Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and regularization is used to enforce prior constraints on the object. In this paper, we propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network that can invert multiple scattered measurements to produce a high-quality image of the refractive index. Our results on both simulated and experimental datasets show that the proposed approach is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.

pdf/efficient-and-accurate-inversion-of-multiple-scattering-with-4sf0nx60kd.pdf

Efficient and accurate inversion of multiple scattering with deep learning

Review of deep learning for photoacoustic imaging

Breast cancer remains one of the world's most dangerous diseases because of the difficulty of finding cost-effective and specific targets for effective and efficient treatment methods. The biodegradability and biocompatibility properties of chitosan-based nanoparticles (ChNPs) have good prospects for targeted drug delivery systems. ChNPs can transfer various antitumor drugs to targeted sites via passive and active targeting pathways. The modification of ChNPs has attracted the researcher to the loading of drugs to targeted cancer cells. The objective of our review was to summarize and discuss the modification in ChNPs in delivering anticancer drugs against breast cancer cells from published papers recorded in Scopus, PubMed, and Google Scholar. In order to improve cellular uptake, drug accumulation, cytotoxicity, and selectivity, we examined different kinds of modification of ChNPs. Notably, these forms of ChNPs use the characteristics of the enhanced permeability and retention (EPR) effect as a proper parameter and different biological ligands, such as proteins, peptides, monoclonal antibodies, and small particles. In addition, as a targeted delivery system, ChNPs provided and significantly improved the delivery of drugs into specific breast cancer cells (MDA-MB-231, 4T1 cells, SK-BR-3, MCF-7, T47D). In conclusion, a promising technique is presented for increasing the efficacy, selectivity, and effectiveness of candidate drug carriers in the treatment of breast cancer.

https://www.mdpi.com/2073-4360/13/11/1717/pdf

Chitosan-Based Nanoparticles of Targeted Drug Delivery System in Breast Cancer Treatment

Photoacoustic imaging - a hybrid biomedical imaging modality finding its way to clinical practices. Although the photoacoustic phenomenon was known more than a century back, only in the last two decades it has been widely researched and used for biomedical imaging applications. In this review we focus on the development and progress of the technology in the last decade (2010-2020). From becoming more and more user friendly, cheaper in cost, portable in size, photoacoustic imaging promises a wide range of applications, if translated to clinic. The growth of photoacoustic community is steady, and with several new directions researchers are exploring, it is inevitable that photoacoustic imaging will one day establish itself as a regular imaging system in the clinical practices.

https://iopscience.iop.org/article/10.1088/1361-6560/abd669/pdf

Another decade of photoacoustic imaging.

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model has received tremendous attention from academic and industrial researchers who have extended it to address the scale and complexity created by medical tasks. These extensions are commonly related to enhancing the U-Net's backbone, bottleneck, or skip connections, or including representation learning, or combining it with a Transformer architecture, or even addressing probabilistic prediction of the segmentation map. Having a compendium of different previously proposed U-Net variants makes it easier for machine learning researchers to identify relevant research questions and understand the challenges of the biological tasks that challenge the model. In this work, we discuss the practical aspects of the U-Net model and organize each variant model into a taxonomy. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. Furthermore, we provide a comprehensive implementation library with trained models. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in a GitHub repository https://github.com/NITR098/Awesome-U-Net. 

Medical Image Segmentation Review: The Success of U-Net

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-24/issue-5/051407/Back-propagation-neural-network-based-reconstruction-algorithm-for-diffuse-optical/10.1117/1.JBO.24.5.051407.pdf

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.

Recently, deep neural networks have attracted great attention in photoacoustic imaging (PAI). In PAI, reconstructing the initial pressure distribution from acquired photoacoustic (PA) signals is a typically inverse problem. In this paper, an end-to-end Unet with residual blocks (Res-Unet) is designed and trained to solve the inverse problem in PAI. The performance of the proposed algorithm is explored and analyzed by comparing a recent model-resolution-based regularization algorithm (MRR) with numerical and physical phantom experiments. The improvement obtained in the reconstructed images was more than 95% in pearson correlation and 39% in peak signal-to-noise ratio in comparison to the MRR. The Res-Unet also achieved superior performance over the state-of-the-art Unet++ architecture by more than 18% in PSNR in simulation experiments.

End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging

Global spatio-temporal synergistic topology learning for skeleton-based action recognition

Unet-based for Photoacoustic Imaging Artifact Removal

The invention discloses a near-infrared spectral tomography reconstruction method based on a neural network and belongs to the field of medical image processing In a Boltzmann radiation transmissionequation, a light transmission process is regarded as an absorption and scattering process of photons in a medium, the interaction between light and tissue is determined by absorption coefficients, scattering coefficients and phase functions of reactive scattering distribution, only particle properties of the light are considered in transmission, and the volatility of the light is not considered;therefore, phenomena of polarization and interference correlated with the volatility of the light are not considered, and only the energy transmission of the light is tracked The reconstruction method based on the BP neural network is used for reconstructing the distribution of the absorption coefficients of the light, and a reconstruction result of the distribution of the absorption coefficientscan be obtained through calculation By means of the method, not only can the distribution of the absorption coefficients be accurately reconstructed, but also high computational efficiency is achieved

A near-infrared spectroscopy tomography reconstruction method based on neural network

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