Journal articles on the topic 'Computational methods in biomedical optical imaging'
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1
Liu, Xueyan, Dong Peng, Wei Guo, Xibo Ma, Xin Yang, and Jie Tian. "Compressed Sensing Photoacoustic Imaging Based on Fast Alternating Direction Algorithm." International Journal of Biomedical Imaging 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/206214.
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Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications.
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Laurino, Annunziatina, Alessandra Franceschini, Luca Pesce, Lorenzo Cinci, Alberto Montalbano, Giacomo Mazzamuto, Giuseppe Sancataldo, et al. "A Guide to Perform 3D Histology of Biological Tissues with Fluorescence Microscopy." International Journal of Molecular Sciences 24, no. 7 (April 4, 2023): 6747. http://dx.doi.org/10.3390/ijms24076747.
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The analysis of histological alterations in all types of tissue is of primary importance in pathology for highly accurate and robust diagnosis. Recent advances in tissue clearing and fluorescence microscopy made the study of the anatomy of biological tissue possible in three dimensions. The combination of these techniques with classical hematoxylin and eosin (H&E) staining has led to the birth of three-dimensional (3D) histology. Here, we present an overview of the state-of-the-art methods, highlighting the optimal combinations of different clearing methods and advanced fluorescence microscopy techniques for the investigation of all types of biological tissues. We employed fluorescence nuclear and eosin Y staining that enabled us to obtain hematoxylin and eosin pseudo-coloring comparable with the gold standard H&E analysis. The computational reconstructions obtained with 3D optical imaging can be analyzed by a pathologist without any specific training in volumetric microscopy, paving the way for new biomedical applications in clinical pathology.
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Zaitsev, Vladimir Y., Sergey Y. Ksenofontov, Alexander A. Sovetsky, Alexander L. Matveyev, Lev A. Matveev, Alexey A. Zykov, and Grigory V. Gelikonov. "Real-Time Strain and Elasticity Imaging in Phase-Sensitive Optical Coherence Elastography Using a Computationally Efficient Realization of the Vector Method." Photonics 8, no. 12 (November 24, 2021): 527. http://dx.doi.org/10.3390/photonics8120527.
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We present a real-time realization of OCT-based elastographic mapping local strains and distribution of the Young’s modulus in biological tissues, which is in high demand for biomedical usage. The described variant exploits the principle of Compression Optical Coherence Elastography (C-OCE) and uses processing of phase-sensitive OCT signals. The strain is estimated by finding local axial gradients of interframe phase variations. Instead of the popular least-squares method for finding these gradients, we use the vector approach, one of its advantages being increased computational efficiency. Here, we present a modified, especially fast variant of this approach. In contrast to conventional correlation-based methods and previously used phase-resolved methods, the described method does not use any search operations or local calculations over a sliding window. Rather, it obtains local strain maps (and then elasticity maps) using several transformations represented as matrix operations applied to entire complex-valued OCT scans. We first elucidate the difference of the proposed method from the previously used correlational and phase-resolved methods and then describe the proposed method realization in a medical OCT device, in which for real-time processing, a “typical” central processor (e.g., Intel Core i7-8850H) is sufficient. Representative examples of on-flight obtained elastographic images are given. These results open prospects for broad use of affordable OCT devices for high-resolution elastographic vitalization in numerous biomedical applications, including the use in clinic.
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Sridhar, Chethana, Piyush Kumar Pareek, R. Kalidoss, Sajjad Shaukat Jamal, Prashant Kumar Shukla, and Stephen Jeswinde Nuagah. "Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ." Journal of Healthcare Engineering 2022 (February 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/2354866.
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Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.
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Hauptman, Ami, Ganesh M. Balasubramaniam, and Shlomi Arnon. "Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming." Bioengineering 10, no. 3 (March 21, 2023): 382. http://dx.doi.org/10.3390/bioengineering10030382.
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Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called “XGBoost” to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.
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Jiang, Yuan, Hao Sha, Shuai Liu, Peiwu Qin, and Yongbing Zhang. "AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging." Biomedical Optics Express 14, no. 9 (August 22, 2023): 4814. http://dx.doi.org/10.1364/boe.498421.
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Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models, and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A transfer learning framework is further devised to allow our AutoUnmix to adapt to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve the highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. For experiments where spectral profiles and morphology are akin to simulated data, our method realizes the quantitative performance demonstrated above. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool for studying the interaction process of different organelles labeled by multiple fluorophores.
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Akman, Ozgur E., Steven Watterson, Andrew Parton, Nigel Binns, Andrew J. Millar, and Peter Ghazal. "Digital clocks: simple Boolean models can quantitatively describe circadian systems." Journal of The Royal Society Interface 9, no. 74 (April 12, 2012): 2365–82. http://dx.doi.org/10.1098/rsif.2012.0080.
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The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day–night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.
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Mostaço-Guidolin, Leila B., Michael S. D. Smith, Mark Hewko, Bernie Schattka, Michael G. Sowa, Arkady Major, and Alex C. T. Ko. "Fractal dimension and directional analysis of elastic and collagen fiber arrangement in unsectioned arterial tissues affected by atherosclerosis and aging." Journal of Applied Physiology 126, no. 3 (March 1, 2019): 638–46. http://dx.doi.org/10.1152/japplphysiol.00497.2018.
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Structural proteins like collagen and elastin are major constituents of the extracellular matrix (ECM). ECM degradation and remodeling in diseases significantly impact the microorganization of these structural proteins. Therefore, tracking the changes of collagen and elastin fiber morphological features within ECM impacted by disease progression could provide valuable insight into pathological processes such as tissue fibrosis and atherosclerosis. Benefiting from its intrinsic high-resolution imaging power and superior biochemical specificity, nonlinear optical microscopy (NLOM) is capable of providing information critical to the understanding of ECM remodeling. In this study, alterations of structural fibrillar proteins such as collagen and elastin in arteries excised from atherosclerotic rabbits were assessed by the combination of NLOM images and textural analysis methods such as fractal dimension (FD) and directional analysis (DA). FD and DA were tested for their performance in tracking the changes of extracellular elastin and fibrillar collagen remodeling resulting from atherosclerosis progression/aging. Although other methods of image analysis to study the organization of elastin and collagen structures have been reported, the simplified calculations of FD and DA presented in this work prove that they are viable strategies for extracting and analyzing fiber-related morphology from disease-impacted tissues. Furthermore, this study also demonstrates the potential utility of FD and DA in studying ECM remodeling caused by other pathological processes such as respiratory diseases, several skin conditions, or even cancer. NEW & NOTEWORTHY Textural analyses such as fractal dimension (FD) and directional analysis (DA) are straightforward and computationally viable strategies to extract fiber-related morphological data from optical images. Therefore, objective, quantitative, and automated characterization of protein fiber morphology in extracellular matrix can be realized by using these methods in combination with digital imaging techniques such as nonlinear optical microscopy (NLOM), a highly effective visualization tool for fibrillar collagen and elastic network. Combining FD and DA with NLOM is an innovative approach to track alterations of structural fibrillar proteins. The results illustrated in this study not only prove the effectiveness of FD and DA methods in extracellular protein characterization but also demonstrate their potential value in clinical and basic biomedical research where protein microstructure characterization is critical.
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Zhang, Huiting, Dong-Hee Kang, Marie Piantino, Daisuke Tominaga, Takashi Fujimura, Noriyuki Nakatani, J. Nicholas Taylor, Tomomi Furihata, Michiya Matsusaki, and Satoshi Fujita. "Rapid Quantification of Microvessels of Three-Dimensional Blood–Brain Barrier Model Using Optical Coherence Tomography and Deep Learning Algorithm." Biosensors 13, no. 8 (August 15, 2023): 818. http://dx.doi.org/10.3390/bios13080818.
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The blood–brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.
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Chen, Duan, Guo-Wei Wei, Wen-Xiang Cong, and Ge Wang. "Computational methods for optical molecular imaging." Communications in Numerical Methods in Engineering 25, no. 12 (December 2009): 1137–61. http://dx.doi.org/10.1002/cnm.1164.
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Hériché, Jean-Karim, Stephanie Alexander, and Jan Ellenberg. "Integrating Imaging and Omics: Computational Methods and Challenges." Annual Review of Biomedical Data Science 2, no. 1 (July 20, 2019): 175–97. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013328.
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Fluorescence microscopy imaging has long been complementary to DNA sequencing- and mass spectrometry–based omics in biomedical research, but these approaches are now converging. On the one hand, omics methods are moving from in vitro methods that average across large cell populations to in situ molecular characterization tools with single-cell sensitivity. On the other hand, fluorescence microscopy imaging has moved from a morphological description of tissues and cells to quantitative molecular profiling with single-molecule resolution. Recent technological developments underpinned by computational methods have started to blur the lines between imaging and omics and have made their direct correlation and seamless integration an exciting possibility. As this trend continues rapidly, it will allow us to create comprehensive molecular profiles of living systems with spatial and temporal context and subcellular resolution. Key to achieving this ambitious goal will be novel computational methods and successfully dealing with the challenges of data integration and sharing as well as cloud-enabled big data analysis.
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Weissleder, Ralph, and Matthias Nahrendorf. "Advancing biomedical imaging." Proceedings of the National Academy of Sciences 112, no. 47 (November 24, 2015): 14424–28. http://dx.doi.org/10.1073/pnas.1508524112.
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Imaging reveals complex structures and dynamic interactive processes, located deep inside the body, that are otherwise difficult to decipher. Numerous imaging modalities harness every last inch of the energy spectrum. Clinical modalities include magnetic resonance imaging (MRI), X-ray computed tomography (CT), ultrasound, and light-based methods [endoscopy and optical coherence tomography (OCT)]. Research modalities include various light microscopy techniques (confocal, multiphoton, total internal reflection, superresolution fluorescence microscopy), electron microscopy, mass spectrometry imaging, fluorescence tomography, bioluminescence, variations of OCT, and optoacoustic imaging, among a few others. Although clinical imaging and research microscopy are often isolated from one another, we argue that their combination and integration is not only informative but also essential to discovering new biology and interpreting clinical datasets in which signals invariably originate from hundreds to thousands of cells per voxel.
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Aruleba, Kehinde, George Obaido, Blessing Ogbuokiri, Adewale Oluwaseun Fadaka, Ashwil Klein, Tayo Alex Adekiya, and Raphael Taiwo Aruleba. "Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review." Journal of Imaging 6, no. 10 (October 8, 2020): 105. http://dx.doi.org/10.3390/jimaging6100105.
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With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.
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Hillman, Elizabeth M. C., Cyrus B. Amoozegar, Tracy Wang, Addason F. H. McCaslin, Matthew B. Bouchard, James Mansfield, and Richard M. Levenson. "In vivo optical imaging and dynamic contrast methods for biomedical research." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, no. 1955 (November 28, 2011): 4620–43. http://dx.doi.org/10.1098/rsta.2011.0264.
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This paper provides an overview of optical imaging methods commonly applied to basic research applications. Optical imaging is well suited for non-clinical use, since it can exploit an enormous range of endogenous and exogenous forms of contrast that provide information about the structure and function of tissues ranging from single cells to entire organisms. An additional benefit of optical imaging that is often under-exploited is its ability to acquire data at high speeds; a feature that enables it to not only observe static distributions of contrast, but to probe and characterize dynamic events related to physiology, disease progression and acute interventions in real time. The benefits and limitations of in vivo optical imaging for biomedical research applications are described, followed by a perspective on future applications of optical imaging for basic research centred on a recently introduced real-time imaging technique called dynamic contrast-enhanced small animal molecular imaging (DyCE).
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Nahm, Werner, Christoph Hornberger, Ute Morgenstern, and Stephan B. Sobottka. "Optical imaging methods in medicine: how can we escape the plausibility trap?" Biomedical Engineering / Biomedizinische Technik 63, no. 5 (October 25, 2018): 507–10. http://dx.doi.org/10.1515/bmt-2018-2001.
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Turchin, Il'ya V. "Methods of biomedical optical imaging: from subcellular structures to tissues and organs." Uspekhi Fizicheskih Nauk 186, no. 5 (2016): 550–67. http://dx.doi.org/10.3367/ufnr.2015.12.037734.
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Turchin, I. V. "Methods of biomedical optical imaging: from subcellular structures to tissues and organs." Physics-Uspekhi 59, no. 5 (May 31, 2016): 487–501. http://dx.doi.org/10.3367/ufne.2015.12.037734.
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Qiu, Panghe, Zhiyuan Ye, Zhichen Bai, Xin Liu, and Su Bo. "Computational Ghost Imaging with Multiplexed Time-Varying Signals." International Journal of Optics 2020 (July 18, 2020): 1–8. http://dx.doi.org/10.1155/2020/4109612.
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This study proposes two methods of optical watermarking based on multiplexed time-varying signals for computational ghost imaging using the Hadamard matrices. The proposed methods can realize image fusion and dual optical encryption. The time-varying signal is encoded into a specific Hadamard coefficient in advance and hidden in the light source of the transmitting end as a multiplicative factor or loaded at the receiving end as an additive factor. Theory and experiments confirm the feasibility of this scheme. Moreover, the scheme is highly scalable and has potential applications in multispectral single-pixel imaging.
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Rundo, Leonardo, Andrea Tangherloni, and Carmelo Militello. "Artificial Intelligence Applied to Medical Imaging and Computational Biology." Applied Sciences 12, no. 18 (September 8, 2022): 9052. http://dx.doi.org/10.3390/app12189052.
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The Special Issue “Artificial Intelligence Applied to Medical Imaging and Computational Biology” of the Applied Sciences Journal has been curated from February 2021 to May 2022, which covered the state-of-the-art and novel algorithms and applications of Artificial Intelligence methods for biomedical data analysis, ranging from classic Machine Learning to Deep Learning [...]
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Zheng, Peixia, Qi Dai, Zile Li, Zhiyuan Ye, Jun Xiong, Hong-Chao Liu, Guoxing Zheng, and Shuang Zhang. "Metasurface-based key for computational imaging encryption." Science Advances 7, no. 21 (May 2021): eabg0363. http://dx.doi.org/10.1126/sciadv.abg0363.
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Optical metasurfaces can offer high-quality multichannel displays by modulating different degrees of freedom of light, demonstrating great potential in the next generation of optical encryption and anti-counterfeiting. Different from the direct imaging modality of metasurfaces, single-pixel imaging (SPI) as a typical computational imaging technique obtains the object image from a decryption-like computational process. Here, we propose an optical encryption scheme by introducing metasurface-images (meta-images) into the encoding and decoding processes as the keys of SPI encryption. Different high-quality meta-images generated by a dual-channel Malus metasurface play the role of keys to encode multiple target images and retrieve them following the principle of SPI. Our work eliminates the conventional digital transmission process of keys in SPI encryption, enables the reusability of a single metasurface in different encryption processes, and thereby paves the way toward a high-security optical encryption between direct and indirect imaging methods.
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Gao, Wendi, Libo Zhao, Zhuangde Jiang, and Dong Sun. "Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications." Applied Sciences 10, no. 20 (October 19, 2020): 7308. http://dx.doi.org/10.3390/app10207308.
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Intracellular micromanipulation assisted by robotic systems has valuable applications in biomedical research, such as genetic diagnosis and genome-editing tasks. However, current studies suffer from a low success rate and a large operation damage because of insufficient information on the operation information of targeted specimens. The complexity of the intracellular environment causes difficulties in visualizing manipulation tools and specimens. This review summarizes and analyzes the current development of advanced biological imaging sampling and computational processing methods in intracellular micromanipulation applications. It also discusses the related limitations and future extension, providing an important reference about this field.
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Farkas, Daniel L. "Biomedical Applications of Translational Optical Imaging: From Molecules to Humans." Molecules 26, no. 21 (November 2, 2021): 6651. http://dx.doi.org/10.3390/molecules26216651.
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Light is a powerful investigational tool in biomedicine, at all levels of structural organization. Its multitude of features (intensity, wavelength, polarization, interference, coherence, timing, non-linear absorption, and even interactions with itself) able to create contrast, and thus images that detail the makeup and functioning of the living state can and should be combined for maximum effect, especially if one seeks simultaneously high spatiotemporal resolution and discrimination ability within a living organism. The resulting high relevance should be directed towards a better understanding, detection of abnormalities, and ultimately cogent, precise, and effective intervention. The new optical methods and their combinations needed to address modern surgery in the operating room of the future, and major diseases such as cancer and neurodegeneration are reviewed here, with emphasis on our own work and highlighting selected applications focusing on quantitation, early detection, treatment assessment, and clinical relevance, and more generally matching the quality of the optical detection approach to the complexity of the disease. This should provide guidance for future advanced theranostics, emphasizing a tighter coupling—spatially and temporally—between detection, diagnosis, and treatment, in the hope that technologic sophistication such as that of a Mars rover can be translationally deployed in the clinic, for saving and improving lives.
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Rodimova, Svetlana, Nikolai Bobrov, Artem Mozherov, Vadim Elagin, Maria Karabut, Ilya Shchechkin, Dmitry Kozlov, et al. "Optical Biomedical Imaging Reveals Criteria for Violated Liver Regenerative Potential." Cells 12, no. 3 (February 2, 2023): 479. http://dx.doi.org/10.3390/cells12030479.
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To reduce the risk of post-hepatectomy liver failure in patients with hepatic pathologies, it is necessary to develop an approach to express the intraoperative assessment of the liver’s regenerative potential. Traditional clinical methods do not enable the prediction of the function of the liver remnant. Modern label-free bioimaging, using multiphoton microscopy in combination with second harmonic generation (SHG) and fluorescence lifetime imaging microscopy (FLIM), can both expand the possibilities for diagnosing liver pathologies and for assessing the regenerative potential of the liver. Using multiphoton and SHG microscopy, we assessed the structural state of liver tissue at different stages of induced steatosis and fibrosis before and after 70% partial hepatectomy in rats. Using FLIM, we also performed a detailed analysis of the metabolic state of the hepatocytes. We were able to determine criteria that can reveal a lack of regenerative potential in violated liver, such as the presence of zones with reduced NAD(P)H autofluorescence signals. Furthermore, for a liver with pathology, there was an absence of the jump in the fluorescence lifetime contributions of the bound form of NADH and NADPH the 3rd day after hepatectomy that is characteristic of normal liver regeneration. Such results are associated with decreased intensity of oxidative phosphorylation and of biosynthetic processes in pathological liver, which is the reason for the impaired liver recovery. This modern approach offers an effective tool that can be successfully translated into the clinic for express, intraoperative assessment of the regenerative potential of the pathological liver of a patient.
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Wijesinghe, Philip, David D. Sampson, and Brendan F. Kennedy. "Computational optical palpation: a finite-element approach to micro-scale tactile imaging using a compliant sensor." Journal of The Royal Society Interface 14, no. 128 (March 2017): 20160878. http://dx.doi.org/10.1098/rsif.2016.0878.
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High-resolution tactile imaging, superior to the sense of touch, has potential for future biomedical applications such as robotic surgery. In this paper, we propose a tactile imaging method, termed computational optical palpation, based on measuring the change in thickness of a thin, compliant layer with optical coherence tomography and calculating tactile stress using finite-element analysis. We demonstrate our method on test targets and on freshly excised human breast fibroadenoma, demonstrating a resolution of up to 15–25 µm and a field of view of up to 7 mm. Our method is open source and readily adaptable to other imaging modalities, such as ultrasonography and confocal microscopy.
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Moon, Inkyu, and Bahram Javidi. "Three-dimensional identification of stem cells by computational holographic imaging." Journal of The Royal Society Interface 4, no. 13 (November 21, 2006): 305–13. http://dx.doi.org/10.1098/rsif.2006.0175.
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We present an optical imaging system and mathematical algorithms for three-dimensional sensing and identification of stem cells. Data acquisition of stem cells is based on holographic microscopy in the Fresnel domain by illuminating the cells with a laser. In this technique, the holograms of stem cells are optically recorded with an image sensor array interfaced with a computer and three-dimensional images of the stem cells are reconstructed from the Gabor-filtered digital holograms. The Gabor wavelet transformation for feature extraction of the digital hologram is performed to improve the process of identification. The inverse Fresnel transformation of the Gabor-filtered digital hologram is performed to reconstruct the multi-scale three-dimensional images of the stem cells at different depths along the longitudinal direction. For recognition and classification of stem cells, a statistical approach using an empirical cumulative density function is introduced. The experiments indicate that the proposed system can be potentially useful for recognizing and classifying stem cells. To the best of our knowledge, this is the first report on using three-dimensional holographic microscopy for automated identification of stem cells.
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Zhang, Lin, and Guanglei Zhang. "Brief review on learning-based methods for optical tomography." Journal of Innovative Optical Health Sciences 12, no. 06 (November 2019): 1930011. http://dx.doi.org/10.1142/s1793545819300118.
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Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.
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Elson, Daniel S., Rui Li, Christopher Dunsby, Robert Eckersley, and Meng-Xing Tang. "Ultrasound-mediated optical tomography: a review of current methods." Interface Focus 1, no. 4 (June 2, 2011): 632–48. http://dx.doi.org/10.1098/rsfs.2011.0021.
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Ultrasound-mediated optical tomography (UOT) is a hybrid technique that is able to combine the high penetration depth and high spatial resolution of ultrasound imaging to overcome the limits imposed by optical scattering for deep tissue optical sensing and imaging. It has been proposed as a method to detect blood concentrations, oxygenation and metabolism at depth in tissue for the detection of vascularized tumours or the presence of absorbing or scattering contrast agents. In this paper, the basic principles of the method are outlined and methods for simulating the UOT signal are described. The main detection methods are then summarized with a discussion of the advantages and disadvantages of each. The recent focus on increasing the weak UOT signal through the use of the acoustic radiation force is explained, together with a summary of our results showing sensitivity to the mechanical shear stiffness and optical absorption properties of tissue-mimicking phantoms.
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Valdés, Pablo A., David W. Roberts, Fa-Ke Lu, and Alexandra Golby. "Optical technologies for intraoperative neurosurgical guidance." Neurosurgical Focus 40, no. 3 (March 2016): E8. http://dx.doi.org/10.3171/2015.12.focus15550.
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Biomedical optics is a broadly interdisciplinary field at the interface of optical engineering, biophysics, computer science, medicine, biology, and chemistry, helping us understand light–tissue interactions to create applications with diagnostic and therapeutic value in medicine. Implementation of biomedical optics tools and principles has had a notable scientific and clinical resurgence in recent years in the neurosurgical community. This is in great part due to work in fluorescence-guided surgery of brain tumors leading to reports of significant improvement in maximizing the rates of gross-total resection. Multiple additional optical technologies have been implemented clinically, including diffuse reflectance spectroscopy and imaging, optical coherence tomography, Raman spectroscopy and imaging, and advanced quantitative methods, including quantitative fluorescence and lifetime imaging. Here we present a clinically relevant and technologically informed overview and discussion of some of the major clinical implementations of optical technologies as intraoperative guidance tools in neurosurgery.
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Grosenick, Dirk, Heidrun Wabnitz, and Rainer Macdonald. "Diffuse near-infrared imaging of tissue with picosecond time resolution." Biomedical Engineering / Biomedizinische Technik 63, no. 5 (October 25, 2018): 511–18. http://dx.doi.org/10.1515/bmt-2017-0067.
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Abstract Optical imaging of biological tissue in vivo at multiple wavelengths in the near-infrared (NIR) spectral range can be achieved with picosecond time resolution at high sensitivity by time-correlated single photon counting. Measuring and analyzing the distribution of times of flight of photons randomly propagated through the tissue has been applied for diffuse optical imaging and spectroscopy, e.g. of human breast tissue and of the brain. In this article, we review the main features and the potential of NIR multispectral imaging with picosecond time resolution and illustrate them by exemplar applications in these fields. In particular, we discuss the experimental methods developed at the Physikalisch-Technische Bundesanstalt (PTB) to record optical mammograms and to quantify the absorption and scattering properties from which hemoglobin concentration and oxygen saturation of healthy and diseased breast tissue have been derived by combining picosecond time-domain and spectral information. Furthermore, optical images of functional brain activation were obtained by a non-contact scanning device exploiting the null source-detector separation approach which takes advantage of the picosecond time resolution as well. The recorded time traces of changes in the oxy- and deoxyhemoglobin concentrations during a motor stimulation investigation show a localized response from the brain.
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Attota, Ravi Kiran. "Through-focus or volumetric type of optical imaging methods: a review." Journal of Biomedical Optics 23, no. 07 (July 6, 2018): 1. http://dx.doi.org/10.1117/1.jbo.23.7.070901.
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Lademann, Jürgen. "Special Section Guest Editorial: Optical Methods of Imaging in the Skin." Journal of Biomedical Optics 18, no. 6 (June 28, 2013): 061201. http://dx.doi.org/10.1117/1.jbo.18.6.061201.
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Medina, R., J. Montagnat, and V. Breton. "DataGrid, Prototype of a Biomedical Grid." Methods of Information in Medicine 42, no. 02 (2003): 143–47. http://dx.doi.org/10.1055/s-0038-1634325.
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Summary Background: The availability of large amounts of data in heterogeneous formats and the rapid progress in fields such as computer based drug design, medical imaging and medical simulations have lead to a growing demand for large computational power and easy accessibility to heterogeneous data sources. Objectives: The goal is to address these needs by deploying computing grids. Grids provide both large scale and distributed storage facilities and an increased computing power. Moreover, Grids are a promising tool to foster the synergy between bioinformatics and computerised medical imaging. Methods: A first biomedical grid is being deployed within the framework of the DataGrid IST project (www.edg.org). The goal of the project is to provide a novel environment to support globally distributed scientific exploration involving up to multi-Perabyte datasets. Results and Conclusions: The first biomedical applications deployed inside the project demonstrate the relevance of the grid paradigm for genomics and medical image processing. They also highlight the specific requirements of the biomedical community.
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Boone, Donald C. "Second Harmonic Generation in Lithiated Silicon Nanowires: Derivations and Computational Methods." European Journal of Applied Physics 3, no. 6 (December 5, 2021): 36–46. http://dx.doi.org/10.24018/ejphysics.2021.3.6.130.
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This research will examine the computational methods to calculate the nonlinear optical process of second harmonic generation (SHG) that will be hypothesized to be present during lithium ion insertion into silicon nanowires. First it will be determined whether the medium in which SHG is conveyed is non-centrosymmetric or whether the medium is inversion symmetric where SHG as a part of the second-order nonlinear optical phenomenon does not exist. It will be demonstrated that the main interaction that determines SHG is multiphoton absorption on lithium ions. The quantum harmonic oscillator (QHO) is used as the background that generates coherent states for electrons and photons that transverse the length of the silicon nanowire. The matrix elements of the Hamiltonian which represents the energy of the system will be used to calculate the probability density of second-order nonlinear optical interactions which includes collectively SHG, sum-frequency generation (SFG) and difference-frequency generation (DFG). As a result, it will be seen that at varies concentrations of lithium ions (Li+) within the crystallized silicon (c-Si) matrix the second-order nonlinear optical process has probabilities substantial enough to create second harmonic generation that could possibly be used for such applications as second harmonic imaging microscopy.
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Liu, Kai, Xiao Yu, Yongsen Xu, Yulei Xu, Yuan Yao, Nan Di, Yefei Wang, Hao Wang, and Honghai Shen. "Computational Imaging for Simultaneous Image Restoration and Super-Resolution Image Reconstruction of Single-Lens Diffractive Optical System." Applied Sciences 12, no. 9 (May 9, 2022): 4753. http://dx.doi.org/10.3390/app12094753.
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Diffractive optical elements (DOEs) are difficult to apply in natural scenes imaging covering the visible bandwidth-spectral due to their strong chromatic aberration and the decrease in diffraction efficiency. Advances in computational imaging make it possible. In this paper, the image quality degradation model of DOE in bandwidth-spectral imaging is established to quantitatively analyze its degradation process. We design a DDZMR network for a single-lens diffractive lens computational imaging system, which can simultaneously perform image restoration and image super-resolution reconstruction on degraded images. The multimodal loss function was created to evaluate the reconstruction of the diffraction imaging degradation by the DDZMR network. The prototype physical prototype of the single-lens harmonic diffraction computational imaging system (SHDCIS) was built to verify the imaging performance. SHDCIS testing showed that optical chromatic aberration is corrected by computational reconstruction, and the computational imaging module can interpret an image and restore it at 1.4 times the resolution. We also evaluated the performance of the DDZMR model using the B100 and Urban100 datasets. Mean Peak Signal to Noise Ratio (PSNR)/Structural Similarity (SSIM) were, respectively, 32.09/0.8975 and 31.82/0.9247, which indicates that DDZMR performed comparably to the state-of-the-art (SOTA) methods. This work can promote the development and application of diffractive imaging systems in the imaging of natural scenes in the bandwidth-spectrum.
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Liu, Yujie, Binxiao Li, Baohong Liu, and Kun Zhang. "Single-Particle Optical Imaging for Ultrasensitive Bioanalysis." Biosensors 12, no. 12 (December 1, 2022): 1105. http://dx.doi.org/10.3390/bios12121105.
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The quantitative detection of critical biomolecules and in particular low-abundance biomarkers in biofluids is crucial for early-stage diagnosis and management but remains a challenge largely owing to the insufficient sensitivity of existing ensemble-sensing methods. The single-particle imaging technique has emerged as an important tool to analyze ultralow-abundance biomolecules by engineering and exploiting the distinct physical and chemical property of individual luminescent particles. In this review, we focus and survey the latest advances in single-particle optical imaging (OSPI) for ultrasensitive bioanalysis pertaining to basic biological studies and clinical applications. We first introduce state-of-the-art OSPI techniques, including fluorescence, surface-enhanced Raman scattering, electrochemiluminescence, and dark-field scattering, with emphasis on the contributions of various metal and nonmetal nano-labels to the improvement of the signal-to-noise ratio. During the discussion of individual techniques, we also highlight their applications in spatial–temporal measurement of key biomarkers such as proteins, nucleic acids and extracellular vesicles with single-entity sensitivity. To that end, we discuss the current challenges and prospective trends of single-particle optical-imaging-based bioanalysis.
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Zhang, Rui, Ya-Zhou Xue, and Xiao-Feng Yang. "Biomedical optical properties of color light and near-infrared fluorescence separated-merged imager." Journal of Innovative Optical Health Sciences 12, no. 06 (November 2019): 1940001. http://dx.doi.org/10.1142/s1793545819400017.
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Objective: We study the biomedical optical properties of the color light and near-infrared fluorescence separated-merged imager. Materials and Methods: The color light and near-infrared fluorescence separated-merged imager can illuminate the visible light and the near-infrared light of [Formula: see text][Formula: see text]nm, receiving the reflected light and [Formula: see text][Formula: see text]nm near-infrared fluorescence, and display the color, fluorescence and merge image. ICG solution of different concentration, including standing time, was allocated to study the best imaging condition in vitro, and the depth of fluorescence penetration was studied with 5% agarose gel; the imaging characteristics of the imager was studied using SD rat; and then the SLNs tracing in 4 cases of penile carcinoma was performed. Results: When the concentration of ICG is 13.11[Formula: see text][Formula: see text]mol/L, the fluorescence intensity and the merge image are the best. The maximum depth of fluorescence imaging is 9[Formula: see text]mm in 5% agarose gel, while the bone has the greatest influence on it. The SLNs tracing shows that the imager can locate the SLNs in vitro, to achieve perioperative navigation during biopsy. Conclusion: There are many factors that affect the imaging effect, but the imaging effect of the imager meets the requirement of vision in a wide range, and can effectively trace the SLNs in perioperative period.
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Lee, Joon-Jae, Donghak Shin, and Hoon Yoo. "Image quality improvement in computational reconstruction of partially occluded objects using two computational integral imaging reconstruction methods." Optics Communications 304 (September 2013): 96–101. http://dx.doi.org/10.1016/j.optcom.2013.04.042.
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Siddique, Sarkar, and James C. L. Chow. "Application of Nanomaterials in Biomedical Imaging and Cancer Therapy." Nanomaterials 10, no. 9 (August 29, 2020): 1700. http://dx.doi.org/10.3390/nano10091700.
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Nanomaterials, such as nanoparticles, nanorods, nanosphere, nanoshells, and nanostars, are very commonly used in biomedical imaging and cancer therapy. They make excellent drug carriers, imaging contrast agents, photothermal agents, photoacoustic agents, and radiation dose enhancers, among other applications. Recent advances in nanotechnology have led to the use of nanomaterials in many areas of functional imaging, cancer therapy, and synergistic combinational platforms. This review will systematically explore various applications of nanomaterials in biomedical imaging and cancer therapy. The medical imaging modalities include magnetic resonance imaging, computed tomography, positron emission tomography, single photon emission computerized tomography, optical imaging, ultrasound, and photoacoustic imaging. Various cancer therapeutic methods will also be included, including photothermal therapy, photodynamic therapy, chemotherapy, and immunotherapy. This review also covers theranostics, which use the same agent in diagnosis and therapy. This includes recent advances in multimodality imaging, image-guided therapy, and combination therapy. We found that the continuous advances of synthesis and design of novel nanomaterials will enhance the future development of medical imaging and cancer therapy. However, more resources should be available to examine side effects and cell toxicity when using nanomaterials in humans.
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Baek, YoonSeok, KyeoReh Lee, Jeonghun Oh, and YongKeun Park. "Speckle-Correlation Scattering Matrix Approaches for Imaging and Sensing through Turbidity." Sensors 20, no. 11 (June 2, 2020): 3147. http://dx.doi.org/10.3390/s20113147.
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The development of optical and computational techniques has enabled imaging without the need for traditional optical imaging systems. Modern lensless imaging techniques overcome several restrictions imposed by lenses, while preserving or even surpassing the capability of lens-based imaging. However, existing lensless methods often rely on a priori information about objects or imaging conditions. Thus, they are not ideal for general imaging purposes. The recent development of the speckle-correlation scattering matrix (SSM) techniques facilitates new opportunities for lensless imaging and sensing. In this review, we present the fundamentals of SSM methods and highlight recent implementations for holographic imaging, microscopy, optical mode demultiplexing, and quantification of the degree of the coherence of light. We conclude with a discussion of the potential of SSM and future research directions.
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Khalid, Husna, Muneeba Rafique, Aimen Qaiser, Fakhar-Ud-Din, and Gul Shahnaz. "Carbon Nanotubes: A Brief Review on Its Use for Biomedical Imaging Purpose." Global Drug Design & Development Review IV, no. I (December 30, 2019): 24–33. http://dx.doi.org/10.31703/gdddr.2019(iv-i).03.
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Carbon nanotubes (CNTs) belong to the fullerene family, also known as graphene. These graphenes are similar to the graphite sheets and when these are turn up in the cylindrical form they are known as carbon nanotubes. Currently, the most common methods used for CNTs preparation are: Electric-arc-discharge methods, Chemical-vapor-deposition method and Laserablation method. In order to cross the cell membrane, functionalization of the pristine CNTs is performed. Because of the sp2 hybridization and closely packed hexagons in their structure, functionalization of the pristine CNTs can be done easily with either therapeutic agent or the imaging agent. They have wide applications in the field of bio-imaging because of their intrinsic optical, mechanical and electrical properties. They can be used as efficient contrast agents and the biosensors as well as efficient carriers for the delivery of therapeutic or imaging agents.
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Zysk, Adam M., and Stephen A. Boppart. "Computational methods for analysis of human breast tumor tissue in optical coherence tomography images." Journal of Biomedical Optics 11, no. 5 (2006): 054015. http://dx.doi.org/10.1117/1.2358964.
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Cysewska-Sobusiak, Anna Romana. "Control of stenting procedures with optical imaging." Photonics Letters of Poland 12, no. 2 (July 1, 2020): 31. http://dx.doi.org/10.4302/plp.v12i2.988.
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The article presents how application of optical methods of imaging may aid control of more and more widespread stenting procedure. Modern gastro-videoendoscopy is especially useful in such therapeutic effects made in particular parts of the alimentary tract. The shown selected examples relate to results of real studies made in cooperation with medical specialists Full Text: PDF ReferencesA. Cysewska-Sobusiak, "Examples of acquisition and application of biooptical signals", Phot. Lett. Poland 11, 2 (2019). CrossRef P. Listewnik and A. Mazikowski, "Automatic system for optical parameters measurements of biological tissues", Phot. Lett. Poland 10, 3 (2018). CrossRef J-S. Park, S. Jeong, and D. H. Lee, "Recent Advances in Gastrointestinal Stent Development", Clin. Endosc. 48, 3 (2015). CrossRef B.S. Dhillon, "Medical device reliability and associated areas" (Boca Raton, CRC Press LLC 2000). CrossRef J.F. Rey, R. Lambert, and The ESGE Quality Assurance Committee, "ESGE Recommendations for Quality Control in Gastrointestinal Endoscopy: Guidelines for Image Documentation in Upper and Lower GI Endoscopy", Endoscopy 33 (2001). CrossRef W.M. Saltzman, "Biomedical engineering. Bridging medicine and technology" (Cambridge University Press 2009). CrossRef F.A. Duck, "Physical properties of tissue: a comprehensive reference book" (San Diego, Academia Press 1990). CrossRef A. Cysewska-Sobusiak, "One-dimensional representation of light-tissue interaction for application in noninvasive oximetry", Opt. Eng. 36, 4 (1997). CrossRef A. Cysewska-Sobusiak, P. Skrzywanek, and A. Sowier, "Utilization of Miniprobes in Modern Endoscopic Ultrasonography", IEEE Sensors Journal 6, 5 (2006). CrossRef T. Mahmood, M.A. Scaffidi, R Khan, and S.Ch. Grover, "Virtual reality simulation in endoscopy training: Current evidence and future directions", World Journal of Gastroenterol. 24, 48 (2018). CrossRef https://medical.olympusamerica.com DirectLink
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Anand, Vijayakumar, Tomas Katkus, Denver P. Linklater, Elena P. Ivanova, and Saulius Juodkazis. "Lensless Three-Dimensional Quantitative Phase Imaging Using Phase Retrieval Algorithm." Journal of Imaging 6, no. 9 (September 20, 2020): 99. http://dx.doi.org/10.3390/jimaging6090099.
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Quantitative phase imaging (QPI) techniques are widely used for the label-free examining of transparent biological samples. QPI techniques can be broadly classified into interference-based and interferenceless methods. The interferometric methods which record the complex amplitude are usually bulky with many optical components and use coherent illumination. The interferenceless approaches which need only the intensity distribution and works using phase retrieval algorithms have gained attention as they require lesser resources, cost, space and can work with incoherent illumination. With rapid developments in computational optical techniques and deep learning, QPI has reached new levels of applications. In this tutorial, we discuss one of the basic optical configurations of a lensless QPI technique based on the phase-retrieval algorithm. Simulative studies on QPI of thin, thick, and greyscale phase objects with assistive pseudo-codes and computational codes in Octave is provided. Binary phase samples with positive and negative resist profiles were fabricated using lithography, and a single plane and two plane phase objects were constructed. Light diffracted from a point object is modulated by phase samples and the corresponding intensity patterns are recorded. The phase retrieval approach is applied for 2D and 3D phase reconstructions. Commented codes in Octave for image acquisition and automation using a web camera in an open source operating system are provided.
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Yuan, Daohe, Connor M. Ellis, and Jason J. Davis. "Mesoporous Silica Nanoparticles in Bioimaging." Materials 13, no. 17 (August 27, 2020): 3795. http://dx.doi.org/10.3390/ma13173795.
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A biomedical contrast agent serves to enhance the visualisation of a specific (potentially targeted) physiological region. In recent years, mesoporous silica nanoparticles (MSNs) have developed as a flexible imaging platform of tuneable size/morphology, abundant surface chemistry, biocompatibility and otherwise useful physiochemical properties. This review discusses MSN structural types and synthetic strategies, as well as methods for surface functionalisation. Recent applications in biomedical imaging are then discussed, with a specific emphasis on magnetic resonance and optical modes together with utility in multimodal imaging.
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Chen, Lingye, Yuyang Shui, Libang Chen, Ming Li, Jinhua Chu, Xia Shen, Yikun Liu, and Jianying Zhou. "Multi-Channel Visibility Distribution Measurement via Optical Imaging." Photonics 10, no. 8 (August 18, 2023): 945. http://dx.doi.org/10.3390/photonics10080945.
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Calibration of the imaging environment is an important step in computational imaging research, as it provides an assessment of the imaging capabilities of an imaging system. Visibility is an important quantity reflecting the transparency of the atmosphere. Currently, transmissometers and optical scatterometers are the primary methods for visibility measurement. Transmissometers measure visibility along a single direction between the transmitter and receiver but encounter challenges in achieving optical alignment under long baseline conditions. Optical scatterometers measure the visibility within a localized area since they collect only a small volume of air. Hence, both transmissometers and optical scatterometers have limitations in accurately representing the visibility distribution of an inhomogeneous atmosphere. In this work, a multi-channel visibility distribution measurement via the optical imaging method is proposed and validated in a standard fog chamber. By calibrating the attenuation of infrared LED arrays, the visibility distribution over the entire field of view can be calculated based on the atmospheric visibility model. Due to the large angle of divergence of the LED, the need for optical alignment is eliminated. In further discussion, the key factors affecting the accuracy of visibility measurement are analyzed, and the results show that increasing the measurement baseline, increasing the dynamic range of the detector, and eliminating background light can effectively improve the accuracy of visibility measurement.
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Carrington, Walter A. "Advances in computational fluorescence microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 926–27. http://dx.doi.org/10.1017/s042482010017236x.
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By combining optical sections of a fluorescently labelled cell with quantitative calibration of the microscope’s blurring in 3-D , we are able to computationally reverse the blurring of the microscope. This computational process is called image restoration or deconvolution We apply this approach to images taken on a conventional wide field microscope using a cooled CCD camera. The resulting computed 3-D images have improved resolution, greater dynamic range, substantially reduced out of focus haze and substantially greater accuracy of fluorescence quantification than the unprocessed images. Requirements imposed by the biology that are addressed by our methods include the need to minimize photodamage and photobleaching and the need to minimize data acquisition time for following high speed events. We address the need for very high resolution with a sub-pixel restoration scheme. We also consider the optical aberrations associated with imaging into living samples with oil immersion objectives. We consider the need for reducing computation time. Finally, we apply our method to a variety of biological samples.
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Trede, Dennis, Jan Hendrik Kobarg, Janina Oetjen, Herbert Thiele, Peter Maass, and Theodore Alexandrov. "On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data." Journal of Integrative Bioinformatics 9, no. 1 (March 1, 2012): 1–11. http://dx.doi.org/10.1515/jib-2012-189.
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Summary In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies.A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.
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Wang, Pengcheng, Hao Sun, Wei Yang, and Yimin Fang. "Optical Methods for Label-Free Detection of Bacteria." Biosensors 12, no. 12 (December 15, 2022): 1171. http://dx.doi.org/10.3390/bios12121171.
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Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments.
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Maddalena, Lucia, Laura Antonelli, Alexandra Albu, Aroj Hada, and Mario Rosario Guarracino. "Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-free Microscopy Imaging." Algorithms 15, no. 9 (August 31, 2022): 313. http://dx.doi.org/10.3390/a15090313.
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Background: Time-lapse microscopy imaging is a key approach for an increasing number of biological and biomedical studies to observe the dynamic behavior of cells over time which helps quantify important data, such as the number of cells and their sizes, shapes, and dynamic interactions across time. Label-free imaging is an essential strategy for such studies as it ensures that native cell behavior remains uninfluenced by the recording process. Computer vision and machine/deep learning approaches have made significant progress in this area. Methods: In this review, we present an overview of methods, software, data, and evaluation metrics for the automatic analysis of label-free microscopy imaging. We aim to provide the interested reader with a unique source of information, with links for further detailed information. Results: We review the most recent methods for cell segmentation, event detection, and tracking. Moreover, we provide lists of publicly available software and datasets. Finally, we summarize the metrics most frequently adopted for evaluating the methods under exam. Conclusions: We provide hints on open challenges and future research directions.
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Kye, Hyunjun, Yuon Song, Tsedendamba Ninjbadgar, Chulhong Kim, and Jeesu Kim. "Whole-Body Photoacoustic Imaging Techniques for Preclinical Small Animal Studies." Sensors 22, no. 14 (July 8, 2022): 5130. http://dx.doi.org/10.3390/s22145130.
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Photoacoustic imaging is a hybrid imaging technique that has received considerable attention in biomedical studies. In contrast to pure optical imaging techniques, photoacoustic imaging enables the visualization of optical absorption properties at deeper imaging depths. In preclinical small animal studies, photoacoustic imaging is widely used to visualize biodistribution at the molecular level. Monitoring the whole-body distribution of chromophores in small animals is a key method used in preclinical research, including drug-delivery monitoring, treatment assessment, contrast-enhanced tumor imaging, and gastrointestinal tracking. In this review, photoacoustic systems for the whole-body imaging of small animals are explored and summarized. The configurations of the systems vary with the scanning methods and geometries of the ultrasound transducers. The future direction of research is also discussed with regard to achieving a deeper imaging depth and faster imaging speed, which are the main factors that an imaging system should realize to broaden its application in biomedical studies.