Karlaftis, V. | Search bioRxiv

bioRxiv2026

Fully automated open-source analysis and interactive visualization of magnetic resonance spectroscopic imaging (MRSI) data in Osprey-MRSI

Purpose: Magnetic resonance spectroscopic imaging (MRSI) is a versatile technique to investigate the spatial distribution of in vivo metabolism. However, processing MRSI data is demanding, and only a few software packages support end-to-end analysis. The goal of this study was to implement fully automated, end-to-end MRSI analysis into the open-source 'Osprey-MRSI' software package. Methods: MRSI-specific analysis and visualization capabilities were implemented, building on the existing Osprey workflow. Modifications included spatial transformation and filtering operations, automated brain masking and tissue segmentation of the MRSI data, improved lipid filtering, rapid integral maps, linear-combination modeling with explicit B0 frequency-shift correction, and generation of quality-control maps and metabolic images. A fully interactive GUI and semi-interactive HTML reports provide a user-friendly way to inspect each step of the analysis. All analysis derivatives are also exported in NIfTI and NIfTI-MRS format for easy visualization and synergies with other toolboxes and modalities. Results: The automated MRSI workflow was successfully used to analyze short- and medium-TE 3T in vivo MRSI datasets from all major vendors (Philips, GE, Siemens) across multiple sites. Correct coregistration of MRSI data and MR images was validated using phantom data from each vendor and existing MRSI processing tools. Conclusion: Osprey-MRSI offers state-of-the-art methods with minimal user interaction available for non-expert users. The modularity of the workflow and the modeling algorithm will foster innovation and development of novel MRSI-specific analysis methods.

neuroscience↗

bioRxiv2026

Scaling up polarization-sensitive optical coherence tomography to image the whole macaque brain

Polarization-sensitive optical coherence tomography (PS-OCT) is a label-free imaging technique that exploits birefringence to visualize myelinated axons at micrometer resolution. However, serial PS-OCT imaging has been limited to small volumes, including tissue blocks from larger species, owing to constraints in acquisition speed, system stability, and data processing. These limitations have prevented its application to whole-brain mapping in large mammals. Here we present a scalable PS-OCT acquisition system and computational pipeline for whole-brain imaging in the rhesus macaque. The framework integrates high-throughput serial imaging with automated reconstruction and processing, enabling volumetric imaging at micrometer-scale resolution across decimeter-scale brain volumes. Using this approach, we acquired two complete macaque brains at a voxel size of 5.5 x 5.5 x 3.4 m and an effective resolution of approximately 10 x 10 x 5.5 m, generating multi-terabyte datasets consisting of multiple contrasts including fiber orientation information. The datasets and associated processing tools are made publicly available. This platform establishes a method for large-scale, high-resolution mapping of white matter architecture in primate brains. The resulting datasets provide a reference for validating MRI models and support the development of neurotechnological applications, including deep brain stimulation, where accurate characterization of axonal organization is required.

neuroscience↗