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Monalisa (Python)

Monalisa reconstructs non-Cartesian and Cartesian MRI data: gridded, iterative (SENSE, L2, total variation) and motion-compensated chain reconstructions. This image contains its Python port, monalisa-python, with everything installed: Python 3.12, NumPy/SciPy/numba, the Siemens and ISMRMRD readers, SimpleITK, the napari viewer, JupyterLab, the tests and tutorial 1. Only Docker is needed on your computer.

Platforms: linux/amd64 (Intel/AMD) and linux/arm64 (Apple silicon, ARM servers).

Tutorial 1 without cloning the repository

mkdir -p monalisa/data monalisa/results

# raw data of tutorial 1 (7.9 GB, once)
docker run --rm -v "$PWD/monalisa/data:/data" mattechlab/monalisa-python \
    python demo/data_demo/data_8_tutorial_1/download_data.py --out /data

# JupyterLab on http://127.0.0.1:8888 (open the link with the token printed in the terminal)
docker run --rm -it -p 127.0.0.1:8888:8888 \
    -v "$PWD/monalisa/data:/data" -e MONALISA_TUTORIAL_DATA=/data \
    -v "$PWD/monalisa/results:/work/examples/tutorial_1/results" \
    mattechlab/monalisa-python

In JupyterLab, open examples/tutorial_1/tutorial_1.ipynb. The results are written to monalisa/results on your computer. Other files changed in the container (the notebook itself) are lost when it stops: use File → Download, or mount a folder of your own (-v "$PWD/notebooks:/work/notebooks").

The same reconstructions as a script:

docker run --rm -v "$PWD/monalisa/data:/data" -v "$PWD/monalisa/results:/results" \
    mattechlab/monalisa-python python examples/tutorial_1/tutorial_1.py --data /data --out /results

Your own scripts and notebooks

import monalisa works in any folder of the container: mount your project folder on /work/mine (it appears in JupyterLab's file browser, and its files stay on your computer).

# JupyterLab with your notebooks in mine/ (File → New → Terminal runs scripts there)
docker run --rm -it -p 127.0.0.1:8888:8888 -v "$PWD:/work/mine" mattechlab/monalisa-python

# a script, without JupyterLab
docker run --rm -v "$PWD:/work/mine" -w /work/mine mattechlab/monalisa-python python my_script.py

Paths in your code are the container's: mount raw data from elsewhere as well (-v /path/to/data:/data:ro, then read /data/scan.dat).

With a clone of the repository

docker compose up in the repository uses the repository folder as the working folder (your notebooks, code changes and results stay there); see docs/docker.md.

Good to know

  • Memory: tutorial 1 needs about 35 GB. With Docker Desktop (macOS, Windows), raise the memory in Settings → Resources; Killed (exit code 137) means it was too low.
  • Linux: the container runs as uid 1000; the mounted folders must be writable by that user (the first user of the computer usually is). Create them before docker run, otherwise Docker creates them as root.
  • Images: in the notebook, show_image shows images with sliders, on every computer. napari windows and interactive dialogs open from the container only on Linux: -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix, plus --gpus all only with an NVIDIA card and the NVIDIA Container Toolkit (without them, Docker refuses --gpus all). On macOS and Windows, use the notebook viewer, or install monalisa without Docker for windows; see docs/docker.md.
  • Tags: latest, and one tag per release (0.1.0, 0.1). Cite a release tag, or its digest, to reproduce a result exactly.

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