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Register a new userWide-Field Mueller Polarimetry of Brain Tissue Sections for Visualization of White Matter Fiber Tracts
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Philippe Schucht
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Identification of white matter fiber tracts of the brain is crucial for delineating the tumor border during neurosurgery. A custom-built Mueller polarimeter was used in reflection configuration for the wide-field imaging of thick section of fixed human brain and fresh calf brain. The experimental images of azimuth of the fast optical axis of linear birefringent medium showed a strong correlation with the silver-stained sample histology image, which is the gold standard for ex-vivo brain fiber tract visualization. The polarimetric images of fresh calf brain tissue demonstrated the same trends in depolarization, scalar retardance and azimuth of the fast optical axis as seen in fixed human brain tissue. Thus, label-free imaging Mueller polarimetry shows promise as an efficient intra-operative modality for the visualization of healthy brain white matter fiber tracts, which could improve the accuracy of tumor border detection and, ultimately, patient outcomes.
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Neurons transmit active potentials through axons, which are essential for the brain to function. In this study, the axonal networks of the murine brain were visualized with X-ray tomographic microscopy, also known as X-ray microtomography or micro-CT. Murine brain samples were freeze-dried to reconstitute the intrinsic contrast of tissue constituents and subjected to X-ray visualization. A whole brain hemisphere visualized by absorption contrast illustrated three-dimensional structures including those of the striatum, corpus callosum, and anterior commissure. Axonal tracts observed in the striatum start from the basal surface of the cerebral cortex and end at various positions in the basal ganglia. The distribution of X-ray attenuation coefficients indicated that differences in water and phospholipid content between the myelin sheath and surrounding tissue constituents account for the observed contrast. A rod-shaped cutout of brain tissue was also analyzed with a phase retrieval method, wherein tissue microstructures could be resolved with up to 2.7 {mu}m resolution. Structures of axonal networks of the striatum were reconstructed by tracing axonal tracts. Such an analysis should be able to delineate the functional relationships of the brain regions involved in the observed network.
Mechanical characterization of brain tissue has been investigated extensively by various research groups over the past fifty years. These properties are particularly important for modelling Traumatic Brain Injury (TBI). In this research, we present the design and calibration of a High Rate Tension Device (HRTD) capable of performing tests up to a maximum strain rate of 90/s. We use experimental and numerical methods to investigate the effects of inhomogeneous deformation of porcine brain tissue during tension at different specimen thicknesses (4.0-14.0 mm), by performing tension tests at a strain rate of 30/s. One-term Ogden material parameters (mu = 4395.0 Pa, alpha = -2.8) were derived by performing an inverse finite element analysis to model all experimental data. A similar procedure was adopted to determine Youngs modulus (E= 11200 Pa) of the linear elastic regime. Based on this analysis, brain specimens of aspect ratio (diameter/thickness) S < 1.0 are required to minimise the effects of inhomogeneous deformation during tension tests.
The foreseen CaLIPSO Positron Emission Tomography (PET) scanner is expected to yield simultaneously a fine image resolution, about 1 mm$^3$, and a high contrast. In this paper we present results of simulations for the full CaLIPSO PET scanner with a cube geometry. We quantify by simulations the expected image resolution and Noise Equivalent Count Rates and compare them to the performance of the most efficient clinically used PET scanner, the High-Resolution Research Tomograph by Siemens. We bring up the issues of the image reconstruction for a scanner with high spatial resolution. We also present simulated brain images for [$^{18}$F]-FDG and [$^{11}$C]-PE2I tracer distributions. Results demonstrate the high potential of the CaLIPSO PET scanner for small animal and brain imaging where combination of high spatial resolution and efficiency is essential.
Convolutional neural networks (CNNs) have achieved stateof-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). These CNNs require a large number of manual delineations of the WM tracts of interest for training, which are generally labor-intensive and costly. The expensive manual delineation can be a particular disadvantage when novel WM tracts, i.e., tracts that have not been included in existing manual delineations, are to be analyzed. To accurately segment novel WM tracts, it is desirable to transfer the knowledge learned about existing WM tracts, so that even with only a few delineations of the novel WM tracts, CNNs can learn adequately for the segmentation. In this paper, we explore the transfer of such knowledge to the segmentation of novel WM tracts in the few-shot setting. Although a classic fine-tuning strategy can be used for the purpose, the information in the last task-specific layer for segmenting existing WM tracts is completely discarded. We hypothesize that the weights of this last layer can bear valuable information for segmenting the novel WM tracts and thus completely discarding the information is not optimal. In particular, we assume that the novel WM tracts can correlate with existing WM tracts and the segmentation of novel WM tracts can be predicted with the logits of existing WM tracts. In this way, better initialization of the last layer than random initialization can be achieved for fine-tuning. Further, we show that a more adaptive use of the knowledge in the last layer for segmenting existing WM tracts can be conveniently achieved by simply inserting a warmup stage before classic fine-tuning. The proposed method was evaluated on a publicly available dMRI dataset, where we demonstrate the benefit of our method for few-shot segmentation of novel WM tracts.
During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a fixed set of diffusion weightings (i.e., b-values). We decompose the spherical mean series at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion processes, characterizing hindered and restricted diffusion stemming respectively from extra- and intra-neurite water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ($mu$FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show maps of these indices for baby brains, demonstrating that microscopic tissue features can be extracted from the developing brain for greater sensitivity and specificity to development related changes. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.
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