scholarly journals Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Xingxing Chen ◽  
Weizhi Qi ◽  
Lei Xi
Keyword(s):  
Deep Learning ◽  
Optical Resolution ◽  
Motion Artifacts ◽  
Photoacoustic Microscopy ◽  
Simulation Studies ◽  
Correction Algorithm ◽  
Brain Vessels ◽  
Capillary Networks ◽  
Motion Corrections

Abstract In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.

scholarly journals Dual-Polarized Fiber Laser Sensor for Photoacoustic Microscopy

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4632 ◽  
Author(s):  
Lin ◽  
Liang ◽  
Jin ◽  
Wang
Keyword(s):  
Fiber Laser ◽  
Optical Resolution ◽  
High Sensitivity ◽  
Low Noise ◽  
Laser Sensor ◽  
Photoacoustic Microscopy ◽  
Label Free ◽  
Laser Sensors ◽  
Dual Polarized

Optical resolution photoacoustic microscopy (OR-PAM) provides high-resolution, label-free and non-invasive functional imaging for broad biomedical applications. Dual-polarized fiber laser sensors have high sensitivity, low noise, a miniature size, and excellent stability; thus, they have been used in acoustic detection in OR-PAM. Here, we review recent progress in fiber-laser-based ultrasound sensors for photoacoustic microscopy, especially the dual-polarized fiber laser sensor with high sensitivity. The principle, characterization and sensitivity optimization of this type of sensor are presented. In vivo experiments demonstrate its excellent performance in the detection of photoacoustic (PA) signals in OR-PAM. This review summarizes representative applications of fiber laser sensors in OR-PAM and discusses their further improvements.

scholarly journals Multifocal optical-resolution photoacoustic microscopy in vivo

2011 ◽  
Vol 36 (7) ◽  
pp. 1236 ◽  
Author(s):  
Liang Song ◽  
Konstantin Maslov ◽  
Lihong V. Wang
Keyword(s):  
Optical Resolution ◽  
Photoacoustic Microscopy

scholarly journals Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo

2014 ◽  
Vol 22 (2) ◽  
pp. 1500 ◽  
Author(s):  
Zhenyuan Yang ◽  
Jianhua Chen ◽  
Junjie Yao ◽  
Riqiang Lin ◽  
Jing Meng ◽  
...  
Keyword(s):  
Optical Resolution ◽  
Photoacoustic Microscopy ◽  
Microvascular Imaging

scholarly journals Rectal Cancer Treatment Management: Deep-Learning Neural Network Based on Photoacoustic Microscopy Image Outperforms Histogram-Feature-Based Classification

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiandong Leng ◽  
Eghbal Amidi ◽  
Sitai Kou ◽  
Hassam Cheema ◽  
Ebunoluwa Otegbeye ◽  
...  
Keyword(s):  
Neural Network ◽  
Rectal Cancer ◽  
Deep Learning ◽  
Linear Models ◽  
Ex Vivo ◽  
Residual Tumor ◽  
Photoacoustic Microscopy ◽  
Feature Based ◽  
Histogram Feature

We have developed a novel photoacoustic microscopy/ultrasound (PAM/US) endoscope to image post-treatment rectal cancer for surgical management of residual tumor after radiation and chemotherapy. Paired with a deep-learning convolutional neural network (CNN), the PAM images accurately differentiated pathological complete responders (pCR) from incomplete responders. However, the role of CNNs compared with traditional histogram-feature based classifiers needs further exploration. In this work, we compare the performance of the CNN models to generalized linear models (GLM) across 24 ex vivo specimens and 10 in vivo patient examinations. First order statistical features were extracted from histograms of PAM and US images to train, validate and test GLM models, while PAM and US images were directly used to train, validate, and test CNN models. The PAM-CNN model performed superiorly with an AUC of 0.96 (95% CI: 0.95-0.98) compared to the best PAM-GLM model using kurtosis with an AUC of 0.82 (95% CI: 0.82-0.83). We also found that both CNN and GLMs derived from photoacoustic data outperformed those utilizing ultrasound alone. We conclude that deep-learning neural networks paired with photoacoustic images is the optimal analysis framework for determining presence of residual cancer in the treated human rectum.

scholarly journals Integrating photoacoustic microscopy, optical coherence tomography, OCT angiography, and fluorescence microscopy for multimodal imaging

2020 ◽  
Vol 245 (4) ◽  
pp. 342-347 ◽  
Author(s):  
Arash Dadkhah ◽  
Shuliang Jiao
Keyword(s):  
Optical Coherence Tomography ◽  
Fluorescence Microscopy ◽  
Multimodal Imaging ◽  
Imaging System ◽  
Optical Resolution ◽  
Biological Tissues ◽  
Large Field ◽  
Optical Coherence ◽  
Photoacoustic Microscopy

We have developed a multimodal imaging system, which integrated optical resolution photoacoustic microscopy, optical coherence tomography, optical coherence tomography angiography, and confocal fluorescence microscopy in one platform. The system is able to image complementary features of a biological sample by combining different contrast mechanisms. We achieved fast imaging and large field of view by combining optical scanning with mechanical scanning, similar to our previous publication. We have demonstrated the capability of the multimodal imaging system by imaging a mouse ear in vivo. Impact statement Photoacoustic microscopy-based multimodal imaging technology can provide high-resolution complementary information for biological tissues in vivo. It will potentially bring significant impact on the research and diagnosis of diseases by providing combined structural and functional information.

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