Imaging Technology Research Articles

Is cutting-edge imaging technology superior to conventional angiography in improving outcomes of coronary artery stenting?

Is cutting-edge imaging technology superior to conventional angiography in improving outcomes of coronary artery stenting?

Automatic Optic Nerve Assessment From Transorbital Ultrasound Images: A Deep Learning-based Approach.

Transorbital Ultrasonography (TOS) is a promising imaging technology that can be used to characterize the structures of the optic nerve and the potential alterations that may occur in those structures as a result of an increase in intracranial pressure (ICP) or the presence of other disorders such as multiple sclerosis (MS) and hydrocephalus. In this paper, the primary objective is to develop a fully automated system that is capable of segmenting and calculating the diameters of structures that are associated with the optic nerve in TOS images. These structures include the optic nerve diameter sheath (ONSD) and the optic nerve diameter (OND). A fully convolutional neural network (FCN) model that has been pre-trained serves as the foundation for the segmentation method. The method that was developed was utilized to collect 464 different photographs from 110 different people, and it was accomplished with the assistance of four distinct pieces of apparatus. An examination was carried out to compare the outcomes of the automatic measurements with those of a manual operator. Both OND and ONSD have a typical inaccuracy of -0.12 0.32 mm and 0.14 0.58 mm, respectively, when compared to the operator. The Pearson correlation coefficient (PCC) for OND is 0.71, while the coefficient for ONSD is 0.64, showing that there is a positive link between the two measuring tools. A conclusion may be drawn that the technique that was developed is automatic, and the average error (AE) that was reached for the ONSD measurement is compatible with the ranges of inter-operator variability that have been discovered in the literature.

Magnetic steering continuum robot for transluminal procedures with programmable shape and functionalities

Millimeter-scale soft continuum robots offer safety and adaptability in transluminal procedures due to their passive compliance, but this feature necessitates interactions with surrounding lumina, leading to potential medical risks and restricted mobility. Here, we introduce a millimeter-scale continuum robot, enabling apical extension while maintaining structural stability. Utilizing phase transition components, the robot executes cycles of tip-based elongation, steered accurately through programmable magnetic fields. Each motion cycle features a solid-like backbone for stability, and a liquid-like component for advancement, thereby enabling autonomous shaping without reliance on environmental interactions. Together with clinical imaging technologies, we demonstrate the capability of navigating through tortuous and fragile lumina to transport microsurgical tools. Once it reaches larger anatomical spaces such as stomach, it can morph into functional 3D structures that serve as surgical tools or sensing units, overcoming the constraints of initially narrow pathways. By leveraging this design paradigm, we anticipate enhanced safety, multi-functionality, and cooperative capabilities among millimeter-scale continuum robots, opening new avenues for transluminal robotic surgery.

Brain Metastasis: an insight into novel molecular targets for theranostic approaches.

Brain metastases (BrM) are common malignant lesions in the central nervous system, and pose a significant threat in advanced-stage malignancies due to delayed diagnosis and limited therapeutic options. Their distinct genomic profiles underscore the need for molecular profiling to tailor effective treatments. Recent advances in cancer biology have uncovered molecular drivers underlying tumor initiation, progression, and metastasis. This, coupled with the advances in molecular imaging technology and radiotracer synthesis, has paved the way for the development of innovative radiopharmaceuticals with enhanced specificity and affinity for BrM specific targets. Despite the challenges posed by the blood-brain barrier to effective drug delivery, several radiolabeled compounds have shown promise in detecting and targeting BrM. This manuscript provides an overview of the recent advances in molecular biomarkers used in nuclear imaging and targeted radionuclide therapy in both clinical and preclinical settings. Additionally, it explores potential theranostic applications addressing the unique challenges posed by BrM.

Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor

The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems.

Neuroinflammation in dementia: A meta-analysis of PET imaging studies.

Dementia is a major public health challenge for aging societies worldwide. Neuroinflammation is thought to be a key factor in dementia development. The aim of this study was to comprehensively assess translocator protein (TSPO) expression by positron emission tomography (PET) imaging to reveal the characteristics of neuroinflammation in dementia. We used a meta-analysis to retrieve literature on TSPO expression in dementia using PET imaging technology, including but not limited to the quality of the study design, sample size, and the type of TSPO ligand used in the study. For the included studies, we extracted key data, including TSPO expression levels, clinical characteristics of the study participants, and specific information on brain regions. Meta-analysis was performed using R software to assess the relationship between TSPO expression and dementia. After screening, 12 studies that met the criteria were included. The results of the meta-analysis showed that the expression level of TSPO was significantly elevated in patients with dementia, especially in the hippocampal region. The OR in the hippocampus was 1.50 with a 95% CI of 1.09 to 1.25, indicating a significant increase in the expression of TSPO in this region compared to controls. Elevated levels of inflammation in the prefrontal lobe and cingulate gyrus are associated with cognitive impairment in patients. This was despite an OR of 1.00 in the anterior cingulate gyrus, indicating that TSPO expression in this region did not correlate significantly with the findings. The overall heterogeneity test showed I² = 51%, indicating moderate heterogeneity. This study summarizes the existing literature on TSPO expression in specific regions of the brain in patients with dementia, and also provides some preliminary evidence on the possible association between neuroinflammation and dementia. However, the heterogeneity of results and limitations of the study suggest that we need to interpret these findings with caution. Future studies need to adopt a more rigorous and consistent methodological design to more accurately assess the role of neuroinflammation in dementia, thereby providing a more reliable evidence base for understanding pathological mechanisms and developing potential therapeutic strategies.

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients.

Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

Structural and functional connectivity in hydrocephalus: a scoping review.

Optimizing the treatment of hydrocephalus remains a major challenge in adult and pediatric neurosurgery. Currently, clinical treatment relies heavily on anatomic imaging of ventricular size and clinical presentation. The emergence of functional and structural brain connectivity imaging has provided the basis for a new paradigm in the management of hydrocephalus. Here we review the pertinent advances in this field. Following PRISMA-ScR guidelines for scoping reviews, we searched PubMed for relevant literature from 1994 to April 2023 using hydrocephalus and MRI-related terms. Included articles reported original MRI data on human subjects with hydrocephalus, while excluding non-English or pre-1994 publications that didn't match the study framework. The review identified 44 studies that investigated functional and/or structural connectivity using various MRI techniques across different hydrocephalus populations. While there is significant heterogeneity in imaging technology and connectivity analysis, there is broad consensus in the literature that 1) hydrocephalus is associated with disruption of functional and structural connectivity, 2) this disruption in cerebral connectivity can be further associated with neurologic compromise 3) timely treatment of hydrocephalus restores both cerebral connectivity and neurologic compromise. The robustness and consistency of these findings vary as a function of patient age, hydrocephalus etiology, and the connectivity region of interest studied. Functional and structural brain connectivity imaging shows potential as an imaging biomarker that may facilitate optimization of hydrocephalus treatment. Future research should focus on standardizing regions of interest as well as identifying connectivity analysis most pertinent to clinical outcome.

Case Report on Caries Assessment Using Intraoral Scanner Compared with Bitewing Radiographs.

Dental caries remains one of the main reasons patients seeing their dentist. They either show up for preventive measures, diagnostics, or treatment of present caries lesions. To date, diagnostics are performed visually, supported by using bitewing radiographs. While radiographic diagnostics should only be performed on a biannual basis, and some caries process will not be seen with visual diagnostics, there remains a lack in regular checkups. Therefore, different technical applications can be used for regular diagnostics. One of those is the near-infrared imaging (NIRI) technology. In this case report, a patient presented with incipient caries lesions. These lesions were diagnosed visually, radiographically, and using NIRI. After diagnosis of incipient caries lesions, the patient was advised to use a hydroxyapatite toothpaste and a hydroxyapatite gel for the remineralization of the lesions and prevention of caries progression. The patient was followed up for 6 months with regular checkups in between. Visual diagnostics and NIRI were used to detecting the caries progress. After 6 months, bitewing radiographs were used in addition. In this clinical investigation we were able to show for the first time that NIRI and bitewing radiographs are able to detect and follow incipient caries lesions. Additionally, this study highlights that hydroxyapatite-containing oral care products are able to arrest and remineralize the caries process.

Interfacial Second-Harmonic Generation via Superposition of Symmetries in a Double-Resonance-Enhanced Plasmonic Nanocavity.

Second-harmonic generation (SHG) has rapidly advanced with the miniaturization of on-chip devices and has found many applications, including optical frequency conversion, nonlinear imaging, and quantum technology. However, owing to the obvious phase-matching constraints involved in nonlinear optical interactions in bulk crystals and the decrease in the length and strength of nonlinear interactions in nanophotonic and surface/interface systems, improving the SHG efficiency and manipulating its optical properties at the nanoscale are challenging tasks. Herein, a monocrystalline silver microplate and nanocube-coupled nanocavity with double-resonance plasmonic modes and an ultrasmall gap were constructed, resulting in efficiently enhanced SHG. In particular, the SHG from the silver microplate (111) is polarization-dependent, and the anisotropy of the SHG in the plasmonic nanocavity can be further controlled via the superposition of symmetries at the interface and plasmonic waveguide-cavity modes. The interfacial SHG provides technology for developing lattice surface atomic arrangement and nanostructure rapid characterization, nonlinear light sources, and on-chip nonlinear nanophotonic devices.

Multilayer lensless camera for improving the condition number

Computational lensless imaging technology implements the thinning of the optical system by replacing optical imaging with image reconstruction processing. The conventional optical design uses a single coded mask and an image sensor; however, researchers have recently proposed optical designs incorporating multiple stacked coded apertures for multidimensional and wide-field imaging. Here, we investigate the effects of multilayering the coded aperture on the performance of two-dimensional spatial imaging. Through simulations and optical experiments, we demonstrate that multilayering the coded aperture enhances the condition number of the optical system’s transmission matrix and consequently improves the accuracy of image reconstruction in lensless imaging.

Pancreatic cancer: from early detection to personalized treatment approaches.

Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.

Adrenal Vein Sampling: Tips and Tricks.

Adrenal vein sampling (AVS) is the standard method for distinguishing unilateral from bilateral sources of autonomous aldosterone production in patients with primary aldosteronism. This procedure has been performed at limited specialized centers due to its technical complexity. With recent advances in imaging technology and knowledge of adrenal vein anatomy in parallel with the development of adjunctive techniques, AVS has become easier to perform, even at nonspecialized centers. Although rare, anatomic variants of the adrenal veins can cause sampling failure or misinterpretation of the sampling results. The inferior accessory hepatic vein and the inferior emissary vein are useful anatomic landmarks for right adrenal vein cannulation, which is the most difficult and crucial step in AVS. Meticulous assessment of adrenal vein anatomy on multidetector CT images and the use of a catheter suitable for the anatomy are crucial for adrenal vein cannulation. Adjunctive techniques such as intraprocedural cortisol assay, cone-beam CT, and coaxial guidewire-catheter techniques are useful tools to confirm right adrenal vein cannulation or to troubleshoot difficult blood sampling. Interventional radiologists should be involved in interpreting the sampling results because technical factors may affect the results. In rare instances, bilateral adrenal suppression, in which aldosterone-to-cortisol ratios of both adrenal glands are lower than that of the inferior vena cava, can be encountered. Repeat sampling may be necessary in this situation. Collaboration with endocrinology and laboratory medicine services is of great importance to optimize the quality of the samples and for smooth and successful operation. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis.

Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments. This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments. Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.

Simulations and performance studies of a MAPS in 65 nm CMOS imaging technology

Simulations and performance studies of a MAPS in 65 nm CMOS imaging technology

Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data

Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challenges in achieving rapid visual characterization of microplastics. In this study, photoacoustic imaging technology is initially employed to capture high-resolution images of diverse microplastic samples. To address the limited dataset issue, an automated data processing pipeline is designed to obtain sample masks while effectively expanding the dataset size. Additionally, we propose Vqdp2, a generative deep learning model with multiple proxy tasks, for predicting six forms of microplastics data. By simultaneously constraining model parameters through two training modes, outstanding morphological category representations are achieved. The results demonstrate Vqdp2's excellent performance in classification accuracy and feature extraction by leveraging the advantages of multi-task training. This research is expected to be attractive for the detection classification and visual characterization of microplastics.

Spinal Cord Injury Identification and Localization Detection Based on MRI Imaging and Deep Learning Technology

Spinal Cord Injury Identification and Localization Detection Based on MRI Imaging and Deep Learning Technology

A Review of Metamaterials Cloaking for Military and Civilian Use

Metamaterials cloaking technology boasts a wide array of potential uses, spanning from military stealth applications to advancements in medical imaging. Metamaterials are specially engineered materials with unique characteristics absent in natural substances, such as negative refractive index, empowering them to manipulate electromagnetic waves in novel ways. The process of cloaking with metamaterials involves the bending of light or other electromagnetic waves around an object, effectively rendering it invisible or significantly reducing its detectability. This is accomplished through the intricate design of metamaterial structures capable of governing the flow of electromagnetic fields, diverting them away from the cloaked object. Among the most prominent applications of metamaterial cloaking is the development of invisibility cloaks. While primarily at the experimental stage, researchers have made substantial progress in crafting cloaking devices capable of concealing objects from specific wavelengths of light. These cloaks function by manipulating the path of light around an object, effectively creating the illusion of its absence. The potential applications of metamaterial cloaking encompass a broad spectrum, including invisibility, antennas and sensors, medical imaging, wireless communication, and stealth technology.

Effect of Incidence Angle on Temperature Measurement of Solar Panel with Unmanned Aerial Vehicle-Based Thermal Infrared Camera

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

Computational imaging with randomness

AbstractImaging is a longstanding research topic in optics and photonics and is an important tool for a wide range of scientific and engineering fields. Computational imaging is a powerful framework for designing innovative imaging systems by incorporating signal processing into optics. Conventional approaches involve individually designed optical and signal processing systems, which unnecessarily increased costs. Computational imaging, on the other hand, enhances the imaging performance of optical systems, visualizes invisible targets, and minimizes optical hardware. Digital holography and computer-generated holography are the roots of this field. Recent advances in information science, such as deep learning, and increasing computational power have rapidly driven computational imaging and have resulted in the reinvention these imaging technologies. In this paper, I survey recent research topics in computational imaging, where optical randomness is key. Imaging through scattering media, non-interferometric quantitative phase imaging, and real-time computer-generated holography are representative examples. These recent optical sensing and control technologies will serve as the foundations of next-generation imaging systems in various fields, such as biomedicine, security, and astronomy.