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centralize b-value management at initialization (#155) #165

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4 changes: 2 additions & 2 deletions WrapImage/nifti_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,7 +100,7 @@ def loop_over_first_n_minus_1_dimensions(arr):

# Pass additional arguments to the algorithm

fit = OsipiBase(algorithm=args.algorithm)
fit = OsipiBase(algorithm=args.algorithm, bvalues=bvals)
f_image = []
Dp_image = []
D_image = []
Expand All @@ -109,7 +109,7 @@ def loop_over_first_n_minus_1_dimensions(arr):
n = data.ndim
total_iteration = np.prod(data.shape[:n-1])
for idx, view in tqdm(loop_over_first_n_minus_1_dimensions(data), desc=f"{args.algorithm} is fitting", dynamic_ncols=True, total=total_iteration):
fit_result = fit.osipi_fit(view, bvals)
fit_result = fit.osipi_fit(view)
f_image.append(fit_result["f"])
Dp_image.append(fit_result["Dp"])
D_image.append(fit_result["D"])
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4 changes: 2 additions & 2 deletions WrapImage/nifti_wrapper_kaapana.py
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Expand Up @@ -127,7 +127,7 @@ def load_config():
affine = np.array(affine_override).reshape(4, 4)

# Initialize model
fit = OsipiBase(algorithm=algorithm)
fit = OsipiBase(algorithm=algorithm, bvalues=bvals)

# Preallocate output arrays
shape = data.shape[:data.ndim - 1]
Expand All @@ -142,7 +142,7 @@ def load_config():
loop_over_first_n_minus_1_dimensions(data),
desc="Fitting IVIM model", dynamic_ncols=True, total=total_iteration
):
fit_result = fit.osipi_fit(view, bvals)
fit_result = fit.osipi_fit(view)
f_image[idx] = fit_result["f"]
Dp_image[idx] = fit_result["Dp"]
D_image[idx] = fit_result["D"]
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5 changes: 2 additions & 3 deletions src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py
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Expand Up @@ -66,12 +66,11 @@ def algorithm(self,dwi_arr, bval_arr, LB0, UB0, x0in):
(f_arr, D_arr, Dx_arr, s0_arr, fitted_dwi_arr, RSS, rms_val, chi, AIC, BIC, R_sq) = results
return D_arr/1000, f_arr, Dx_arr/1000, s0_arr

def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -81,7 +80,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):

initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]

bvalues=np.array(bvalues)
bvalues=np.array(self.bvalues)
LB = np.array(bounds[0])[[1,0,2,3]]
UB = np.array(bounds[1])[[1,0,2,3]]

Expand Down
5 changes: 2 additions & 3 deletions src/standardized/DT_IIITN_WLS.py
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Expand Up @@ -66,17 +66,16 @@ def __init__(self, bvalues=None, thresholds=None,
)
self.method = method.upper()

def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **kwargs):
"""Perform the IVIM fit using the selected method (WLS or RLM).

Args:
signals (array-like): Signal intensities at each b-value.
bvalues (array-like, optional): b-values for the signals.

Returns:
dict: Dictionary with keys "D", "f", "Dp".
"""
bvalues = np.array(bvalues)
bvalues = np.array(self.bvalues)

# Use threshold as cutoff if available
cutoff = 200
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6 changes: 2 additions & 4 deletions src/standardized/ETP_SRI_LinearFitting.py
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Expand Up @@ -59,20 +59,18 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
# Check the inputs


def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
def ivim_fit(self, signals, linear_fit_option=False, **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.
linear_fit_option (bool, optional): This fit has an option to only run a linear fit. Defaults to False.

Returns:
_type_: _description_
"""
signals[signals<0.0000001]=0.0000001
if bvalues is None:
bvalues = self.bvalues
bvalues = self.bvalues

if self.thresholds is None:
ETP_object = LinearFit()
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17 changes: 5 additions & 12 deletions src/standardized/IAR_LU_biexp.py
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Expand Up @@ -71,12 +71,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
self.IAR_algorithm = None


def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -88,10 +87,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]

if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
Expand All @@ -110,12 +106,11 @@ def ivim_fit(self, signals, bvalues, **kwargs):
return results


def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
def ivim_fit_full_volume(self, signals, **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_
Expand All @@ -125,16 +120,14 @@ def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
[self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0)

self.IAR_algorithm = IvimModelBiExp(gtab, bounds=bounds, initial_guess=initial_guess)
bvalues = np.asarray(self.bvalues)
b0_index = np.where(bvalues == 0)[0][0]
mask = signals[...,b0_index]>0
fit_results = self.IAR_algorithm.fit(signals, mask=mask)
Expand Down
8 changes: 2 additions & 6 deletions src/standardized/IAR_LU_modified_mix.py
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Expand Up @@ -68,12 +68,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
self.IAR_algorithm = None


def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -83,10 +82,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
[self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]]

if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
Expand Down
8 changes: 2 additions & 6 deletions src/standardized/IAR_LU_modified_topopro.py
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Expand Up @@ -72,12 +72,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
self.IAR_algorithm = None


def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -86,10 +85,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
[self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]]

if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
Expand Down
8 changes: 2 additions & 6 deletions src/standardized/IAR_LU_segmented_2step.py
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Expand Up @@ -72,12 +72,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
self.IAR_algorithm = None


def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -90,10 +89,7 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]

if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
Expand Down
8 changes: 2 additions & 6 deletions src/standardized/IAR_LU_segmented_3step.py
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Expand Up @@ -75,12 +75,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
self.IAR_algorithm = None


def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -93,10 +92,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]

if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
Expand Down
8 changes: 2 additions & 6 deletions src/standardized/IAR_LU_subtracted.py
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Expand Up @@ -72,12 +72,11 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
self.IAR_algorithm = None


def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **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_
Expand All @@ -90,10 +89,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]

if self.IAR_algorithm is None:
if bvalues is None:
bvalues = self.bvalues
else:
bvalues = np.asarray(bvalues)
bvalues = np.asarray(self.bvalues)

bvec = np.zeros((bvalues.size, 3))
bvec[:,2] = 1
Expand Down
10 changes: 2 additions & 8 deletions src/standardized/IVIM_NEToptim.py
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Expand Up @@ -89,18 +89,15 @@ def initialize(self, bounds, initial_guess, fitS0, traindata, SNR, n):
self.algorithm =lambda data: deep.predict_IVIM(data, self.bvalues, self.net, self.arg)


def ivim_fit(self, signals, bvalues, **kwargs):
def ivim_fit(self, signals, **kwargs):
"""Perform the IVIM fit

Args:
signals (array-like)
bvalues (array-like): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:
_type_: _description_
"""
if not np.array_equal(bvalues, self.bvalues):
raise ValueError("bvalue list at fitting must be identical as the one at initiation, otherwise it will not run")

paramsNN = deep.predict_IVIM(signals, self.bvalues, self.net, self.arg)

Expand All @@ -112,18 +109,15 @@ def ivim_fit(self, signals, bvalues, **kwargs):
return results


def ivim_fit_full_volume(self, signals, bvalues, retrain_on_input_data=False, **kwargs):
def ivim_fit_full_volume(self, signals, retrain_on_input_data=False, **kwargs):
"""Perform the IVIM fit

Args:
signals (array-like)
bvalues (array-like): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:
_type_: _description_
"""
if not np.array_equal(bvalues, self.bvalues):
raise ValueError("bvalue list at fitting must be identical as the one at initiation, otherwise it will not run")
minimum_bvalue = np.min(self.bvalues) # We normalize the signal to the minimum bvalue. Should be 0 or very close to 0.
b0_indices = np.where(self.bvalues == minimum_bvalue)[0]
normalization_factor = np.mean(signals[..., b0_indices],axis=-1)
Expand Down
11 changes: 5 additions & 6 deletions src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py
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Expand Up @@ -73,12 +73,11 @@ def initialize(self, bounds=None, initial_guess=None, fitS0=True, prior_in=None,
self.neg_log_prior = empirical_neg_log_prior(prior_in[0], prior_in[1], prior_in[2])
self.fitS0=fitS0

def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
def ivim_fit(self, signals, initial_guess=None, **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_
Expand All @@ -88,7 +87,7 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):

initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]

bvalues=np.array(bvalues)
bvalues=np.array(self.bvalues)

epsilon = 0.000001
fit_results = fit_segmented(bvalues, signals, bounds=bounds, cutoff=self.thresholds, p0=initial_guess)
Expand All @@ -105,11 +104,10 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):

return results

def ivim_fit_full_volume(self, signals, bvalues, njobs=4, **kwargs):
def ivim_fit_full_volume(self, signals, njobs=4, **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_
"""
Expand All @@ -122,6 +120,7 @@ def ivim_fit_full_volume(self, signals, bvalues, njobs=4, **kwargs):
# Get index of b=0
shape=np.shape(signals)

bvalues=np.array(self.bvalues)
b0_index = np.where(bvalues == 0)[0][0]
# Mask of voxels where signal at b=0 >= 0.5
valid_mask = signals[..., b0_index] >= 0
Expand All @@ -133,7 +132,7 @@ def ivim_fit_full_volume(self, signals, bvalues, njobs=4, **kwargs):
normalization_factor = np.mean(signals[..., b0_indices],axis=-1)
signals = signals / np.repeat(normalization_factor[...,np.newaxis],np.shape(signals)[-1],-1)

bvalues=np.array(bvalues)
bvalues=np.array(self.bvalues)

epsilon = 0.000001
fit_results = np.array(fit_segmented_array(bvalues, signals, bounds=bounds, cutoff=self.thresholds, p0=initial_guess))
Expand Down
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