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Biomedical Optics|6 Article(s)
Three-dimensional tracking of multiple particles in large depth of field using dual-objective bifocal plane imaging
Aiwang Huang, Danni Chen, Heng Li, Dexiang Tang, Bin Yu, Jia Li, and Junle Qu
Tracking moving particles in cells by single particle tracking is an important optical approach widely used in biological research. In order to track multiple particles within a whole cell simultaneously, a parallel tracking approach with large depth of field was put forward. It was based on distorted grating and dual-objective bifocal imaging, making use of the distorted grating to expand the depth of field, dual-objective to gather as many photons as possible, and bifocal plane imaging to realize three-dimensional localization. Simulation of parallel tracking of two particles moving along the z axis demonstrated that even when the two are axially separated by 10 μm, they can both be localized simultaneously with transversal precision better than 5 nm and axial precision better than 20 nm. Tracking moving particles in cells by single particle tracking is an important optical approach widely used in biological research. In order to track multiple particles within a whole cell simultaneously, a parallel tracking approach with large depth of field was put forward. It was based on distorted grating and dual-objective bifocal imaging, making use of the distorted grating to expand the depth of field, dual-objective to gather as many photons as possible, and bifocal plane imaging to realize three-dimensional localization. Simulation of parallel tracking of two particles moving along the z axis demonstrated that even when the two are axially separated by 10 μm, they can both be localized simultaneously with transversal precision better than 5 nm and axial precision better than 20 nm.
Chinese Optics Letters
- Publication Date: Jul. 10, 2020
- Vol. 18, Issue 7, 071701 (2020)
Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure
Xiaorong Ma, Hong Cheng, Junwei Hou, Zhenhong Jia, Guohua Wu, Xiaoyi Lü, Hongyi Li, Xiangxiang Zheng, and Chen Chen
In this Letter, the surface-enhanced Raman scattering (SERS) signal of early breast cancer (BRC) patient serum is obtained by a composite silver nanoparticles (Ag NPs) PSi Bragg reflector SERS substrate. Based on these advantages, the serum SERS signals of 30 normal people and 30 early BRC patients were detected by this substrate. After a baseline correction of the experimental data, principal component analysis and linear discriminant analysis were used to complete the data processing. The results showed that the diagnostic accuracy, specificity, and sensitivity of the composite Ag NPs PSi Bragg reflector SERS substrate were 95%, 96.7%, and 93.3%, respectively. The results of this exploratory study prove that the detection of early BRC serum based on a composite Ag NPs PSi Bragg reflector SERS substrate is with a stable strong SERS signal, and an unmarked and noninvasive BRC diagnosis technology. In the future, this technology can serve as a noninvasive clinical tool to detect cancer diseases and have a considerable impact on clinical medical detection. In this Letter, the surface-enhanced Raman scattering (SERS) signal of early breast cancer (BRC) patient serum is obtained by a composite silver nanoparticles (Ag NPs) PSi Bragg reflector SERS substrate. Based on these advantages, the serum SERS signals of 30 normal people and 30 early BRC patients were detected by this substrate. After a baseline correction of the experimental data, principal component analysis and linear discriminant analysis were used to complete the data processing. The results showed that the diagnostic accuracy, specificity, and sensitivity of the composite Ag NPs PSi Bragg reflector SERS substrate were 95%, 96.7%, and 93.3%, respectively. The results of this exploratory study prove that the detection of early BRC serum based on a composite Ag NPs PSi Bragg reflector SERS substrate is with a stable strong SERS signal, and an unmarked and noninvasive BRC diagnosis technology. In the future, this technology can serve as a noninvasive clinical tool to detect cancer diseases and have a considerable impact on clinical medical detection.
Chinese Optics Letters
- Publication Date: May. 10, 2020
- Vol. 18, Issue 5, 051701 (2020)
Bone mineral density value evaluation based on photoacoustic spectral analysis combined with deep learning method
Xue Zhou, Zhibin Jin, Ting Feng, Qian Cheng, Xueding Wang, Yao Ding, Hongchen Zhan, and Jie Yuan
The diagnosis of osteoporosis is eventually converted to the measurement of bone mineral density (BMD) in clinical trials. Since our previous work had proved the ability of using photoacoustic spectral analysis (PASA) to efficiently detect osteoporosis, in this contribution, we proposed a fully connected multi-layer deep neural network combined with PASA to semi-quantify BMD values corresponding to varying degrees of bone loss and to further evaluate the degree of osteoporosis. Experiments were carried out on swine femur heads, and the performance of our proposed method is satisfying for future clinical screening. The diagnosis of osteoporosis is eventually converted to the measurement of bone mineral density (BMD) in clinical trials. Since our previous work had proved the ability of using photoacoustic spectral analysis (PASA) to efficiently detect osteoporosis, in this contribution, we proposed a fully connected multi-layer deep neural network combined with PASA to semi-quantify BMD values corresponding to varying degrees of bone loss and to further evaluate the degree of osteoporosis. Experiments were carried out on swine femur heads, and the performance of our proposed method is satisfying for future clinical screening.
Chinese Optics Letters
- Publication Date: Apr. 10, 2020
- Vol. 18, Issue 4, 041701 (2020)
Tomography-assisted Doppler photoacoustic microscopy: proof of concept
Xinkun Wang, Kedi Xiong, Xin Jin, and Sihua Yang
The previous methods to measure flow speed by photoacoustic microscopy solely focused on either the transverse or the axial flow component, which did not reflect absolute flow speed. Here, we present absolute flow speed maps by combining Doppler bandwidth broadening with volumetric photoacoustic microscopy. Photoacoustic Doppler bandwidth broadening and photoacoustic tomographic images were applied to measure the transverse flow component and the Doppler angle, respectively. Phantom experiments quantitatively demonstrated that ranges of 55° to 90° Doppler angle and 0.5 to 10 mm/s flow speed can be measured. This tomography-assisted method provides the foundation for further measurement in vivo. The previous methods to measure flow speed by photoacoustic microscopy solely focused on either the transverse or the axial flow component, which did not reflect absolute flow speed. Here, we present absolute flow speed maps by combining Doppler bandwidth broadening with volumetric photoacoustic microscopy. Photoacoustic Doppler bandwidth broadening and photoacoustic tomographic images were applied to measure the transverse flow component and the Doppler angle, respectively. Phantom experiments quantitatively demonstrated that ranges of 55° to 90° Doppler angle and 0.5 to 10 mm/s flow speed can be measured. This tomography-assisted method provides the foundation for further measurement in vivo.
Chinese Optics Letters
- Publication Date: Oct. 10, 2020
- Vol. 18, Issue 10, 101702 (2020)
Automated superpixels-based identification and mosaicking of cone photoreceptor cells for adaptive optics scanning laser ophthalmoscope
Yiwei Chen, Yi He, Jing Wang, Wanyue Li, Lina Xing, Feng Gao, and Guohua Shi
An automated superpixels identification/mosaicking method is presented for the analysis of cone photoreceptor cells with the use of adaptive optics scanning laser ophthalmoscope (AO-SLO) images. This is an image oversegmentation method used for the identification and mosaicking of cone photoreceptor cells in AO-SLO images. It includes image denoising, estimation of the cone photoreceptor cell number, superpixels segmentation, merging of superpixels, and final identification and mosaicking processing steps. The effectiveness of the presented method was confirmed based on its comparison with a manual method in terms of precision, recall, and F1-score of 77.3%, 95.2%, and 85.3%, respectively. An automated superpixels identification/mosaicking method is presented for the analysis of cone photoreceptor cells with the use of adaptive optics scanning laser ophthalmoscope (AO-SLO) images. This is an image oversegmentation method used for the identification and mosaicking of cone photoreceptor cells in AO-SLO images. It includes image denoising, estimation of the cone photoreceptor cell number, superpixels segmentation, merging of superpixels, and final identification and mosaicking processing steps. The effectiveness of the presented method was confirmed based on its comparison with a manual method in terms of precision, recall, and F1-score of 77.3%, 95.2%, and 85.3%, respectively.
Chinese Optics Letters
- Publication Date: Oct. 10, 2020
- Vol. 18, Issue 10, 101701 (2020)
Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction
Jiaju Cheng, and Jianwen Luo
For fluorescence molecular tomography (FMT), image quality could be improved by incorporating a sparsity constraint. The L1 norm regularization method has been proven better than the L2 norm, like Tikhonov regularization. However, the Tikhonov method was found capable of achieving a similar quality at a high iteration cost by adopting a zeroing strategy. By studying the reason, a Tikhonov-regularization-based projecting sparsity pursuit method was proposed that reduces the iterations significantly and achieves good image quality. It was proved in phantom experiments through time-domain FMT that the method could obtain higher accuracy and less oversparsity and is more applicable for heterogeneous-target reconstruction, compared with several regularization methods implemented in this Letter. For fluorescence molecular tomography (FMT), image quality could be improved by incorporating a sparsity constraint. The L1 norm regularization method has been proven better than the L2 norm, like Tikhonov regularization. However, the Tikhonov method was found capable of achieving a similar quality at a high iteration cost by adopting a zeroing strategy. By studying the reason, a Tikhonov-regularization-based projecting sparsity pursuit method was proposed that reduces the iterations significantly and achieves good image quality. It was proved in phantom experiments through time-domain FMT that the method could obtain higher accuracy and less oversparsity and is more applicable for heterogeneous-target reconstruction, compared with several regularization methods implemented in this Letter.
Chinese Optics Letters
- Publication Date: Jan. 10, 2020
- Vol. 18, Issue 1, 011701 (2020)
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