This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications. The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest. Polarization information is increasingly incorporated into convolutional neural networks (CNN) as a supplemental feature of objects to improve performance in computer vision task applications. Polarimetric imaging and deep learning can extract abundant information to address various challenges. Therefore, this article briefly reviews recent developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection, and biomedical imaging. Furthermore, we synthetically analyze the input, datasets, and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages. We also highlight the significance of data-driven polarimetric imaging in future research and development.

The aim of this study was to discover a simple/convenient geometrical arrangement of radiation sources and detector to acquire finger-photoplethysmograms (PPGs) with wavelength regions of blood glucose (BGL) absorption, toward practical noninvasive BGL measurement. First, we compared PPGs with three wavelengths: 808 nm (without water absorption), 1160 nm (with weak water absorption), and 1600 nm (with nearly peak BGL absorption and strong water absorption), while the source-detector spacing was successively increased circumferentially around a fingertip. In 10 healthy subjects, we observed clear cardiac-related pulsatile components of PPG signals at 808 and 1160 nm in any incident positions with more than 15 dB of signal-to-noise ratio ($S/N$), but reliable PPG detections at 1600 nm with more than 10 dB of $S/N$ was only possible when the source-detector distance was less than 3 mm around the fingertip circumference. Second, with this arrangement, an experiment was performed using six wavelengths to cover glucose absorption bands (from 1550 to 1749 nm), obtaining pulsatile PPG signals with more or less 15 dB of $S/N$. Through the present experiments, this orthogonal arrangement of the source and detector to detect forward- and side-scattered radiation through the tissue is appropriate for PPG measurements with wavelength regions where there is potential for BGL measurement.

Using near-infrared (NIR) light with 700–1200 nm wavelength, transillumination images of small animals and thin parts of a human body such as a hand or foot can be obtained. They are two-dimensional (2D) images of internal absorbing structures in a turbid medium. A three-dimensional (3D) see-through image is obtainable if one can identify the depth of each part of the structure in the 2D image. Nevertheless, the obtained transillumination images are blurred severely because of the strong scattering in the turbid medium. Moreover, ascertaining the structure depth from a 2D transillumination image is difficult. To overcome these shortcomings, we have developed a new technique using deep learning principles. A fully convolutional network (FCN) was trained with 5,000 training pairs of clear and blurred images. Also, a convolutional neural network (CNN) was trained with 42,000 training pairs of blurred images and corresponding depths in a turbid medium. Numerous training images were provided by the convolution with a point spread function derived from diffusion approximation to the radiative transport equation. The validity of the proposed technique was confirmed through simulation. Experiments demonstrated its applicability. This technique can provide a new tool for the NIR imaging of animal bodies and biometric authentication of a human body.

The classical transillumination technique has been revitalized through recent advancements in optical technology, enhancing its applicability in the realm of biomedical research. With a new perspective on near-axis scattered light, we have harnessed near-infrared (NIR) light to visualize intricate internal light-absorbing structures within animal bodies. By leveraging the principle of differentiation, we have extended the applicability of the Beer–Lambert law even in cases of scattering-dominant media, such as animal body tissues. This approach facilitates the visualization of dynamic physiological changes occurring within animal bodies, thereby enabling noninvasive, real-time imaging of macroscopic functionality in vivo. An important challenge inherent to transillumination imaging lies in the image blur caused by pronounced light scattering within body tissues. By extracting near-axis scattered components from the predominant diffusely scattered light, we have achieved cross-sectional imaging of animal bodies. Furthermore, we have introduced software-based techniques encompassing deconvolution using the point spread function and the application of deep learning principles to counteract the scattering effect. Finally, transillumination imaging has been elevated from two-dimensional to three-dimensional imaging. The effectiveness and applicability of these proposed techniques have been validated through comprehensive simulations and experiments involving human and animal subjects. As demonstrated through these studies, transillumination imaging coupled with emerging technologies offers a promising avenue for future biomedical applications.

Despite continued improvement in therapies to prevent acute allograft rejection, transplant-associated arteriosclerosis remains a major long-term limitation to successful solid organ transplantation. Ongoing intimal hyperplasia of arterioles results in progressive vascular stenosis with consequent graft ischemia. The process is driven by successive waves of inflammatory cell infiltrates beginning with cells of acute and chronic innate immunity and culminating in alloantigen-specific T cells. Finally, smooth muscle cells, largely derived from host precursor cells, are also recruited into the vessel intima where they proliferate and produce extracellular matrix. The process of inflammatory cell and smooth muscle cell recruitment depends on the coordinated production of chemokines, produced both by graft endothelial cells as well as by previously recruited host inflammatory cells. This article reviews recent progress in our understanding of these small molecular weight mediators and suggests potential novel therapeutic intervention via blockade of chemokine and chemokine receptor interactions.