Image Visualization Research Articles

Dual-Modality Accurate Visualization of Drug Synergy Based on Mass Spectrometry and Fluorescence Imaging.

There is a potential synergistic effect between nonsteroidal anti-inflammatory drugs and hydrogen sulfide (H2S), but direct evidence for the study is lacking. With a single fluorescence detection method, it is difficult to accurately confirm the effectiveness of the synergistic effect. In this study, the fluorescent probe and the nonsteroidal anti-inflammatory drug naproxen were combined via different self-immolative spacer groups to obtain a diagnostic and therapeutic integrated fluorescent probe Nap-NP-NSB, which can release H2S. The quantitative release of H2S by Nap-NP-NSB was evaluated in vitro and in cells, and the synergistic effect of H2S and naproxen was confirmed by monitoring the treatment process of cellular inflammation and oxidative damage of gastric mucosa cells. Finally, in vivo fluorescence imaging and mass spectrometry imaging of the liver and stomach tissues and their sections were performed in the mouse model of acute hepatitis. The dual-modal detection method not only confirmed that Nap-NP-NSB had better anti-inflammatory activity and less gastric mucosal damage, but also enabled a more accurate visualization of the drug synergistic effect of naproxen and H2S. This work provides a dual visualization imaging method combining fluorescence and mass spectrometry imaging and develops a new idea for studying drug synergies based on self-immolative structures.

Segmentation and visualization of the Shampula dragonfly eye glass bead CT images using a deep learning method

Micro-computed tomography (CT) of ancient Chinese glass dragonfly eye beads has enabled detailed exploration of their internal structures, contributing to our understanding of their manufacture. Segmentation of these CT images is essential but challenging due to variation in grayscale values and the presence of bubbles. This study introduces a U-Net-based model called EBV-SegNet, which enables efficient and accurate segmentation and visualization of these beads. We developed, trained, and tested the model using a dataset comprising four typical Shampula dragonfly eye beads, and the results demonstrated high-precision segmentation and precise delineation of the beads’ complex structures. These segmented data were further analyzed using the Visualization Toolkit for advanced volume rendering and reconstruction. Our application of EBV-SegNet to Shampula beads suggests the likelihood of two distinct manufacturing techniques, underscoring the potential of the model for enhancing the analysis of cultural artifacts using three-dimensional visualization and deep learning.

Evaluation of monocular and binocular contrast perception on virtual reality head-mounted displays.

Visualization of medical images on a virtual reality (VR) head-mounted display (HMD) requires binocular fusion of a stereoscopic pair of graphical views. However, current image quality assessment on VR HMDs for medical applications has been primarily limited to time-consuming monocular optical bench measurement on a single eyepiece. As an alternative to optical bench measurement to quantify the image quality on VR HMDs, we developed a WebXR test platform to perform contrast perceptual experiments that can be used for binocular image quality assessment. We obtained monocular and binocular contrast sensitivity responses (CSRs) from participants on a Meta Quest 2 VR HMD using varied interpupillary distance (IPD) configurations. The perceptual result shows that contrast perception on VR HMDs is primarily affected by optical aberration of the VR HMD. As a result, monocular CSR degrades at a high spatial frequency greater than 4 cycles per degree when gazing at the periphery of the display field of view, especially for mismatched IPD settings consistent with optical bench measurements. On the contrary, binocular contrast perception is dominated by the monocular view with superior image quality measured by the contrast. We developed a test platform to investigate monocular and binocular contrast perception by performing perceptual experiments. The test method can be used to evaluate monocular and/or binocular image quality on VR HMDs for potential medical applications without extensive optical bench measurements.

Mining and Visualization of Tourism Cultural Image Based on the Information Transmission Model of Tourism Cultural Map—Taking Nanjing Xuanwu Lake Tourist Attraction as an Example

Mining and Visualization of Tourism Cultural Image Based on the Information Transmission Model of Tourism Cultural Map—Taking Nanjing Xuanwu Lake Tourist Attraction as an Example

Determining the degree of proximal nerve fascicle rotation in healthy controls using ultrahigh-frequency neuromuscular ultrasound: A pilot study.

Ultrahigh-frequency ultrasound (UHFUS) allows improved visualization and higher resolution images of nerve fascicles than standard high-frequency ultrasound. Dynamic UHFUS may detect the presence of fascicular entwinement, the recently described sonographic phenomenon of pathologic fascicular rotation seen in neuralgic amyotrophy. This pilot study aims to establish normative reference values and degrees of fascicular rotation for the proximal portions of commonly involved upper limb nerves in healthy controls using UHFUS. Twenty healthy participants underwent sonographic examination of the median, musculocutaneous, and radial nerves on both upper limbs using UHFUS with a 48 MHz linear transducer. A single rater assessed the degree of fascicular rotation in each peripheral nerve. Fascicular rotation appears to occur in the proximal portion of each of these nerves. The mean degree of fascicular rotation for each of the measured nerves was median 94.5°, musculocutaneous 97.9°, and radial 50.9°. The maximum observed fascicular rotation in each nerve was 180°. Age, sex, height, weight, body mass index, and race did not predict degree of fascicular rotation (all p > .103). A single-factor ANOVA test showed the degree of fascicular rotation differed in median, musculocutaneous, and radial nerves (F = 4.748, p = .011). UHFUS allows quantification of fascicular rotation in healthy controls in the median, musculocutaneous, and radial nerves, and provides normative data. The data from this pilot study may serve as control data for future comparative studies in conditions where fascicular rotation occurs, such as neuralgic amyotrophy.

Leveraging mixed reality in mitraclip implantation: A preliminary case series toward a pilot study

Abstract Introduction The advent of mixed reality (MR) technology, particularly when combined with Hololens 2.0 and specialized software for three-dimensional DICOM image visualization, offers a promising avenue for enhancing the precision and spatial awareness in interventional cardiology procedures. This case series aims to preliminarily evaluate the impact of MR on MitraClip implantation processes, setting a foundation for an ensuing comprehensive pilot study. Methods We conducted a comparative analysis within a case series of four MitraClip implantations, categorizing them into two groups based on the utilization of MR support. We examined procedural efficiency, radiation metrics (SKIA and DAP), and operator stress indicators, including cortisol levels, heart rate variability, QTc interval, and subjective assessments through NASA TLX, STAI Y, and SSQ questionnaires. Results Initial findings from the MR-assisted procedures suggest a notable improvement in procedural efficiency and a reduction in operator stress. Specifically, MR use was associated with shorter procedural times (ranging from 60 to 130 minutes) and lower radiation exposure, with SKIA durations between 15.5 and 40.5 minutes. Moreover, operators in the MR group showed reduced cortisol levels (8.2-9.2 mmol/L), indicating decreased physiological stress. Heart rate variability analysis further supported superior stress management among MR-supported operators. Subjective evaluations reinforced these physiological findings. NASA TLX scores in the MR group (13-20) markedly contrasted with those in the control group (30-45), signifying a significant reduction in perceived workload. SSQ scores remained minimal, implying good MR tolerability, and STAI Y scores (30-35) were lower than those in the control group (40-50), highlighting reduced anxiety levels during MR-assisted procedures. Conclusion This explorative case series lays the groundwork for a forthcoming pilot study, revealing MR's potential to not only streamline MitraClip implantation but also to alleviate operator stress. Both objective and subjective data advocate for expanded research to definitively ascertain MR's benefits in cardiac interventions, promising to refine procedural workflows and enhance patient outcomes. This series acts as a stepping stone towards a larger-scale evaluation that could solidify MR's role in the future of interventional cardiology.Operator's View and Technology PreviewFunction diagram

Synthesis and Evaluation of 68Ga- and 177Lu-Labeled [Pro14]bombesin(8-14) Derivatives for Detection and Radioligand Therapy of Gastrin-Releasing Peptide Receptor-Expressing Cancer.

The gastrin-releasing peptide receptor (GRPR) is overexpressed in a variety of cancers and represents a promising target for diagnosis and therapy. However, the extremely high accumulation in the pancreas observed for most of the clinically evaluated GRPR-targeted radiopharmaceuticals could limit their applications. In this study, we synthesized one GRPR antagonist (ProBOMB5) and two GRPR agonists (LW02056 and LW02057) by replacing the 4-thiazolidinecarboxylic acid (Thz14) residue in our previously reported GRPR-targeted tracers with Pro14. The 68Ga and 177Lu labeling were conducted in HEPES (2 M, pH 5.0) buffer and acetate (0.1 M, pH 4.5) buffer, respectively, and the radiolabeled products were obtained in a 24-57% decay-corrected radiochemical yield and >92% radiochemical purity. The binding affinities (Ki) of Ga-ProBOMB5, Ga-LW02056, Ga-LW02057, and Lu-ProBOMB5 were measured via in vitro competition binding assays and were 12.2 ± 1.89, 14.7 ± 4.81, 13.8 ± 2.24, and 13.6 ± 0.25 nM, respectively. The PET imaging and ex vivo biodistribution studies were conducted in PC-3 tumor-bearing mice at 1 h post injection. [68Ga]Ga-ProBOMB5, [68Ga]Ga-LW02056, and [68Ga]Ga-LW02057 enabled clear tumor visualization in PET images. The tumor uptake values of [68Ga]Ga-ProBOMB5, [68Ga]Ga-LW02056, and [68Ga]Ga-LW02057 were 12.4 ± 1.35, 8.93 ± 1.96, and 7.64 ± 0.55%ID/g, respectively, and their average pancreas uptake values were minimal (0.60-1.37%ID/g). Longitudinal SPECT imaging and ex vivo biodistribution studies were also conducted for [177Lu]Lu-ProBOMB5 and clinically validated [177Lu]Lu-RM2. Despite comparable tumor uptake at 1 h post injection ([177Lu]Lu-ProBOMB5:8.09 ± 1.70%ID/g; [177Lu]Lu-RM2:7.73 ± 0.96%ID/g), a faster clearance from PC-3 tumor xenografts was observed for [177Lu]Lu-ProBOMB5, leading to a lower radiation-absorbed dose delivered to tumors. Our data demonstrate that [68Ga]Ga-ProBOMB5 is a promising tracer for clinical translation for detecting GRPR-expressing tumor lesions. However, further optimizations are needed for [177Lu]Lu-ProBOMB5 to prolong tumor retention for therapeutic applications.

VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation.

Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral blood vessels, the traditional convolutional kernel is weak in perceiving the structure of elongated blood vessels in the brain, and it is easy to lose the feature information of the elongated blood vessels during the network training process. In this paper, a vascular convolutional U-network (VCU-Net) is proposed to address these problems. This network utilizes a new convolution (vascular convolution) instead of the traditional convolution kernel, to extract features of elongated blood vessels in the brain with different morphologies and orientations by adaptive convolution. In the network encoding stage, a new feature splicing method is used to combine the feature tensor obtained through vascular convolution with the original tensor to provide richer feature information. Experiments show that the DSC and IOU of the proposed method are 53.57% and 69.74%, which are improved by 2.11% and 2.01% over the best performance of the GVC-Net among several typical models. In image visualization, the proposed network has better segmentation performance for complex cerebrovascular structures, especially in dealing with elongated blood vessels in the brain, which shows better integrity and continuity.

Clinical safety and efficacy of elliptical thin-flap LASIK using a low-pulse energy femtosecond laser.

This study assessed the clinical safety and efficacy of elliptical thin-flap LASIK with a low-pulse energy femtosecond laser in 3 thickness subgroups (85, 90, and 100μm). A total of 80 patients who underwent bilateral LASIK surgery at Taiwan between April and September 2019 were retrospectively enrolled. Elliptical corneal flaps with wide temporal hinges and inverted-angled side cuts were created. Target flap thickness was calculated on the basis of residual stromal bed thickness and percent tissue altered. Before flap creation, an optical coherence tomography image for visualization of the precut flap position was obtained with the built-in camera. At postoperative month 1, the overall mean logMAR uncorrected distance visual acuity (UDVA) was - 0.04 ± 0.07 (20/18 Snellen), with 96% of eyes achieving UDVA of 20/20 or better. Postoperative mean manifest spherical equivalent was - 0.37 ± 0.42 D at 1 month. The visual and refractive outcomes in each of the 3 subgroups were similar. The achieved flap thicknesses were found to be highly predictable and consistent in the respective thickness subgroups. Optical coherence tomography-guided thin-flap LASIK performed with the low-pulse energy femtosecond laser was found to be safe with no intraoperative or postoperative complications.

Visualisation of Images and Performance in Music Genres

The purpose of the article is to analyse the visual component in the musical genre (songs) and musical-theatrical genre (ballet), which is an element for the formation of spectacle. The work methodology includes analytical, structural-functional, generalising scientific methods; techniques of comparative analysis to reveal the specifics of modern artistic practices in revealing the specifics of the visual as a component of spectacle. The scientific novelty of the work consists in the analysis of the song “Smerekova Khata”, the musical and stage performance of the “Gerdan” theatre, the ballet “Signs” with the determination of the visualisation of the images laid down by the authors to form the spectacle. Conclusions. Based on the analysis of M. Bakay and P. Dvorskyi's song “Smerekova Khata”, the dependence of visualisation in musical-poetic composition and music, metaphoricity in musical-poetic images in various performing interpretations with a certain amount of spectacularity according to the definition of “spectacularity” in the dictionary of Efron and Brockhaus is determined as a concert, performance. The analysis of the concert performance of the Chernivtsi City Folklore Theatre-Studio “Gherdan” based on the musical and textual material, stage design and costumes of the performers made it possible to reveal the degree of visualisation of musical and stage images that contribute to spectacle in art according to K. Stanislavska's concept as a spectacle with an artistic structure, which is a system of means of expression with external manifestations. The French-American dancer-choreographer Carolyn Carlson's ballet “Signes” (1997, Paris Opera) with scenery by the French artist Olivier Debre and electronic music by the French composer René Aubry fully meets all the parameters of spectacle: a) unusual events that appeal to history with famous heroes Leonardo da Vinci and Gioconda with her outstanding smile; b) entertainment in the presentation of material with the use of three-dimensional pictures that are constantly moving; c) seven different locations; d) lack of verbal text (words), which are compensated by electronic music and choreographic movements.

Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma

BackgroundConcurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways.MethodsOverall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing.ResultsThe DL model exhibited favorable predictive performance, with an area under the curve of 0.86 (95% confidence interval [CI] 0.79–0·92) in the training cohort and 0.84 (95% CI 0.75–0.94) in the validation cohort. The DL model (low score vs. high score) was an independent predictive factor; it was significantly associated with progression-free survival and overall survival in both the training (hazard ratio [HR] = 0.54 [0.36−0.80], P = 0.002; 0.44 [0.28−0.68], P < 0.001) and validation cohorts (HR = 0.46 [0.24−0.88], P = 0.008; 0.30 [0.14−0.60], P < 0.001). The DL model was also positively related to the cell adhesion molecules, the P53 signaling pathway, and natural killer cell-mediated cytotoxicity. Single-cell analysis revealed that differentially expressed genes were enriched in different immune cells.ConclusionThe DL model demonstrated a strong predictive ability for determining the response in patients with NSCLC undergoing CCRT. Our findings contribute to understanding the potential biological mechanisms underlying treatment responses in these patients.

Automated matching and visualisation of magnetic flux leakage data in shale gas pipeline based on ICP and DBSCAN algorithm

ABSTRACT Magnetic flux leakage (MFL) is a non-destructive testing method for shale gas pipeline safety monitoring. Despite many pipelines have completed two or more rounds of internal inspection and have accumulated substantial data, effectively analyzing and utilizing this MFL data remains a significant challenge. To that end, this study proposes a novel combined model that integrates Iterative Closest Point (ICP) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) fusion to match multiple MFL data. The model outputs the matching result and visualisation images of MFL data acquired in different years. Furthermore, RMSE (root mean square error) and overlap rate have been used to evaluate the model. The results indicate that the average distance error of matched defects between two datasets decreased by 72.5%, and the overlap rate increased by 20%. Additionally, the DBSCAN Fusion shows better computational efficiency with an increase in defect quantity within the pipe segments. Finally, the impact of different datasets on the model’s matching accuracy is discussed in this study. The findings show that small-scale datasets or higher false detection rates lead to decreased accuracy. The proposed model fully leverages MFL data to achieve rapid matching of defects, offering a dependable and effective technical solution for safety monitoring and corrosion prediction in shale gas pipelines.

2D23D: 3D images from 2D images using statistical filters and color shading

Below is the development of an algorithm that allows generating 3D models from 2D images for analysis and visualization, with an application focused on images from the health and space sectors, domains chosen for their contrast characteristics. The characteristics used in the project are presented and determined, as well as 3D programming and visualization environment tools that allow the generation of voxel depth, as well as the management of image formats from the medical or spatial sector, including the results obtained with images. from both sectors, from which the 3D models were created.

A comparative study of flow boiling performance in smooth and multi-stage enhanced open microchannels

Flow boiling in open microchannels is currently one of the research focuses in the field of phase-change heat transfer. The present paper described a comparative and experimental research on flow boiling heat transfer of SF-33, an environmentally friendly working fluid, in smooth surfaces open microchannels (SOM) and multi-stage enhanced open microchannels (MSEOM). The study investigated the flow boiling performance of SF-33 in two open microchannels with different mass fluxes, heat fluxes and inlet subcooling. The different boiling flow patterns in two open microchannels were analyzed with high-speed visualization. The results indicated that under different inlet cooling conditions, the MSEOM improved the flow boiling heat transfer performance significantly and improved the wall temperature uniformity. The visualization images indicated that the flow pattern transition was different due to the different surface structures of two open microchannels. The SOM experienced plug-stratified flow at high heat flux, while MSEOM transformed into churn flow due to its sintered copper powder and wire mesh micro-nanocomposite structures. The multi-stage enhanced structures exhibited excellent hydrophilicity and wicking capability, leading to a less pressure drop in the MSEOM compared with that in SOM. The pressure drops for both open microchannels decreased as inlet subcooling increased.

Transient behavior of ablation and swelling for C/C composite and HfC-coated C/C composite in an arc-heated wind tunnel

This study investigated and modeled the transient behavior of surface recession, defined as the difference between ablative length and swelling length, for thermal protection materials within a high-enthalpy flow. Needle punch carbon-carbon (NPCC) and hafnium carbide coated carbon-carbon (HfC-coated NPCC) were exposed to high-enthalpy flow with a heat flux of 7.67 MW/m2 generated by an arc-heated facility. The NPCC and the HfC-coated NPCC represent an ablative surface and a non-ablative surface, respectively. Surface recession histories were estimated through an image analysis and visualizations monitored by a high-speed camera. Moreover, measured data including surface temperature histories, mass loss, and total length were presented. We proposed the models for the transient recession on the ablative surface and the non-ablative surface considering the ablation and the swelling. The ablative length was calculated using non-dimensional parameter B′, which computes ablation rates under equilibrium air conditions. While B’ modeling accurately predicted the ablation rates, it could not reflect the swelling phenomenon during an initial heating phase. The swelling was modeled with consideration of the thermal expansion. Specifically, the effect of increased porosity near the ablative surface was incorporated into the thermal expansion coefficient. The model developed in this study well agreed with the experimental data.

Real-time electrochemical-strain distribution and state-of-charge mapping via distributed optical fiber for lithium-ion batteries

Deep learning has opened new avenues for estimating the dynamic state of charge in batteries. However, hindered by macroscopic and limited electrochemical inputs, data-driven models still struggle to accurately capture local states inside batteries. This study aims to leverage distributed fiber optic sensing and long short-term memory to enhance the accuracy and reliability of the state of charge estimation and to visualize its distribution. First, the internal strain is closely linked to the lithium embedded state of graphite particles, with a coefficient of 0.992 between strain amplitude and capacity over long cycles. Incorporating strain as an additional physical parameter significantly improves accuracy. The proposed model achieves a prediction error of only 2.01 %, representing a 41.2 % improvement compared to models that exclude stain data. Factors such as current and ambient temperature are considered to prove the effectiveness of this method. Furthermore, by combining the trained model with internal strain distribution, the developed intuitive imaging for local state visualization reveals aging tendencies and uneven behavior in lithium-ion batteries. This predictive and imaging approach offers valuable insights for local state analysis and advances battery research and design.

A Generative Adversarial Network for Upsampling of Direct Volume Rendering Images

AbstractDirect volume rendering (DVR) is an important tool for scientific and medical imaging visualization. Modern GPU acceleration has made DVR more accessible; however, the production of high‐quality rendered images with high frame rates is computationally expensive. We propose a deep learning method with a reduced computational demand. We leveraged a conditional generative adversarial network (cGAN) to upsample DVR images (a rendered scene), with a reduced sampling rate to obtain similar visual quality to that of a fully sampled method. Our dvrGAN is combined with a colour‐based loss function that is optimized for DVR images where different structures such as skin, bone, etc. are distinguished by assigning them distinct colours. The loss function highlights the structural differences between images, by examining pixel‐level colour, and thus helps identify, for instance, small bones in the limbs that may not be evident with reduced sampling rates. We evaluated our method in DVR of human computed tomography (CT) and CT angiography (CTA) volumes. Our method retained image quality and reduced computation time when compared to fully sampled methods and outperformed existing state‐of‐the‐art upsampling methods.

Experimental investigation into thermal characteristics of subcooled flow boiling in horizontal concentric annuli for cooling ultra-fast electric vehicle charging cables

In an effort to reduce the charging time of electric vehicles (EVs), the thermal management of the charging system has emerged as one of the most urgent issues. The charging rate has been restricted to avoid thermal failure especially in charging cable. Recently, a direct contact cooling technique that utilizes subcooled flow boiling for charging cables has been proposed and has shown promising thermal management performance. However, there has been a lack of clear explanation regarding its thermal characteristics due to its intrinsically complicated flow features. This study investigates the thermal characteristics of subcooled flow boiling in a horizontally aligned concentric annular tube intended to simulate the direct contact cooling technique employed for charging cables. For the experimental investigation, the wall temperature measurements and high-speed flow visualization images acquired from the transparent test module annulus, with inner and outer diameters of 6.35 mm and 22.0 mm, were utilized. Three key axial thermal characteristics of subcooled flow boiling in annulus have been analyzed, and they are flow regime transition, variation of heat transfer mechanisms, and occurrence of critical heat flux (CHF). The transition of flow regimes and the variation of dominant heat transfer mechanisms in the axial direction are mutually influencing due to the coupled effects between the hydrodynamic and thermal characteristics of simultaneously developing flows. The flow regime changes from partially developed boiling (PDB) to fully developed boiling (FDB) as the intensities of nucleate boiling and bubble recondensation vary in the axial direction, while the dominant heat transfer mechanism shifts from single-phase convection to nucleate boiling, corresponding to the flow regime transition from PDB to FDB. The departure from nucleate boiling (DNB) type CHF is observed, manifested by the wavy interface propagating from the far downstream to the upstream region, and the intensities of nucleate boiling and bubble recondensation are also identified as the dominant parameters in determining the occurrence of CHF. Charging time estimation reveals that the proposed method can achieve an 80 % charge for 100-kWh EV batteries in 98 s, while maintaining the cable wire temperature below the safety limit of 80 °C. This strongly supports its potential as a promising thermal management technique for future ultra-fast charging systems.

Apple Vision Pro: A Paradigm Shift in Medical Technology.

The introduction of Apple Vision Pro (AVP) marks a significant milestone in the intersection of technology and healthcare, offering unique capabilities in mixed reality, which Apple terms "spatial computing." This narrative review aims to explore the various applications of AVP in medical technology, emphasizing its impact on patient care, clinical practices, medical education, and future directions. The review synthesizes findings from multiple studies and articles published between January 2023 and May 2024, highlighting AVP's potential to enhance visualization in diagnostic imaging and surgical planning, assist visually impaired patients, and revolutionize medical education through immersive learning environments. Despite its promise, challenges remain in integrating AVP into existing healthcare systems and understanding its long-term impact on patient outcomes. As research continues, AVP is poised to play a pivotal role in the future of medicine, offering a transformative tool for healthcare professionals.

Mitochondrial STED Imaging and Membrane Potential Monitoring with a Cationic Molecular Probe.

Mitochondria are essential organelles that not only undergo dynamic morphological changes but also exhibit functional activities such as mitochondrial membrane potential (MMP). While super-resolution techniques such as stimulated emission depletion (STED) nanoscopy can visualize the ultrastructure of mitochondria and the MMP probe can monitor mitochondria function, few dyes meet both demands. Here, a small molecule (MitoPDI-90) based on perylene diimide with cationic groups is reported and used for mitochondrial STED imaging and MMP indication. Characterized by excellent photostability, biocompatibility, and high quantum yield, MitoPDI-90 exhibits STED imaging compatibility, facilitating visualization of mitochondrial cristae and time-lapse imaging of highly dynamic mitochondria in living cells. Besides, MitoPDI-90 targets the mitochondria through electrical potential, also enabling live-cell MMP monitoring. MitoPDI-90 allows for super-resolution visualization and time-lapse imaging of mitochondria, and more importantly, indication of changes in MMP, providing insight into the functional activity of live-cell mitochondria.