Meningeal vascular network is significant in neurology and neurosurgery. However, high-resolution imaging of intact meningeal vascular network is lacking. In this work, we develop a practical experimental method to ensure that the intact meninges are morphologically unfolded and fixed in an agarose gel. With the help of high-brightness polymer dots (Pdots) as probe, macroscopic and detailed imaging of the vascular network on the intact dorsal meninges can be performed. Meningeal vessels are symmetrically distributed along the superior sagittal sinus, and the distribution of meningeal vessels had a certain degree of hierarchy. The meninges are thicker blood vessels and capillary networks from the outside to the inside. Moreover, the diameter of the capillaries is 3.96 ± 0.89 μm. Interestingly, meningeal primo vessels in the central nervous system of mice is imaged with the diameter of 4.18 ± 1.18 μm, which has not been reported previously. It is worth mentioning that we found that orthotopic xenografts of brain tumors caused the appearance of corneal neovascularization and morphological changes in optic nerve microvessels. In conclusion, our work provides an effective Pdots-based imaging method for follow-up research on meningeal vascular-related diseases, and illustrates that the eye can serve as a window for the prevention and diagnosis of brain diseases.

Single-pixel imaging (SPI) is an alternative method for obtaining images using a single photodetector, which has numerous advantages over the traditional matrix-based approach. However, most experimental SPI realizations provide relatively low resolution compared to matrix-based imaging systems. Here, we show a simple yet effective experimental method to scale up the resolution of SPI. Our imaging system utilizes patterns based on Hadamard matrices, which, when reshaped to a variable aspect ratio, allow us to improve resolution along one of the axes, while sweeping of patterns improves resolution along the second axis. This work paves the way towards novel imaging systems that retain the advantages of SPI and obtain resolution comparable to matrix-based systems.

High-resolution imaging methods of the iridocorneal angle (ICA) will lead to enhanced understanding of aqueous humor outflow mechanisms and a characterization of the trabecular meshwork (TM) morphology at the cellular level will help to better understand glaucoma mechanics (e.g., cellular level biomechanics of the particulate glaucomas). This information will translate into immense clinical value, leading to more informed and customized treatment selection, and improved monitoring of procedural interventions that lower intraocular pressure (IOP). Given ICA anatomy, imaging modalities that yield intrinsic optical sectioning or 3D imaging capability will be useful to aid in the visualization of TM layers. This minireview examines advancements in imaging the ICA in high-resolution.

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.

X-rays can penetrate deeply into biological cells and thus allow for examination of their internal structures with high spatial resolution. In this study, X-ray phase-contrast imaging and tomography is combined with an X-ray-compatible optical stretcher and microfluidic sample delivery. Using this setup, individual cells can be kept in suspension while they are examined with the X-ray beam at a synchrotron. From the recorded holograms, 2D phase shift images that are proportional to the projected local electron density of the investigated cell can be calculated. From the tomographic reconstruction of multiple such projections the 3D electron density can be obtained. The cells can thus be studied in a hydrated or even living state, thus avoiding artifacts from freezing, drying or embedding, and can in principle also be subjected to different sample environments or mechanical strains. This combination of techniques is applied to living as well as fixed and stained NIH3T3 mouse fibroblasts and the effect of the beam energy on the phase shifts is investigated. Furthermore, a 3D algebraic reconstruction scheme and a dedicated mathematical description is used to follow the motion of the trapped cells in the optical stretcher for multiple rotations.

Rhizoremediation of wetland plants is an environmentally friendly strategy for sediment phosphorous (P) removal, the basic underlying principle of which is the complex interactions between roots and microorganisms. This study investigated the immobilization and mobilization mechanisms of P in the rhizosphere of wetland plants using high-resolution spatial visualization techniques and metagenomic sequencing. Two-dimensional visualization of the spatial distribution of P, iron (Fe) and manganese (Mn) indicated that the sequestration of Fe-oxides rather than Mn-oxides caused the depletion of labile P, resulting in an increase in the Fe-adsorbed P fraction. Plants altered the rhizospheric environments and P-cycling microbial community to mobilize low-availability P from sediments. Mineral P solubilization and organic P mineralization were enhanced by local acidification and increased phosphatase activity, respectively. Microbial P mobilization also increased with increasing relative abundances of P solubilization and mineralization genes (gcd and phnW) and decreasing P transportation genes (ugpA, ugpB, and pit) genes in the rhizosphere. These processes led to the remobilization of 10.04 % of inorganic P, and 15.23 % of organic P, in the rhizosphere during the incubation period. However, the resupply of P via the above processes did not compensate for the depletion of rhizospheric P via root uptake and mineral sequestration. Our results provide novel insights into the mechanisms of rhizospheric P cycling, which will help to inform future phytoremediation strategies.

In musculoskeletal imaging, CT is used in a wide range of indications, either alone or in a synergistic approach with MRI. While MRI is the preferred modality for the assessment of soft tissues and bone marrow, CT excels in the imaging of high-contrast structures, such as mineralized tissue. Additionally, the introduction of dual-energy CT in clinical practice two decades ago opened the door for spectral imaging applications. Recently, the advent of photon-counting detectors (PCDs) has further advanced the potential of CT, at least in theory. Compared to conventional energy-integrating detectors (EIDs), PCDs provide superior spatial resolution, reduced noise, and intrinsic spectral imaging capabilities. This review briefly describes the technical advantages of PCDs. For each technical feature, the corresponding applications in musculoskeletal imaging will be discussed, including high-spatial resolution imaging for the assessment of bone and crystal deposits, low-dose applications such as whole-body CT, as well as spectral imaging applications including the characterization of crystal deposits and imaging of metal hardware. Finally, we will highlight the potential of PCD-CT in emerging applications, underscoring the need for further preclinical and clinical validation to unleash its full clinical potential.

This research presents a groundbreaking approach in virus-related research, addressing challenges in electron microscopy (EM). This imaging technique has been crucial in exploring virus structures; however, traditional methods involve complex sample preparations and the risk of contamination. Herein, we introduce an approach that overcomes these obstacles, enabling high-resolution virus imaging without toxic staining procedures. Focusing on Begomovirus particles, an economically significant plant virus genus, our images confirm their non-enveloped structure and their twin icosahedral symmetry. Our methods involve sample collection, purification, and crystallization, followed by transmission electron microscopy - selected area electron diffraction (TEM-SAED) analysis. Notably, this study achieves 2D and 3D virus imaging through standard TEM, providing a new avenue for virus structure analysis and advancing virus-related research. Remarkable high image quality stemmed from the crystallization process, offering exciting possibilities for improving virus research and diagnosis while eliminating staining limitations.

Abnormal endo-lysosomal morphology is an early cytopathological feature of Alzheimer\'s disease (AD) and genome-wide association studies (GWAS) have implicated genes involved in the endo-lysosomal network (ELN) as conferring increased risk for developing sporadic, late-onset AD (LOAD). Characterization of ELN pathology and the underlying pathophysiology is a promising area of translational AD research and drug development. However, rigorous study of ELN vesicles in AD and aged control brains poses a unique constellation of methodological challenges due in part to the small size of these structures and subsequent requirements for high-resolution imaging. Here we provide a detailed protocol for high-resolution 3D morphological quantification of neuronal endosomes in postmortem AD brain tissue, using immunofluorescent staining, confocal imaging with image deconvolution, and Imaris software analysis pipelines. To demonstrate these methods, we present neuronal endosome morphology data from 23 sporadic LOAD donors and one aged non-AD control donor. The techniques described here were developed across a range of AD neuropathology to best optimize these methods for future studies with large cohorts. Application of these methods in research cohorts will help advance understanding of ELN dysfunction and cytopathology in sporadic AD.

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.