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Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI.
Whelan, Brendan M; Liu, Paul Z Y; Shan, Shanshan; Waddington, David E J; Dong, Bin; Jameson, Michael G; Keall, Paul J.
Afiliação
  • Whelan BM; Image-X Institute, School of Health Sciences, University of Sydney, Sydney, NSW, Australia.
  • Liu PZY; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
  • Shan S; Image-X Institute, School of Health Sciences, University of Sydney, Sydney, NSW, Australia.
  • Waddington DEJ; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
  • Dong B; Image-X Institute, School of Health Sciences, University of Sydney, Sydney, NSW, Australia.
  • Jameson MG; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
  • Keall PJ; Image-X Institute, School of Health Sciences, University of Sydney, Sydney, NSW, Australia.
Med Phys ; 2024 Aug 07.
Article em En | MEDLINE | ID: mdl-39111826
ABSTRACT

BACKGROUND:

Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance.

PURPOSE:

To develop and test a suite of open-source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI.

METHODS:

An open-source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150 mm. This phantom was imaged on a CT scanner and a novel split-bore 1.0 T MRI magnet. The CT images provide distortion-free dataset. These data were used to test all modules of the open-source software.

RESULTS:

All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0 ± 0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5 ± 3.1 to 1.6 ± 0.8 mm.

CONCLUSION:

Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article