Strain Tensor Imaging: Cardiac-induced brain tissue deformation in humans quantified with high-field MRI

A characterization of cardiac-induced noise in R2 * maps of the brain

PURPOSE

Cardiac pulsation increases the noise level in brain maps of the transverse relaxation rate R2 *. Cardiac-induced noise is challenging to mitigate during the acquisition of R2 * mapping data because its characteristics are unknown. In this work, we aim to characterize cardiac-induced noise in brain maps of the MRI parameter R2 *.

METHODS

We designed a sampling strategy to acquire multi-echo 3D data in 12 intervals of the cardiac cycle, monitored with a fingertip pulse-oximeter. We measured the amplitude of cardiac-induced noise in this data and assessed the effect of cardiac pulsation on R2 * maps computed across echoes. The area of k-space that contains most of the cardiac-induced noise in R2 * maps was then identified. Based on these characteristics, we introduced a tentative sampling strategy that aims to mitigate cardiac-induced noise in R2 * maps of the brain.

RESULTS

In inferior brain regions, cardiac pulsation accounts for R2 * variations of up to 3 s-1 across the cardiac cycle (i.e., ∼35% of the overall variability). Cardiac-induced fluctuations occur throughout the cardiac cycle, with a reduced intensity during the first quarter of the cycle. A total of 50% to 60% of the overall cardiac-induced noise is localized near the k-space center (k < 0.074 mm-1 ). The tentative cardiac noise mitigation strategy reduced the variability of R2 * maps across repetitions by 11% in the brainstem and 6% across the whole brain.

CONCLUSION

We provide a characterization of cardiac-induced noise in brain R2 * maps that can be used as a basis for the design of mitigation strategies during data acquisition.

Estimating the viscoelastic properties of the human brain at 7 T MRI using intrinsic MRE and nonlinear inversion

Intrinsic actuation magnetic resonance elastography (MRE) is a phase-contrast MRI technique that allows for in vivo quantification of mechanical properties of the brain by exploiting brain motion that arise naturally due to the cardiac pulse. The mechanical properties of the brain reflect its tissue microstructure, making it a potentially valuable parameter in studying brain disease. The main purpose of this study was to assess the feasibility of reconstructing the viscoelastic properties of the brain using high-quality 7 T MRI displacement measurements, obtained using displacement encoding with stimulated echoes (DENSE) and intrinsic actuation. The repeatability and sensitivity of the method for detecting normal regional variation in brain tissue properties was assessed as secondary goal. The displacement measurements used in this analysis were previously acquired for a separate study, where eight healthy subjects (27 ± 7 years) were imaged with repeated scans (spatial resolution approx. 2 mm isotropic, temporal resolution 75 ms, motion sensitivity 0.35 mm/2π for displacements in anterior-posterior and left-right directions, and 0.7 mm/2π for feet-head displacements). The viscoelastic properties of the brain were estimated using a subzone based non-linear inversion scheme. The results show comparable consistency to that of extrinsic MRE between the viscoelastic property maps obtained from repeated displacement measurements. The shear stiffness maps showed fairly consistent spatial patterns. The whole-brain repeatability coefficient (RC) for shear stiffness was (mean ± standard deviation) 8 ± 8% relative to the mean whole-brain stiffness, and the damping ratio RC was 28 ± 17% relative to the whole-brain damping ratio. The shear stiffness maps showed similar statistically significant regional trends as demonstrated in a publicly available atlas of viscoelastic properties obtained with extrinsic actuation MRE at 50 Hz. The damping ratio maps showed less consistency, likely due to data-model mismatch of describing the brain as a viscoelastic material under low frequencies. While artifacts induced by fluid flow within the brain remain a limitation of the technique in its current state, intrinsic actuation based MRE allow for consistent and repeatable estimation of the mechanical properties of the brain. The method provides enough sensitivity to investigate regional variation in such properties in the normal brain, which is likely sufficient to also investigate pathological changes.

Rethinking the Origin of the Primary Respiratory Mechanism

Spheno-occipital synchondrosis (SOS) is the joint regarded as the most important foundation for understanding cranial osteopathy and craniosacral therapy. SOS is the origin of the primary respiratory mechanism (PRM), a movement between the posterior surface of the body of the sphenoid bone and the anterior surface of the base of the occipital bone. From the PRM perspective, an alteration of the position between the two bone surfaces would create cranial and/or craniosacral dysfunction. These positional alterations of the SOS (in adults and children) would determine specific and schematical movements of the bones of the entire skull, whose movements are recognizable by palpation by trained operators. PRM expression is influenced by other elements, such as movement of the cranial bones, inherent movement of the central nervous system, cyclic movement of cerebrospinal fluid (CSF), mechanical tension of the cranial meninges, and passive movement of the sacral bone between the iliac bones. The article reviews the most up-to-date information on the evolution of cranial sutures/joints and meninges in adulthood, the fluctuations of the CSF, brain, and spinal mass movements. Research should reconsider the motivations that induce the operator to discriminate the palpable cranial rhythmic impulse, and probably, to rethink new cranial dysfunctional patterns.

Abnormalities in cardiac-induced brain tissue deformations are now detectable with MRI: A case-report of a patient who underwent craniotomy after trauma

BACKGROUND

Heartbeat and respiration induce cyclic brain tissue deformations, which receive increasing attention as potential driving force for brain clearance. These deformations can now be assessed using a novel 3D strain tensor imaging (STI) method at 7 T MRI.

METHODS

An 18-year-old man had suffered a traumatic brain injury and was treated with a craniotomy with a maximal diameter of 12 cm. STI was employed to capture cardiac-induced brain tissue deformations and additional time-resolved 2D flow measurements were acquired to capture cerebrospinal fluid (CSF) flow towards the spinal canal.

RESULTS

The craniotomy caused major changes in all aspects of the brain's mechanical dynamics as compared to healthy volunteer references. Tissue strains increased, particularly around the craniotomy, and directionality of deformations showed large abnormalities, also in the contralateral hemisphere. As the brain tissue could pulsate outward from the skull, physiological pulsatile CSF flow at the foramen magnum was abolished.

CONCLUSIONS

This work illustrates how STI can assess physiological patterns of brain tissue deformation and how craniotomy leads to widespread deformation abnormalities that can be detected at a single patient level. While this case is meant to provide proof of concept, application of STI in other conditions of abnormal brain mechanical dynamics warrants further study.

Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks

Image registration aims to align two images through a spatial transformation. It plays a significant role in brain imaging analysis. In this research, we propose a symmetric diffeomorphic image registration model based on multi-label segmentation masks to solve the problems in brain MRI registration. We first introduce the similarity metric of the multi-label masks to the energy function, which improves the alignment of the brain region boundaries and the robustness to the noise. Next, we establish the model on the diffeomorphism group through the relaxation method and the inverse consistent constraint. The algorithm is designed through the local linearization and least-squares method. We then give spatially adaptive parameters to coordinate the descent of the energy function in different regions. The results show that our approach, compared with the mainstream methods, has better accuracy and noise resistance, and the transformations are more smooth and more reasonable.

Dynamic brain ADC variations over the cardiac cycle and their relation to tissue strain assessed with DENSE at high-field MRI

Purpose

The ADC of brain tissue slightly varies over the cardiac cycle. This variation could reflect physiology, including mixing of the interstitial fluid, relevant for brain waste clearance. However, it is known from cardiac diffusion imaging that tissue deformation by itself affects the magnitude of the MRI signal, leading to artificial ADC variations as well. This study investigates to what extent tissue deformation causes artificial ADC variations in the brain.

Theory and Methods

We implemented a high‐field MRI sequence with stimulated echo acquisition mode that simultaneously measures brain tissue deformation and ADC. Based on the measured tissue deformation, we simulated the artificial ADC variation by combining established theoretical frameworks and compared the results with the measured ADC variation. We acquired data in 8 healthy volunteers with diffusion weighting b = 300 and b = 1000 s/mm².

Results

Apparent diffusion coefficient variation was largest in the feet‐to‐head direction and showed the largest deviation from the mean ADC at peak systole. Artificial ADC variation estimated from tissue deformation was 1.3 ± 0.37·10⁻⁵ mm²/s in the feet‐to‐head direction for gray matter, and 0.75 ± 0.29·10⁻⁵ mm²/s for white matter. The measured ADC variation in the feet‐to‐head direction was 5.6·10⁻⁵ ± 1.5·10⁻⁵ mm²/s for gray matter and 3.2·10⁻⁵ ± 1.0·10⁻⁵ mm²/s for white matter, which was a factor of 3.5 ± 0.82 and 3.4 ± 0.57 larger than the artificial diffusion variations. The measured diffusion variations in the right‐to‐left/anterior‐to‐posterior direction were a factor of 1.5 ± 1.0/1.7 ± 1.4 and 2.0 ± 0.91/2.5 ± 0.94 larger than the artificial diffusion variations for gray matter and white matter, respectively.

Conclusion

Apparent diffusion coefficient variations in the brain likely largely reflect physiology.