Skip to main content

Multimodal models of 20 normal brains

Project description

The relevant file is README.ipynb, accessible via any of the following:

BrainWeb: Multimodal models of 20 normal brains

Download and Preprocessing for PET-MR Simulations

This notebook will not re-download/re-process files if they already exist.

  • Output data

  • ~/.brainweb/subject_*.npz: dtype(shape): float32(127, 344, 344)

  • Raw data source

  • ~/.brainweb/subject_*.bin.gz: dtype(shape): uint16(362, 434, 362)

  • Prerequisites

  • Python: requirements.txt (e.g. pip install -r ../../requirements.txt)


from __future__ import print_function, division
%matplotlib notebook
import brainweb
from brainweb import volshow
import numpy as np
from os import path
from tqdm.auto import tqdm
import logging
logging.basicConfig(level=logging.INFO)

Raw Data

# download
files = brainweb.get_files()

# read last file
data = brainweb.load_file(files[-1])

# show last subject
print(files[-1])
volshow(data, cmaps=['gist_ncar']);
~/.brainweb/subject_54.bin.gz
raw.png

Transform

Convert raw image data:

  • Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)

  • PET/T1/T2/uMap intensities

  • randomised structure for PET/T1/T2

  • t (1 + g [2 G_sigma(r) - 1]), where

    • r = rand(127, 344, 344) in [0, 1),

    • Gaussian smoothing sigma = 1,

    • g = 1 for PET; 0.75 for MR, and

    • t = the PET or MR piecewise constant phantom

brainweb.seed(1337)

for f in tqdm(files, desc="mMR ground truths", unit="subject"):
    vol = brainweb.get_mmr_fromfile(
        f,
        petNoise=1, t1Noise=0.75, t2Noise=0.75,
        petSigma=1, t1Sigma=1, t2Sigma=1)
# show last subject
print(f)
volshow([vol['PET' ][:, 100:-100, 100:-100],
         vol['uMap'][:, 100:-100, 100:-100],
         vol['T1'  ][:, 100:-100, 100:-100],
         vol['T2'  ][:, 100:-100, 100:-100]],
        cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],
        titles=["PET", "uMap", "T1", "T2"]);
~/.brainweb/subject_54.bin.gz
mMR.png
# add some lesions
brainweb.seed(1337)
im3d = brainweb.add_lesions(vol['PET'])
volshow(im3d[:, 100:-100, 100:-100], cmaps=['hot']);
lesions.png

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

brainweb-0.3.1.tar.gz (7.8 kB view hashes)

Uploaded Source

Built Distribution

brainweb-0.3.1-py2.py3-none-any.whl (8.1 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page