News | November 24, 2009

Dynamic MRI Detects Where Static Doesn’t

Axial T2 weighted MRI showing lateral deviation of the right vaginal wall.

November 24, 2009 - Dynamic MRI assists in diagnosing pelvic organ prolapse, which is a condition often undiagnosed on static MRI and physical examinations.

In the study, which appears in the December issue of the American Journal of Roentgenology, researchers conducted the dynamic MRI study, while the patient performed a straining maneuver, such as bearing down. Static MRI was performed while the patient is at rest.

The study, performed at NYU Langone Medical Center in New York, included 84 women with lower urinary tract symptoms who underwent dynamic and static MRI scans for a suspected urethra abnormality. Ten of the 84 patients were found to have an abnormality of the urethra. Thirty-three of the patients were diagnosed with pelvic organ prolapse, of whom 29 were diagnosed exclusively on dynamic imaging, explained lead author of the study, Genevieve L. Bennett, M.D., assistant professor of radiology at NYU Langone Medical Center.

"Dynamic imaging allows for the detection of pelvic organ prolapse, which may not be evident at rest but only detected when the woman strains," said Bennett.

Researchers concluded that in women with lower urinary tract symptoms who undergo MRI for evaluation of a suspected urethra abnormality, the addition of dynamic MRI permits detection of pelvic organ prolapse that may not be evident on static at rest images and that may also go undetected at physical examination.

For more information: www.ajronline.org/

Related Content

Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used “Deep Learning,“ a form of machine learning, which is a type of artificial intelligence. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Videos | RSNA | April 03, 2019
ITN Editor Dave Fornell takes a tour of some of the most interesting new medical imaging technologies displa
NIH Study of Brain Energy Patterns Provides New Insights into Alcohol Effects

NIH scientists present a new method for combining measures of brain activity (left) and glucose consumption (right) to study regional specialization and to better understand the effects of alcohol on the human brain. Image courtesy of Ehsan Shokri-Kojori, Ph.D., of NIAAA.

News | Neuro Imaging | March 22, 2019
March 22, 2019 — Assessing the patterns of energy use and neuronal activity simultaneously in the human brain improve
Book Chapter Reports on Fonar Upright MRI for Hydrocephalus Imaging

Rotary misalignment of atlas (C1) and axis (C2). Image courtesy of Scott Rosa, DC, BCAO.

News | Magnetic Resonance Imaging (MRI) | March 20, 2019
Fonar Corp. reported publication of a chapter where the physician-author-researchers utilized the Fonar Upright Multi-...
Non-Contrast MRI Effective in Monitoring MS Patients
News | Neuro Imaging | March 18, 2019
Brain magnetic resonance imaging (MRI) without contrast agent is just as effective as the contrast-enhanced approach...
New MRI Sensor Can Image Activity Deep Within the Brain
News | Magnetic Resonance Imaging (MRI) | March 15, 2019
Calcium is a critical signaling molecule for most cells, and it is especially important in neurons. Imaging calcium in...