yong Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
White Light ◽  
Imaging System ◽  
Light Diffraction ◽  
Phase Image ◽  
Phase Imaging ◽  
The Neural Network ◽  
Phase Images ◽  
Measured Phase

In white-light diffraction phase imaging, when used with insufficient spatial filtering, phase image exhibits object-dependent artifacts, especially around the edges of the object, referred to the well-known halo effect. Here we present a new deep-learning-based approach for recovering halo-free white-light diffraction phase images. The neural network-based method can accurately and rapidly remove the halo artifacts not relying on any priori knowledge. First, the neural network, namely HFDNN (deep neural network for halo free), is designed. Then, the HFDNN is trained by using pairs of the measured phase images, acquired by white-light diffraction phase imaging system, and the true phase images. After the training, the HFDNN takes a measured phase image as input to rapidly correct the halo artifacts and reconstruct an accurate halo-free phase image. We validate the effectiveness and the robustness of the method by correcting the phase images on various samples, including standard polystyrene beads, living red blood cells and monascus spores and hyphaes. In contrast to the existing halo-free methods, the proposed HFDNN method does not rely on the hardware design or does not need iterative computations, providing a new avenue to all halo-free white-light phase imaging techniques.

scholarly journals Auto-focusing and quantitative phase imaging using deep learning for the incoherent illumination microscopy system

2021 ◽  
Author(s):  
Hao Ding ◽  
Fajing Li ◽  
Zhang Meng ◽  
Shaoteng Feng ◽  
Jun Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Auto Focusing

Second harmonic phase imaging enabled by deep learning

2021 ◽  
Author(s):  
Tianrun Chen ◽  
Weiru Fan ◽  
Dawei Wang ◽  
Vlad V. Yakovlev ◽  
Delong Zhang
Keyword(s):  
Deep Learning ◽  
Second Harmonic ◽  
Phase Imaging ◽  
Harmonic Phase ◽  
Harmonic Phase Imaging
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