Screenshot showing polar angle surface map. The map is calculated after analyzing a population receptive field (pRF) retinotopic mapping experiment.
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BrainVoyager
Flagship Product
Our flagship product BrainVoyager is a powerful neuroimaging software package for data management and data analysis. It started as a tool for the analysis of anatomical and functional MRI data sets but has evolved over the years into a multi-modal analysis tool for fMRI, DWI, EEG and MEG data. It also has inspired and interacts with our Satori fNIRS software.
The software is highly optimized and user friendly running on all major computer platforms; the current version runs on Windows (11), Linux (e.g. Ubuntu, SUSE, Fedora) and macOS (13 and higher). BrainVoyager is only available as a 64 bit program supporting the allocation of memory blocks larger than 3 GB.
High Performance
To provide maximum speed on each platform, BrainVoyager has been programmed in C++ with optimized and highly efficient statistical, numerical, and image processing routines exploitng modern multi-core, multi-processor hardware. It supports on all platforms fast CPU-parallelized linear algebra math routines using the Intel Math Kernel Library (MKL) on Intel hardware, and the Apple Accelerate framework on macOS (Apple Silicon). Multiple parallel processing pipelines of modern GPU’s (Metal shaders on macOS, OpenCL on Windows / Linux) are used for massively parallel tasks, including deep learning based cortex segmentation, anatomical and functional spatial transformations, real-time volume rendering, data filtering, and sinc interpolation for e.g. motion correctionn.
BrainVoyager provides a comprehensive cross-platform solution. The software allows easy exchange of data between operating systems and file formats. Data analyzed on one platform - for example Windows - can be moved to another platform - for example macOS - and processed further without any problems.
Screenshot showing franewise displacement. Users can move the playhead in the ‘Time Course Movie' dialog to inspect frame-to-frame motion in the three orthographic volume slices.
Screenshot showing deep-learning based segmentation result. The ‘DNN Segmentation Postprocessing' dialog provides tools to convert the probability tissue classes obtained fron deep learning segmentation into discrete tissue segments ( volumes-of-interest) and generates segmented grey and white matter volumes and corresponding mesh surfaces - here the outer grey matter mesh is shown.
Interactive Graphics
The surface (meshes) rendering environment has been implemented using modern GPU shaders supporting Metal on macOS, Direct3D 11/12 on Windows and OpenGL on Linux. The interactive graphical user interface (GUI) has been built using the award-winning cross-platform Qt C++/QML toolkit. Using cross-platform C++/QML code for all aspects of the program, BrainVoyager provides a native and responsive user interface and powerful computational routines on all supported platforms.
Open Interface
BrainVoyager integrates Python, JavaScript and Matlab (on Windows) for scripting and development. The offered Python IDE (shown on the right), for example, offers side- by-side code editors, search and replace, multi-line indentation and shell commands. BrainVoyager also enables development of cross-platform custom C++ plugins extending the core functionality of BrainVoyager. For C++ and Python coding, access to internal functionality is provided (BrainVoyager API).
Screenshot showing Python Development window.
Screenshot showing BV Notebook. Like the Python developer window, BV notebooks have full access to the BrainVoyager API. Extra functions allow, for example, to embed screenshots of produced results (shown above), or interactive widgets of volumes and meshes.
BV Python Notebooks
Inspired by Jupyter notebooks, BV notebooks provide sharable rich interactive documents containing code, explanatory text, images and animations in a sequence of cells. Furthermore, BV notebooks also contain interactive BrainVoyager Viewers that enable browsing through 3D volumes or inspecting brain surfaces with overlaid maps right inside notebooks. These "living documents" can be rerun step-by-step (or in one go) to reproduce processing steps.
A main unique feature of BV notebooks is that they not only offer (script) programmers a means to write and document code but they also support non-programmers to learn to code by converting essential GUI actions automatically into corresponding code. If, for example, the GO button in the "Func Preprocessing" dialog is clicked, all selected preprocessing operations, including parameters, are converted into Python code, which is added to the current notebook (if enabled).
SELECTED APPLICATION FEATURES
Modern Graphical User Interface
Elegant, easy-to-use user interface with hight and dark mode appearance.
Surface Mesh 3D Viewer
Visualize and interact with multiple meshes in scenes containing head and cortex meshes.
Advanced Volume Rendering
Visualize high-quality results from advanced real-time volume renderer.
Python Developer Window
IDE with side-by-side code editors, syntax highlighting, search and replace, automatic code indentation and much more.
Python Notebooks
Develop rich sharable interactive documents containing code, markdown text, images, animation and interactive brain viewers.
Data Analysis Manager
Build reproducible analysis pipelines using Python code or Data Analysis Manager window.
C++ Plugins
Develop C++ plugins to extend the functionality of BrainVoyager - launch plugins from the ‘Plugins’ menu.
Build Custom User Interfaces
Use a simple declarative language (QML) to build user interfaces for JS scripts and C++ plugins.
BIDS Layout, NIfTI and GIFTI Files
Projects are stored in BIDS format; NIfTI volume and GIFTI surface can be easily shared and exchanged with other software.