We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App

Generative AI Models Could Find Application in Low-Dose X-Ray CT and Accelerated MRI

By MedImaging International staff writers
Posted on 18 Jan 2024
Print article
Image: Researchers are improving generative AI models for real-world medical imaging (Photo courtesy of 123RF)
Image: Researchers are improving generative AI models for real-world medical imaging (Photo courtesy of 123RF)

Diffusion models are a type of deep generative models that are extremely successful in applications like image generation and audio synthesis, as well as medical imaging and molecule design. Diffusion models are designed to learn the data distribution, which is important for deciphering large-scale and complex real-world data. Presently, there are several limitations regarding the practical applications of diffusion models. For instance, the training and inference of diffusion models are both data-intensive and computationally demanding, which limits their usage across various scientific disciplines. The images generated in real-world medical imaging are always high-resolution and high-dimensional, far beyond what can be managed by existing diffusion models in terms of memory and time efficiency. Additionally, diffusion models have an undesirably lengthy inference time due to the iterative sampling procedure.

The Michigan Engineering research team at the University of Michigan (Ann Arbor, MI, USA) is working on developing new and more efficient diffusion models that can surpass the current limitations. The team is focusing on examining how diffusion models can be applied to inverse problems, which is when a set of observations are utilized to determine the factors that generated the results. The team is working to improve the practical applicability and mathematical interpretability of diffusion models by developing new architecture designs and latent embeddings.

The researchers are also developing new techniques to improve the training and sampling efficiency of diffusion models. They are working to create computationally efficient diffusion models for high-dimensional data that could further improve data, memory, and time efficiency. This could significantly improve applications such as high-dimensional, high-resolution biomedical imaging, as well as motion prediction based on high-dimensional dynamic imaging.

“Generative models are one of the hottest topics in machine learning right now, and I’m excited to have the opportunity to investigate their potential for solving inverse problems, especially in medical imaging,” said Fessler, the William L. Root Collegiate Professor of EECS. “We’re hoping to apply the methods developed in this project to large-scale 3D medical imaging applications, like low-dose X-ray CT and accelerated MRI.”

Related Links:
University of Michigan

Gold Member
Solid State Kv/Dose Multi-Sensor
Gold Member
Radiation QA Tool
Accu-Gold 3
MRI System
uMR 588
Silver Member
Mobile X-Ray Barrier
Lead Acrylic Mobile X-Ray Barriers

Print article



view channel
Image: LumiGuide enables doctors to navigate through blood vessels using light instead of X-ray (Photo courtesy of Philips)

3D Human GPS Powered By Light Paves Way for Radiation-Free Minimally-Invasive Surgery

In vascular surgery, doctors frequently employ endovascular surgery techniques using tools such as guidewires and catheters, often accessing through arteries like the femoral artery. This method is known... Read more


view channel
Image: Intravascular ultrasound provides a more accurate and specific picture of the coronary arteries (Photo courtesy of 123RF)

Intravascular Imaging Significantly Improves Outcomes in Cardiovascular Stenting Procedures

Individuals with coronary artery disease, which involves plaque accumulation in the arteries leading to symptoms like chest pain, shortness of breath, and heart attacks, often undergo a non-surgical procedure... Read more

Nuclear Medicine

view channel
Image: The PET imaging technique can noninvasively detect active inflammation before clinical symptoms arise (Photo courtesy of 123RF)

New PET Tracer Detects Inflammatory Arthritis Before Symptoms Appear

Rheumatoid arthritis, the most common form of inflammatory arthritis, affects 18 million people globally. It is a complex autoimmune disease marked by chronic inflammation, leading to cartilage and bone... Read more

General/Advanced Imaging

view channel
Image: Routine chest CT holds untapped potential for revealing patients at risk for cardiovascular disease (Photo courtesy of Johns Hopkins)

Routine Chest CT Exams Can Identify Patients at Risk for Cardiovascular Disease

Coronary artery disease (CAD) is the primary cause of death globally. Adults without symptoms but at risk can be screened using EKG-gated coronary artery calcium (CAC) CT scans, which are crucial in assessing... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more

Industry News

view channel
Image: The acquisition will expand IBA’s medical imaging quality assurance offering (Photo courtesy of Radcal)

IBA Acquires Radcal to Expand Medical Imaging Quality Assurance Offering

Ion Beam Applications S.A. (IBA, Louvain-La-Neuve, Belgium), the global leader in particle accelerator technology and a world-leading provider of dosimetry and quality assurance (QA) solutions, has entered... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.