Motion-robust <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> quantification from low-resolution gradient echo brain MRI with physics-informed deep learning.
Authors
Affiliations (7)
Affiliations (7)
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany.
- School of Computation, Information & Technology, Technical University of Munich, Munich, Germany.
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine & Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- TUM-Neuroimaging Center, School of Medicine & Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- Philips GmbH Market DACH, Hamburg, Germany.
- Clinic of Neurology, School of Medicine & Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.
Abstract
<math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to its high sensitivity to magnetic field inhomogeneities, which are influenced by motion and might cause signal loss. Thus, motion correction is crucial to obtain high-quality <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> maps. We extend PHIMO, our previously introduced learning-based physics-informed motion correction method for low-resolution <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> mapping. Our extended version, PHIMO+, utilizes acquisition knowledge to enhance the reconstruction performance for challenging motion patterns and increase PHIMO's robustness to varying strengths of magnetic field inhomogeneities across the brain. We perform comprehensive evaluations regarding motion detection accuracy and image quality for data with simulated and real motion. PHIMO+ outperforms the learning-based baseline methods both qualitatively and quantitatively with respect to line detection and image quality. Moreover, PHIMO+ performs on par with a conventional state-of-the-art motion correction method for <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> quantification from gradient echo MRI, which relies on redundant data acquisition. PHIMO+'s competitive motion correction performance, combined with a reduction in acquisition time by over 40% compared to the state-of-the-art method, makes it a promising solution for motion-robust <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {\mathrm{T}}_2^{\ast } $$</annotation></semantics> </math> quantification in research settings and clinical routine.