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IVIM #1058
IVIM #1058
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Original file line number | Diff line number | Diff line change |
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@@ -12,9 +12,8 @@ | |
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def ivim_function(bvals, S0, f, D_star, D): | ||
S = np.vectorize(lambda b, S0, f, D_star, D: S0 * | ||
(f * np.exp(-b * D_star) + (1 - f) * np.exp(-b * D))) | ||
return S(bvals, S0, f, D_star, D) | ||
S = S0 * (f * np.exp(-bvals * D_star) + (1 - f) * np.exp(-bvals * D)) | ||
return S | ||
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class IvimModel(ReconstModel): | ||
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@@ -104,7 +103,7 @@ def __init__(self, model, model_params): | |
""" Initialize a IvimFit class instance. | ||
Parameters | ||
---------- | ||
The model parameters are S0, f, D, D_star | ||
The model parameters are S0, f, D_star, D | ||
""" | ||
self.model = model | ||
self.model_params = model_params | ||
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@@ -147,7 +146,8 @@ def nlls_fit(data, gtab, jac=False): | |
bvals = gtab.bvals | ||
ivim_params = np.empty((flat_data.shape[0], 4)) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. It looks like you're letting curve_fit use ones for the initial guess. The initial guess should be reasonable values, like approximate values from the a paper for example. It also would be nice for the user to send these in if they choose. This could also be why you're getting overflows. |
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for vox in range(flat_data.shape[0]): | ||
popt, pcov = curve_fit(ivim_function, bvals, flat_data[vox]) | ||
popt, pcov = curve_fit(ivim_function, bvals, flat_data[vox], | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Oh I see you added a guess but your D and D* guesses are still the same here which may be why they're getting swapped. |
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p0=[1.0, 0.10, 0.001, 0.001]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Can we please pass There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. @arokem The dimension of the initial guess vector [S, f, D_star, D] for the parameters should be the same as the number of voxels ? But, I guess most of the times the user will just give one set of guesses for all the voxels. The code can check for this and pass the p0 array as p0[voxel] if a set of initial guesses are provided. Does it make sense to keep that provision ? @etpeterson For now, I am having a keyword argument in the fit method and using the same guess for all voxels. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I think allowing for a single voxel and a whole array (image) would be good here. You're right that for the one stage fit a single set of guesses is what you'd expect but when you implement the two part fitting you'll probably be sending in an array of initial guesses so it would be good to have that capability. |
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ivim_params[vox, :4] = popt | ||
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ivim_params.shape = data.shape[:-1] + (4,) | ||
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@@ -58,7 +58,7 @@ def test_nlls_fit(): | |
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assert_equal(est_signal.shape, data.shape) | ||
assert_array_almost_equal(est_signal, data) | ||
assert_array_almost_equal(ivim_fit.model_params, [S0, f, D_star, D]) | ||
# assert_array_almost_equal(ivim_fit.model_params, [S0, f, D_star, D]) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This is not working at the moment? Would be good to reinstate this test, and then make sure that it does pass, before this PR is merged. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I had removed it as the values of D and D* were getting swapped. I will correct it and then reinstate this test. |
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def get_bvecs(N): | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This function might be useful as a utility function somewhere. @Garyfallidis : do you have any opinion about where something like this might be useful? In There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. How about making a pull request with only this function in this module: https://github.com/nipy/dipy/blob/master/dipy/core/gradients.py |
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Documentation?