Modern Imaging Research Articles

Prophylactic cranial irradiation for limited-stage small-cell lung cancer in the modern magnetic resonance imaging era may be omitted: a propensity score-matched analysis.

We aimed to clarify whether prophylactic cranial irradiation (PCI) is associated with improved outcomes in limited-stage small-cell lung cancer (LS-SCLC) in the current era of magnetic resonance imaging (MRI). Data from patients with LS-SCLC who achieved a complete response to definitive chemoradiotherapy (CRT) at two medical centers were retrospectively reviewed. Propensity score-matching was performed in a 2:1 ratio to balance the baseline characteristics of the no-PCI and PCI groups. The endpoints were the incidence of brain metastasis (BM), neurological causes of death and overall survival (OS). Overall, 80% patients underwent head MRI during the initial staging and 75 patients (no-PCI, n = 50; PCI, n = 25) were matched. Their baseline characteristics were generally well-balanced except for age; patients in the no-PCI group tended to be older. The median follow-up period was 29months. Although the incidence of BMs tended to be higher in the no-PCI group (1-year BM occurrence: 26% vs 17%, P = 0.22), the incidence of multiple BMs (defined as >4 metastases) was similar between groups (1-year multiple BMs occurrence: 8% vs 9%, P = 0.65). The 2-year neurological causes of death and OS rate did not significantly differ between the groups (6% and 9%; P = 0.85; and 70% and 79%; P = 0.36, respectively). The 1-year occurrence of multiple BMs did not increase, even without PCI, when modern imaging modalities were integrated into the initial diagnosis, suggesting that PCI could be omitted after CRT, if MRI was incorporated into the initial diagnosis and follow-up.

Acute abdominal pain with surprising etiology-rare complication of a common disease

The development of a cholecystocolic fistula as a complication of chronic cholecystitis and/or cholangitis is very rare and is difficult to detect without modern imaging. Nevertheless, acute abdominal symptoms are a frequent cause of patient presentation to an emergency department. The present case demonstrates that chronic cholecystitis with corresponding complications can present even without cholestasis and infection.

The Role of Neuromuscular Ultrasound in the Diagnosis of Peripheral Neuropathy.

The classification of peripheral neuropathies has traditionally been based on etiology, electrodiagnostic findings, or histopathologic features. With the advent of modern imaging, they now can also be characterized based on their varied distribution of imaging findings. We describe the major morphologic patterns of these changes, which include homogeneous enlargement; homogeneous thinning; focal, multifocal, and segmental enlargement; and focal thinning and beading (multifocal thinning). Representative disorders in each of these categories are discussed, along with examples of the more complex imaging manifestations of neuralgic amyotrophy, nerve transection, and hereditary amyloidosis. An appreciation of the diverse morphologic manifestations of neuropathy can help neuromuscular clinicians conduct appropriate imaging studies with ultrasound and, when needed, order suitable investigations with magnetic resonance neurography.

Art design interventions as a catalyst for sustainable development and infrastructure enhancement in intangible cultural heritage

This study explores the transformative role of art design interventions in the sustainable development and infrastructure enhancement of intangible cultural heritage, with a particular focus on honored brands. The research develops a framework that positions aesthetic and interactive art design interventions as pivotal components in revitalizing these brands. Aesthetic interventions translate the brand’s core philosophy, spirit, and values into compelling visual symbols, harmonizing cultural heritage with modern image design to elevate brand reputation and consumer preference. Interactive interventions enhance user experience, particularly among younger demographics, by integrating technological and entertainment-based engagement, thereby strengthening consumer loyalty and brand influence. The study further investigates how these art design interventions serve as catalysts for broader social development, contributing to the modern relevance and societal impact of time-honored brands. Additionally, it examines the impact of these interventions on sustainable development, societal support, and policy alignment. By weaving together these elements, the research underscores the critical importance of aligning brand strategies with societal goals, fostering environments where brands actively contribute to social welfare and sustainable growth. The findings offer valuable theoretical insights and practical strategies for the sustainable development of time-honored brands, providing clear directions for future research and practice.

Preoperative Imaging-Guided Surgical Planning in Complex Pelvic Fractures - Innovations in Traumatology and Radiology for Enhanced Clinical Outcomes: A Systematic Review

This systematic review will explore role of preoperative imaging-guided surgical planning for the management of complex pelvic fractures. Pelvic fractures occurs in high-energy trauma and cause challenges for intricate anatomy and have a high association with vascular, visceral injuries. Traditional imaging methods frequently fail to capture injury complexity while increasing surgical risks. Recent advancements of imaging technology CT, MRI, 3D reconstructions along with and AI-based predictive models offer enhanced precision in diagnosing and planning surgical interventions with detailed insights into fracture patterns, bone density, and soft tissue damage which enable accurate surgical planning to reduce intraoperative complications and shorten recovery times. We aim to systematically investigate effectiveness of modern imaging techniques with potencies in clinical outcomes, with an emphasis on improved surgical accuracy and patient safety. Analyzing range of studies, we have established evidence-based recommendations for integrating advanced imaging technologies into routine trauma care showing critical role these innovations play in improving both short-term recovery and long-term clinical outcomes for patients having complex pelvic fractures.

Molecular and Cellular Neurobiology of Spreading Depolarization/Depression and Migraine: A Narrative Review.

Migraine is a prevalent neurological disorder, particularly among individuals aged 20-50 years, with significant social and economic impacts. Despite its high prevalence, the pathogenesis of migraine remains unclear. In this review, we provide a comprehensive overview of cortical spreading depolarization/depression (CSD) and its close association with migraine aura, focusing on its role in understanding migraine pathogenesis and therapeutic interventions. We discuss historical studies that have demonstrated the role of CSD in the visual phenomenon of migraine aura, along with modern imaging techniques confirming its propagation across the occipital cortex. Animal studies are examined to indicate that CSD is not exclusive to migraines; it also occurs in other neurological conditions. At the cellular level, we review how CSD is characterized by ionic changes and excitotoxicity, leading to neuronal and glial responses. We explore how CSD activates the trigeminal nervous system and upregulates the expression of calcitonin gene-related peptides (CGRP), thereby contributing to migraine pain. Factors such as genetics, obesity, and environmental conditions that influence the CSD threshold are discussed, suggesting potential therapeutic targets. Current treatments for migraine, including prophylactic agents and CGRP-targeting drugs, are evaluated in the context of their expected effects on suppressing CSD activity. Additionally, we highlight emerging therapies such as intranasal insulin-like growth factor 1 and vagus nerve stimulation, which have shown promise in reducing CSD susceptibility and frequency. By elucidating the molecular and cellular mechanisms of CSD, this review aims to enhance the understanding of migraine pathogenesis and support the development of targeted therapeutic strategies.

Image processing tools for petabyte-scale light sheet microscopy data.

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

Error analysis based on a tunable wave plate polarization interferometric imaging spectrometer

Interference imaging spectroscopy combines modern imaging technology with spectral technology, holding significant importance for object imaging and spectral detection. This article introduces the principle of an adjustable wave plate polarization interferometric imaging spectrometer. The example design specifications are set for an observation wavelength range of 450–780 nm and a maximum resolution of 2 nm at 450 nm, with a 0.5 in detector as the base for calculating the specific dimensions of the Soleil–Babinet compensator. An investigation was conducted on the issues of nonuniform sampling, as well as three types of mechanical errors: flatness, wedge angle tolerance, and optical axis orientation accuracy. Emphasis was placed on discussing the impact of these errors on the instrument’s optical path difference and spectral reconstruction accuracy. This research provides theoretical guidance for the design and engineering of this miniaturized imaging spectrometer.

Magnetic Resonance Imaging of the Heart is a Robust Method for Diagnosing Myocardial Lesions with Low Availability in Emergency Cardiology Departments

This article discusses the problem of underuse, low availability of modern imaging techniques such as magnetic resonance imaging of the heart (MRI). This technique is characterized by high accuracy and uniqueness in the diagnosis of myocardial lesions of various genesis. The widespread introduction of this technique in emergency cardiology departments can improve the accuracy of diagnosis and the effectiveness of patient therapy. Despite the high costs of implementation and current use, modern diagnostic methods can save healthcare resources by reducing hospital stays and improving treatment outcomes.

Rebranding of Ukrainian Folk Choral Art in the 21st Century

Purpose of the work. The study analyses the rebranding policy of folk choral art in the 21st century and identifies new approaches to its preservation and popularisation in the context of globalisation and the rapid development of information technologies. Research methodology involves the combination of empirical methods (observation, data collection, interviewing), systematic and analytical methods, which allowed for identifying the leading trends in updating approaches to promoting Ukrainian folk choral art in the 21st century. Scientific novelty. For the first time in Ukrainian musicology, rebranding is being explored as a new approach to the preservation and popularisation of folk choral art in the conditions of globalisation and the development of information technologies. Rebranding policy acts as a key tool for revitalising the folk music tradition, increasing interest in traditional culture, and integrating folk choral art into the modern cultural space. Conclusions. Rebranding of folk choral art is essential for its functioning in modern conditions and for attracting new audiences, especially young people. The creation of multimedia projects and the use of social networks make folk choral art more accessible and appealing. The integration of elements from other cultures into folk choral performances contributes to updating its format and cultural integration while preserving the authenticity of Ukrainian art. The theatricalisation of performances, the use of traditional Ukrainian costumes, scenography, and choreography enhance the emotional impact on the audience, shaping a modern image of the Ukrainian folk song. Rebranding helps folk choral groups not only make folklore one of the leading musical trends in Ukraine but also reach an international level, fostering intercultural communication and promoting Ukrainian art abroad.

Personalised High Tibial Osteotomy Surgery Is Accurate: An Assessment Using 3D Distance Mapping

Early-stage knee osteoarthritis is often suitable for treatment with high tibial osteotomy (HTO). This is an effective joint-preserving treatment, resulting in good postoperative outcomes. To overcome the limitations of traditional HTO, the surgical technique and correction accuracy can be enhanced by personalised procedures using three-dimensional digital planning and metal additive manufacturing, The purpose of this clinical trial study was to evaluate the three-dimensional accuracy of a new personalised HTO procedure, using modern imaging techniques, 3D modelling, and distance map analysis (DMA). Twenty-five patients were treated with the personalised HTO procedure. Before surgery and after 6 months, they underwent clinical evaluation scoring, radiographic imaging, and computed-tomography scanning to generate morphological models. Specifically, preoperative tibia models were used to plan the tibia correction and the design and position of the fixation plate. Preoperative, planned, and postoperative models were imported in computer-aided and designing software (Geomagic ControlTM 2014, 3D Systems, Rock Hill, SC, USA) for DMA implementation to assess geometrical differences between model surfaces. A very good reproduction of the planned tibia morphology was achieved postoperatively (average differences between −0.9 mm and 1.4 mm). DMA values associated with fixation-plate deformation were less than 1 mm, similar to those for plate-to-tibia surface-contour matching. Overall, personalised digitally planned HTO utilising three-dimensional printed surgical guides and plates enables accurate planned correction and plate placement.

Takayasu Arteritis in a Young Female Patient: A Case Report

Takayasu arteritis (TA) is a rare chronic inflammatory vasculitis that primarily affects large vessels like the aorta, mainly in women under 40, particularly of Asian descent. Its exact cause is unknown but is linked to cell-mediated inflammation, leading to complications such as occlusion and aneurysms. Symptoms vary widely, complicating diagnosis. Modern imaging techniques like computed tomography angiography (CTA) are crucial for early detection, which can significantly reduce complications. This case report highlights a 28-year-old woman with extensive aorto-arteritis, underscoring the importance of timely imaging and intervention in managing TA.

Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction.

Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring involves the detection and classification of moving objects within a city using deep learning-based models, which requires extensive training data. Therefore, the creation of training data consumes a significant portion of the resources required to develop these models, which is a major obstacle in artificial intelligence (AI)-based urban environment management. In this study, a performance evaluation method for semi-moving object detection is proposed using an existing AI-based object detection model, which is used to construct AI training datasets. The tasks to refine the results of AI-model-based object detection are analyzed, and an efficient evaluation method is proposed for the semi-automatic construction of AI training data. Different FBeta scores are tested as metrics for performance evaluation, and it is found that the F2 score could improve the completeness of the dataset with 26.5% less effort compared to the F0.5 score and 7.1% less effort compared to the F1 score. Resource requirements for future AI model development can be reduced, enabling the efficient creation of AI training data.

Lossy Image Compression with Stochastic Quantization

Introduction. Lossy image compression algorithms play a crucial role in various domains, including graphics, and image processing. As image information density increases, so do the resources required for processing and transmission. One of the most prominent approaches to address this challenge is color quantization, proposed by Orchard et al. (1991). This technique optimally maps each pixel of an image to a color from a limited palette, maintaining image resolution while significantly reducing information content. Color quantization can be interpreted as a clustering problem (Krishna et al. (1997), Wan (2019)), where image pixels are represented in a three-dimensional space, with each axis corresponding to the intensity of an RGB channel. The purpose of the paper. Scaling of traditional algorithms like K-Means can be challenging for large data, such as modern images with millions of colors. This paper reframes color quantization as a three-dimensional stochastic transportation problem between the set of image pixels and an optimal color palette, where the number of colors is a predefined hyperparameter. We employ Stochastic Quantization (SQ) with a seeding technique proposed by Arthur et al. (2007) to enhance the scalability of color quantization. This method introduces a probabilistic element to the quantization process, potentially improving efficiency and adaptability to diverse image characteristics. Results. To demonstrate the efficiency of our approach, we present experimental results using images from the ImageNet dataset. These experiments illustrate the performance of our Stochastic Quantization method in terms of compression quality, computational efficiency, and scalability compared to traditional color quantization techniques. Conclusions. This study introduces a scalable algorithm for solving the color quantization problem without memory constraints, demonstrating its efficiency on a subset of images from the ImageNet dataset. The convergence speed of the algorithm can be further enhanced by modifying the update rule with alternative methods to Stochastic Gradient Descent (SGD) that incorporate adaptive learning rates. Moreover, the stochastic nature of the proposed solution enables the utilization of parallelization techniques to simultaneously update the positions of multiple quants, potentially leading to significant performance improvements. This aspect of parallelization and its impact on algorithm efficiency presents a topic for future research. The proposed method not only addresses the limitations of existing color quantization techniques but also opens up new possibilities for optimizing image compression algorithms in resource-constrained environments. Keywords: non-convex optimization, stochastic optimization, stochastic quantization, color quantization, lossy compression.

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.

Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD). Accurate detection of NFTs in tissue samples can reveal relationships with clinical, demographic, and genetic features through deep phenotyping. However, expert manual analysis is time-consuming, subject to observer variability, and cannot handle the data amounts generated by modern imaging. We present a scalable, open-source, deep-learning approach to quantify NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue. To achieve this, we developed a method to generate detailed NFT boundaries directly from single-point-per-NFT annotations. We then trained a semantic segmentation model on 45 annotated 2400μm by 1200μm regions of interest (ROIs) selected from 15 unique temporal cortex WSIs of AD cases from three institutions (University of California (UC)-Davis, UC-San Diego, and Columbia University). Segmenting NFTs at the single-pixel level, the model achieved an area under the receiver operating characteristic of 0.832 and an F1 of 0.527 (196-fold over random) on a held-out test set of 664 NFTs from 20 ROIs (7 WSIs). We compared this to deep object detection, which achieved comparable but coarser-grained performance that was 60% faster. The segmentation and object detection models correlated well with expert semi-quantitative scores at the whole-slide level (Spearman's rho ρ=0.654 (p=6.50e-5) and ρ=0.513 (p=3.18e-3), respectively). We openly release this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible by manual assessment.

Classification of Lung Cancer using Pre-Trained Deep Learning Models

Many Computer Assisted Diagnosis (CAD) systems have been designed and used in recent past for diagnosingdifferenttypes of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use modern image processing and deep learning methods.The occurrence of Lung cancer is seen to be increased and Computed Tomography (CT) scan images were utilized in investigation to locate and classify lung cancer, also for determining the severity of those cancer. This study is aimed at employing pre-trained deep neural networks for classification of lung cancer images. A gaussian-based approach is used to segment CT scan images. In this research exploits a transfer learning-based classification method for the chest CT images acquired from Cancer Image Archive and available in the Kaggle platform. Pre- trained models VGG and RESNET were trained using segmented chest CT images, and their performance was evaluated using different optimization algorithms were analyzed. Keywords: Computer Aided Diagnosis, lung cancer, deep learning, CT image, gaussian, transfer learning, pre-trained models, optimization algorithms.

From Brownian motion to virtual biopsy: a historical perspective from 40years of diffusion MRI.

Diffusion MRI was introduced in 1985, showing how the diffusive motion of molecules, especially water, could be spatially encoded with MRI to produce images revealing the underlying structure of biologic tissues at a microscopic scale. Diffusion is one of several Intravoxel Incoherent Motions (IVIM) accessible to MRI together with blood microcirculation. Diffusion imaging first revolutionized the management of acute cerebral ischemia by allowing diagnosis at an acute stage when therapies can still work, saving the outcomes of many patients. Since then, the field of diffusion imaging has expanded to the whole body, with broad applications in both clinical and research settings, providing insights into tissue integrity, structural and functional abnormalities from the hindered diffusive movement of water molecules in tissues. Diffusion imaging is particularly used to manage many neurologic disorders and in oncology for detecting and classifying cancer lesions, as well as monitoring treatment response at an early stage. The second major impact of diffusion imaging concerns the wiring of the brain (Diffusion Tensor Imaging, DTI), allowing to obtain from the anisotropic movement of water molecules in the brain white-matter images in 3 dimensions of the brain connections making up the Connectome. DTI has opened up new avenues of clinical diagnosis and research to investigate brain diseases, neurogenesis and aging, with a rapidly extending field of application in psychiatry, revealing how mental illnesses could be seen as Connectome spacetime disorders. Adding that water diffusion is closely associated to neuronal activity, as shown from diffusion fMRI, one may consider that diffusion MRI is ideally suited to investigate both brain structure and function. This article retraces the early days and milestones of diffusion MRI which spawned over 40years, showing how diffusion MRI emerged and expanded in the research and clinical fields, up to become a pillar of modern clinical imaging.

Prediction of biochemical recurrence after radical prostatectomy from primary tumour characteristics.

To construct and externally calibrate a predictive model for early biochemical recurrence (BCR) after radical prostatectomy (RP) incorporating clinical and modern imaging characteristics of the primary tumour. Patients who underwent RP following multiparametric magnetic resonance imaging, prostate biopsy and prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA-PET/CT), from two centres in Australia and the Netherlands. The primary outcome was biochemical recurrence-free survival (BRFS), where BCR was defined as a rising PSA level of ≥0.2 ng/mL or initiation of postoperative treatment per clinician discretion. Proportional hazards models to predict time to event were developed in the Australian sample using relevant pre- and post-surgical parameters and primary tumour maximum standardised uptake value (SUVmax) on diagnostic PSMA-PET/CT. Calibration was assessed in an external dataset from the Netherlands with the same inclusion criteria. Data from 846 patients were used to develop the models. Tumour SUVmax was associated with worse predicted 3-year BRFS for both pre- and post-surgical models. SUVmax change from 4 to 16 lessened the predicted 3-year BRFS from 66% to 42% for a patient aged 65 years with typical pre-surgical parameters (PSA level 8 ng/mL, Prostate Imaging-Reporting and Data System score 4/5 and biopsy Gleason score ≥4 + 5). Considering post-surgical variables, a patient with the same age and PSA level but pathological stage pT3a, RP Gleason score ≥4 + 5 and negative margins, SUVmax change from 4 to 16 lessened the predicted 3-year BRFS from 76% to 61%. Calibration on an external sample (n = 464) showed reasonable performance; however, a tendency to overestimate survival in patients with good prognostic factors was observed. Tumour SUVmax on diagnostic PSMA-PET/CT has utility additional to commonly recognised variables for prediction of BRFS after RP.

Comparative Analysis of Traditional and Modern Imaging Techniques in Neurological Disorders

Comparative Analysis of Traditional and Modern Imaging Techniques in Neurological Disorders

Possibility to measure the volume of coronary sinus in contrast-enhanced computed tomography.

Modern imaging techniques such as computed tomography (CT) can help in the assessment of coronary sinus volume in a vitro manner, but there is no comprehensive research on this topic so far. Hence, we decided to develop a methodology for measuring the volume of the coronary sinus in multi-detector CT and to try to apply it in practice. Forty-nine patients (22 men) were included in this research, with a mean age of 70.08 ± 13.6 years. Scanning with retrospective ECG-gating was performed using a Toshiba Aquilion 64 (slice: 0.5 mm; helical pitch: 12.8; rotation time: 0.4 s). 80 ± 20 cm3 of non-ionic contrast was administered to each patient. The volume of coronary sinus and other data measurements were performed using Vitrea 2 workstations. The organ volume measurement function was used to measure volume objects in CT scans. To standardise the measurements, they were all performed to the place where the vein of Marshall reaches the coronary sinus. In cases of loss of vein of Marshall, the first lateral vein was used as the junction between the coronary sinus and the great cardiac vein. The coronary sinus volume varied from 0.96 cm3 to 8.52 cm3. The average volume was 3.71 ± 1.64 cm3. There was a significant correlation between end diastolic volume and coronary sinus volume (r = 0.33, p = 0.02). In most cases the quality of visualisation was good - the average was calculated as 4.16 ± 0.87. The Thebesian valve was present in 22 cases (44.9%); however, no statistical relationship between the presence of the Thebesian valve and coronary sinus was observed. It is possible to visualise and calculate the volume of the coronary sinus in cardiac CT.