Quantitative magnetic resonance imaging (qMRI) goes beyond conventional MRI, which aims primarily at local image contrast. It provides specific physical parameters related to the nuclear spin of protons in water, such as relaxation times. These parameters carry information about the local microstructural environment of the protons (such as myelin in the brain). Non-invasive in vivo histology using MRI (hMRI) aims to use this information to directly characterize biological tissue microstructure, partially replacing or complementing classical invasive histology. The understanding of MRI tissue contrast provided by hMRI is, in turn, crucial for further improvements of qMRI, and they should be considered closely interlinked. We discuss concepts, models and validation approaches, pointing out challenges and the latest advances in this field. Further, we point out links to physics, including computational and analytical approaches and developments in materials science and photonics, that aid in reference data acquisition and model validation.
Quantitative magnetic resonance imaging (qMRI) provides quantitative measurements of specific physical parameters related to the nuclear spin of protons in water.
Water proton spins act as intrinsic probes of the surrounding tissue microstructure.
qMRI parameters, including longitudinal and transverse relaxation rates, magnetic susceptibility, proton density and magnetization transfer, carry important information about myelination, iron and cell membranes in the living brain.
In vivo histology using MRI (hMRI) aims to provide quantitative whole-brain measures of brain microstructure in health and disease.
qMRI and hMRI promise much needed sensitive biomarkers in health and disease.
Model building and validation require comprehensive reference data of brain microstructure that capture all features relevant for the MRI contrast.
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The authors thank H. Möller (MPI-CBS, Leipzig), J. Schmidt (MPI-CBS, Leipzig) and R. Valiullin (Leipzig University) for their very helpful comments on earlier versions of the manuscript. They thank T. Reinert (MPI-CBS, Leipzig) and M. Morozova (MPI-CBS, Leipzig) for providing data for illustrations, including electron microscopy and PIXE. They also thank J. Grant (MPI-CBS, Leipzig) for proofreading an earlier version of the manuscript. N.W. received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 616905. N.W. also received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 681094 and the Federal Ministry of Education and Research (BMBF; 01EW1711A and B) in the framework of ERA-NET NEURON. G.H. was funded by the Swedish Research Council (NT 2014-6193). S.M. was supported by the ERA-NET NEURON (hMRIofSCI), the BMBF (01EW1711A and B) and the German Research Foundation (DFG Priority Program 2041 ‘Computational Connectomics’ (AL 1156/2-1; GE 2967/1-1; MO 2397/5-1; MO 2249/3-1), DFG Emmy Noether Stipend: MO 2397/4-1).
The Max Planck Institute for Human Cognitive and Brain Sciences has an institutional research agreement with Siemens Healthcare. N.W. holds a patent on MRI data acquisition during spoiler gradients (United States Patent 10,401,453). N.W. was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses.
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- MRI pulse sequence
Sequence of radio frequency pulses, spatially varying magnetic field gradients and data acquisition periods executed on the MRI scanner for creating, manipulating and measuring the MRI signal.
- Longitudinal relaxation time
The characteristic time T1 for the return of the net magnetization of the spin ensemble to its thermal equilibrium value parallel to the external magnetic field (Box 1).
- Transverse relaxation time
The characteristic time T2 describing the irreversible loss of the magnetization transverse to the static magnetic field (Box 1).
- Effective transverse relaxation time
The characteristic time T2* describing the decay of the magnetization transverse to the static magnetic field due to reversible and irreversible processes (Box 1).
- Proton density
Proton density reflects the content of magnetic-resonance-visible free water in the tissue, which is often expressed as a percentage of the proton concentration in water.
Iron is accumulated in the brain to cover demands for oxygen transport, myelination and neurotransmitter synthesis. Iron overload in ageing leads to cellular damage and neurodegeneration.
- Inverse problem
In physics, this refers to inferring unknown physical properties of a system from measurements.
- Forward model
A forward model predicts measurements from physical properties of a system.
- Ill-posed problem
A problem is regarded as ill-posed (in contrast to well-posed problems) when a solution either does not exist, is not unique (often the case in qMRI/hMRI) or is unstable in the presence of small perturbations (such as noise).
- Signal-to-noise ratio
A measure comparing the level of signal of interest to the level of noise. Noise may include thermal and instrumental noise, as well as physiological processes of no interest.
- Specific absorption rate
(SAR). Measure of radio frequency power deposition leading to tissue heating, typically given in Watts per kilogram of tissue.
- Peripheral nerve stimulation
(PNS). Stimulation of peripheral nerve fibres due to the electric field induced by the fast switching of magnetic field gradients.
- RF coil arrays
Coils for receiving and transmitting radio frequency fields used to manipulate the spin system and read out its magnetization state.
- Gradient systems
Systems consisting of a power amplifier and a set of three gradient coils providing switchable magnetic field gradients for spatial and diffusion encoding along the three spatial axes.
Long projection of a neuronal cell body that transmits neuronal signals over long distances.
Short, low-resolution, self-contained acquisitions inserted into a pulse sequence to measure and correct for phase instabilities or motion.
Shimming increases the magnetic field (B0) spatial homogeneity in the imaged body part or object. This is achieved using additional resistive coils that can generate various field distributions (linear and higher order) to compensate for inhomogeneities.
The smallest 3D volume element in an imaging volume (typically represented as a cuboid in a 3D grid) as a logical extension of a 2D pixel (picture element).
- Glial cells
Non-neuronal cells in the nervous system that support and protect neurons, maintain homeostasis and form myelin.
A lipid-rich insulating substance surrounding axons that increases nerve conduction velocity.
- Bloembergen–Purcell–Pound theory
Explains that the main determinant of longitudinal and transverse relaxation rates in liquids is molecular motion stochastically modulating intramolecular and intermolecular dipolar interactions.
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Weiskopf, N., Edwards, L.J., Helms, G. et al. Quantitative magnetic resonance imaging of brain anatomy and in vivo histology. Nat Rev Phys 3, 570–588 (2021). https://doi.org/10.1038/s42254-021-00326-1
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