Medical Imaging

Background-In vivo confocal scanning laser microscopy (CSLM) is a recently-developed noninvasive technique for visualizing microscopic structures with the skin. CSLM has been used to characterize proliferative and inflammatory skin diseases, neoplastic skin lesions and pigmented lesions. Objective-Here, we assessed the ability of CSLM to evaluate the formation of neogenic hair follicles after a full thickness wound in mice. Methods-Full-thickness wounds were made on the dorsal skin of 3-week old mice. After scab detachment (SD), the number, width, length, space and volume of neogenic hair follicles were analyzed using CSLM. The results were compared with those from conventional methods, including staining for alkaline phosphatase (AP) and keratin 17 (K17) as well as histology. Results-Quantification of neogenic hair follicles using CSLM compared favorably with results from direct measurements on isolated epidermal tissue after immunostaining for K17, a marker for the epithelial portion of new hair follicles. CSLM detected 89% of K17-stained follicles. CSLM more accurately quantitated the number of new follicles compared to AP staining, which detects the dermal portion of the new follicle. The width and length measurement from CSLM and histology were very close and correlated with each other. The minimum length of a neogenic hair follicle that could be detected by CSLM was 21 μm. The space between neogenic hair follicles was decreased in histological sections compared to CSLM. Conclusions-CSLM is an accurate and valuable method for counting and measuring neogenic hair follicles non-invasively. CSLM produces images similar to histology in mice. Measurements of microstructures using CSLM more accurately reflect actual sizes since this technique avoids fixation artifact. In vivo visualization of developing follicles with CSLM permits detection of serial changes in hair follicle formation, thus conserving numbers of mice required for studies and improving detection of temporal changes in developing hair follicles.

The relative retardation A, the orientation of the main axis p, the polarization-dependent loss angle XI, and the reciprocal and the nonreciprocal rotation angle p of optical components can be determined, in principle, by intracavity ellipsometry. The component under test is placed inside a phase-modulated cw laser, which causes a change in the beat frequency, beam polarization, and intensity. Using the diode-pumped Nd:YAG-laser ellipsometer, we demonstrate with our preliminary measurements of A and p the feasibility and precision of intracavity ellipsometry for measuring optically anisotropic components.

Background and objectives: the present study is an attempt to investigate the prevalence and type of lumbar spinal MRI incidental findings in patients diagnosed with discopathy. Materials and Methods: This cross-sectional study was conducted from 26/09/2012 to 14/01/2013 on MRI findings of 444 patients clinically diagnosed with Discopathy and approved MRI reports. A Radiologist studied the MRI results in terms of single vertebral hemangioma, multiple vertebral hemangiomas, kidney cyst, liver cyst and Perineural cyst. Data were collected and analyzed through Fisher Exact test. Results: 73 patients (16%) out of a total of 444 patients (215 males (48.4%) and 229 females (51.6) with a mean age of 42 years and age range of 13 to 87 years), provided incidental findings. The frequency of single vertebral hemangioma, multiple vertebral hemangioma, kidney cysts, liver cysts and Perineural cyst was 31 cases (7.0%), 11 cases (2.5%), 13 cases (2.9%), 2 cases (0.5%) and 3 cases (0.7%) respectively. There was no significant correlation between the frequency distribution of findings in terms of sex (P-value = 0.08), but there was a significant correlation between the frequency distribution of findings in terms of age (P-value = 0.006). Conclusion: incidental findings are fairly common findings in the MRI of patients with discopathy. There is a significant correlation between the frequency distributions of these findings and the patients' age, however, no correlation was observed between these findings and patients' sex.

Purpose: We present a phase-contrast x-ray tomography study of wild type C57BL/6 mouse hearts as a nondestructive approach to the microanatomy on the scale of the entire excised organ. Based on the partial coherence at a home-built phase-contrast μ-CT setup installed at a liquid metal jet source, we exploit phase retrieval and hence achieve superior image quality for heart tissue, almost comparable to previous synchrotron data on the whole organ scale. Approach: In our work, different embedding methods and heavy metal-based stains have been explored. From the tomographic reconstructions, quantitative structural parameters describing the three-dimensional (3-D) architecture have been derived by two different fiber tracking algorithms. The first algorithm is based on the local gradient of the reconstructed electron density. By performing a principal component analysis on the local structure-tensor of small subvolumes, the dominant direction inside the volume can be determined. In addition to this approach, which is already well established for heart tissue, we have implemented and tested an algorithm that is based on a local 3-D Fourier transform. Results: We showed that the choice of sample preparation influences the 3-D structure of the tissue, not only in terms of contrast but also with respect to the structural preservation. A heart prepared with the evaporation-of-solvent method was used to compare both algorithms. The results of structural orientation were very similar for both approaches. In addition to the determination of the fiber orientation, the degree of filament alignment and local thickness of single muscle fiber bundles were obtained using the Fourier-based approach. Conclusions: Phase-contrast x-ray tomography allows for investigating the structure of heart tissue with an isotropic resolution below 10 μm. The fact that this is possible with compact laboratory instrumentation opens up new opportunities for screening samples and optimizing sample preparation, also prior to synchrotron beamtimes. Further, results from the structural analysis can help in understanding cardiovascular diseases or can be used to improve computational models of the heart.

A novel technique for calibration transfer called the Modified Four Point Interpolant (MFPI) method is introduced for near infrared spectra. The method is founded on physical intuition and utilizes a series of quasiconformal maps in the frequency domain to transfer spectra from a slave instrument to a master instrument's approximated space. Comparisons between direct standardization (DS), piecewise direct standardization (PDS), and MFPI for two publicly available datasets are detailed herein. The results suggest that MFPI can outperform DS and PDS with respect to root mean squared errors of transfer and prediction. Combinations of MFPI with DS/PDS are also shown to reduce predictive errors after transfer.

Effective management of glaucoma is essential for preventing irreversible vision loss. This study introduces a novel deep learning-based network designed to enhance performance while minimizing computational complexity. The system comprises two models: the first is a hybrid model combining a customized U-Net architecture integrated with you only look at coefficients (YOLACT) is utilized to achieve accurate segmentation of the optic disc (OD) and optic cup (OC), providing detailed diagnostic insights for ophthalmologists. The second model employs you only look once version 5 (YOLOv5) for real-time glaucoma prediction, delivering outstanding performance with an accuracy of 97.89% and F1 score of 98% on the primary dataset. On an independent dataset without further training, the model achieved 96% accuracy, with sensitivity and specificity of 98.9% and 93.3%, respectively. These results highlight the model's robustness, generalizability, and adaptability, demonstrating its potential for effective glaucoma screening and early detection in diverse clinical environments. This approach offers a promising advancement in improving the accessibility and efficiency of glaucoma management.

Anaphylaxis to contrast media is rare but potentially fatal. A coronial inquest in Victoria highlighted a gap in processes and skills for managing contrast-related anaphylaxis in a private, stand-alone radiology clinic. Key findings were that all staff (radiographers and radiologists) had little to no practical training in the management of contrast reactions, the radiologist present was not aware of the current guidelines prescribed by the clinic or of any visual displays of the guidelines and the radiologist was inadequately prepared for the medical emergency. The Coroner recommended that specific training in the management of anaphylaxis should be implemented for radiologists and radiographers. These recommendations were directly at professional bodies but, because they relate to clinical governance, they are also relevant to health services and private companies providing radiology services. This gap in knowledge, confidence and processes was not unknown. An Australian survey published in 2014 reported that about 40% of radiologists/radiology trainees assessed themselves as lacking knowledge in managing a contrast-related anaphylactic reaction and in dosing and administration of adrenaline. The reported deficit in knowledge was larger for clinicians who had completed basic life support training more than 3 years previously. Similar findings have been reported in American and UK studies. In 2021, The Australian Commission for Safety and Quality in Healthcare released its Acute Anaphylaxis Clinical Care Standard. The first two quality statements focus on prompt recognition of anaphylaxis and immediate injection of intramuscular adrenaline, before any other treatment including asthma medicines, corticosteroids and antihistamines. As at the date of writing, the Royal Australian and New Zealand College of Radiologists and the Medical Radiation Practice Board are not listed as having endorsed this document. Several small studies have shown that education, especially targeted training involving simulation, can improve knowledge and confidence for treating contrast-related anaphylaxis. 7 Unfortunately, to the authors' knowledge to date, these strategies are not widely embedded in hospital mandatory competency requirements for radiographers and radiologists or in credentialing requirements for stand-alone radiology clinics. As clinical competency to manage foreseeable emergencies is a matter of clinical governance, there is a strong case for health service and corporate governance teams (for stand-alone clinics) to take a lead in making appropriate training mandatory and in partnering with training providers to ensure it is available to staff in a format and location that facilitates participation and skill development. Reliance on an ambulance response, given the known delays, is not enough.

Reflectance imaging spectroscopy (RIS) in the visible and near infrared is revolutionizing the way cultural heritage investigators may analyze paintings nondestructively. In an ongoing, highly collaborative project, the authors are investigating a painting by American artist John White Alexander . The painting, titled A Study in Pink, is in the collection of the Virginia Museum of Fine Arts (VMFA). An x-radiograph taken in 2014 revealed that there was a painting of another female figure, rotated 180 degrees, underneath the current composition. With this discovery, A Study in Pink became an ideal case study for evaluating the advantages and limitations of nondestructive instrumentation for the purpose of providing useful art historical information on cultural heritage objects. The study examined the information obtained using low cost (US$15,000) as well as high cost (US$300,000) imaging instrumentation. Additionally, the authors are seeking ways of making these technologies robust for in-field non-experts, as well as practical research instruments for undergraduate and graduate education. The preliminary research results revealed that the face of the female figure underneath A Study in Pink could be partially retrieved using RIS multi-spectral imaging equipment operating from 900-1700 nm and more completely revealed using RIS equipment operating from 400-2350 nm.

Most cases of superior vena cava syndrome are easily diagnosed bya clinical examination alone, but several diagnostic tests and procedures can be helpful. When a patient presentswith a suspected diagnosis of superior vena cava syndrome, the first step is to obtain an imaging study that confirms the diagnosis and aids treatment decisions. Magnetic resonance imaging, contrast-enhanced CT scanning, radionuclide flow studies and traditional venography are all appropriate techniques. Still, the CT scan is the most readily available technology in most centers. The CT scan and magnetic resonance imaging also provide information on possible etiologies and can therefore direct the approach towards a tissue diagnosis.

Although an infinity of three-dimensional (3-D) objects could generate any given silhouette, we usually infer only one 3-D object from its two-dimensional (2-D) projection. What are the constraints that restrict this infinity of choices? We identify three mathematical properties of smooth surfaces plus one simple viewing constraint that seem to drive our preferred interpretation of 3-D shape from 2-D contour. The constraint is an extension of the notion of general position. Taken together, our interpretation rules predict that "dents" in a 3-D surface should never be inferred from a smooth 2-D silhouette.

SEEING THE UNSEEN-AI IN MEDICAL IMAGING AND DIAGNOSTICS edical imaging has long been a cornerstone of clinical diagnosis-X-rays, MRIs, CT scans, and ultrasounds give physicians a window into the human body. But even the most experienced radiologists can miss details or face limitations due to time, fatigue, or the sheer volume of data. Enter ArtiEicial Intelligence. AI systems are now being trained to see patterns invisible to the human eye, analyze massive datasets in seconds, and provide faster, more consistent diagnoses. From identifying tumors at earlier stages to detecting anomalies in thousands of scans, AI is transforming medical imaging into a precisionpowered science. From Pixels to Predictions: How AI Understands Images At the heart of this revolution lies deep learning, a branch of machine learning inspired by the human brain. Using convolutional neural networks (CNNs), AI can "learn" to identify speciEic features in medical images, such as the irregular shape of a tumor or the early signs of pneumonia. Here's how it works: M 1. Training: AI is fed thousands-sometimes millions-of labeled images (e.g., chest X-rays diagnosed by radiologists). 2. Learning: The algorithm recognizes patterns associated with each diagnosis. 3. Inference: When shown a new image, the trained AI can predict the likely diagnosis with impressive accuracy. This process is not just about speed-it's about consistency and early detection that can save lives. Case Studies in AI-Powered Imaging 1. Breast Cancer Screening Companies like Google Health have developed AI models that outperform radiologists in detecting early signs of breast cancer. In one study, their model

In constructing the ADK theory, the Coulomb interaction was not included as the first-order correction into the all phases, i.e., the turning point was calculated to the zero-order degree of approximation with regard to the Coulomb interaction. This was correct for fields (up to 10 12 W/cm 2 ) much smaller than the atomic field (10 16 W/cm 2 ). But as lately in experiments are used fields even greater than the atomic one (in this case ADK theory does not work), we decided to reconsider the influence of the Coulomb interaction on the turning point for the fields that are strong enough to make a difference and yet are smaller than the atomic fields (i.e., for fields of order of magnitude of 10 14 W/cm 2 ). So, in this paper we have included the Coulomb interaction where it has been completely neglected earlier. We have shown that this more accurate approach gives somewhat different result for the ionization probability.

Purpose: Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast. Methods: To investigate these issues we have created a three-dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom. Results: Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9-35.8 phantom, 29.9-34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other. The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

This review provides a high-level overview of the most influential neural network architectures that have shaped modern artificial intelligence. The paper traces the historical development of key models, including Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transformer Networks, and Autoencoders. For each architecture, it highlights their conceptual roles, historical significance, typical application areas, and general strengths and limitations. By summarizing their impact across domains such as computer vision, natural language processing, and anomaly detection, this review aims to offer readers a broad understanding of the capabilities and relevance of these machine learning architectures in both research and applications.

Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not yet captured by hand-crafted algorithms. With deep learning, it may be possible to encapsulate this knowledge in an automatic method. We have built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms. The CNNs are trained using whole-image mammograms, each labeled with the average VAS score of two independent readers. Each CNN learns a mapping between mammographic appearance and VAS score so that at test time, they can predict VAS score for an unseen image. Networks were trained using 67,520 mammographic images from 16,968 women and for model selection we used a dataset of 73,128 images. Two case-control sets of contralateral mammograms of screen detected cancers and prior images of women with cancers detected subsequently, matched to controls on age, menopausal status, parity, HRT and BMI, were used for evaluating performance on breast cancer prediction. In the case-control sets, odd ratios of cancer in the highest versus lowest quintile of percentage density were 2.49 (95% CI: 1.59 to 3.96) for screendetected cancers and 4.16 (2.53 to 6.82) for priors, with matched concordance indices of 0.587 (0.542 to 0.627) and 0.616 (0.578 to 0.655), respectively. There was no significant difference between reader VAS and predicted VAS for the prior test set (likelihood ratio chi square, p ¼ 0.134). Our fully automated method shows promising results for cancer risk prediction and is comparable with human performance.

In this paper, we study the effects of different ASE noise models on the performance of turbo code (TC) decoders. A soft-decoding algorithm, the Bahl, Cocke, Jelinek, and Raviv (BCJR) decoding algorithm [1], is generally used in the TC decoders. The BCJR algorithm is a maximum a posteriori probability (MAP) algorithm, and is very sensitive to the noise statistics. The Gaussian approximation of the ASE noise is widely used in the study of optical-fiber communication systems [2]-[8], and there exist standard TCs for additive white Gaussian noise (AWGN) channels. We show that using a MAP decoding algorithm based on the Gaussian noise assumptions, however, may significantly degrade the TC decoder performance in an optical-fiber channel with non-Gaussian ASE noise. To take full advantage of TC, the accurate noise statistics in optical-fiber transmissions should be used in the MAP decoding algorithm.

This paper describes a fully automated segmentation and recognition scheme, which is designed to recognize lung anatomical structures in the human chest by segmenting the different chest internal organ and tissue regions sequentially from high-resolution chest CT images. A sequential region-splitting process is used to segment lungs, airway of bronchus, lung lobes and fissures based on the anatomical structures and statistical intensity distributions in CT images. The performance of our scheme is evaluated by segmenting lung structures from high-resolution multi-slice chest CT images from 44 patients; the validity of our method was proved by preliminary experimental results.

Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimer's disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects as input and classifies them into one of three classes: AD, mild cognitive impairment (MCI) and cognitively normal (CN). Our pipeline consists of three stages -(1) a segmentation layer where brain MRI data is divided into clinically relevant regions; (2) a classification layer that uses relational learning algorithms to make pairwise predictions between the three classes; and (3) a combination layer that combines the results of the different classes to obtain the final classification. One of the key features of our proposed approach is that it allows for domain expert's knowledge to guide the learning in all the layers. We evaluate our pipeline on 397 patients acquired from the Alzheimer's Disease Neuroimaging Initiative and demonstrate that it obtains state-ofthe-art performance with minimal feature engineering.

A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR modalities remains a challenging as well as computationally intensive task. In this paper, we propose a novel automated, fast, and approximate segmentation technique. The input is a patient study consisting of a set of MR slices. The output is a corresponding set of the slices that circumscribe the tumors with axis-parallel bounding boxes. The proposed approach is based on an unsupervised change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms. We prove that this score function admits a very fast (linear in image height and width) search to locate the bounding box. The average dice coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52 respectively, which significantly exceeds the scores for two other competitive region-based bounding box techniques.

Purpose:To introduce a hybrid volumetric modulated arc therapy/intensity modulated radiation therapy (VMAT/IMRT) optimization strategy called FusionArc that combines the delivery efficiency of single‐arc VMAT with the potentially desirable intensity modulation possible with IMRT.Methods:A beamlet‐based inverse planning system was enhanced to combine the advantages of VMAT and IMRT into one comprehensive technique. In the hybrid strategy, baseline single‐arc VMAT plans are optimized and then the current cost function gradients with respect to the beamlets are used to define a metric for predicting which beam angles would benefit from further intensity modulation. Beams with the highest metric values (called the gradient factor) are converted from VMAT apertures to IMRT fluence, and the optimization proceeds with the mixed variable set until convergence or until additional beams are selected for conversion. One phantom and two clinical cases were used to validate the gradient factor a...

Photon detectors are indispensable in many areas of fundamental physics research, particularly in the emerging fields of particle astrophysics, nuclear and particle physics, as well as in medical equipment (i.e. PET), in physical check-ups and diagnosis as in-vitro inspection (Radioimmunoassay and Enzyme immunoassay as luminescent, fluorescent,

Philip de Melo is an American data scientist renowned for his contributions to biomedical research. He holds a Ph.D. in mathematics and computer science and has served as a faculty member at Columbia University and Georgia Tech and as a UNESCO professor. He is the creator of PM GenAI, a groundbreaking tool that significantly enhances the accuracy of prediction algorithms. A pioneer in big data analytics for public health, he has authored over 100 scientific papers, six monographs, and holds two patents. His latest book, Public Health Informatics and Technology, examines the critical intersection of information technology, computer science, and public health practice.

Convolutional Neural Networks (CNNs) have revolutionized computer vision by enabling automatic hierarchical feature learning from raw pixel data, leading to stateof-the-art performance in image classification, object detection, and segmentation. This review synthesizes the architectural foundations of CNNs, emphasizing their multilayered abstraction capabilities and the advantages of hierarchical deep CNNs for complex classification tasks. We discuss dimensional adaptations-1D, 2D, and 3D CNNs-and highlight their application domains, computational characteristics, and output structures. Comparative analysis demonstrates that CNNs outperform traditional artificial neural networks (ANNs) and recurrent neural networks (RNNs) in spatial data tasks, and, while Vision Transformers (ViTs) excel in large-scale settings, CNNs remain more data-efficient and computationally practical for many real-world applications. Despite these strengths, standard CNNs are more susceptible to high levels of image noise compared to human observers; however, targeted training with blurred or noisy images significantly narrows this gap, improving robustness and

Purpose: Coronary artery calcium (CAC) scoring with computed tomography (CT) has been proposed as a screening tool for coronary artery disease, but concerns remain regarding the radiation dose of CT CAC scoring. Photon counting detectors and iterative reconstruction (IR) are promising approaches for patient dose reduction, yet the preservation of CAC scores with IR has been questioned. The purpose of this study was to investigate the applicability of IR for quantification of CAC using a photon counting flat-detector. Approach: We imaged a cardiac rod phantom with calcium hydroxyapatite (CaHA) inserts with different noise levels using an experimental photon counting flat-detector CT setup to simulate the clinical CAC scoring protocol. We applied filtered back projection (FBP) and two IR algorithms with different regularization strengths. We compared the air kerma values, image quality parameters [noise magnitude, noise power spectrum, modulation transfer function (MTF), and contrast-to-noise ratio], and CaHA quantification accuracy between FBP and IR. Results: IR regularization strength influenced CAC scores significantly (p < 0.05). The CAC volumes and scores between FBP and IRs were the most similar when the IR regularization strength was chosen to match the MTF of the FBP reconstruction. When the regularization strength is selected to produce comparable spatial resolution with FBP, IR can yield comparable CAC scores and volumes with FBP. Nonetheless, at the lowest radiation dose setting, FBP produced more accurate CAC volumes and scores compared to IR, and no improved CAC scoring accuracy at low dose was demonstrated with the utilized IR methods.

L'imagerie optique de fluorescence permet de localiser des marqueurs fluorescents spécifiques injectés au patient qui s'accumulent autour de tumeurs cancéreuses. Une fois les régions dŠintérêt illuminées, un signal de fluorescence est émis par les marqueurs mais également par les tissus sains environnants. Lors de l'analyse de tissus épais, alors que le signal de fluorescence décroit avec le parcours de la lumière, l'autofluorescence des tissus prévient la détection des marqueurs profonds. Un approche spectroscopique basée sur la Factorisation en Matrices Non-négatives (FMN) est proposée pour séparer les spectres de fluorescence et éliminer l'autofluorescence des tissus. Afin de limiter le problème de non-unicité de la décomposition, l'ajout d'a priori à la méthode classique développée par Lee et Seung est proposé; la pertinence de ces contraintes est illustrée sur des exemples d'acquisitions de fluorescence in vivo.

Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signalto-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.

Although liposomes are widely used as carriers of drugs and imaging agents, they suffer from a lack of stability and the slow release of the encapsulated contents at the targeted site. Polymersomes (vesicles of amphiphilic polymers) are considerably more stable compared to liposomes; however, they also demonstrate a slow release for the encapsulated contents, limiting their efficacy as a drug-delivery tool. As a solution, we prepared and characterized echogenic polymersomes which are programmed to release the encapsulated drugs rapidly when incubated with cytosolic concentrations of glutathione. These vesicles encapsulated air bubbles inside and efficiently reflected diagnostic frequency ultrasound. Folate-targeted polymersomes showed an enhanced uptake by breast and pancreatic-cancer cells in a monolayer as well as in threedimensional spheroid cultures. Polymersomes encapsulated with the anticancer drugs gemcitabine and doxorubicin showed significant cytotoxicity to these cells. With further improvements, these vesicles hold the promise to serve as multifunctional nanocarriers, offering a triggered release as well as diagnostic ultrasound imaging.

Purpose of Review Cardiovascular infections are serious disease associated with high morbidity and mortality. Their diagnosis is challenging, requiring a proper management for a prompt recognition of the clinical manifestations, and a multidisciplinary approach involving cardiologists, cardiothoracic surgeons, infectious diseases specialist, imagers, and microbiologists. Imaging plays a central role in the diagnostic workout, including molecular imaging techniques. In this setting, two different strategies might be used to image infections: the first is based on the use of agents targeting the microorganism responsible for the infection. Alternatively, we can target the components of the pathophysiological changes of the inflammatory process and/or the host response to the infectious pathogen can be considered. Understanding the strength and limitations of each strategy is crucial to select the most appropriate imaging tool. Recent Findings Currently, multislice computed tomography (MSCT) and nuclear imaging ( 18 F-fluorodeoxyglucose positron emission tomography/computed tomography, and leucocyte scintigraphy) are part of the diagnostic strategies. The main role of nuclear medicine imaging (PET/CT and SPECT/CT) is the confirmation of valve/CIED involvement and/or associated perivalvular infection and the detection of distant septic embolism. Proper patients' preparation, imaging acquisition, and reconstruction as well as imaging reading are crucial to maximize the diagnostic information. Summary In this manuscript, we described the use of molecular imaging techniques, in particular WBC imaging, in patients with infective endocarditis, cardiovascular implantable electronic device infections, and infections of composite aortic graft, underlying the strength and limitations of such approached as compared to the other imaging modalities. This article is part of the Topical Collection on Nuclear Cardiology

We analyze the transition from passive mode locking into the novel modulational-instability regime of erbiumdoped f iber lasers. By including in the cavity an anisotropic loss, we observed, depending on the polarization setting, the generation of either single pulses as short as 435 fs or a continuous-wave train of 3.5-ps-long solitons at a repetition rate of 76 GHz. Even with no anisotropic losses in the cavity, soliton trains at a repetition rate of as high as 130 GHz were still observed. We believe that this is the f irst observation of a self-induced modulational-instability laser.

We analyze the main methods for high-repetition-rate soliton generation in fiber lasers in view of the applications to fiber transmission. Particular emphasis is given to the problem of intracavity soliton interactions and stabilization methods. Because of the interactions we find that the maximum duty cycle achievable in a soliton source is 0.22. We also find that the relative phase difference between adjacent solitons may be controlled by means of an intracavity étalon, whereas without the étalon the solitons are stable only when they are out of phase. Moreover the introduction of the intracavity étalon has led to a strong reduction in the fluctuations of the soliton parameters induced by quantum noise.

We characterize the soliton-train emission from an Er -Yb-doped fiber loop laser. We discuss the self-starting dynamics and pulse-repetition-rate control in this sliding-frequency soliton laser. We show that the laser truly self-starts after only one cavity round trip. In the steady state the laser emits a closely spaced train of solitons. We also show that the output pulse width may be controlled by the interplay of continuous frequency shifting, bandwidth-limited amplification, and nonlinear polarization rotation of the circulating solitons. The repetition rate is fixed by means of a weak intracavity feedback. The laser is tunable by shifting of the filter wavelength through the whole spectral band of the active fiber.

With the advent of picture archival and communication systems (PACS), the importance of design surrounding prima~y review workstations has become apparent. To help acceptance of filmless medical imaging, workstations must be developed that serve the needs of both radiologists and referring clinicians. This report will discuss integral requirements of workstation design, including information creation, medical management, medical knowledge, and enabling technologies. Copyright 9 1998 by W.B. Saunders Company p ICTURE ARCH]VING and communication systems (PACS) have made possible the viewing of radiographic images on computer workstations both where interpretation occurs and where clinical care is delivered. With the advent of this technology, much development has been focused towards improving both aspects of these deployments from an image-centric perspective. This report will serve to discuss the experiences of the Massachusetts General Hospital-Advanced Imaging Laboratory's (MGH-AIL) design and development of primary interpretation and clinical review workstations that include not only image data, but also the integration of a variety of other essential patient information and medical knowledge. The ideal primary radiologic interpretation workstation serves to provide the radiologist with not only the sets of images for clinical interpretation, buta host of tools and other information necessary to make this process more efficient and accurate. While our currently deployed workstations are primarily DICOM-based image display stations running C+ + display applications in UNIX OS, future workstations will run Active X components on NT OS layers. Because of this trend, the features added to the existing workstations by the MGH-AIL ate occurred on Microsoft OS platforms using Active X modules. To interface to existing UNIX

MIL-STD-1678 is more than just a checklist—it’s a foundational requirement for robust, high-performance fiber optic systems in defense applications. Learn more in MIL-STD-1678 Training | Fiber Optic Cabling Systems Requirements and Measurements by Tonex.

Sum frequency generation (SFG) microscopy images of cotton cellulose fibers were observed at the infrared wavenumber of ; 2945 cm À1 and with a spatial resolution of 2 lm. Domains of different cellulose microfibril bunches were observed and they showed different second-order nonlinear responses. The intensity of the peak of the asymmetric CH 2 stretching mode at 2945 cm À1 depended strongly on the orientation of the electric fields of the incident visible and infrared light with respect to the cellulose fiber axis. The second-order nonlinear susceptibility arising from the chirality in the cellulose structure was found to be dominant. The SFG of the cross section of the cellulose fiber was relatively weak and showed a different spectrum from that measured from the side of the fiber axis.

For debate Over the last two decades, there have been major advances in methods of medical imaging that have brought great benefits to patients both for diagnosis and for treatment. There has been an increasing tendency for some of these new imaging modalities to be dependent on highly sophisticated technology. The belief has developed amongst doctors who use these techniques that newer is necessarily better. Just as patients (and some doctors) now often believe that anything done with a laser represents the best that can be offered, so there is a tendency to demand that newer techniques in imaging should always be offered as the preferred option. In urological practice that philosophy is questionable.

The inverse sonification problem is investigated in this article in order to detect hardly capturing details in a medical image. The direct problem consists in converting the image data into sound signals by a transformation which involves three steps -data, acoustics parameters and sound representations. The inverse problem is reversing back the sound signals into image data. By using the known sonification operator, the inverse approach does not bring any gain in the sonified medical imaging. The replication of the image already known does not help the diagnosis and surgical operation. In order to bring gains in the medical imaging, a new sonification operator is advanced in this paper, by using the Burgers equation of sound propagation. The sonified medical imaging is useful in interpreting the medical imaging that, however powerful they may be, are never good enough to aid tumour surgery. The inverse approach is exercised on several medical images used to surgical operations. A significant effort has been devoted in recent years to improve the quality of medical images used to surgery 1 . The Roentgen's discovery of X-rays (1895) was followed by the computed tomography, magnetic resonance imaging, nuclear imaging, and ultrasound-positioned medical imaging used for diagnoses and surgery. To our knowledge, we are the first to apply the sonification theory to uncover hidden details in medical images, such as vessels, organs or tumors that cannot be directly seen with the eye. The beginnings of the sonification theory are dated in 1952 when Pollack evaluates auditory displays as a visualization tool by using the information theory 2,3 . The International Community for Auditory Display Conference organized by Kramer in 1992 has generated great interest for this multi-disciplinary theory, from the science and technology to the arts 4,5 . Licht pursues the history of the sound art in 2007 by highlighting ancient art such as Sonic Youth and contemporary art that led to challenging applications, including the works of Christian Marclay, LaMonte Young, Janet Cardiff, Rodney Graham and Laurie Anderson among many others 6 . The nano-guitar built at Cornell University from crystalline silicon no larger than a single human blood cell, invites bacteria inside a pacient to sing so that it can be easily detectable by a stethoscope 7 . The quantum whistle produces a nano-scale sound capable of detecting oscillations in the superfluid gases predicted by quantum theory 8 . The sonification theory allows new perspectives in the diagnosis of diseases, such as the Alzheimers's dementia 9 and therapies in body movements, such as walking, twisting, lifting the arms or moving the legs 10 . The inverse problem of sonification, that is, the reversal of sound samples in new images is less studied so far, to the best of our knowledge. This is due to the fact that the known sonification operator does not bring any improvement of the medical image, because the theory behind it is the linear theory of sound motion. This article introduces a new sonification operator based on the nonlinear Burgers theory of the sound motion. The new operator has proved its ability to solve the inverse problem of sonification and to obtain essential gains in improving the medical image. The paper is organized as follows: Section 2 is devoted to description of the direct problem of sonification. A description of the new sonification operator based on the Burgers equation of sound propagation is presented in Section 3. The methodology is described in Section 4, the applications in Section 5, while Section 6 contains the conclusions.

We present an overview of the design and test of an image processing procedure for detecting all important anatomical structures in color fundus images. These structures are the optic disk, the macula and the retinal network. The algorithm proceeds through five main steps: (1) automatic mask generation using pixels value statistics and color threshold, (2) visual image quality assessment using histogram matching and Canny edge distribution modeling, (3) optic disk localization using pyramidal decomposition, Hausdorff-based template matching and confidence assignment, (4) macula localization using pyramidal decomposition and ( ) vessel network tracking using recursive dual edge tracking and connectivity recovering. The procedure has been tested on a database of about 40 color fundus images acquired from a digital non-mydriatic fundus camera. The database is composed of images of various types (macula-and optic disk-centered) and of various visual quality (with or without abnormal bright or dark regions, blurred, etc).

Purpose: To commemorate the 50th anniversary of the first SPIE Medical Imaging meeting, we highlight some of the important publications published in the conference proceedings. Approach: We determined the top cited and downloaded papers. We also asked members of the editorial board of the Journal of Medical Imaging to select their favorite papers. Results: There was very little overlap between the three methods of highlighting papers. The downloads were mostly recent papers, whereas the favorite papers were mostly older papers. The three different methods combined provide an overview of the highlights of the papers published in the SPIE Medical Imaging conference proceedings over the last 50 years.

A light intensity-modulated curvature measuring principle for gauging bending angles within a range between −120◦ and +130◦ based on speckle image processing is presented in this paper.Coherent light with a wavelength of 632.8 nm is propagated through a looped Polymeric Optical Fiber (POF) and received by a Charge-Coupled-Device camera (CCD), positioned on its end face.After applying a chain of image processing techniques the determination of the angle is realized by using the average of the pixel intensity and the amount of speckles within a Region Of Interest (ROI). Practical results revealed the existence of a non-linear dependence in the range of −120◦ and +130◦ between the geometrical arrangements of the POF, the average pixel intensity, the amount of speckles and the bent angle. Keywords— Polymeric optical fiber, POF, Curvature sensor, Bending sensor, Speckle image processing, Light intensitymodulated curvature sensor, Transmission loss sensor. Resumo— O presente trabalho apre...

In response to recent human monkeypox outbreaks, the imperative of swiftly identifying and isolating infected individuals to curb transmission underscores the significance of innovative solutions. This study introduces an Android mobile application harnessing deep learning capabilities to address this urgent need. Developed using Java within Android Studio and Android SDK 12, the application leverages the device's camera via the Camera 2 API for real-time image capture. The captured video images are processed by a deep convolutional neural network (CNN) embedded within the device. Training the CNN involved utilizing a dataset containing skin lesion images from monkeypox-infected individuals and other skin conditions, employing a deep transfer learning methodology. The training and testing phases were executed using Matlab, with the selected network further trained using TensorFlow and adapted into a TensorFlow Lite model for mobile deployment. Successful testing on various devices yielded average inference times of 197 ms, 91 ms and 138 ms, affirming the application's efficiency. Facilitating swift preliminary diagnosis, the application empowers individuals with skin lesions to seek prompt medical attention, potentially curtailing disease transmission. Notably, the system exhibits a commendable 91.11% accuracy in classifying images, indicating its reliability. Moreover, its adaptable architecture suggests broader utility for training in diagnosing diverse skin diseases, reflecting a promising avenue for future healthcare innovations.

Objectif : Le produit de contraste occupe une place importante en imagerie médicale. L'objectif de cette étude était d'évaluer les connaissances des patients avant toute injection d'un produit de contraste. Méthodologie : Il s'agissait d'une étude transversale et descriptive sur 3 mois (de mars à mai

(ASL) shows frequently good agreement with other independent results such as White cell measurements of methane at deuterium fluoride laser lines (3.5-4.1 micrometer wavelengths), AFGL predictions at CO 2 laser lines (9.2-10.8 micrometer wavelengths) for ozone, and Lorenz-Mie calculations for quartz and calcite aerosol particles at the CO. laser wavelengths. In a number of cases, significant changes to prior published values have also been well substantiated. Using verified absorption coefficients for the atmospheric gases, an on-line/off-line gas analysis at DF laser frequencies was performed for a site at Oscura Peak, White Sands Missile Range (WSMR), NM. Atmospheric gases at the field site were retained in sampling tanks and taken to the laboratory where ,oncentrations of the known gaseous constituents (water vapor, N2'O, and CH4A were determined using a self-consistency calculational scheme. Realtime field measurements of atmospheric absorbing gases and dust were then made at a WSMR desert site. Field spectrophone measurements of absorption by desert dust lofted by a driven vehicle were also made and were correlated with Lorenz-Mie predictions based on measured "Knollenberg" particle size distributions. The theoretical calculations overestimate the spectrophone values by various amounts up to a factor of three. Possible errors and uncertainties that are introduced into the measurement technique and into the "Knollenberg" instrument are discussed.

Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dualenergy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p < 0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p < 0.01) for FBP-Ramp images and 53.2% to 67.7% (all p < 0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.