Measuring geometric accuracy in magnetic resonance imaging with 3D-printed phantom and nonrigid image registration

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Nousiainen , K & Mäkelä , T 2020 , ' Measuring geometric accuracy in magnetic resonance imaging with 3D-printed phantom and nonrigid image registration ' , Magnetic Resonance Materials in Physics, Biology and Medicine. , vol. 33 , no. 3 , pp. 401-410 . https://doi.org/10.1007/s10334-019-00788-6

Title: Measuring geometric accuracy in magnetic resonance imaging with 3D-printed phantom and nonrigid image registration
Author: Nousiainen, Katri; Mäkelä, Teemu
Contributor: University of Helsinki, Department of Diagnostics and Therapeutics
University of Helsinki, HUS Medical Imaging Center
Date: 2020-06
Number of pages: 10
Belongs to series: Magnetic Resonance Materials in Physics, Biology and Medicine.
ISSN: 0968-5243
URI: http://hdl.handle.net/10138/316151
Abstract: Objective We aimed to develop a vendor-neutral and interaction-free quality assurance protocol for measuring geometric accuracy of head and brain magnetic resonance (MR) images. We investigated the usability of nonrigid image registration in the analysis and looked for the optimal registration parameters. Materials and methods We constructed a 3D-printed phantom and imaged it with 12 MR scanners using clinical sequences. We registered a geometric-ground-truth computed tomography (CT) acquisition to the MR images using an open-source nonrigid-registration-toolbox with varying parameters. We applied the transforms to a set of control points in the CT image and compared their locations to the corresponding visually verified reference points in the MR images. Results With optimized registration parameters, the mean difference (and standard deviation) of control point locations when compared to the reference method was (0.17 +/- 0.02) mm for the 12 studied scanners. The maximum displacements varied from 0.50 to 1.35 mm or 0.89 to 2.30 mm, with vendors' distortion correction on or off, respectively. Discussion Using nonrigid CT-MR registration can provide a robust and relatively test-object-agnostic method for estimating the intra- and inter-scanner variations of the geometric distortions.
Subject: Magnetic resonance imaging
Artifacts
Quality control
Healthcare quality assurance
MR-IMAGES
DISTORTION CORRECTION
3126 Surgery, anesthesiology, intensive care, radiology
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