Eisai U.S. Neurology’s Post

Check out this insightful editorial by Thomas Beyer, PhD, MBA, and E. V. Moser on "Editor’s Challenge in Medical Physics and Imaging: Quantitative Medical Imaging." Explore how recent advancements in quantitative imaging enhance diagnostic precision and patient care. #medicalphysics #imaging #medicalimaging #quantitativeimaging

Imaging scientists at our research center at NYU Langone Health, together with colleagues from Meta AI Research, have taken another step toward faster clinical MRI exams by refining deep-learning image reconstruction for accelerated MRI. The team created a variational network with a multichannel "feature space" and an artifact-specific attention layer, and integrated it with an end-to-end variational network previously developed at our research center. The scientists tested the resulting architecture on retrospectively undersampled MRI data from brain and knee scans, and found that it "excelled in preserving anatomical details, including blood vessels" that the prior model sometimes blurred. Details are described in a paper published in May in Scientific Reports: https://lnkd.in/epD5FnFZ The authors evaluated the new model at four-, five-, and eightfold accelerations. The team conducted a radiologist reader study and compared the new network with top-performing models in the fastMRI leaderboard. "We have demonstrated ... improved reconstruction performance both quantitatively and qualitatively," write the authors. "The proposed approaches could enable clinically acceptable reconstructions at higher acceleration factors than currently possible." CC: NYU Grossman School of Medicine, Ilias Giannakopoulos, Matthew Muckley, Patricia Johnson, Riccardo Lattanzi, et al. #AI, #ML, & #DL in #radiology #DeepLearning #innovation in #MRI #MachineLearning #research in #medicalimaging #fastMRI for #OpenScience

I welcome you to my five-video series on how Artificial Intelligence is evolving in Medical Imaging. It has shown significant promise in assisting #radiologists and clinicians by providing faster and more accurate diagnoses. As #patients, #doctors, #hospitals, and consultants, we will see and use them in 2024. Let me share one example in this series video 1: Google DeepMind - DeepMind, a subsidiary of Google, developed algorithms for analyzing retinal images to detect eye diseases such as diabetic retinopathy and age-related macular degeneration. Their system uses deep learning to interpret optical coherence tomography (OCT) scans, aiding ophthalmologists in diagnosing eye conditions. See you in video 2. #radiology #health #medicine #doctor #medical #deepmind #google

Imaging technology captures how neurons communicate with new clarity https://lnkd.in/dqXZsA2G Insights from advanced imaging technology show how neurons communicate at the atomic level for the first time.

Our latest work on MR image reconstruction

Read about a new study using our Model 4400 SWIR hyperspectral imaging platform to help enable automated cancer detection processes and assist in a doctor’s diagnosis. Published in the IEEE Sensors Journal titled “Ex Vivo Tissue Classification Using Broadband Hyperspectral Imaging Endoscopy and AI: A Pilot Study”, it explores the combination of hyperspectral imaging and deep learning/convolutional neural networks as a “promising measure” with high validation accuracy for real-time classification of cancerous and healthy tissue samples.   #cancer #endoscopy #diagnosis #medtech #machinelearning #ai #deeplearning #convolutionalneuralnetworks #CNN #neuralnetworks #SWIR #optics #hyperspectral #hyperspectralimaging #spectroscopy  April Shuyan Zhang IEEE Sensors Council TOKYO ELECTRON LIMITED Tokyo Electron US TEL Venture Capital Institute of Materials Research and Engineering (IMRE) NUS Department of Biomedical Engineering NUS Department of Biomedical Engineering A*STAR - Agency for Science, Technology and Research https://lnkd.in/gyy2Q8eB

Scientists with Case Western Reserve University are working to develop an artificial intelligence-based alternative to chemical imaging contrast agents, the Cleveland institution announced Wednesday (—> https://lnkd.in/eizqPaAu). CWRU recently scored a four-year, $1.125 million grant from the National Science Foundation (NSF) to help fuel its work. They hope to create a new approach during CT, MR and X-ray exams, enhancing images without injecting substances such as gadolinium. “Virtual contrast-enhanced imaging could save time and money while continuing to provide the best care to patients,” project leader Shuo Li, PhD, an associate professor at the Case School of Engineering, said in a Jan. 3 announcement. Contrast enhancement is the gold standard for diagnosing many diseases, Li and colleagues noted. However, costs, potential shortages and side effects can pose “significant challenges” for providers. They hope to leverage the engineering and medical expertise of the research team to create a new chemical-free “AI contrast agent.” The team is particularly focused on the development and validation of new models for use during MR exams, according to the announcement. #contrastagents #radiology #artificialintelligence #digitalhealth #AI

Radiomics is a field of medical research that involves extracting quantitative features from medical imaging data, such as CT scans or MRI images. In the context of detecting heart disease, a researcher using radiomics would analyze these images to identify subtle patterns, textures, and shapes not visible to the naked eye. By applying advanced computational techniques, the researcher aims to convert imaging data into a set of quantitative features. These features can then be used to develop models or algorithms capable of predicting the presence, severity, or specific characteristics of heart disease. Radiomics provides a more detailed and nuanced analysis of medical images, potentially enhancing diagnostic accuracy and aiding in personalized treatment strategies. #radiomics #heartattack #heartfailure #cardiology #farheennaz #research #postdoc #jobhiring

Technologies enable 3D imaging of whole human brain hemispheres at subcellular resolution https://lnkd.in/edVsMDtt