arXiv subjects

Vartan Kurtcuoglu

Publications and source records attributed to Vartan Kurtcuoglu.

4 recordsLinked to original sources

arXiv2025

In vivo imaging of central nervous system fluid spaces using synchrotron radiation-based micro computed tomography

Current approaches to in vivo imaging of the mouse central nervous system (CNS) do not offer a combination of micrometer resolution and a whole-brain field of view. To address this limitation, we introduce an approach based on synchrotron radiation-based hard X-ray micro computed tomography (SR$μ$CT). We performed intravital SR$μ$CT acquisitions of mouse CNS fluid spaces at three synchrotron radiation facilities. Imaging was conducted on both anesthetized free-breathing and ventilated animals, with and without retrospective cardiac gating. We achieved whole-brain imaging at 6.3 $μ$m uniform voxel size, observed the distribution of cerebrospinal fluid (CSF) contrast agent over time and quantified choroid plexus movement. SR$μ$CT bridges the gap between multiphoton microscopy and magnetic resonance imaging, offering dynamic imaging with micrometer-scale resolution and whole-organ field of view. Intravital SR$μ$CT will play a crucial role in validating and integrating hypotheses on CSF dynamics and solute transport by providing unique data that cannot be acquired otherwise.

physics.med-ph↗

arXiv2024

Synchrotron radiation-based tomography of an entire mouse brain with sub-micron voxels: augmenting interactive brain atlases with terabyte data

Synchrotron radiation-based X-ray microtomography is uniquely suited for post mortem three-dimensional visualization of organs such as the mouse brain. Tomographic imaging of the entire mouse brain with isotropic cellular resolution requires an extended field-of-view and produces datasets of multiple terabytes in size. These data must be processed and made accessible to domain experts who may have only limited image processing knowledge. We present extended-field X-ray microtomography with 0.65 $\mu$m voxel size covering an entire mouse brain. The 4,495 projections from 8 x 8 offset acquisitions were stitched to reconstruct a volume of 15,000$^3$ voxels. The microtomography volume was non-rigidly registered to the Allen Mouse Brain Common Coordinate Framework v3 based on a combination of image intensity and landmark pairs. The data were transformed block-wise and stored in a public repository with a hierarchical format for navigation and overlay with anatomical annotations in online viewers such as Neuroglancer or siibra-explorer. This study demonstrates X-ray imaging and data processing for a full mouse brain, augmenting current atlases by improving resolution in the third dimension by an order of magnitude. The 3.3-teravoxel dataset is publicly available and easily accessible for domain experts via browser-based viewers.

physics.med-ph↗

arXiv2023

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks.

cs.CV↗

arXiv2023

Tomographic imaging of microvasculature with a purpose-designed, polymeric X-ray contrast agent

Imaging of microvasculature is primarily performed with X-ray contrast agents, owing to the wide availability of absorption-contrast laboratory source microCT compared to phase contrast capable devices. Standard commercial contrast agents used in angiography are not suitable for high-resolution imaging ex vivo, however, as they are small molecular compounds capable of diffusing through blood vessel walls within minutes. Large nanoparticle-based blood pool contrast agents on the other hand exhibit problems with aggregation, resulting in clogging in the smallest blood vessels. Injection with solidifying plastic resins has, therefore, remained the gold standard for microvascular imaging, despite the considerable amount of training and optimization needed to properly perfuse the viscous compounds. Even with optimization, frequent gas and water inclusions commonly result in interrupted vessel segments. This lack of suitable compounds has led us to develop the polymeric, cross-linkable X-ray contrast agent XlinCA. As a water-soluble organic molecule, aggregation and inclusions are inherently avoided. High molecular weight allows it to be retained even in the highly fenestrated vasculature of the kidney filtration system. It can be covalently crosslinked using the same aldehydes used in tissue fixation protocols, leading to stable and permanent contrast. These properties allowed us to image whole mice and individual organs in 6 to 12-month-old C57BL/6J mice without requiring lengthy optimizations of injection rates and pressures, while at the same time achieving greatly improved filling of the vasculature compared to resin-based vascular casting. This work aims at illuminating the rationales, processes and challenges involved in creating this recently developed contrast agent.

q-bio.TO↗