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Motivation

The motivation behind creating this container stems from the necessity for a dedicated environment catering to the intricate demands of MRI imaging analysis and NODDI (Neurite Orientation Dispersion and Density Imaging). This section aims to address the challenges encountered in this domain, offering a cohesive solution for researchers and professionals seeking efficient and reliable tools for their work in MRI imaging analysis.

Solution

The solution is a Docker container designed to encapsulate all essential tools and libraries required for advanced MRI imaging analysis and NODDI model fitting. The container should provide a seamless environment for processing complex MRI data, supporting multiple CUDA versions depended applications, and efficiently managing licenses for critical tools like FreeSurfer and FSL. By offering a standardized and comprehensive toolkit, this repository simplifies the complexities associated with MRI data analysis, enabling researchers to focus on their scientific endeavors without worrying about technical intricacies.

Key Features

Here are the highlights that the container should follow:

  • Comprehensive Toolkit: It should include a wide array of neuroimaging tools (e.g., FreeSurfer, FSL), microstructure modeling software (e.g., MOT, MDT), and image conversion utilities, enabling users to perform a broad spectrum of imaging analyses and model fittings within a single environment.

  • BIDS Compliance Support: With tools and auxiliary scripts tailored for Brain Image Data Structure (BIDS) compliance, facilitates the organization and management of neuroimaging data, enhancing interoperability and standardization in data analysis.

  • Multi-Version CUDA Support: The inclusion of CUDA 12.1 and OpenCL 3.0 ensures compatibility with a variety of NVIDIA GPUs, catering to diverse computational needs and optimizing performance for intensive computational tasks.

  • Efficient License Management: It should simplify the management of licenses for critical software such as FreeSurfer and FSL, addressing a common logistical challenge in deploying these tools.

  • Streamlined Setup: By providing a Dockerfile and supporting scripts (e.g., entrypoint.sh, setup_user.sh), the container can simplify the setup process, making it accessible even to those with limited technical expertise in Docker or Linux environments.

  • Enhanced Accessibility: The inclusion of JupyterLab and compatibility with Visual Studio Code allows users to interact with the container through familiar interfaces, facilitating code development, data analysis, and visualization.

  • Prerequisites and Easy Installation: Clear guidelines on prerequisites (e.g., NVIDIA GPU, NVIDIA Container Runtime) and detailed steps for installation ensure users can efficiently deploy and utilize the container with minimal setup overhead.

Challenges

Building a container like this poses several challenges, especially in the context of complex scientific computing environments where dependencies are often version-sensitive. Let's discuss the main challenges:

Dependency Management and Version Conflicts: Different tools and libraries may require specific versions of the same dependency, leading to conflicts. For example, CUDA-enabled applications often necessitate particular CUDA versions, which might not be compatible with other tools in the same environment.

Environment Configuration and Permissions: Installing software as root can lead to permission issues in the finallized container, especially when switching to a non-root user. This can prevent the non-root user from using, modifying packages in certain directories.

Ensuring Reproducibility: Docker builds can produce different results over time if they rely on external resources that change, such as software repositories. This can lead to the dreaded "it works on my machine" problem. For now it is ok but perhaps pinning software versions in Dockerfile and requirements.txt file is a good idea to ensure reproducibility. For system packages, use specific versions in the apt-get install command. For Python packages, specify versions in requirements.txt. However, the best practice is to maintain the Dockerfile and perform versioning!

Managing Large Images: Including a wide array of tools and libraries, especially those with heavy dependencies like CUDA, can lead to very large Docker images, which are slow to build, push, and pull.

Handling Timezone and Locale Settings: Configuring timezones and locales may seem straightforward but can lead to unexpected behavior if not handled correctly, especially in a containerized environment where the host and container settings may differ.

Best Practices for Maintenance and Updates: Regularly updating Dockerfile to accommodate new software versions and security patches is a good idea. This is crucial for maintaining a secure and efficient containerized environment.