Articles tagged with Medical Imaging
Scientists have developed a novel pretargeting system to boost cancer imaging using DARPin G3, which achieves specific tumor targeting with reduced kidney and liver uptake. GRPR is emerging as a powerful tool for both imaging and treatment in various cancers, offering personalized cancer care across multiple tumor types.
The introduction of standardized criteria for amino acid PET in brain metastases diagnosis and treatment could improve patient care and accelerate innovative treatment strategies. The new criteria may allow a more precise distinction between true tumour changes and therapy-related effects.
Researchers at University of East Anglia developed 4D flow MRI scan to diagnose aortic stenosis more accurately and reliably than current ultrasound techniques. The technology offers more accurate measurements of blood flow through heart valves, leading to better prediction of when patients need surgery.
The journal is inviting submissions on AI applications in biomedical engineering, focusing on medical device development, personalized diagnostics, and patient outcome prediction. Accepted articles will be published as part of a themed section showcasing current research on AI applications in biomedical engineering.
Researchers have developed new imaging tools to visualize protein changes in Alzheimer's, heart and lung issues in long COVID patients, and a common marker in nasopharyngeal cancer. A first-in-human trial tests a next-generation radiopharmaceutical for advanced prostate cancer.
A new MRI technique measures a person's 'functional age' of their heart, revealing how unhealthy lifestyles add years to their heart's aging process. This method has the potential to transform heart disease diagnosis and offer a lifeline to millions by catching problems early.
Researchers developed fluorescent polyionic nanoclays that can be customized for medical imaging, sensor technology, and environmental protection. These tiny clay-based materials exhibit high brightness and versatility, enabling precise tuning of optical properties.
Current computed tomography (CT) imaging may lead to up to 103,000 future cancers over a patient's lifetime. If practices persist, CT-associated cancer could account for 5% of all new diagnoses annually.
A new study suggests AI-driven smart devices can revolutionize healthcare by detecting cardiac issues early, triggering emergency responses. The Internet of Medical Things (IoMT) technology enables real-time patient monitoring and analysis, improving efficiency and reducing costs.
The study reveals that muscle fibers shorten as forces increase during fast hopping, allowing for faster motion. This counterintuitive behavior enhances leg stiffness, which could impact sports training, rehabilitation, and robotics.
The device enables precise control over terahertz wave polarization, revolutionizing applications such as data transmission, imaging, and sensing. This innovation promises to transform fields like wireless communication and biomedical imaging.
A new study has shown that high-resolution ultrasound can diagnose prostate cancer as effectively as MRI, with improved resolution and lower costs. The technology, called micro-ultrasound, is expected to speed up diagnosis, reduce hospital visits, and make imaging more accessible, particularly in less developed healthcare systems.
Researchers explore new techniques to improve prostate cancer diagnosis and treatment, including dual-target PET/CT for enhanced lymph node staging and novel PSMA-targeted radioligands for more precise tumor targeting.
PRISm is a form of Pre-Chronic Obstructive Pulmonary Disease (Pre-COPD) characterized by respiratory symptoms and reduced lung function. Imaging assessments, such as computed tomography scans and magnetic resonance imaging, play a crucial role in diagnosing and predicting the prognosis of PRISm.
A new study reveals that schizophrenia's complexity is reflected in the individual brain structures of patients, with some experiencing perceptual disturbances and others cognitive impairments. The research suggests a precision medicine approach could tailor therapies to each patient's unique neurobiological profile.
Researchers introduce a dual threshold classifier to interpret circulating tumor DNA levels, enabling earlier detection of disease progression and personalized treatment. The study confirms that ctDNA levels are superior to traditional biomarkers like CA15-3.
A new technique using optical coherence tomography (OCT) probe aims to improve kidney biopsy imaging, reducing failed surgeries and improving patient outcomes. The technology could also help identify tumor types and reduce cases of renal hemorrhage.
Researchers have developed a perovskite X-ray detector that uses cascade engineering to reduce dark current, enabling high-quality medical images at ultra-low doses. The device achieved a detection limit of 100 nGy·s−1, a significant improvement over previous limits.
Researchers from Tokyo Metropolitan University developed a new dye that strongly absorbs second near-IR radiation, transforming it to heat. This breakthrough enables clearer imaging and better delivery of heat for therapies in deep tissue medicine.
A novel bio-inspired camera capable of ultra-high-speed imaging and high sensitivity has been developed by KAIST researchers. The camera mimics the visual structure of insect eyes and achieves frame rates thousands of times faster than conventional cameras, while providing clear images in low-light conditions.
The Tomosynthesis Mammographic Imaging Screening Trial (TMIST) has successfully enrolled over 108,000 women worldwide for a randomized controlled trial comparing 2D and 3D mammography. The study aims to determine whether 3D mammography is more effective in reducing advanced breast cancer risk.
A new AI model will analyze combined data from calcium-scoring CT scans, clinical risk factors, and demographics to identify high-risk patients. The project aims to accurately predict cardiovascular events, including heart failure, and provide personalized treatment options.
Researchers developed a software tool called clipping spline to visualize complex structures in 3D images. The tool provides unprecedented capabilities to analyze 4D OCT images of embryonic mouse heart development, revealing unseen dynamics and processes.
A recent study published in Nature Medicine found that AI significantly improves breast cancer detection rates, detecting an additional cancer case per 1,000 women screened. The results show improved efficiency without increasing false positives or unnecessary follow-ups.
Osaka University researchers have reported a method that gives high-resolution Raman microscopy images of biological samples, up to eight times brighter than previous methods. This technique uses no stains and doesn't require chemicals to fix cells in position, providing a highly representative view of processes and cell behavior.
A new scanning method allows experts to see the effects of treatment on lung function in real time and enable early identification of decline. The scan method enables researchers to quantify the degree of improvement in ventilation when patients have a treatment, which could be valuable in clinical trials.
Scientists have created a new class of microscopic flower particles that can guide medicines to specific locations in the body and be easily tracked using ultrasound. The particles' unique shape and structure allow them to absorb large amounts of active substance molecules, making them an effective carrier for targeted medication deliv...
A Virginia Tech researcher has received a collaborative grant to improve cancer therapies by developing 3D liver organoids and employing cutting-edge microscopy technology. The project aims to identify the most effective treatments for cancer, enabling better-targeted treatments.
A new study found significantly higher injury rates among transgender women compared to cisgender women, with increased risk of head, facial, and chest injuries. The findings emphasize the need for timely IPV screening and support for vulnerable populations.
Scientists at IOCB Prague have developed a new compound that securely binds metal elements, known as lanthanides, inside molecules of medical drugs. This breakthrough discovery, called 'ClickZip', improves diagnostics and accelerates drug development by making pharmaceuticals more stable.
Researchers at the University of Rochester Medical Center have discovered a potential biomarker for psychosis diagnosis by analyzing brain connectivity patterns. The 'somato-visual' biomarker shows promise in predicting psychosis onset and stratifying patients into clinically meaningful subgroups.
Researchers developed a new approach to analyzing brain scans, allowing accurate predictions of cognitive functioning in psychiatric patients using hundreds, rather than thousands, of subjects. The technique, called meta-matching, leverages data from large population studies to boost accuracy.
Researchers introduce persistence barcodes, a tool that reduces diagnostic bias and uncovers subtle details in medical images. The method preserves key structural details, enabling clearer insights into the data.
A 10-year study found that short-term exposure to heat and air pollution was associated with an increase in medical imaging exams, particularly for chest, neuro, and musculoskeletal imaging. High temperatures had a greater impact on X-ray utilization, while high particulate matter levels were linked to increased CT scans.
Researchers developed BiomedParse, an AI medical image analysis model that works across nine types of medical images to predict systemic diseases. This enables non-specialists to analyze and identify subtle variations in medical images, leading to improved diagnosis.
Researchers have developed a highly sensitive and foldable detector that produces good quality images with smaller dosages of X-rays. The new technology reduces detection limits and paves the way for safer medical imaging and industrial monitoring.
A study by Niigata University found that AI analysis of PET/CT images can predict the occurrence of interstitial lung disease, a serious side effect of immunotherapy. Patients with high inflammation in non-cancerous lungs are at a higher risk of developing this condition.
Scientists have designed bioluminescent proteins that can produce multiple colors of light for real-time imaging in cellular and animal models. These proteins are small, efficient, highly stable and can be used for non-invasive bioimaging, diagnostics, drug discovery and more.
Researchers at Aalto University have designed realistic photonic time crystals that exponentially amplify light, paving the way for faster and more compact optical devices. The discovery has potential applications in nanosensing, imaging, and communication.
TU Graz researcher Gerhard A. Holzapfel leads a six-year project to develop AI-based methods for analyzing soft tissue mechanical properties using transcriptomics and microstructure imaging. The team aims to improve disease diagnosis and therapy in clinical practice.
Aston University researcher has developed a new technique harnessing Orbital Angular Momentum (OAM) light to improve imaging and data transmission through skin and biological tissues. The OAM-based approach shows unmatched sensitivity and accuracy, paving the way for non-invasive medical diagnostics and imaging.
Researchers developed an AI model that uses imaging data to detect less frequent gastrointestinal tract diseases. The model's anomaly detection approach allows it to reliably identify rare diseases without extensive training data.
The new fringe photometric stereo technique reduces scanning time by over two-thirds while achieving micrometer-level accuracy, ideal for real-time scanning applications such as industrial inspection and medical procedures. The approach 'feels' the surface by projecting light patterns, improving precision measurements.
College of Medicine – Tucson researchers are using a $3.3 million grant to test advanced breast computed tomography (CT) scans that create high-resolution 3-D images without physical compression, aiming to enhance early cancer detection and diagnosis for women with dense breasts. The technology has the potential to detect more breast c...
Researchers used fluorescein angiography to visualize neural blood flow in rats and rabbits with chronic nerve compression neuropathy, correlating findings with electrodiagnostic testing. The study showed promising results for the method's potential to improve diagnosis and treatment outcomes in carpal tunnel surgery.
A study by Osaka Metropolitan University found that ChatGPT's diagnostic performance for brain tumors was comparable to that of neuroradiologists, with an accuracy rate of 73%. The model's performance varied depending on the type of clinical report written, with higher accuracy when using reports from neuroradiologist writers.
The new framework, called SLIViT, has been developed by UCLA researchers and achieved accurate disease risk biomarkers detection from medical scans. It consistently achieves better performance compared to domain-specific state-of-the-art models.
Researchers at TU Graz have developed a new machine learning method that generates precise live MRI images of the beating heart using only a few MRI measurement data. This breakthrough enables faster and cheaper MRI applications, including quantitative MRI for diagnoses.
Researchers found that gold nanoparticles can accurately assess kidney injuries using X-rays, correlating with nanoparticle accumulation. However, caution is needed when employing nanomedicines to patients with compromised kidneys.
The integration of artificial intelligence in intratumoral immunotherapy can refine diagnoses, guide interventions, predict treatment responses, and adapt therapeutic strategies. This enables the enhancement of patient outcomes, including improved survival rates and quality of life.
Researchers have developed a new technique to study anisotropic materials, capturing full complexity of light behavior in these materials. The method revealed detailed insights into how light scatters differently along various directions within materials, allowing retrieval of scattering tensor coefficients.
Researchers have introduced DSFN to improve the speed and accuracy of diagnoses of retinal disorders. This AI-powered medical imaging technique combines retina images with vascular distribution information to accurately locate the fovea in complex clinical scenarios, enabling doctors to detect early signs of ocular diseases.
Researchers at Stanford University developed a method to apply an FDA-approved dye to make mouse skin transparent, allowing for non-invasive visualization of internal organs. This breakthrough enables new approaches to biological and diagnostic testing, with potential applications in cancer treatment, blood draws, and cosmetic procedures.
A large cohort study found that false-positive mammography results lead to a significant decrease in subsequent screening rates among women. The study, published in Annals of Internal Medicine, highlights the potential unintended consequence of false positives on routine breast cancer screening participation.
Aston University researcher has developed a new method of analysing crystals in dehydrated blood samples using polarisation-based image reconstruction technique. The technique showed a 90% accuracy rate for early diagnosis and classification of cancer, making it less traumatic and risky for patients.
Researchers at Boston University have developed low-cost, high-impact metamaterials solutions to improve MRI technology, enabling clearer imaging in low-resource areas. Wearable and deployable devices can be tailored to specific body parts, boosting signal-to-noise ratio and reducing scanning time.
Researchers explore AI's role in addressing challenges in immunotherapy, developing new biomarkers for precise disease characterization and predicting treatment response. A comprehensive review applied AI/radiomics to cross-sectional imaging, showcasing the current landscape in IO treatment.
Researchers developed Stain SAN, a novel domain adaptation technique to correct color differences in stained histopathology images. The method improves consistency and comparability of data, leading to better performance in machine-learning-based classifiers.
Researchers at Osaka Metropolitan University found that AI model GPT-4 outperformed a weaker version of the model and was on par with radiology residents in diagnosing musculoskeletal conditions. However, it struggled to match the accuracy of board-certified radiologists.
A research team developed an unsupervised domain adaptation approach called DDSP, achieving high-precision cross-modality segmentation without target modality labels. The framework diverges from GANs and incorporates inter-channel similarity feature alignment to boost flexibility and precision.