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.

MedImaging

Download Mobile App
Recent News Radiography MRI Ultrasound Nuclear Medicine General/Advanced Imaging Imaging IT Industry News

Quantitative Research Tool Devised to Sharpen Focus, Improve Utility of Medical Imaging

By MedImaging International staff writers
Posted on 28 Apr 2011
The notion of investigating the body's insides with radiation goes back to X-ray research in the 1800s, but more than 100 years later, images captured with radiologic scanning still are not considered effective enough to, for example, serve as the only indicator of the efficacy of a cancer treatment. A biochemist and a few dozen of her colleagues across North America have set out to change that drawback.

The group of radiology specialists from a number of institutions has published two articles that Dr. Lisa Karam, from the US National Institute of Standards and Technology (NIST; Gaithersburg, MD, USA) describes as part of a major effort to transform medical imaging computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI) scans, and X-rays into a quantitative research tool, one that generates reliable numbers. Dr. Karam stated that her hope is that the group's efforts will enable scientists to determine whether a new drug or treatment method is working within weeks rather than months or years, thereby slashing the time it takes to get an effective new therapy approved for patients. The articles were published April 12, 2011, in the NIST Tech Beat, and the March 2011 and February 15, 2011 (online) issues of the journal Radiology.

"Let's say doctors are studying an experimental drug that might destroy lung tumors," said Dr. Karam, "CT scans might show that patients' lung tumors shrink after a few weeks on the drug, but regulators would not accept this as evidence that the drug works because legitimate concerns exist about other variables that might be responsible for the apparent change in the image. What we want to do is get control over enough of those variables so that these concerns will fall away."

The many coauthors of the two papers are members of a subgroup of the Radiological Society of North America called the Quantitative Imaging Biomarker Alliance (QIBA). NIST's chief role, Dr. Karam reported, is in helping to get control of anything involved in the imaging process that has to do with measurable physical values, from the radiation beams to the properties of the tissues being imaged.

In the example of lung tumors, according to Dr. Karam, one problem is that lung tissue has a spongy quality and moves during the scan as a patient breathes. "If a patient moves a couple of millimeters during a scan, it can affect your measurement of a 10-mm tumor. So we are creating a plastic ‘benchmark' that can be used to quantify this movement and account for it in the image analysis. We also are creating objects of defined density to calibrate scanners, so you can be sure of what you are measuring even if the surrounding tissue's density varies."

The first of the two articles discusses how the radiology community can best come together to make quantitative imaging the norm in medicine; the second, how to overcome longstanding issues that have discouraged the health care community from supporting the idea. Dr. Karam reported that more papers from QIBA are in the works: a near-term goal is to offer a method of handling data so that it can be useful in evaluating treatment methods.

"We're trying to develop a milieu for medical imaging," concluded Dr. Karam. "We want to show the world that a medical image can be a useful tool for medical decision-making. It can give you hard numbers you can take to your insurance company and use as justification to get treatment."

Related Links:
US National Institute of Standards and Technology




New
MRI System
Ingenia Prodiva 1.5T CS
Ultra-Flat DR Detector
meX+1717SCC
New
Transducer Covers
Surgi Intraoperative Covers
Radiation Therapy Treatment Software Application
Elekta ONE

Latest General/Advanced Imaging News

Deep Learning Based Algorithms Improve Tumor Detection in PET/CT Scans

New Technology Provides Coronary Artery Calcification Scoring on Ungated Chest CT Scans

Deep Learning Model Accurately Diagnoses COPD Using Single Inhalation Lung CT Scan