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0.592716 0.523027 0.070047 -0.134611 0.205839 -0.453718 -0.77397 -0.443555 0.0508823 0.00558113 -0.307793 -0.786104 -0.993357 -0.607984 0.280602 -0.0194886 0.683997 0.992152 0.712727 0.608007 -0.421108 0.51701 -0.175164 -0.294931 0.63883 0.378 diff --git a/src/eddymotion/model/dmri_utils.py b/src/eddymotion/model/dmri_utils.py new file mode 100644 index 00000000..079bbcc5 --- /dev/null +++ b/src/eddymotion/model/dmri_utils.py @@ -0,0 +1,94 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2024 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY kIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +import numpy as np + +DEFAULT_NUM_BINS = 15 +"""Number of bins to classify b-values.""" + +DEFAULT_MULTISHELL_BIN_COUNT_THR = 7 +"""Default bin count to consider a multishell scheme.""" + +DEFAULT_MAX_BVAL = 8000 +"""Maximum b-value cap.""" + + +def find_shelling_scheme( + bvals, + num_bins=DEFAULT_NUM_BINS, + multishell_nonempty_bin_count_thr=DEFAULT_MULTISHELL_BIN_COUNT_THR, + bval_cap=DEFAULT_MAX_BVAL, +): + """ + Find the shelling scheme on the given b-values. + + Computes the histogram of the b-values according to ``num_bins`` + and depending on the nonempty bin count, classify the shelling scheme + as single-shell if they are 2 (low-b and a shell); multi-shell if they are + below the ``multishell_nonempty_bin_count_thr`` value; and DSI otherwise. + + Parameters + ---------- + bvals : :obj:`list` or :obj:`~numpy.ndarray` + List or array of b-values. + num_bins : :obj:`int`, optional + Number of bins. + multishell_nonempty_bin_count_thr : :obj:`int`, optional + Bin count to consider a multi-shell scheme. + + Returns + ------- + scheme : :obj:`str` + Shelling scheme. + bval_groups : :obj:`list` + List of grouped b-values. + bval_estimated : :obj:`list` + List of 'estimated' b-values as the median value of each b-value group. + + """ + + # Bin the b-values: use -1 as the lower bound to be able to appropriately + # include b0 values + hist, bin_edges = np.histogram(bvals, bins=num_bins, range=(-1, min(max(bvals), bval_cap))) + + # Collect values in each bin + bval_groups = [] + bval_estimated = [] + for lower, upper in zip(bin_edges[:-1], bin_edges[1:], strict=False): + # Add only if a nonempty b-values mask + if (mask := (bvals > lower) & (bvals <= upper)).sum(): + bval_groups.append(bvals[mask]) + bval_estimated.append(np.median(bvals[mask])) + + nonempty_bins = len(bval_groups) + + if nonempty_bins < 2: + raise ValueError("DWI must have at least one high-b shell") + + if nonempty_bins == 2: + scheme = "single-shell" + elif nonempty_bins < multishell_nonempty_bin_count_thr: + scheme = "multi-shell" + else: + scheme = "DSI" + + return scheme, bval_groups, bval_estimated diff --git a/src/eddymotion/model/tests/test_dmri_utils.py b/src/eddymotion/model/tests/test_dmri_utils.py new file mode 100644 index 00000000..c6c475e7 --- /dev/null +++ b/src/eddymotion/model/tests/test_dmri_utils.py @@ -0,0 +1,640 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2024 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +from pathlib import Path + +import numpy as np +import pytest + +from eddymotion.model.dmri_utils import ( + find_shelling_scheme, +) + +_datadir = (Path(__file__).parent.parent.parent / "data").absolute() + + +@pytest.mark.parametrize( + ("bvals", "exp_scheme", "exp_bval_groups", "exp_bval_estimated"), + [ + ( + np.asarray( + [ + 5, + 300, + 300, + 300, + 300, + 300, + 305, + 1005, + 995, + 1000, + 1000, + 1005, + 1000, + 1000, + 1005, + 995, + 1000, + 1005, + 5, + 995, + 1000, + 1000, + 995, + 1005, + 995, + 1000, + 995, + 995, + 2005, + 2000, + 2005, + 2005, + 1995, + 2000, + 2005, + 2000, + 1995, + 2005, + 5, + 1995, + 2005, + 1995, + 1995, + 2005, + 2005, + 1995, + 2000, + 2000, + 2000, + 1995, + 2000, + 2000, + 2005, + 2005, + 1995, + 2005, + 2005, + 1990, + 1995, + 1995, + 1995, + 2005, + 2000, + 1990, + 2010, + 5, + ] + ), + "multi-shell", + [ + np.asarray([5, 5, 5, 5]), + np.asarray([300, 300, 300, 300, 300, 305]), + np.asarray( + [ + 1005, + 995, + 1000, + 1000, + 1005, + 1000, + 1000, + 1005, + 995, + 1000, + 1005, + 995, + 1000, + 1000, + 995, + 1005, + 995, + 1000, + 995, + 995, + ] + ), + np.asarray( + [ + 2005, + 2000, + 2005, + 2005, + 1995, + 2000, + 2005, + 2000, + 1995, + 2005, + 1995, + 2005, + 1995, + 1995, + 2005, + 2005, + 1995, + 2000, + 2000, + 2000, + 1995, + 2000, + 2000, + 2005, + 2005, + 1995, + 2005, + 2005, + 1990, + 1995, + 1995, + 1995, + 2005, + 2000, + 1990, + 2010, + ] + ), + ], + [5, 300, 1000, 2000], + ), + ], +) +def test_find_shelling_scheme_array(bvals, exp_scheme, exp_bval_groups, exp_bval_estimated): + obt_scheme, obt_bval_groups, obt_bval_estimated = find_shelling_scheme(bvals) + assert obt_scheme == exp_scheme + assert all( + np.allclose(obt_arr, exp_arr) + for obt_arr, exp_arr in zip(obt_bval_groups, exp_bval_groups, strict=True) + ) + assert np.allclose(obt_bval_estimated, exp_bval_estimated) + + +@pytest.mark.parametrize( + ("dwi_btable", "exp_scheme", "exp_bval_groups", "exp_bval_estimated"), + [ + ( + "ds000114_singleshell", + "single-shell", + [ + np.asarray([0, 0, 0, 0, 0, 0, 0]), + np.asarray( + [ + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + ] + ), + ], + [0.0, 1000.0], + ), + ( + "hcph_multishell", + "multi-shell", + [ + np.asarray([0, 0, 0, 0, 0, 0]), + np.asarray([700, 700, 700, 700, 700, 700, 700, 700, 700, 700, 700, 700]), + np.asarray( + [ + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + 1000, + ] + ), + np.asarray( + [ + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + 2000, + ] + ), + np.asarray( + [ + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + 3000, + ] + ), + ], + [0.0, 700.0, 1000.0, 2000.0, 3000.0], + ), + ( + "ds004737_dsi", + "DSI", + [ + np.asarray([5, 5, 5, 5, 5, 5, 5, 5, 5]), + np.asarray([995, 995, 800, 800, 995, 995, 795, 995]), + np.asarray([1195, 1195, 1195, 1195, 1000, 1195, 1195, 1000]), + np.asarray([1595, 1595, 1595, 1600.0]), + np.asarray( + [ + 1800, + 1795, + 1795, + 1790, + 1995, + 1800, + 1795, + 1990, + 1990, + 1795, + 1990, + 1795, + 1795, + 1995, + ] + ), + np.asarray([2190, 2195, 2190, 2195, 2000, 2000, 2000, 2195, 2195, 2190]), + np.asarray([2590, 2595, 2600, 2395, 2595, 2600, 2395]), + np.array([2795, 2790, 2795, 2795, 2790, 2795, 2795, 2790, 2795]), + np.array([3590, 3395, 3595, 3595, 3395, 3395, 3400]), + np.array([3790, 3790]), + np.array([4195, 4195]), + np.array([4390, 4395, 4390]), + np.array( + [ + 4790, + 4990, + 4990, + 5000, + 5000, + 4990, + 4795, + 4985, + 5000, + 4795, + 5000, + 4990, + 4990, + 4790, + 5000, + 4990, + 4795, + 4795, + 4990, + 5000, + 4990, + ] + ), + ], + [ + 5.0, + 995.0, + 1195.0, + 1595.0, + 1797.5, + 2190.0, + 2595.0, + 2795.0, + 3400.0, + 3790.0, + 4195.0, + 4390.0, + 4990.0, + ], + ), + ], +) +def test_find_shelling_scheme_files(dwi_btable, exp_scheme, exp_bval_groups, exp_bval_estimated): + bvals = np.loadtxt(_datadir / f"{dwi_btable}.bval") + + obt_scheme, obt_bval_groups, obt_bval_estimated = find_shelling_scheme(bvals) + assert obt_scheme == exp_scheme + assert all( + np.allclose(obt_arr, exp_arr) + for obt_arr, exp_arr in zip(obt_bval_groups, exp_bval_groups, strict=True) + ) + assert np.allclose(obt_bval_estimated, exp_bval_estimated)