Congress:
EuroSafe Imaging 2017
Keywords:
Equipment, MR-Angiography, MR, MR physics, Action 3 - Image quality assessment based on clinical indications, Action 2 - Clinical diagnostic reference levels (DRLs), Action 4 - Quality of radiological equipment, Action 3 - Optimisation, diagnostic reference levels, image quality, Physics, Quality assurance
Authors:
K. Sergunova, S. Morozov, S. Kim, N. Potrakhov, A. Petraikin, E. Gaysina, D. Semenov
DOI:
10.1594/esi2017/ESI-0058
Background/Introduction
Magnetic Resonance Imaging (MRI) is one the most popular methods of diagnosis.
The main goal of it,
as all diagnostic methods,
is to produce high quality images to provide accessible,
accurate,
early-stage diagnostics.
To obtain high resolution images from technologically sophisticated equipment a large number of electronic components and data processing are used,
which affect the quality and thus the results of diagnosis.
In order to ensure the proper functioning of these systems and the required image quality over the lifetime of the equipment regular and adequate Quality Assurance (QA) procedures are conducted,
including image Quality Control (QC).
The development of MR image QA program began in 1988 as part of a research project of the European Economic Community conducted under the direction of Lerski.
He was able to identify a core list of control parameters,
which are now used in QA programs of several professional groups such as the National Electrical Manufacturers Association (NEMA),
the American Association of Physicists in Medicine (AAPM) and the American College of Radiology (ACR).
Currently existing QA programs describe the assessment methods of measuring such parameters as signal-to-noise ratio,
non-uniformity,
spatial resolution,
non-linearity and slice thickness and etc.
Today MRI can not only provide morphological information,
but also assess dynamic parameters,
such as linear velocity and flow rate (also for cerebrospinal fluid motion) and etc.
Described above QA programs don’t include the control of such parameters.
However,
to enhance quantitative measurements and diagnostic efficiency it is important to assess the accuracy of estimated parameters and standardize the data obtained on different scanners.
In this research,
we intend to develop,
design and test phantoms for establishing and maintaining an adequate and effective imaging QA program for controlling quantitative parameters.