High-quality Visualization Research Articles

EHerbarium: A Global Platform for Exploring and Sharing Herbarium and Live Plant Images to Enhance Biodiversity Preservation

Historically, herbaria have been instrumental in preserving and studying plant biodiversity. The widespread use of smartphones has led to a surge in the digital documentation of plants. Over the last decade, herbarium digitizing projects are also underway. Many of the digitized herbarium images are available online through different portals. The examples of these portals include Kew Gardens and New York Botanic Garden Herbarium. Combining the latest mobile, cloud and AI technologies as well as plant taxonomy and herbarium artifacts, the mission of eHerbarium*1 is to provide an accessible digital platform for plant enthusiasts, including botanists, researchers, students, as well as the general public around the world, to collect, identify and share live plant and herbarium images. It promotes data collection and data sharing around the globe for biodiversity preservation. It further provides educational benefits about plants and the plant biodiversity around us and the world. eHerbarium offers access to an extensive collection of herbarium images from Kew’s Herbarium at the Royal Botanic Gardens, Kew. The platform is also extensible, allowing it to host images from other herbaria. In addition, this platform provides high-quality botanical visuals, enabling users to capture and share live plant photos or herbarium images, or upload from existing collections. Customizable licensing options facilitate community use and enhance collaborative botanical research. The platform’s AI-powered tools streamline plant identification, offering autocomplete suggestions for taxonomy details such as scientific name, family name or common names. Our proprietary technology ensures seamless and accurate botanical data entry by automatically retrieving corresponding scientific or common names. Additionally, eHerbarium enriches botanical records by automatically associating image locations with Biodiversity Information Standards (TDWG) Level 2 regions. TDWG Level 2 regions are specific geographical areas defined by TDWG to standardize the recording and reporting of biodiversity data. These regions, which typically correspond to large countries or significant subnational areas, help ensure consistent and accurate geographical referencing in botanical studies and herbarium records, while also respecting image owners' location privacy. eHerbarium has more than 40,000 worldwide downloads since its debut less than six months ago on March 18, 2024. We encourage further collaborations to expand our repositories for herbarium and live plant images. We also welcome partnerships to tailor the application to specific needs for natural history and biodiversity data collection.

3D 4K Exo-Endoscopic Temporal Bone Dissection: A Novel Approach for Sharing the Anatomy.

Temporal bone dissection is overwide recognized as an ideal training method for otologic surgeons. The knowledge of temporal bone anatomy and especially of the course of infratemporal facial nerve is pivotal in practice. The 3D exoscope is an innovative and promising tool, that was recently introduced in ear surgery. A high-definition 3D exoscope (3D VITOM®) mounted on the VERSACRANETM holding system (Karl Storz) was used to perform two temporal bone dissection, with the aim to study the anatomy of infratemporal facial nerve. The 3D endoscope (TIPCAM®1 S 3D ORL, Karl Storz) was used in combination to provide a close-up high-quality view and to provide a different angle of view on fine anatomical relationships. The high-definition 3D exoscope allowed to conduct the dissection with high quality visualization and to share the same surgical field with trainees. Moreover, it showed a high interchangeability with the 3D endoscope. 3D 4K Exo-endoscopic temporal bone dissection seems to have benefits in terms of educational purpose, especially concerning anatomy understanding. The superiority in teaching value of this tool should be further investigated in cohort studies.

Benzyloxychalcone Hybrids as Prospective Acetylcholinesterase Inhibitors against Alzheimer's Disease: Rational Design, Synthesis, In Silico ADMET Prediction, QSAR, Molecular Docking, DFT, and Molecular Dynamic Simulation Studies.

Acetylcholinesterase inhibitors (AChEIs) are crucial therapeutic targets for both the early and severe stages of Alzheimer's disease (AD). Chalcones and their chromone-based derivatives are well-known building blocks with anti-Alzheimer properties. This study synthesized 4-benzyloxychalcone derivatives and characterized their structures using IR, 1H NMR, 13C NMR, and HRMS. Additionally, the synthesized 4-benzyloxychalcone derivatives were tested for anti-acetylcholinesterase (AChE) activity. The synthesized compounds outperformed galantamine, which is used as a positive control against acetylcholinesterase. Utilizing an acetylcholinesterase (AChE) receptor (PDB ID: 4EY7)-chalcone derivative (12a-c), a molecular docking investigation was performed on the synthesized compounds. The goal was to predict the binding sites and energies of the derivatives with respect to the receptor amino acids. The dynamic behavior of the ligand-receptor complex resulting from the interaction of the best docking compounds 12a and 12c with the acetylcholinesterase receptor was used to analyze the stability via MD simulation. MM/GBSA and MM/PBSA were used to calculate free binding energies using snapshots from system trajectories. Advanced computational approaches incorporating long-range corrections were utilized to calculate the molecular characteristics of chalcone derivatives 12a-c at the DFT/wB97XD/6-311++G(d,p) level. We used the molecular electrostatic surface potential (MESP) with high-quality data and visualization to find the most active site in these molecules. Reactivity descriptors, including the condensed Fukui function, chemical hardness (η), dual descriptors, chemical potential (μ), and electrophilicity (ω), were calculated for the chalcone derivatives.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800[Formula: see text]kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Non-invasive laser speckle contrast imaging (LSCI) of extra-embryonic blood vessels in intact avian eggs at early developmental stages.

Imaging blood vessels in early-stage avian embryos has a wide range of practical applications for developmental biology studies, drug and vaccine testing, and early sex determination. Optical imaging, such as brightfield transmission imaging, offers a compelling solution due to its safe non-ionizing radiation, and operational benefits. However, it comes with challenges, such as eggshell opacity and light scattering. To address these, we have revisited an approach based on laser speckle contrast imaging (LSCI) and demonstrated a high-quality, comprehensive, and non-invasive visualization of blood vessels in few-days-old chicken eggs, with blood vessels as small as 100 µm in diameter (with LSCI profile full-width-at-half-maximum of 275 µm). We present its non-invasive use for monitoring blood flow, measuring the embryo's heartbeat, and determining the embryo's developmental stages using machine learning with 85% accuracy from stage HH15 to HH22. This method can potentially be used for non-invasive longitudinal studies of cardiovascular development and angiogenesis, as well as egg screening for the poultry industry.

Optimized quantization parameter selection for video-based point cloud compression

Point clouds are sets of points used to visualize three-dimensional (3D) objects. Point clouds can be static or dynamic. Each point is characterized by its 3D geometry coordinates and attributes such as color. High-quality visualizations often require millions of points, resulting in large storage and transmission costs, especially for dynamic point clouds. To address this problem, the moving picture experts group has recently developed a compression standard for dynamic point clouds called video-based point cloud compression (V-PCC). The standard generates two-dimensional videos from the geometry and color information of the point cloud sequence. Each video is then compressed with a video coder, which converts each frame into frequency coefficients and quantizes them using a quantization parameter (QP). Traditionally, the QPs are severely constrained. For example, in the low-delay configuration of the V-PCC reference software, the quantization parameter values of all the frames in a group of pictures are set to be equal. We show that the rate-distortion performance can be improved by relaxing this constraint and treating the QP selection problem as a multi-variable constrained combinatorial optimization problem, where the variables are the QPs. To solve the optimization problem, we propose a variant of the differential evolution (DE) algorithm. Differential evolution is an evolutionary algorithm that has been successfully applied to various optimization problems. In DE, an initial population of randomly generated candidate solutions is iteratively improved. At each iteration, mutants are generated from the population. Crossover between a mutant and a parent produces offspring. If the performance of the offspring is better than that of the parent, the offspring replaces the parent. While DE was initially introduced for continuous unconstrained optimization problems, we adapt it for our constrained combinatorial optimization problem. Also, unlike standard DE, we apply individual mutation to each variable. Furthermore, we use a variable crossover rate to balance exploration and exploitation. Experimental results for the low-delay configuration of the V-PCC reference software show that our method can reduce the average bitrate by up to 43% compared to a method that uses the same QP values for all frames and selects them according to an interior point method.

VISAtlas: An Image-Based Exploration and Query System for Large Visualization Collections via Neural Image Embedding.

High-quality visualization collections are beneficial for a variety of applications including visualization reference and data-driven visualization design. The visualization community has created many visualization collections, and developed interactive exploration systems for the collections. However, the systems are mainly based on extrinsic attributes like authors and publication years, whilst neglect intrinsic property (i.e., visual appearance) of visualizations, hindering visual comparison and query of visualization designs. This paper presents VISAtlas, an image-based approach empowered by neural image embedding, to facilitate exploration and query for visualization collections. To improve embedding accuracy, we create a comprehensive collection of synthetic and real-world visualizations, and use it to train a convolutional neural network (CNN) model with a triplet loss for taxonomical classification of visualizations. Next, we design a coordinated multiple view (CMV) system that enables multi-perspective exploration and design retrieval based on visualization embeddings. Specifically, we design a novel embedding overview that leverages contextual layout framework to preserve the context of the embedding vectors with the associated visualization taxonomies, and density plot and sampling techniques to address the overdrawing problem. We demonstrate in three case studies and one user study the effectiveness of VISAtlas in supporting comparative analysis of visualization collections, exploration of composite visualizations, and image-based retrieval of visualization designs. The studies reveal that real-world visualization collections (e.g., Beagle and VIS30K) better accord with the richness and diversity of visualization designs than synthetic collections (e.g., Data2Vis), inspiring composite visualizations are identified in real-world collections, and distinct design patterns exist in visualizations from different sources.

Virtual robots based on digital images and machine learning in green landscape design

Virtual robots, as an intelligent technology, can provide efficient and accurate green landscape design solutions. This study explores the application of virtual robots in green landscape design based on digital images and machine learning techniques. The research and design of a green landscape environment mapping model enables virtual robots to accurately describe green areas. By using advanced sensor technology, virtual robots can perceive key nodes in the environment and construct an accurate map of green areas. Panoramic image synthesis technology utilizes image sensors carried by virtual robots to obtain multiple images in the environment, and concatenates them to generate panoramic images. Through panoramic images, virtual robots can obtain a broader field of view, and improve the accuracy and authenticity of green landscape design. By applying image dehazing algorithms, the impact of fog is effectively reduced, and the clarity and authenticity of the image are improved, enabling designers to better observe and evaluate the greening effect. By perceiving the environment, establishing green area maps, using panoramic images, and applying machine learning processing techniques, virtual robots can accurately perceive the situation of green areas and provide high-quality data and visualization effects for green design.

ANALYZING VISUALS OF SHOE PRODUCT PHOTOS IN 2023 THROUGH INSTAGRAM MEDIA USING QUALITATIVE SEMIOTIC METHODOLOGY

Product photography plays a pivotal role in business, demanding cameras attuned to lighting nuances and medium capturing. High-quality product visuals foster consumer trust, convey sophistication, and reinforce brand recognition. Particularly in platforms like Instagram, visually appealing photos are instrumental in driving engagement and sales. This study delves into innovative camera methods and background preparation to craft captivating product imagery, aiming to enrich comprehension of crafting unique, consumer-attracting visuals aligning with brand attributes. This research contributes as a knowledge repository, sparks creativity in image creation, and bolsters consumer appeal on social media. Employing a qualitative approach, it entails observation, literature review, and documentation, progressing through initial research, image curation, and analysis of design, material quality, and lifestyle integration. Findings underscore the significance of photography techniques, background curation, and camera angles, furnishing insights to elevate product aesthetics, notably on social platforms

Novel intravital approaches to quantify deep vascular structure and perfusion in the aging mouse brain using ultrasound localization microscopy (ULM)

Intra-vital visualization of deep cerebrovascular structures and blood flow in the aging brain has been a difficult challenge in the field of neurovascular research, especially when considering the key role played by the cerebrovasculature in the pathogenesis of both vascular cognitive impairment and dementia (VCID) and Alzheimer’s disease (AD). Traditional imaging methods face difficulties with the thicker skull of older brains, making high-resolution imaging and cerebral blood flow (CBF) assessment challenging. However, functional ultrasound (fUS) imaging, an emerging non-invasive technique, provides real-time CBF insights with notable spatial-temporal resolution. This study introduces an enhanced longitudinal fUS method for aging brains. Using elderly (24-month C57BL/6) mice, we detail replacing the skull with a polymethylpentene window for consistent fUS imaging over extended periods. Ultrasound localization mapping (ULM), involving the injection of a microbubble (<<10 μm) suspension allows for recording of high-resolution microvascular vessels and flows. ULM relies on the localization and tracking of single circulating microbubbles in the blood flow. A FIJI-based analysis interprets these high-quality ULM visuals. Testing on older mouse brains, our method successfully unveils intricate vascular specifics even in-depth, showcasing its utility for longitudinal studies that require ongoing evaluations of CBF and vascular aspects in aging-focused research.

Electrochemical visualization of latent fingerprints using polyphenazine dyes on brass cartridges

This work is focused on the visualization of latent fingerprints left on unfired brass cartridges. Polymer films were prepared from 2 mM neutral red or 5 mM toluidine blue using two different electrochemical methods (cyclic voltammetry or chronoamperometry) with relatively short polymerization times. The conditions for the deposition of conductive polymers, poly(neutral red) and poly(toluidine blue), from a neutral medium (phosphate buffer with 0.1 M KNO3 or 0.1 M KNO3, respectively) were optimized to produce high-quality visualization of the remaining fingerprints on the brass substrates. The surface morphology and quality of the polymer films after the electrochemical deposition of both polyphenazine dyes were characterized by stereomicroscope. Phenazine dyes, which were used for the visualization of fingerprints, have been shown to provide different degrees of homogeneity in the deposited film. Furthermore, the dependence of the stability of the monomer solutions on their age, use, and storage conditions are discussed. Finally, a methodology is proposed for how to apply this technique of visualizing latent fingerprints with observed details of papillary lines in forensic practice.Graphical abstract

Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data

A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field.

Python tools for structural tasks in chemistry.

In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.

Reliability of Three-Dimensional Micro-Computed Tomography Root Canal Analysis Using Drishti Software: A Pilot Study

In recent years, micro-computed tomography (micro-CT) has become a promising tool for non-destructive imaging and the study of root canal morphology in endodontics. However, the reliability of such analyses heavily relies on the accuracy and consistency of the software used for image processing. Objective: This study aims to investigate the reliability of two different micro-CT voxel sizes using open-source three-dimensional (3D) software (Drishti v3.0) used to identify and quantify the Apical Foramen (AF) and Apical Constriction (AC) in mandibular premolars. Methods: Eight mandibular premolars were divided into two groups, with four samples in each group. Samples were scanned using a ZEISS X-Radia 520 Micro-CT system with two different voxel sizes of 23 µm (Group A) and 17 µm (Group B), respectively. The micro-CT data were reconstructed and transferred to Drishti Software, and individual image rendering was performed to visualize the images for data analysis. The position and measurement of AF and AC to the anatomical apex were measured individually and independently by single operators. Results: The reliability of the measurements assessed using Intraclass Correlation Coefficients (ICCs) between two voxel sizes was excellent and on par with previous research. This pilot study suggested that results for voxel sizes of 23 µm and 17 µm were comparable. Both voxel size 3D images demonstrated excellent and high-quality visualization of the root canal configuration, lateral and accessory canals, apical deltas, major AF, and AC. Conclusion: It is feasible and reliable to visualize and analyze 3D micro-CT images using open-source software (Drishti v3.0). Both voxel sizes of 23 µm and 17 µm are recommended for use in root canal study.

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

BackgroundThe integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.ResultsTo expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes.ConclusionscDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction.

Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm2, respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar.

Interactive Virtual Simulation with Haptics for Neurosurgery

We established a unique pre-surgical simulation method by applying interactive virtual simulation(IVS)using multi-fusion three-dimensional imaging data, presenting high-quality visualization of microsurgical anatomies. Our IVS provided a realistic environment for imitating surgical manipulations, such as dissecting bones, retracting brain tissues, and removing tumors, with tactile and kinesthetic sensations delivered through a specific haptic device. The great advantage of our IVS was in deciding the most appropriate craniotomy and bone resection to create the optimal surgical window and obtain the best working space with a thorough understanding of the lesion-bone relationship. Particularly for skull-base tumors, tailoring the procedures to individual patients for craniotomy and bone resection was sufficiently achieved using our IVS. In cases of large skull base meningiomas, our IVS was also helpful preoperatively regarding tumors, as several compartments were achievable in every potentially usable surgical direction. Additionally, the non-risky realistic microsurgical environments of the IVS provided improvement in the microsurgical senses and skills of young trainees through the repetition of surgical tasks. Finally, our presurgical IVS simulation method provided a realistic environment for practicing microsurgical procedures virtually and enabled us to ascertain the complex microsurgical anatomy, determine optimal surgical strategies, and efficiently educate neurosurgical trainees.

Development of reddish orange-emitting Y2Sn2O7:Eu3+phosphors for latent fingerprint detection and anti-counterfeit security ink

In forensic science, fingerprint detection and anti-counterfeiting technologies are crucial. Inorganic phosphors are potentially suitable materials for high-quality visualization and security. We synthesized novel Y2Sn2O7:Eu3+ (YSO:Eu3+) phosphors by a sol–gel method. They have a cubic structure and emit reddish orange emission from the Eu3+ ions electronic transition of 5D0→7F1. The optimal doping concentration of Eu3+ is 7 mol% and dipole-dipole interaction causes the quenching mechanism. The chromaticity coordinates of YSO:7Eu3+ phosphor are (0.6086, 0.3906). We used the optimized YSO:7Eu3+ phosphors for forensic applications, such as latent fingerprints and security ink. The latent fingerprints stained with YSO:7Eu3+ phosphors showed clear images and reliable level 1–3 characteristics. The security ink was visible on different surfaces. Our results indicate that the YSO:7Eu3+ phosphor can be used for visualizing latent fingerprints and anti-counterfeiting applications.

Stereo attention-based all-in-one super-resolution for robot-assisted minimally invasive surgery.

Robot-assisted minimally invasive surgery (MIS) faces challenges in obtaining high-quality imaging results due to the limited spatial environment. In this paper, we present an all-in-one image super-resolution (SR) algorithm designed to tackle this challenge. By utilizing the stereo information from binocular images, we effectively convert low-resolution images into high-resolution ones. Our model architecture amalgamates the prowess of Convolutional Neural Networks (CNNs) and Transformers, capitalizing on the advantages of both methodologies. To achieve super-resolution across all scale factors, we employ a trainable upsampling module within our proposed network. We substantiate the effectiveness of our method through extensive quantitative and qualitative experiments. The results of our evaluations provide strong evidence supporting the superior performance of our approach in enhancing the quality of surgical images. Our method improves the resolution and thus the overall image quality, which allows the surgeon to perform precise operations conveniently. Simultaneously, it also facilitates the scaling of the region of interest (ROI) to achieve high-quality visualization during surgical procedures. Furthermore, it has the potential to enhance the image quality during telesurgery.

Significance of nuclear medicine methods for diagnosis and predicting the course of renal transplantation complications

Renal transplantation is a replacement-based therapeutic method in end-stage kidney failure.It is based on the interdisciplinary collaboration between scientists and clinicians, which match together the achievements of surgery, immunology, and radiation diagnostics. After transplantation the complications appear, which can be associated with status of the graft, the immunological factors of compatibility between the donor and the recipient, the surgical intervention. Early diagnosis and forehanded treatment of such complications is important, as it helps to evaluate and predict the functionality of the graft. In this article we discuss the complications after kidney transplantation: the reasons of their occurrence, pathogenesis, clinical features, and radiation imaging signs. The opportunities of the radiology methods for the differential diagnostics of complications which appear before, during or after the surgery, are shown. We pay the main attention to the nuclear medicine methods, especially the dynamic renal scintigraphy (DRSG) as a method for the evaluation of graft functionality. This is a safe, accessible and non-invasive method for evaluation the quantitative and qualitative parameters of kidney function. DRSG consequently characterizes the perfusion, the extraction and the excretion in the studied organ. We display the standard DRSG protocols using [99mTc] Tc-MAG3 and [99mTc] Tc-DTPA radiopharmaceuticals. We analyze investigations of kinetic perfusion and parenchymal DRSG parameters and their importance for differential diagnosis of complications, prognosis on delayed and slow graft function, short-term and long-term graft functionality assessment. We make an accent on the prognostic importance of the dynamic changes visible on DRSG. We discuss the perspectives of further development of scintigraphic methods for the assessment of kidney grafts. It is related to the implementation of high-quality 3-dimentional visualization, newest radiopharmaceuticals. We conclude the importance of the nuclear medicine as an element of multidisciplinary approach in transplantology.