@@ -53,9 +53,10 @@ def algorithm(self,dwi_arr, bval_arr, LB0, UB0, x0in):
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LB0 = matlab .double (LB0 .tolist ())
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UB0 = matlab .double (UB0 .tolist ())
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x0in = matlab .double (x0in .tolist ())
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- f_arr , D_arr , Dx_arr , s0_arr , fitted_dwi_arr , RSS , rms_val , chi , AIC , BIC , R_sq = self .eng .IVIM_standard_bcin (
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- dwi_arr , bval_arr , 0.0 , LB0 , UB0 , x0in , False , 0 , 0 )
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- return D_arr , f_arr , Dx_arr , s0_arr
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+ results = self .eng .IVIM_standard_bcin (
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+ dwi_arr , bval_arr , 0.0 , LB0 , UB0 , x0in , False , 0 , 0 ,nargout = 11 )
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+ (f_arr , D_arr , Dx_arr , s0_arr , fitted_dwi_arr , RSS , rms_val , chi , AIC , BIC , R_sq ) = results
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+ return D_arr / 1000 , f_arr , Dx_arr / 1000 , s0_arr
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def initialize (self , bounds , initial_guess ):
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if bounds is None :
@@ -87,7 +88,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
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LB = np .array (self .bounds [0 ])
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UB = np .array (self .bounds [1 ])
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- fit_results = self .algorithm (np .array (signals ), bvalues , LB , UB , np .array (self .initial_guess ))
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+ fit_results = self .algorithm (np .array (signals )[:, np . newaxis ] , bvalues , LB , UB , np .array (self .initial_guess ))
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results = {}
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results ["D" ] = fit_results [0 ]
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