A Novel Multiparametric Approach to 3D Quantitative MRI of the Brain

Magnetic Resonance properties of tissues can be quantified in several respects: relaxation processes, density of imaged nuclei, magnetism of environmental molecules, etc. In this paper, we propose a new comprehensive approach to obtain 3D high resolution quantitative maps of arbitrary body districts, mainly focusing on the brain. The theory presented makes it possible to map longitudinal (R 1), pure transverse (R 2) and free induction decay (R2*) rates, along with proton density (PD) and magnetic susceptibility (χ), from a set of fast acquisition sequences in steady-state that are highly insensitive to flow phenomena. A novel denoising scheme is described and applied to the acquired datasets to enhance the signal to noise ratio of the derived maps and an information theory approach compensates for biases from radio frequency (RF) inhomogeneities, if no direct measure of the RF field is available. Finally, the results obtained on sample brain scans of healthy controls and multiple sclerosis patients are presented and discussed.

[1]  B. Rutt,et al.  Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state , 2003, Magnetic resonance in medicine.

[2]  Paul S Babyn,et al.  Steady-state MR imaging sequences: physics, classification, and clinical applications. , 2008, Radiographics : a review publication of the Radiological Society of North America, Inc.

[3]  Ferdinand Schweser,et al.  Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? , 2011, NeuroImage.

[4]  Peter Lundberg,et al.  Application of Quantitative MRI for Brain Tissue Segmentation at 1.5 T and 3.0 T Field Strengths , 2013, PloS one.

[5]  W. Hänicke,et al.  An analytical solution for the SSFP signal in MRI , 2003, Magnetic resonance in medicine.

[6]  Nikolaus Weiskopf,et al.  Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation , 2013, Front. Neurosci..

[7]  Dwight G Nishimura,et al.  Parameter estimation approach to banding artifact reduction in balanced steady‐state free precession , 2014, Magnetic resonance in medicine.

[8]  Petre Stoica,et al.  Signal processing algorithms for removing banding artifacts in MRI , 2011, 2011 19th European Signal Processing Conference.

[9]  Bruno Alfano,et al.  Unbiased noise estimation and denoising in parallel magnetic resonance imaging , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  T. Peters,et al.  High‐resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2 , 2005, Magnetic resonance in medicine.

[11]  S. Deoni,et al.  Transverse relaxation time (T2) mapping in the brain with off‐resonance correction using phase‐cycled steady‐state free precession imaging , 2009, Journal of magnetic resonance imaging : JMRI.

[12]  M. Bronskill,et al.  T1, T2 relaxation and magnetization transfer in tissue at 3T , 2005, Magnetic resonance in medicine.

[13]  E. Haacke,et al.  Quantitative susceptibility mapping: current status and future directions. , 2015, Magnetic resonance imaging.

[14]  M. Maier.  Quantitative MRI of the brain—measuring changes caused by disease , 2004 .

[15]  Salvatore Cuomo,et al.  3D Non-Local Means denoising via multi-GPU , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[16]  J. Thiran,et al.  Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis , 2014, Annals of Clinical and Translational Neurology.

[17]  B. Alfano,et al.  T2 & T2ρ maps: Sequence Development and Clinical Impact on Joint Study , 2012 .

[18]  Sean C L Deoni,et al.  Quantitative Relaxometry of the Brain , 2010, Topics in magnetic resonance imaging : TMRI.

[19]  Paul Strauss,et al.  Magnetic Resonance Imaging Physical Principles And Sequence Design , 2016 .

[20]  E. Soscia.  Relaxometric Maps: Sequence Development and Clinical Impact. Initial Observations. , 2011 .

[21]  M. L. Wood,et al.  Motion‐insensitive, steady‐state free precession imaging , 1990, Magnetic resonance in medicine.

[22]  Peter Lundberg,et al.  Brain Characterization Using Normalized Quantitative Magnetic Resonance Imaging , 2013, PloS one.

[23]  J. Leigh,et al.  High-precision mapping of the magnetic field utilizing the harmonic function mean value property. , 2001, Journal of magnetic resonance.

[24]  Rohit Bakshi,et al.  3 T MRI relaxometry detects T2 prolongation in the cerebral normal-appearing white matter in multiple sclerosis , 2009, NeuroImage.

[25]  Richard S. Frackowiak,et al.  Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ) , 2011, NeuroImage.

[26]  E. Mark Haacke,et al.  Susceptibility Weighted Imaging in MRI: Basic Concepts and Clinical Applications , 2011 .

[27]  Derek K. Jones,et al.  Gleaning multicomponent T1 and T2 information from steady‐state imaging data , 2008, Magnetic resonance in medicine.

[28]  Bernd Weber,et al.  Multicentre absolute myelin water content mapping: Development of a whole brain atlas and application to low-grade multiple sclerosis☆ , 2012, NeuroImage: Clinical.

[29]  P. Lundberg,et al.  Rapid magnetic resonance quantification on the brain: Optimization for clinical usage , 2008, Magnetic resonance in medicine.

[30]  Oliver Bieri,et al.  Triple echo steady‐state (TESS) relaxometry , 2014, Magnetic resonance in medicine.

[31]  Oliver Bieri,et al.  Triple‐echo steady‐state T2 relaxometry of the human brain at high to ultra‐high fields , 2014, NMR in biomedicine.

[32]  Francis Lilley,et al.  Fast and robust three-dimensional best path phase unwrapping algorithm. , 2007, Applied optics.

[33]  P. E. Morris,et al.  Water proton T1 measurements in brain tissue at 7, 3, and 1.5T using IR-EPI, IR-TSE, and MPRAGE: results and optimization , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

[34]  Bruno Alfano,et al.  Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach , 2015, PloS one.