dcmri 0.6.10

A python toolbox for dynamic contrast MRI

A python toolbox for dynamic contrast MRI

• Documentation: https://dcmri.org

• Source code: https://github.com/dcmri/dcmri

Note: dcmri is under construction. At this stage, the API may still change and features may be deprecated without warning.

Install

Install the latest version of dcmri:

$ pip install dcmri

ROI-based analysis

import dcmri as dc

time, aif, roi, _ = dc.fake_tissue(CNR=50)   # Generate some test data
tissue = dc.Tissue(aif=aif, t=time)          # Launch a tissue model
tissue.train(time, roi)                      # Train the tissue on the data
tissue.plot(time, roi)                       # Check the fit to the data

tissue.print(round_to=3)                     # Print the fitted parameters

--------------------------------
Free parameters with their stdev
--------------------------------

Blood volume (vb): 0.018 (0.002) mL/cm3
Interstitial volume (vi): 0.174 (0.004) mL/cm3
Permeability-surface area product (PS): 0.002 (0.0) mL/sec/cm3

----------------------------
Fixed and derived parameters
----------------------------

Plasma volume (vp): 0.01 mL/cm3
Interstitial mean transit time (Ti): 74.614 sec

Pixel-based analysis

n = 128
time, signal, aif, _ = dc.fake_brain(n)      # Generate some test data
image = dc.TissueArray((n, n),               # Launch an array model
   aif = aif,
   t = time,
   kinetics = '2CU',
   verbose = 1)
image.train(time, roi)                       # Train the tissue on the data
image.plot(time, roi)                        # Plot the parameter maps

License

Released under the Apache 2.0 license:

Copyright (C) 2023-2024 dcmri developers