The Outpost is a comprehensive collection of curated artificial intelligence software tools that cater to the needs of small business owners, bloggers, artists, musicians, entrepreneurs, marketers, writers, and researchers.
© 2024 TheOutpost.AI All rights reserved
Curated by THEOUTPOST
On October 15, 2024
2 Sources
[1]
Enhancing MRI with AI to improve diagnosis of brain disorders
At the intersection of AI and medical science, there is growing interest in using machine learning to enhance imaging data captured by magnetic resonance imaging (MRI) technology. Recent studies show that ultra-high-field MRI at 7 Tesla (7T) could have far greater resolution and clinical advantages over high-field MRI at 3T in delineating anatomical structures that are important for identifying and monitoring pathological tissue, particularly in the brain. Most clinical MRI exams in the U.S. are performed with 1.5T or 3T MRI systems. As recently as 2022, the National Institutes of Health documented only about 100 7T MRI machines being used for diagnostic imaging worldwide. Researchers from UC San Francisco developed a machine learning algorithm to enhance 3T MRIs by synthesizing 7T-like images that approximate real 7T MRIs. Their model enhanced pathological tissue with more fidelity for clinical insights and represents a new step toward evaluating clinical applications of synthetic 7T MRI models. The study was presented Oct. 7 at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). "Our paper introduces a machine-learning model to synthesize high-quality MRIs from lower-quality images. We demonstrate how this AI system improves the visualization and identification of brain abnormalities captured by MRIs in Traumatic Brain Injury," said senior study author Reza Abbasi-Asl, Ph.D., UCSF Assistant Professor of Neurology. "Our findings highlight the promise of AI and machine learning to improve the quality of medical images captured by less advanced imaging systems." Better to see TBI and multiple sclerosis with UCSF researchers collected imaging data from patients diagnosed with mild traumatic brain injury (TBI) at UCSF. They designed and trained three neural network models to perform image enhancement and 3D image segmentation using the generated synthetic-7T MRIs from the standard 3T MRIs. The images generated with the new models provided enhanced pathological tissue for patients with mild TBI. They selected an example region with white matter lesions and microbleeds in subcortical areas to use for comparison. They found pathological tissue was easier to see in synthesized 7T images. This was evident in the separation of adjacent lesions and the sharper contours of subcortical microbleeds. Additionally, the synthesized 7T images better captured the diverse features within white matter lesions. These observations also highlight the promise of using this technology to improve diagnostic accuracy in neurodegenerative disorders such as multiple sclerosis. While synthetization techniques based on machine learning frameworks demonstrate remarkable performance, their application in clinical settings will require extensive validation. The researchers believe that future work should include extensive clinical assessment of the model findings, clinical rating of model-generated images, and quantification of uncertainties in the model.
[2]
Enhancing MRI with AI to Improve Diagnosis of Brai | Newswise
Newswise -- At the intersection of AI and medical science, there is growing interest in using machine learning to enhance imaging data captured by magnetic resonance imaging (MRI) technology. Recent studies show that ultra-high-field MRI at 7 Tesla (7T) could have far greater resolution and clinical advantages over high-field MRI at 3T in delineating anatomical structures that are important for identifying and monitoring pathological tissue, particularly in the brain. Most clinical MRI exams in the U.S. are performed with 1.5T or 3T MRI systems. As recently as 2022, the National Institutes of Health documented only about 100 7T MRI machines being used for diagnostic imaging worldwide. Researchers from UC San Francisco developed a machine learning algorithm to enhance 3T MRIs by synthesizing 7T-like images that approximate real 7T MRIs. Their model enhanced pathological tissue with more fidelity for clinical insights and represents a new step toward evaluating clinical applications of synthetic 7T MRI models. The study was presented Oct. 7 at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). "Our paper introduces a machine-learning model to synthesize high-quality MRIs from lower-quality images. We demonstrate how this AI system improves the visualization and identification of brain abnormalities captured by MRIs in Traumatic Brain Injury", said senior study author Reza Abbasi-Asl, PhD, UCSF Assistant Professor of Neurology. "Our findings highlight the promise of AI and machine learning to improve the quality of medical images captured by less advanced imaging systems." Better to see TBI and Multiple Sclerosis with UCSF researchers collected imaging data from patients diagnosed with mild traumatic brain injury (TBI) at UCSF. They designed and trained three neural network models to perform image enhancement and 3D image segmentation using the generated synthetic-7T MRIs from the standard 3T MRIs. The images generated with the new models provided enhanced pathological tissue for patients with mild TBI. They selected an example region with white matter lesions and microbleeds in subcortical areas to use for comparison. They found pathological tissue was easier to see in synthesized 7T images. This was evident in the separation of adjacent lesions and the sharper contours of subcortical microbleeds. Additionally, the synthesized 7T images better captured the diverse features within white matter lesions. These observations also highlight the promise of using this technology to improve diagnostic accuracy in neurodegenerative disorders such as multiple sclerosis. While synthesization techniques based on machine learning frameworks demonstrate remarkable performance, their application in clinical settings will require extensive validation. The researchers believe that future work should include extensive clinical assessment of the model findings, clinical rating of model-generated images, and quantification of uncertainties in the model. About UCSF Health: UCSF Health is recognized worldwide for its innovative patient care, reflecting the latest medical knowledge, advanced technologies and pioneering research. It includes the flagship UCSF Medical Center, which is a top-ranked hospital, as well as UCSF Benioff Children's Hospitals, with campuses in San Francisco and Oakland; Langley Porter Psychiatric Hospital and Clinics; UCSF Benioff Children's Physicians; and the UCSF Faculty Practice. These hospitals serve as the academic medical center of the University of California, San Francisco, which is world-renowned for its graduate-level health sciences education and biomedical research. UCSF Health has affiliations with hospitals and health organizations throughout the Bay Area. Visit https://ucsfhealth.org. Follow UCSF Health on Facebook or on Twitter.
Share
Share
Copy Link
Researchers at UC San Francisco have developed an AI algorithm that enhances 3T MRI images to approximate 7T MRI quality, potentially revolutionizing brain disorder diagnosis and accessibility to advanced imaging.
Researchers at the University of California, San Francisco (UCSF) have developed a groundbreaking machine learning algorithm that could revolutionize the diagnosis of brain disorders. This innovative technology enhances standard 3 Tesla (3T) MRI images to approximate the quality of ultra-high-field 7 Tesla (7T) MRIs, potentially expanding access to advanced diagnostic capabilities [1][2].
Recent studies have shown that 7T MRI offers superior resolution and clinical advantages over 3T MRI, particularly in delineating anatomical structures crucial for identifying and monitoring brain pathologies. However, as of 2022, only about 100 7T MRI machines were being used for diagnostic imaging worldwide, according to the National Institutes of Health [1][2].
The UCSF team's machine learning model synthesizes high-quality MRI images from lower-quality 3T scans, effectively creating "synthetic" 7T-like images. This advancement represents a significant step towards evaluating clinical applications of synthetic 7T MRI models [1][2].
The researchers collected imaging data from patients diagnosed with mild traumatic brain injury (TBI) at UCSF. They designed and trained three neural network models to perform image enhancement and 3D image segmentation using the generated synthetic-7T MRIs [1][2].
Key findings include:
The enhanced imaging capabilities show promise for improving diagnostic accuracy in neurodegenerative disorders such as multiple sclerosis. The technology's ability to better visualize white matter lesions could lead to earlier and more precise diagnoses [1][2].
While the AI-enhanced MRI technique shows remarkable performance, its application in clinical settings will require extensive validation. The researchers emphasize the need for:
This technology has the potential to democratize access to high-quality brain imaging. By enhancing images from more widely available 3T MRI machines, it could bring advanced diagnostic capabilities to a broader range of healthcare facilities and patients [1][2].
As AI continues to intersect with medical science, innovations like this MRI enhancement algorithm demonstrate the transformative potential of machine learning in improving medical imaging and, ultimately, patient care.
Reference
[1]
Medical Xpress - Medical and Health News
|Enhancing MRI with AI to improve diagnosis of brain disordersResearchers at TU Graz have developed a novel machine learning technique that generates precise real-time MRI images of the beating heart using minimal data, potentially revolutionizing medical imaging practices.
3 Sources
Recent studies highlight the potential of artificial intelligence in medical settings, demonstrating improved diagnostic accuracy and decision-making. However, researchers caution about the need for careful implementation and human oversight.
2 Sources
Researchers have developed an AI-powered system that enhances EEG analysis, potentially revolutionizing early dementia detection. This breakthrough could lead to more timely interventions and improved patient outcomes.
3 Sources
Recent studies showcase AI's potential in revolutionizing brain tumor diagnosis. An AI system outperforms radiologists in accuracy, while ChatGPT demonstrates utility in preoperative MRI analysis, marking significant advancements in medical imaging and diagnostics.
2 Sources
A new artificial intelligence model has demonstrated superior performance in predicting Alzheimer's disease progression compared to traditional clinical tests. This breakthrough could revolutionize early diagnosis and treatment of dementia.
5 Sources