NIST's mission to make a better MRI phantom

Sunday, November 26 | 11:25 a.m.-11:35 a.m. | SSA22-05 | Room S405AB
In this session, researchers from the U.S. National Institute of Standards and Technology (NIST) will detail their efforts to increase the accuracy of MRI phantoms by developing primary calibration structures that follow International System of Units guidelines.

"Our goal in the MRI standards effort is to help medical imaging transition to a precise and comprehensive metrology where radiologists are given enough information that they can make accurate and consistent diagnoses," said Stephen Russek, PhD, project leader in imaging physics at the NIST. "Our work helps to provide methods to determine the accuracy and stability of quantitative imaging methods as well as associated analysis."

To that end, the NIST works closely with the RSNA's Quantitative Imaging Biomarkers Alliance (QIBA) to advance quantitative imaging and the use of imaging biomarkers in clinical trials and clinical practice.

"We provide reference objects with traceability to the [International System of Units] so that researchers can improve and verify their imaging techniques," Russek told AuntMinnie.com. "These reference objects include water diffusion standards, T1 and T2 standards, and resolution and geometric distortion test structures."

Three NIST phantoms are commercially available. Currently, the devices are used to do the following:

  • Validate MR fingerprinting developed by Case Western Reserve University
  • Help homogenize scan protocols for Alzheimer's Disease Neuroimaging Initiative (ADNI) research at the Mayo Clinic
  • Evaluate advanced MRI protocols for breast cancer at the University of California, San Francisco
  • Evaluate diffusion imaging for traumatic brain injury at the U.S. Department of Veterans Affairs' imaging sites

"Future reference objects will include tissue mimics with precisely defined magnetic susceptibility and electromagnetic properties," Russek said. "Our research ultimately provides a basis for understanding the accuracy of image-based in vivo measurements."

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