Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm3) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm3) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) than Conv-VWI, both in normal walls (basilar artery; SNR 4.83 ± 1.23 vs. 3.02 ± 0.59, P < .001) and lesions (contrast-enhanced images; SNR 22.12 ± 11.68 vs. 8.33 ± 3.26, P < .001). In the assessment of 86 lesions, DL-VWI showed higher confidence of detection (4.56 ± 0.55 vs. 2.62 ± 0.77, P < .001), more concordant IPH characterization (Cohen\'s Kappa 0.85 vs. 0.59) and greater enhancement. For culprit plaque identification, IPH exhibited higher sensitivity in DL-VWI compared to Conv-VWI (70.6% vs. 23.5%) and excellent specificity (94.3% vs. 94.3%). Deep learning application of intracranial vessel wall images successfully improved the quality and resolution of the images. This aided in detecting vessel wall lesions and intraplaque hemorrhage, and in identifying potentially culprit atherosclerotic plaques.

Magnetic resonance imaging (MRI) is commonly used for studying infant brain development. However, due to the lengthy image acquisition time and limited subject compliance, high-quality infant MRI can be challenging. Without imposing additional burden on image acquisition, image super-resolution (SR) can be used to enhance image quality post-acquisition. Most SR techniques are supervised and trained on multiple aligned low-resolution (LR) and high-resolution (HR) image pairs, which in practice are not usually available. Unlike supervised approaches, Deep Image Prior (DIP) can be employed for unsupervised single-image SR, utilizing solely the input LR image for de novo optimization to produce an HR image. However, determining when to stop early in DIP training is non-trivial and presents a challenge to fully automating the SR process. To address this issue, we constrain the low-frequency k-space of the SR image to be similar to that of the LR image. We further improve performance by designing a dual-modal framework that leverages shared anatomical information between T1-weighted and T2-weighted images. We evaluated our model, dual-modal DIP (dmDIP), on infant MRI data acquired from birth to one year of age, demonstrating that enhanced image quality can be obtained with substantially reduced sensitivity to early stopping.

Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model\'s overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.

Since its invention, super-resolution microscopy has become a popular tool for advanced imaging of biological structures, allowing visualisation of subcellular structures at a spatial scale below the diffraction limit. Thus, it is not surprising that recently, different super-resolution techniques are being applied in neuroscience, e.g. to resolve the clustering of neurotransmitter receptors and protein complex composition in presynaptic terminals. Still, the vast majority of these experiments were carried out either in cell cultures or very thin tissue sections, while there are only a few examples of super-resolution imaging in deeper layers (30 - 50 µm) of biological samples. In that context, the mammalian whole-mount retina has rarely been studied with super-resolution microscopy. Here, we aimed at establishing a stimulated-emission-depletion (STED) microscopy protocol for imaging whole-mount retina. To this end, we developed sample preparation including horizontal slicing of retinal tissue, an immunolabeling protocol with STED-compatible fluorophores and optimised the image acquisition settings. We labelled subcellular structures in somata, dendrites, and axons of retinal ganglion cells in the inner mouse retina. By measuring the full width at half maximum of the thinnest filamentous structures in our preparation, we achieved a resolution enhancement of two or higher compared to conventional confocal images. When combined with horizontal slicing of the retina, these settings allowed visualisation of putative GABAergic horizontal cell synapses in the outer retina. Taken together, we successfully established a STED protocol for reliable super-resolution imaging in the whole-mount mouse retina at depths between 30 and 50 µm, which enables investigating, for instance, protein complex composition and cytoskeletal ultrastructure at retinal synapses in health and disease.

Lysosomes are dynamic cellular structures that adaptively remodel their membrane in response to stimuli, including membrane damage. We previously uncovered a process we term LYTL (LYsosomal Tubulation/sorting driven by Leucine-Rich Repeat Kinase 2 [LRRK2]), wherein damaged lysosomes generate tubules sorted into mobile vesicles. LYTL is orchestrated by the Parkinson\'s disease-associated kinase LRRK2 that recruits the motor adaptor protein and RHD family member JIP4 to lysosomes via phosphorylated RAB proteins. To identify new players involved in LYTL, we performed unbiased proteomics on isolated lysosomes after LRRK2 kinase inhibition. Our results demonstrate that there is recruitment of RILPL1 to ruptured lysosomes via LRRK2 activity to promote phosphorylation of RAB proteins at the lysosomal surface. RILPL1, which is also a member of the RHD family, enhances the clustering of LRRK2-positive lysosomes in the perinuclear area and causes retraction of LYTL tubules, in contrast to JIP4 which promotes LYTL tubule extension. Mechanistically, RILPL1 binds to p150Glued, a dynactin subunit, facilitating the transport of lysosomes and tubules to the minus end of microtubules. Further characterization of the tubulation process revealed that LYTL tubules move along tyrosinated microtubules, with tubulin tyrosination proving essential for tubule elongation. In summary, our findings emphasize the dynamic regulation of LYTL tubules by two distinct RHD proteins and pRAB effectors, serving as opposing motor adaptor proteins: JIP4, promoting tubulation via kinesin, and RILPL1, facilitating tubule retraction through dynein/dynactin. We infer that the two opposing processes generate a metastable lysosomal membrane deformation that facilitates dynamic tubulation events.

With the introduction of deep learning, a significant amount of research has been conducted in the field of computer vision in the past decade. In particular, research on object detection (OD) continues to progress rapidly. However, despite these advances, some limitations need to be overcome to enable real-world applications of deep learning-based OD models. One such limitation is inaccurate OD when image quality is poor or a target object is small. The performance degradation phenomenon for small objects is similar to the fundamental limitations of an OD model, such as the constraint of the receptive field, which is a difficult problem to solve using only an OD model. Therefore, OD performance can be hindered by low image quality or small target objects. To address this issue, this study investigates the compatibility of super-resolution (SR) and OD techniques to improve detection, particularly for small objects. We analyze the combination of SR and OD models, classifying them based on architectural characteristics. The experimental results show a substantial improvement when integrating OD detectors with SR models. Overall, it was demonstrated that, when the evaluation metrics (PSNR, SSIM) of the SR models are high, the performance in OD is correspondingly high as well. Especially, evaluations on the MS COCO dataset reveal that the enhancement rate for small objects is 9.4% higher compared to all objects. This work provides an analysis of SR and OD model compatibility, demonstrating the potential benefits of their synergistic combination. The experimental code can be found on our GitHub repository.

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.

UNASSIGNED: Stimulated emission depletion (STED) is a powerful super-resolution microscopy technique that can be used for imaging live cells. However, the high STED laser powers can cause significant photobleaching and sample damage in sensitive biological samples. The dynamic intensity minimum (DyMIN) technique turns on the STED laser only in regions of the sample where there is fluorescence signal, thus saving significant sample photobleaching. The reduction in photobleaching allows higher resolution images to be obtained and longer time-lapse imaging of live samples. A stand-alone module to perform DyMIN is not available commercially.
UNASSIGNED: In this work, we developed an open-source design to implement three-step DyMIN on a STED microscope and demonstrated reduced photobleaching for timelapse imaging of beads, cells, and tissue.
UNASSIGNED: The DyMIN system uses a fast multiplexer circuit and inexpensive field-programmable gate array controlled by Labview software that operates as a stand-alone module for a STED microscope. All software and circuit diagrams are freely available.
UNASSIGNED: We compared time-lapse images of bead samples using our custom DyMIN system to conventional STED and recorded a ∼ 46 % higher signal when using DyMIN after a 50-image sequence. We further demonstrated the DyMIN system for time-lapse STED imaging of live cells and brain tissue slices.
UNASSIGNED: Our open-source DyMIN system is an inexpensive add-on to a conventional STED microscope that can reduce photobleaching. The system can significantly improve signal to noise for dynamic time-lapse STED imaging of live samples.

The cilium, a pivotal organelle crucial for cell signaling and proper cell function, relies on meticulous macromolecular transport from the cytoplasm for its formation and maintenance. While the intraflagellar transport (IFT) pathway has traditionally been the focus of extensive study concerning ciliogenesis and ciliary maintenance, recent research highlights a complementary and alternative mechanism-vesicle-assisted transport (VAT) in cytoplasm to cilium trafficking. Despite its potential significance, the VAT pathway remains largely uncharacterized. This review explores recent studies providing evidence for the dynamics of vesicle-related diffusion and transport within the live primary cilium, employing high-speed super-resolution light microscopy. Additionally, we analyze the spatial distribution of vesicles in the cilium, mainly relying on electron microscopy data. By scrutinizing the VAT pathways that facilitate cargo transport into the cilium, with a specific emphasis on recent advancements and imaging data, our objective is to synthesize a comprehensive model of ciliary transport through the integration of IFT-VAT mechanisms.

Ultra-low field (ULF) magnetic resonance imaging (MRI) holds the potential to make MRI more accessible, given its cost-effectiveness, reduced power requirements, and portability. However, signal-to-noise ratio (SNR) drops with field strength, necessitating imaging with lower resolution and longer scan times. This study introduces a novel Fourier-based Super Resolution (FouSR) approach, designed to enhance the resolution of ULF MRI images with minimal increase in total scan time. FouSR combines spatial frequencies from two orthogonal ULF images of anisotropic resolution to create an isotropic T2-weighted fluid-attenuated inversion recovery (FLAIR) image. We hypothesized that FouSR could effectively recover information from under-sampled slice directions, thereby improving the delineation of multiple sclerosis (MS) lesions and other significant anatomical features. Importantly, the FouSR algorithm can be implemented on the scanner with changes to the k-space trajectory. Paired ULF (Hyperfine SWOOP, 0.064 tesla) and high field (Siemens, Skyra, 3 Tesla) FLAIR scans were collected on the same day from a phantom and a cohort of 10 participants with MS or suspected MS (6 female; mean ± SD age: 44.1 ± 4.1). ULF scans were acquired along both coronal and axial planes, featuring an in-plane resolution of 1.7 mm × 1.7 mm with a slice thickness of 5 mm. FouSR was evaluated against registered ULF coronal and axial scans, their average (ULF average) and a gold standard SR (ANTs SR). FouSR exhibited higher SNR (47.96 ± 12.6) compared to ULF coronal (36.7 ± 12.2) and higher lesion conspicuity (0.12 ± 0.06) compared to ULF axial (0.13 ± 0.07) but did not exhibit any significant differences contrast-to-noise-ratio (CNR) compared to other methods in patient scans. However, FouSR demonstrated superior image sharpness (0.025 ± 0.0040) compared to all other techniques (ULF coronal 0.021 ± 0.0037, q = 5.9, p-adj. = 0.011; ULF axial 0.018 ± 0.0026, q = 11.1, p-adj. = 0.0001; ULF average 0.019 ± 0.0034, q = 24.2, p-adj. < 0.0001) and higher lesion sharpness (-0.97 ± 0.31) when compared to the ULF average (-1.02 ± 0.37, t(543) = -10.174, p = <0.0001). Average blinded qualitative assessment by three experienced MS neurologists showed no significant difference in WML and sulci or gyri visualization between FouSR and other methods. FouSR can, in principle, be implemented on the scanner to produce clinically useful FLAIR images at higher resolution on the fly, providing a valuable tool for visualizing lesions and other anatomical structures in MS.