import numpy as np
from dipy.core.gradients import gradient_table
from src.wrappers.OsipiBase import OsipiBase
from src.original.IAR_LundUniversity.ivim_fit_method_biexp import IvimModelBiExp
[docs]
class IAR_LU_biexp(OsipiBase):
"""
Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University
"""
# I'm thinking that we define default attributes for each submission like this
# And in __init__, we can call the OsipiBase control functions to check whether
# the user inputs fulfil the requirements
# Some basic stuff that identifies the algorithm
id_author = "Ivan A. Rashid, LU"
id_algorithm_type = "Bi-exponential fit"
id_return_parameters = "f, D*, D"
id_units = "seconds per milli metre squared or milliseconds per micro metre squared"
# Algorithm requirements
required_bvalues = 4
required_thresholds = [0,0] # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
required_bounds = False
required_bounds_optional = True # Bounds may not be required but are optional
required_initial_guess = False
required_initial_guess_optional = True
accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
"""
Everything this algorithm requires should be implemented here.
Number of segmentation thresholds, bounds, etc.
Our OsipiBase object could contain functions that compare the inputs with
the requirements.
"""
super(IAR_LU_biexp, self).__init__(bvalues, thresholds, bounds, initial_guess)
# Check the inputs
# Initialize the algorithm
if self.bvalues is not None:
bvec = np.zeros((self.bvalues.size, 3))
bvec[:,2] = 1
gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
self.IAR_algorithm = IvimModelBiExp(gtab)
else:
self.IAR_algorithm = None
[docs]
def ivim_fit(self, signals, bvalues, **kwargs):
"""Perform the IVIM fit
Args:
signals (array-like)
bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
Returns:
_type_: _description_
"""
if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
gtab = gradient_table(bvalues, bvec, b0_threshold=0)
self.IAR_algorithm = IvimModelBiExp(gtab)
fit_results = self.IAR_algorithm.fit(signals)
f = fit_results.model_params[1]
Dstar = fit_results.model_params[2]
D = fit_results.model_params[3]
return f, Dstar, D