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
GLOBETECH PUBLISHING LLC

Download Mobile App




Largest-Ever Open-Source Dataset Released to Speed Up MRIs Using AI

By MedImaging International staff writers
Posted on 18 Dec 2018
Print article
Image: NYU School of Medicine Department of Radiology chair, Michael Recht, MD; Daniel Sodickson, MD, PhD, vice chair for research and director of the Center for Advanced Imaging Innovation and Research; and Yvonne Lui, MD, director of artificial intelligence, watch an MRI exam take place with at NYU Langone Health in New York in August 2018.
Image: NYU School of Medicine Department of Radiology chair, Michael Recht, MD; Daniel Sodickson, MD, PhD, vice chair for research and director of the Center for Advanced Imaging Innovation and Research; and Yvonne Lui, MD, director of artificial intelligence, watch an MRI exam take place with at NYU Langone Health in New York in August 2018.
The NYU School of Medicine’s (New York, NY, USA) Department of Radiology is releasing the first large-scale MRI dataset of its kind as part of fastMRI, a collaborative effort with Facebook AI Research (New York, NY, USA) to speed up MRI scans with artificial intelligence (AI).

The collaboration is aimed at sharing open source tools and spurring the development of AI systems to make MRI scans 10 times faster. The collaboration will promote research reproducibility, provide consistent evaluation methods, and empower the larger community of AI and medical imaging scientists.

Using AI, researchers believe it will be possible to capture less data, and therefore image faster, while preserving or even enhancing the rich information contained in MR images. Leaders of the study say that, if successful, fastMRI could benefit a wide range of people who may have difficulty tolerating lengthy scans, including young children, elderly patients, and claustrophobic individual. It could also decrease the need for anesthesia or sedation. Additionally, the project could expand access to this key diagnostic tool, particularly in areas where there is a shortage of MRI scanners and patients face long wait-times for their scans.

The initial dataset release includes more than 1.5 million anonymous MR images of the knee, drawn from 10,000 scans, in addition to raw measurement data from nearly 1,600 scans. While other sets of radiological images have been released previously, this dataset represents the largest public release of raw MRI data to date. The first phase of the project will involve data from knee MRI scans, but future releases will include data from liver and brain scans. The joint team will also provide a suite of tools, including baseline metrics to compare results, and a leaderboard to keep track of progress as part of an organized challenge to be announced in the near future.

"fastMRI not only could have an important impact in the medical field, it's also an interesting research challenge that will help to advance the field of AI," said Larry Zitnick, Research Manager, Facebook AI Research. "To be medically useful, our AI-reconstructed images need to be more than just good-looking, they must also be accurate representations of the ground-truth, even though they're created from significantly less data. The dataset NYU Langone is releasing and the baseline models we've open-sourced will enable other researchers to join us in working on this challenging problem, and we believe this open approach will bring about positive results more quickly."

“This collaboration focuses on applying the strengths of machine learning to reconstruct high-value images in new ways. Rather than using existing images to train AI algorithms, we will radically change the way medical images are acquired in the first place,” said Daniel Sodickson, MD, PhD, professor of radiology and neuroscience and physiology and director of CAIR. “Our aim is not merely enhanced data mining with AI, but rather creating new capabilities for medical visualization, to benefit human health.”

Related Links:
NYU School of Medicine
Facebook AI Research

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Laptop Ultrasound Scanner
PL-3018
New
Silver Member
Mobile X-Ray Barrier
Lead Acrylic Mobile X-Ray Barriers
New
Brachytherapy Planning System
Oncentra Brachy

Print article

Channels

Radiography

view channel
:	Image: The AI model could be a valuable adjunct to human radiologists in breast cancer diagnoses and risk prediction (Photo courtesy of 123RF)

AI Model Predicts 5-Year Breast Cancer Risk from Mammograms

Approximately 13% of U.S. women, or one in every eight, are predicted to develop invasive breast cancer over their lifetime, with 1 in 39 women (3%) succumbing to the illness, according to the American... Read more

Nuclear Medicine

view channel
Image: The AI system uses scintigraphy imaging for early diagnosis of cardiac amyloidosis (Photo courtesy of 123RF)

AI System Automatically and Reliably Detects Cardiac Amyloidosis Using Scintigraphy Imaging

Cardiac amyloidosis, a condition characterized by the buildup of abnormal protein deposits (amyloids) in the heart muscle, severely affects heart function and can lead to heart failure or death without... Read more

General/Advanced Imaging

view channel
Image: The CIARTIC Move self-driving mobile C-arm has received FDA clearance (Photo courtesy of Siemens)

Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery

Intraoperative imaging faces significant challenges due to staff shortages and the high demands placed on surgical teams in the operating room (OR). A common challenge during many OR procedures is the... 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
Copyright © 2000-2024 Globetech Media. All rights reserved.