gevi

Main files:

Anime.py : Is a class used for displaying 2d Movies from a 3D Matrix.

ImageAlignment.py: Is a class used for aligning two images (move and rotate as well as an unused scale option). This source code is based on https://github.com/matejak/imreg_dft . Is was ported to PyTorch such it can run in GPUs.

DataContailer.py: Main class for data pre-processing of the gevi raw data.

TODO / Known problems

Support for several part files was included but, due to missing data with several part files, never tested.

Installation

The code was tested on a Python 3.11.2 (Linux) with the following pip packages installed:

numpy scipy pandas flake8 pep8-naming black matplotlib seaborn ipython jupyterlab mypy dataclasses-json dataconf mat73 ipympl torch torchtext pywavelets scikit-image opencv-python scikit-learn tensorflow_datasets tensorboard tqdm argh sympy jsmin pybind11 pybind11-stubgen pigar asciichartpy torchvision torchaudio tensorflow natsort roipoly

Not all packages are necessary (probably these are enougth: torch torchaudio torchvision roipoly natsort numpy matplotlib) but this is our default in-house installation plus roipoly.

We used a RTX 3090 as test GPU.

Data processing chain

SVD (requires donor and acceptor time series)

• start automatic_load
  • try to load previous mask
  • start cleaned_load_data
    • start load_data
    • work in XXXX.npy
      • np.load
      • organize acceptor (to GPU memory)
      • organize donor (to GPU memory)
      • move axis (move the time axis of the tensor)
      • move intra timeseries
        • donor time series and donor reference image
        • acceptor time series and acceptor reference image
      • rotate inter timeseries
      • move inter timeseries
    • spatial pooling
    • data(x,y,t) = data(x,y,t) / data(x,y,t).mean(t) + 1
    • remove the heart beat via SVD
    • remove mean
    • remove linear trends
  • remove heart beat (heartbeat_scale)
    • apply bandpass donor_residuum (filtfilt)
    • apply bandpass acceptor_residuum (filtfilt)
    • calculate mask (optinal)
  • don't use regression
  • scale acceptor signal (result_a(x,y,t)) and donor signal (result_d(x,y,t))
  • result(x,y,t) = 1.0 + result_a(x,y,t) - result_d(x,y,t)
  • update inital mask
• end automatic_load

Classic (requires donor, acceptor, volume, and oxygenation time series)

• start automatic_load
  • try to load previous mask
  • start cleaned_load_data
    • start load_data
      • work in XXXX.npy
      • np.load
      • organize acceptor (to GPU memory)
      • organize donor (to GPU memory)
      • organize oxygenation (to GPU memory)
      • organize volume (to GPU memory)
      • move axis (move the time axis of the tensor)
      • move intra timeseries
        • donor time series and donor reference image; transformation also used on volume
        • acceptor time series and acceptor reference image; transformation also used on oxygenation
      • rotate inter timeseries
      • move inter timeseries
    • spatial pooling
    • data(x,y,t) = data(x,y,t) / data(x,y,t).mean(t) + 1
    • frame shift
  • measure heart rate (measure_heartbeat_frequency)
  • use "regression" (i.e. iterative non-orthogonal basis decomposition)
  • donor: measure heart beat spectral power (measure_heartbeat_power)
  • acceptor: measure heart beat spectral power (measure_heartbeat_power)
  • scale acceptor and donor signals
  • result(x,y,t) = 1.0 + result_a(x,y,t) - result_d(x,y,t)
• end automatic_load