# sims

## Module: sims.phantom

 SingleTensor(gtab[, S0, evals, evecs, snr]) Simulated Q-space signal with a single tensor. add_noise(vol[, snr, S0, noise_type]) Add noise of specified distribution to a 4D array. diff2eigenvectors(dx, dy, dz) numerical derivatives 2 eigenvectors get_fnames([name]) provides filenames of some test datasets or other useful parametrisations gradient_table(bvals[, bvecs, big_delta, …]) A general function for creating diffusion MR gradients. orbital_phantom([gtab, evals, func, t, …]) Create a phantom based on a 3-D orbit f(t) -> (x,y,z). vec2vec_rotmat(u, v) rotation matrix from 2 unit vectors

## Module: sims.voxel

 MultiTensor(gtab, mevals[, S0, angles, …]) Simulate a Multi-Tensor signal. SingleTensor(gtab[, S0, evals, evecs, snr]) Simulated Q-space signal with a single tensor. SticksAndBall(gtab[, d, S0, angles, …]) Simulate the signal for a Sticks & Ball model. add_noise(signal, snr, S0[, noise_type]) Add noise of specified distribution to the signal from a single voxel. all_tensor_evecs(e0) Given the principle tensor axis, return the array of all eigenvectors column-wise (or, the rotation matrix that orientates the tensor). callaghan_perpendicular(q, radius) Calculates the perpendicular diffusion signal E(q) in a cylinder of radius R using the Soderman model [1]. cylinders_and_ball_soderman(gtab, tau[, …]) Calculates the three-dimensional signal attenuation E(q) originating from within a cylinder of radius R using the Soderman approximation [1]. dki_design_matrix(gtab) Constructs B design matrix for DKI dki_signal(gtab, dt, kt[, S0, snr]) Simulated signal based on the diffusion and diffusion kurtosis tensors of a single voxel. dot(a, b[, out]) Dot product of two arrays. gaussian_parallel(q, tau[, D]) Calculates the parallel Gaussian diffusion signal. kurtosis_element(D_comps, frac, ind_i, …) Computes the diffusion kurtosis tensor element (with indexes i, j, k and l) based on the individual diffusion tensor components of a multicompartmental model. multi_tensor(gtab, mevals[, S0, angles, …]) Simulate a Multi-Tensor signal. multi_tensor_dki(gtab, mevals[, S0, angles, …]) Simulate the diffusion-weight signal, diffusion and kurtosis tensors based on the DKI model multi_tensor_msd(mf[, mevals, tau]) Simulate a Multi-Tensor rtop. multi_tensor_odf(odf_verts, mevals, angles, …) Simulate a Multi-Tensor ODF. multi_tensor_pdf(pdf_points, mevals, angles, …) Simulate a Multi-Tensor ODF. multi_tensor_rtop(mf[, mevals, tau]) Simulate a Multi-Tensor rtop. single_tensor(gtab[, S0, evals, evecs, snr]) Simulated Q-space signal with a single tensor. single_tensor_msd([evals, tau]) Simulate a Multi-Tensor rtop. single_tensor_odf(r[, evals, evecs]) Simulated ODF with a single tensor. single_tensor_pdf(r[, evals, evecs, tau]) Simulated ODF with a single tensor. single_tensor_rtop([evals, tau]) Simulate a Single-Tensor rtop. sphere2cart(r, theta, phi) Spherical to Cartesian coordinates sticks_and_ball(gtab[, d, S0, angles, …]) Simulate the signal for a Sticks & Ball model. vec2vec_rotmat(u, v) rotation matrix from 2 unit vectors

### SingleTensor

dipy.sims.phantom.SingleTensor(gtab, S0=1, evals=None, evecs=None, snr=None)

Simulated Q-space signal with a single tensor.

Parameters: gtab : GradientTable Measurement directions. S0 : double, Strength of signal in the presence of no diffusion gradient (also called the b=0 value). evals : (3,) ndarray Eigenvalues of the diffusion tensor. By default, values typical for prolate white matter are used. evecs : (3, 3) ndarray Eigenvectors of the tensor. You can also think of this as a rotation matrix that transforms the direction of the tensor. The eigenvectors need to be column wise. snr : float Signal to noise ratio, assuming Rician noise. None implies no noise. S : (N,) ndarray Simulated signal: S(q, tau) = S_0 e^(-b g^T R D R.T g).

References

 [1] M. Descoteaux, “High Angular Resolution Diffusion MRI: from Local Estimation to Segmentation and Tractography”, PhD thesis, University of Nice-Sophia Antipolis, p. 42, 2008.
 [2] E. Stejskal and J. Tanner, “Spin diffusion measurements: spin echos in the presence of a time-dependent field gradient”, Journal of Chemical Physics, nr. 42, pp. 288–292, 1965.

dipy.sims.phantom.add_noise(vol, snr=1.0, S0=None, noise_type='rician')

Add noise of specified distribution to a 4D array.

Parameters: vol : array, shape (X,Y,Z,W) Diffusion measurements in W directions at each (X, Y, Z) voxel position. snr : float, optional The desired signal-to-noise ratio. (See notes below.) S0 : float, optional Reference signal for specifying snr (defaults to 1). noise_type : string, optional The distribution of noise added. Can be either ‘gaussian’ for Gaussian distributed noise, ‘rician’ for Rice-distributed noise (default) or ‘rayleigh’ for a Rayleigh distribution. vol : array, same shape as vol Volume with added noise.

Notes

SNR is defined here, following [1], as S0 / sigma, where sigma is the standard deviation of the two Gaussian distributions forming the real and imaginary components of the Rician noise distribution (see [2]).

References

 [1] (1, 2) Descoteaux, Angelino, Fitzgibbons and Deriche (2007) Regularized, fast and robust q-ball imaging. MRM, 58: 497-510
 [2] (1, 2) Gudbjartson and Patz (2008). The Rician distribution of noisy MRI data. MRM 34: 910-914.

Examples

>>> signal = np.arange(800).reshape(2, 2, 2, 100)
>>> signal_w_noise = add_noise(signal, snr=10, noise_type='rician')


### diff2eigenvectors

dipy.sims.phantom.diff2eigenvectors(dx, dy, dz)

numerical derivatives 2 eigenvectors

### get_fnames

dipy.sims.phantom.get_fnames(name='small_64D')

provides filenames of some test datasets or other useful parametrisations

Parameters: name : str the filename/s of which dataset to return, one of: ‘small_64D’ small region of interest nifti,bvecs,bvals 64 directions ‘small_101D’ small region of interest nifti,bvecs,bvals 101 directions ‘aniso_vox’ volume with anisotropic voxel size as Nifti ‘fornix’ 300 tracks in Trackvis format (from Pittsburgh Brain Competition) ‘gqi_vectors’ the scanner wave vectors needed for a GQI acquisitions of 101 directions tested on Siemens 3T Trio ‘small_25’ small ROI (10x8x2) DTI data (b value 2000, 25 directions) ‘test_piesno’ slice of N=8, K=14 diffusion data ‘reg_c’ small 2D image used for validating registration ‘reg_o’ small 2D image used for validation registration ‘cb_2’ two vectorized cingulum bundles fnames : tuple filenames for dataset

Examples

>>> import numpy as np
>>> from dipy.data import get_fnames
>>> fimg,fbvals,fbvecs=get_fnames('small_101D')
>>> import nibabel as nib
>>> data=img.get_data()
>>> data.shape == (6, 10, 10, 102)
True
>>> bvals.shape == (102,)
True
>>> bvecs.shape == (102, 3)
True


dipy.sims.phantom.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters: bvals : can be any of the four options an array of shape (N,) or (1, N) or (N, 1) with the b-values. a path for the file which contains an array like the above (1). an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped. a path for the file which contains an array like the one at (3). bvecs : can be any of two options an array of shape (N, 3) or (3, N) with the b-vectors. a path for the file which contains an array like the previous. big_delta : float acquisition pulse separation time in seconds (default None) small_delta : float acquisition pulse duration time in seconds (default None) b0_threshold : float All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting. atol : float All b-vectors need to be unit vectors up to a tolerance. gradients : GradientTable A GradientTable with all the gradient information.

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
2. We assume that the minimum number of b-values is 7.
3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt.bvecs.shape == bvecs.T.shape
False


### orbital_phantom

dipy.sims.phantom.orbital_phantom(gtab=None, evals=array([0.0015, 0.0004, 0.0004]), func=None, t=array([0., 0.00628947, 0.01257895, 0.01886842, 0.0251579, 0.03144737, 0.03773685, 0.04402632, 0.0503158, 0.05660527, 0.06289475, 0.06918422, 0.0754737, 0.08176317, 0.08805265, 0.09434212, 0.1006316, 0.10692107, 0.11321055, 0.11950002, 0.1257895, 0.13207897, 0.13836845, 0.14465792, 0.15094739, 0.15723687, 0.16352634, 0.16981582, 0.17610529, 0.18239477, 0.18868424, 0.19497372, 0.20126319, 0.20755267, 0.21384214, 0.22013162, 0.22642109, 0.23271057, 0.23900004, 0.24528952, 0.25157899, 0.25786847, 0.26415794, 0.27044742, 0.27673689, 0.28302637, 0.28931584, 0.29560531, 0.30189479, 0.30818426, 0.31447374, 0.32076321, 0.32705269, 0.33334216, 0.33963164, 0.34592111, 0.35221059, 0.35850006, 0.36478954, 0.37107901, 0.37736849, 0.38365796, 0.38994744, 0.39623691, 0.40252639, 0.40881586, 0.41510534, 0.42139481, 0.42768429, 0.43397376, 0.44026323, 0.44655271, 0.45284218, 0.45913166, 0.46542113, 0.47171061, 0.47800008, 0.48428956, 0.49057903, 0.49686851, 0.50315798, 0.50944746, 0.51573693, 0.52202641, 0.52831588, 0.53460536, 0.54089483, 0.54718431, 0.55347378, 0.55976326, 0.56605273, 0.57234221, 0.57863168, 0.58492115, 0.59121063, 0.5975001, 0.60378958, 0.61007905, 0.61636853, 0.622658, 0.62894748, 0.63523695, 0.64152643, 0.6478159, 0.65410538, 0.66039485, 0.66668433, 0.6729738, 0.67926328, 0.68555275, 0.69184223, 0.6981317, 0.70442118, 0.71071065, 0.71700013, 0.7232896, 0.72957907, 0.73586855, 0.74215802, 0.7484475, 0.75473697, 0.76102645, 0.76731592, 0.7736054, 0.77989487, 0.78618435, 0.79247382, 0.7987633, 0.80505277, 0.81134225, 0.81763172, 0.8239212, 0.83021067, 0.83650015, 0.84278962, 0.8490791, 0.85536857, 0.86165805, 0.86794752, 0.87423699, 0.88052647, 0.88681594, 0.89310542, 0.89939489, 0.90568437, 0.91197384, 0.91826332, 0.92455279, 0.93084227, 0.93713174, 0.94342122, 0.94971069, 0.95600017, 0.96228964, 0.96857912, 0.97486859, 0.98115807, 0.98744754, 0.99373702, 1.00002649, 1.00631597, 1.01260544, 1.01889491, 1.02518439, 1.03147386, 1.03776334, 1.04405281, 1.05034229, 1.05663176, 1.06292124, 1.06921071, 1.07550019, 1.08178966, 1.08807914, 1.09436861, 1.10065809, 1.10694756, 1.11323704, 1.11952651, 1.12581599, 1.13210546, 1.13839494, 1.14468441, 1.15097389, 1.15726336, 1.16355283, 1.16984231, 1.17613178, 1.18242126, 1.18871073, 1.19500021, 1.20128968, 1.20757916, 1.21386863, 1.22015811, 1.22644758, 1.23273706, 1.23902653, 1.24531601, 1.25160548, 1.25789496, 1.26418443, 1.27047391, 1.27676338, 1.28305286, 1.28934233, 1.29563181, 1.30192128, 1.30821075, 1.31450023, 1.3207897, 1.32707918, 1.33336865, 1.33965813, 1.3459476, 1.35223708, 1.35852655, 1.36481603, 1.3711055, 1.37739498, 1.38368445, 1.38997393, 1.3962634, 1.40255288, 1.40884235, 1.41513183, 1.4214213, 1.42771078, 1.43400025, 1.44028973, 1.4465792, 1.45286867, 1.45915815, 1.46544762, 1.4717371, 1.47802657, 1.48431605, 1.49060552, 1.496895, 1.50318447, 1.50947395, 1.51576342, 1.5220529, 1.52834237, 1.53463185, 1.54092132, 1.5472108, 1.55350027, 1.55978975, 1.56607922, 1.5723687, 1.57865817, 1.58494765, 1.59123712, 1.59752659, 1.60381607, 1.61010554, 1.61639502, 1.62268449, 1.62897397, 1.63526344, 1.64155292, 1.64784239, 1.65413187, 1.66042134, 1.66671082, 1.67300029, 1.67928977, 1.68557924, 1.69186872, 1.69815819, 1.70444767, 1.71073714, 1.71702662, 1.72331609, 1.72960557, 1.73589504, 1.74218451, 1.74847399, 1.75476346, 1.76105294, 1.76734241, 1.77363189, 1.77992136, 1.78621084, 1.79250031, 1.79878979, 1.80507926, 1.81136874, 1.81765821, 1.82394769, 1.83023716, 1.83652664, 1.84281611, 1.84910559, 1.85539506, 1.86168454, 1.86797401, 1.87426349, 1.88055296, 1.88684243, 1.89313191, 1.89942138, 1.90571086, 1.91200033, 1.91828981, 1.92457928, 1.93086876, 1.93715823, 1.94344771, 1.94973718, 1.95602666, 1.96231613, 1.96860561, 1.97489508, 1.98118456, 1.98747403, 1.99376351, 2.00005298, 2.00634246, 2.01263193, 2.01892141, 2.02521088, 2.03150035, 2.03778983, 2.0440793, 2.05036878, 2.05665825, 2.06294773, 2.0692372, 2.07552668, 2.08181615, 2.08810563, 2.0943951, 2.10068458, 2.10697405, 2.11326353, 2.119553, 2.12584248, 2.13213195, 2.13842143, 2.1447109, 2.15100038, 2.15728985, 2.16357932, 2.1698688, 2.17615827, 2.18244775, 2.18873722, 2.1950267, 2.20131617, 2.20760565, 2.21389512, 2.2201846, 2.22647407, 2.23276355, 2.23905302, 2.2453425, 2.25163197, 2.25792145, 2.26421092, 2.2705004, 2.27678987, 2.28307935, 2.28936882, 2.2956583, 2.30194777, 2.30823724, 2.31452672, 2.32081619, 2.32710567, 2.33339514, 2.33968462, 2.34597409, 2.35226357, 2.35855304, 2.36484252, 2.37113199, 2.37742147, 2.38371094, 2.39000042, 2.39628989, 2.40257937, 2.40886884, 2.41515832, 2.42144779, 2.42773727, 2.43402674, 2.44031622, 2.44660569, 2.45289516, 2.45918464, 2.46547411, 2.47176359, 2.47805306, 2.48434254, 2.49063201, 2.49692149, 2.50321096, 2.50950044, 2.51578991, 2.52207939, 2.52836886, 2.53465834, 2.54094781, 2.54723729, 2.55352676, 2.55981624, 2.56610571, 2.57239519, 2.57868466, 2.58497414, 2.59126361, 2.59755308, 2.60384256, 2.61013203, 2.61642151, 2.62271098, 2.62900046, 2.63528993, 2.64157941, 2.64786888, 2.65415836, 2.66044783, 2.66673731, 2.67302678, 2.67931626, 2.68560573, 2.69189521, 2.69818468, 2.70447416, 2.71076363, 2.71705311, 2.72334258, 2.72963206, 2.73592153, 2.742211, 2.74850048, 2.75478995, 2.76107943, 2.7673689, 2.77365838, 2.77994785, 2.78623733, 2.7925268, 2.79881628, 2.80510575, 2.81139523, 2.8176847, 2.82397418, 2.83026365, 2.83655313, 2.8428426, 2.84913208, 2.85542155, 2.86171103, 2.8680005, 2.87428998, 2.88057945, 2.88686892, 2.8931584, 2.89944787, 2.90573735, 2.91202682, 2.9183163, 2.92460577, 2.93089525, 2.93718472, 2.9434742, 2.94976367, 2.95605315, 2.96234262, 2.9686321, 2.97492157, 2.98121105, 2.98750052, 2.99379, 3.00007947, 3.00636895, 3.01265842, 3.0189479, 3.02523737, 3.03152684, 3.03781632, 3.04410579, 3.05039527, 3.05668474, 3.06297422, 3.06926369, 3.07555317, 3.08184264, 3.08813212, 3.09442159, 3.10071107, 3.10700054, 3.11329002, 3.11957949, 3.12586897, 3.13215844, 3.13844792, 3.14473739, 3.15102687, 3.15731634, 3.16360582, 3.16989529, 3.17618476, 3.18247424, 3.18876371, 3.19505319, 3.20134266, 3.20763214, 3.21392161, 3.22021109, 3.22650056, 3.23279004, 3.23907951, 3.24536899, 3.25165846, 3.25794794, 3.26423741, 3.27052689, 3.27681636, 3.28310584, 3.28939531, 3.29568479, 3.30197426, 3.30826374, 3.31455321, 3.32084268, 3.32713216, 3.33342163, 3.33971111, 3.34600058, 3.35229006, 3.35857953, 3.36486901, 3.37115848, 3.37744796, 3.38373743, 3.39002691, 3.39631638, 3.40260586, 3.40889533, 3.41518481, 3.42147428, 3.42776376, 3.43405323, 3.44034271, 3.44663218, 3.45292166, 3.45921113, 3.4655006, 3.47179008, 3.47807955, 3.48436903, 3.4906585, 3.49694798, 3.50323745, 3.50952693, 3.5158164, 3.52210588, 3.52839535, 3.53468483, 3.5409743, 3.54726378, 3.55355325, 3.55984273, 3.5661322, 3.57242168, 3.57871115, 3.58500063, 3.5912901, 3.59757958, 3.60386905, 3.61015852, 3.616448, 3.62273747, 3.62902695, 3.63531642, 3.6416059, 3.64789537, 3.65418485, 3.66047432, 3.6667638, 3.67305327, 3.67934275, 3.68563222, 3.6919217, 3.69821117, 3.70450065, 3.71079012, 3.7170796, 3.72336907, 3.72965855, 3.73594802, 3.7422375, 3.74852697, 3.75481644, 3.76110592, 3.76739539, 3.77368487, 3.77997434, 3.78626382, 3.79255329, 3.79884277, 3.80513224, 3.81142172, 3.81771119, 3.82400067, 3.83029014, 3.83657962, 3.84286909, 3.84915857, 3.85544804, 3.86173752, 3.86802699, 3.87431647, 3.88060594, 3.88689542, 3.89318489, 3.89947436, 3.90576384, 3.91205331, 3.91834279, 3.92463226, 3.93092174, 3.93721121, 3.94350069, 3.94979016, 3.95607964, 3.96236911, 3.96865859, 3.97494806, 3.98123754, 3.98752701, 3.99381649, 4.00010596, 4.00639544, 4.01268491, 4.01897439, 4.02526386, 4.03155334, 4.03784281, 4.04413228, 4.05042176, 4.05671123, 4.06300071, 4.06929018, 4.07557966, 4.08186913, 4.08815861, 4.09444808, 4.10073756, 4.10702703, 4.11331651, 4.11960598, 4.12589546, 4.13218493, 4.13847441, 4.14476388, 4.15105336, 4.15734283, 4.16363231, 4.16992178, 4.17621126, 4.18250073, 4.1887902, 4.19507968, 4.20136915, 4.20765863, 4.2139481, 4.22023758, 4.22652705, 4.23281653, 4.239106, 4.24539548, 4.25168495, 4.25797443, 4.2642639, 4.27055338, 4.27684285, 4.28313233, 4.2894218, 4.29571128, 4.30200075, 4.30829023, 4.3145797, 4.32086918, 4.32715865, 4.33344812, 4.3397376, 4.34602707, 4.35231655, 4.35860602, 4.3648955, 4.37118497, 4.37747445, 4.38376392, 4.3900534, 4.39634287, 4.40263235, 4.40892182, 4.4152113, 4.42150077, 4.42779025, 4.43407972, 4.4403692, 4.44665867, 4.45294815, 4.45923762, 4.4655271, 4.47181657, 4.47810604, 4.48439552, 4.49068499, 4.49697447, 4.50326394, 4.50955342, 4.51584289, 4.52213237, 4.52842184, 4.53471132, 4.54100079, 4.54729027, 4.55357974, 4.55986922, 4.56615869, 4.57244817, 4.57873764, 4.58502712, 4.59131659, 4.59760607, 4.60389554, 4.61018502, 4.61647449, 4.62276396, 4.62905344, 4.63534291, 4.64163239, 4.64792186, 4.65421134, 4.66050081, 4.66679029, 4.67307976, 4.67936924, 4.68565871, 4.69194819, 4.69823766, 4.70452714, 4.71081661, 4.71710609, 4.72339556, 4.72968504, 4.73597451, 4.74226399, 4.74855346, 4.75484294, 4.76113241, 4.76742188, 4.77371136, 4.78000083, 4.78629031, 4.79257978, 4.79886926, 4.80515873, 4.81144821, 4.81773768, 4.82402716, 4.83031663, 4.83660611, 4.84289558, 4.84918506, 4.85547453, 4.86176401, 4.86805348, 4.87434296, 4.88063243, 4.88692191, 4.89321138, 4.89950086, 4.90579033, 4.9120798, 4.91836928, 4.92465875, 4.93094823, 4.9372377, 4.94352718, 4.94981665, 4.95610613, 4.9623956, 4.96868508, 4.97497455, 4.98126403, 4.9875535, 4.99384298, 5.00013245, 5.00642193, 5.0127114, 5.01900088, 5.02529035, 5.03157983, 5.0378693, 5.04415878, 5.05044825, 5.05673772, 5.0630272, 5.06931667, 5.07560615, 5.08189562, 5.0881851, 5.09447457, 5.10076405, 5.10705352, 5.113343, 5.11963247, 5.12592195, 5.13221142, 5.1385009, 5.14479037, 5.15107985, 5.15736932, 5.1636588, 5.16994827, 5.17623775, 5.18252722, 5.1888167, 5.19510617, 5.20139564, 5.20768512, 5.21397459, 5.22026407, 5.22655354, 5.23284302, 5.23913249, 5.24542197, 5.25171144, 5.25800092, 5.26429039, 5.27057987, 5.27686934, 5.28315882, 5.28944829, 5.29573777, 5.30202724, 5.30831672, 5.31460619, 5.32089567, 5.32718514, 5.33347462, 5.33976409, 5.34605356, 5.35234304, 5.35863251, 5.36492199, 5.37121146, 5.37750094, 5.38379041, 5.39007989, 5.39636936, 5.40265884, 5.40894831, 5.41523779, 5.42152726, 5.42781674, 5.43410621, 5.44039569, 5.44668516, 5.45297464, 5.45926411, 5.46555359, 5.47184306, 5.47813254, 5.48442201, 5.49071148, 5.49700096, 5.50329043, 5.50957991, 5.51586938, 5.52215886, 5.52844833, 5.53473781, 5.54102728, 5.54731676, 5.55360623, 5.55989571, 5.56618518, 5.57247466, 5.57876413, 5.58505361, 5.59134308, 5.59763256, 5.60392203, 5.61021151, 5.61650098, 5.62279046, 5.62907993, 5.6353694, 5.64165888, 5.64794835, 5.65423783, 5.6605273, 5.66681678, 5.67310625, 5.67939573, 5.6856852, 5.69197468, 5.69826415, 5.70455363, 5.7108431, 5.71713258, 5.72342205, 5.72971153, 5.736001, 5.74229048, 5.74857995, 5.75486943, 5.7611589, 5.76744838, 5.77373785, 5.78002732, 5.7863168, 5.79260627, 5.79889575, 5.80518522, 5.8114747, 5.81776417, 5.82405365, 5.83034312, 5.8366326, 5.84292207, 5.84921155, 5.85550102, 5.8617905, 5.86807997, 5.87436945, 5.88065892, 5.8869484, 5.89323787, 5.89952735, 5.90581682, 5.9121063, 5.91839577, 5.92468524, 5.93097472, 5.93726419, 5.94355367, 5.94984314, 5.95613262, 5.96242209, 5.96871157, 5.97500104, 5.98129052, 5.98757999, 5.99386947, 6.00015894, 6.00644842, 6.01273789, 6.01902737, 6.02531684, 6.03160632, 6.03789579, 6.04418527, 6.05047474, 6.05676422, 6.06305369, 6.06934316, 6.07563264, 6.08192211, 6.08821159, 6.09450106, 6.10079054, 6.10708001, 6.11336949, 6.11965896, 6.12594844, 6.13223791, 6.13852739, 6.14481686, 6.15110634, 6.15739581, 6.16368529, 6.16997476, 6.17626424, 6.18255371, 6.18884319, 6.19513266, 6.20142214, 6.20771161, 6.21400108, 6.22029056, 6.22658003, 6.23286951, 6.23915898, 6.24544846, 6.25173793, 6.25802741, 6.26431688, 6.27060636, 6.27689583, 6.28318531]), datashape=(64, 64, 64, 65), origin=(32, 32, 32), scale=(25, 25, 25), angles=array([0., 0.2026834, 0.40536679, 0.60805019, 0.81073359, 1.01341699, 1.21610038, 1.41878378, 1.62146718, 1.82415057, 2.02683397, 2.22951737, 2.43220076, 2.63488416, 2.83756756, 3.04025096, 3.24293435, 3.44561775, 3.64830115, 3.85098454, 4.05366794, 4.25635134, 4.45903473, 4.66171813, 4.86440153, 5.06708493, 5.26976832, 5.47245172, 5.67513512, 5.87781851, 6.08050191, 6.28318531]), radii=array([0.2, 0.56, 0.92, 1.28, 1.64, 2. ]), S0=100.0, snr=None)

Create a phantom based on a 3-D orbit f(t) -> (x,y,z).

Parameters: gtab : GradientTable Gradient table of measurement directions. evals : array, shape (3,) Tensor eigenvalues. func : user defined function f(t)->(x,y,z) It could be desirable for -1=

Examples

>>> def f(t):
...    x = np.sin(t)
...    y = np.cos(t)
...    z = np.linspace(-1, 1, len(x))
...    return x, y, z

>>> data = orbital_phantom(func=f)


### vec2vec_rotmat

dipy.sims.phantom.vec2vec_rotmat(u, v)

rotation matrix from 2 unit vectors

u, v being unit 3d vectors return a 3x3 rotation matrix R than aligns u to v.

In general there are many rotations that will map u to v. If S is any rotation using v as an axis then R.S will also map u to v since (S.R)u = S(Ru) = Sv = v. The rotation R returned by vec2vec_rotmat leaves fixed the perpendicular to the plane spanned by u and v.

The transpose of R will align v to u.

Parameters: u : array, shape(3,) v : array, shape(3,) R : array, shape(3,3)

Examples

>>> import numpy as np
>>> from dipy.core.geometry import vec2vec_rotmat
>>> u=np.array([1,0,0])
>>> v=np.array([0,1,0])
>>> R=vec2vec_rotmat(u,v)
>>> np.dot(R,u)
array([ 0.,  1.,  0.])
>>> np.dot(R.T,v)
array([ 1.,  0.,  0.])


### MultiTensor

dipy.sims.voxel.MultiTensor(gtab, mevals, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=20)

Simulate a Multi-Tensor signal.

Parameters: gtab : GradientTable mevals : array (K, 3) each tensor’s eigenvalues in each row S0 : float Unweighted signal value (b0 signal). angles : array (K,2) or (K,3) List of K tensor directions in polar angles (in degrees) or unit vectors fractions : float Percentage of the contribution of each tensor. The sum of fractions should be equal to 100%. snr : float Signal to noise ratio, assuming Rician noise. If set to None, no noise is added. S : (N,) ndarray Simulated signal. sticks : (M,3) Sticks in cartesian coordinates.

Examples

>>> import numpy as np
>>> from dipy.sims.voxel import multi_tensor
>>> from dipy.data import get_fnames
>>> fimg, fbvals, fbvecs = get_fnames('small_101D')
>>> bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
>>> mevals=np.array(([0.0015, 0.0003, 0.0003],[0.0015, 0.0003, 0.0003]))
>>> e0 = np.array([1, 0, 0.])
>>> e1 = np.array([0., 1, 0])
>>> S = multi_tensor(gtab, mevals)


### SingleTensor

dipy.sims.voxel.SingleTensor(gtab, S0=1, evals=None, evecs=None, snr=None)

Simulated Q-space signal with a single tensor.

Parameters: gtab : GradientTable Measurement directions. S0 : double, Strength of signal in the presence of no diffusion gradient (also called the b=0 value). evals : (3,) ndarray Eigenvalues of the diffusion tensor. By default, values typical for prolate white matter are used. evecs : (3, 3) ndarray Eigenvectors of the tensor. You can also think of this as a rotation matrix that transforms the direction of the tensor. The eigenvectors need to be column wise. snr : float Signal to noise ratio, assuming Rician noise. None implies no noise. S : (N,) ndarray Simulated signal: S(q, tau) = S_0 e^(-b g^T R D R.T g).

References

 [1] M. Descoteaux, “High Angular Resolution Diffusion MRI: from Local Estimation to Segmentation and Tractography”, PhD thesis, University of Nice-Sophia Antipolis, p. 42, 2008.
 [2] E. Stejskal and J. Tanner, “Spin diffusion measurements: spin echos in the presence of a time-dependent field gradient”, Journal of Chemical Physics, nr. 42, pp. 288–292, 1965.

### SticksAndBall

dipy.sims.voxel.SticksAndBall(gtab, d=0.0015, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[35, 35], snr=20)

Simulate the signal for a Sticks & Ball model.

Parameters: gtab : GradientTable Signal measurement directions. d : float Diffusivity value. S0 : float Unweighted signal value. angles : array (K,2) or (K, 3) List of K polar angles (in degrees) for the sticks or array of K sticks as unit vectors. fractions : float Percentage of each stick. Remainder to 100 specifies isotropic component. snr : float Signal to noise ratio, assuming Rician noise. If set to None, no noise is added. S : (N,) ndarray Simulated signal. sticks : (M,3) Sticks in cartesian coordinates.

References

 [1] Behrens et al., “Probabilistic diffusion tractography with multiple fiber orientations: what can we gain?”, Neuroimage, 2007.

dipy.sims.voxel.add_noise(signal, snr, S0, noise_type='rician')

Add noise of specified distribution to the signal from a single voxel.

Parameters: signal : 1-d ndarray The signal in the voxel. snr : float The desired signal-to-noise ratio. (See notes below.) If snr is None, return the signal as-is. S0 : float Reference signal for specifying snr. noise_type : string, optional The distribution of noise added. Can be either ‘gaussian’ for Gaussian distributed noise, ‘rician’ for Rice-distributed noise (default) or ‘rayleigh’ for a Rayleigh distribution. signal : array, same shape as the input Signal with added noise.

Notes

SNR is defined here, following [1], as S0 / sigma, where sigma is the standard deviation of the two Gaussian distributions forming the real and imaginary components of the Rician noise distribution (see [2]).

References

 [1] (1, 2) Descoteaux, Angelino, Fitzgibbons and Deriche (2007) Regularized, fast and robust q-ball imaging. MRM, 58: 497-510
 [2] (1, 2) Gudbjartson and Patz (2008). The Rician distribution of noisy MRI data. MRM 34: 910-914.

Examples

>>> signal = np.arange(800).reshape(2, 2, 2, 100)
>>> signal_w_noise = add_noise(signal, 10., 100., noise_type='rician')


### all_tensor_evecs

dipy.sims.voxel.all_tensor_evecs(e0)

Given the principle tensor axis, return the array of all eigenvectors column-wise (or, the rotation matrix that orientates the tensor).

Parameters: e0 : (3,) ndarray Principle tensor axis. evecs : (3,3) ndarray Tensor eigenvectors, arranged column-wise.

### callaghan_perpendicular

dipy.sims.voxel.callaghan_perpendicular(q, radius)

Calculates the perpendicular diffusion signal E(q) in a cylinder of radius R using the Soderman model [1]. Assumes that the pulse length is infinitely short and the diffusion time is infinitely long.

Parameters: q : array, shape (N,) q-space value in 1/mm radius : float cylinder radius in mm E : array, shape (N,) signal attenuation

References

 [1] (1, 2, 3) Söderman, Olle, and Bengt Jönsson. “Restricted diffusion in cylindrical geometry.” Journal of Magnetic Resonance, Series A 117.1 (1995): 94-97.

### cylinders_and_ball_soderman

dipy.sims.voxel.cylinders_and_ball_soderman(gtab, tau, radii=[0.005, 0.005], D=0.0007, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[35, 35], snr=20)

Calculates the three-dimensional signal attenuation E(q) originating from within a cylinder of radius R using the Soderman approximation [1]. The diffusion signal is assumed to be separable perpendicular and parallel to the cylinder axis [2]. This function is basically an extension of the ball and stick model. Setting the radius to zero makes them equivalent.

Parameters: gtab : GradientTable Signal measurement directions. tau : float diffusion time in s radii : float cylinder radius in mm D : float diffusion constant S0 : float Unweighted signal value. angles : array (K,2) or (K, 3) List of K polar angles (in degrees) for the sticks or array of K sticks as unit vectors. fractions : [float] Percentage of each stick. Remainder to 100 specifies isotropic component. snr : float Signal to noise ratio, assuming Rician noise. If set to None, no noise is added. E : array, shape (N,) signal attenuation

References

 [1] (1, 2, 3) Söderman, Olle, and Bengt Jönsson. “Restricted diffusion in cylindrical geometry.” Journal of Magnetic Resonance, Series A 117.1 (1995): 94-97.
 [2] (1, 2) Assaf, Yaniv, et al. “New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter.” Magnetic Resonance in Medicine 52.5 (2004): 965-978.

### dki_design_matrix

dipy.sims.voxel.dki_design_matrix(gtab)

Constructs B design matrix for DKI

Measurement directions.
Returns: B : array (N, 22) Design matrix or B matrix for the DKI model B[j, :] = (Bxx, Bxy, Bzz, Bxz, Byz, Bzz, Bxxxx, Byyyy, Bzzzz, Bxxxy, Bxxxz, Bxyyy, Byyyz, Bxzzz, Byzzz, Bxxyy, Bxxzz, Byyzz, Bxxyz, Bxyyz, Bxyzz, BlogS0)

### dki_signal

dipy.sims.voxel.dki_signal(gtab, dt, kt, S0=150, snr=None)

Simulated signal based on the diffusion and diffusion kurtosis tensors of a single voxel. Simulations are preformed assuming the DKI model.

Parameters: gtab : GradientTable Measurement directions. dt : (6,) ndarray Elements of the diffusion tensor. kt : (15, ) ndarray Elements of the diffusion kurtosis tensor. S0 : float (optional) Strength of signal in the presence of no diffusion gradient. snr : float (optional) Signal to noise ratio, assuming Rician noise. None implies no noise. S : (N,) ndarray Simulated signal based on the DKI model: .. math:: S=S_{0}e^{-bD+frac{1}{6}b^{2}D^{2}K}

References

 [1] R. Neto Henriques et al., “Exploring the 3D geometry of the diffusion kurtosis tensor - Impact on the development of robust tractography procedures and novel biomarkers”, NeuroImage (2015) 111, 85-99.

### dot

dipy.sims.voxel.dot(a, b, out=None)

Dot product of two arrays. Specifically,

• If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).

• If both a and b are 2-D arrays, it is matrix multiplication, but using matmul() or a @ b is preferred.

• If either a or b is 0-D (scalar), it is equivalent to multiply() and using numpy.multiply(a, b) or a * b is preferred.

• If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.

• If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b:

dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])

Parameters: a : array_like First argument. b : array_like Second argument. out : ndarray, optional Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. output : ndarray Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If out is given, then it is returned. ValueError If the last dimension of a is not the same size as the second-to-last dimension of b.

vdot
Complex-conjugating dot product.
tensordot
Sum products over arbitrary axes.
einsum
Einstein summation convention.
matmul
‘@’ operator as method with out parameter.

Examples

>>> np.dot(3, 4)
12


Neither argument is complex-conjugated:

>>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)


For 2-D arrays it is the matrix product:

>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])

>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128


### gaussian_parallel

dipy.sims.voxel.gaussian_parallel(q, tau, D=0.0007)

Calculates the parallel Gaussian diffusion signal.

Parameters: q : array, shape (N,) q-space value in 1/mm tau : float diffusion time in s D : float diffusion constant E : array, shape (N,) signal attenuation

### kurtosis_element

dipy.sims.voxel.kurtosis_element(D_comps, frac, ind_i, ind_j, ind_k, ind_l, DT=None, MD=None)

Computes the diffusion kurtosis tensor element (with indexes i, j, k and l) based on the individual diffusion tensor components of a multicompartmental model.

Parameters: D_comps : (K,3,3) ndarray Diffusion tensors for all K individual compartment of the multicompartmental model. frac : [float] Percentage of the contribution of each tensor. The sum of fractions should be equal to 100%. ind_i : int Element’s index i (0 for x, 1 for y, 2 for z) ind_j : int Element’s index j (0 for x, 1 for y, 2 for z) ind_k : int Element’s index k (0 for x, 1 for y, 2 for z) ind_l: int Elements index l (0 for x, 1 for y, 2 for z) DT : (3,3) ndarray (optional) Voxel’s global diffusion tensor. MD : float (optional) Voxel’s global mean diffusivity. wijkl : float kurtosis tensor element of index i, j, k, l

Notes

wijkl is calculated using equation 8 given in [1]

References

 [1] (1, 2) R. Neto Henriques et al., “Exploring the 3D geometry of the diffusion kurtosis tensor - Impact on the development of robust tractography procedures and novel biomarkers”, NeuroImage (2015) 111, 85-99.

### multi_tensor

dipy.sims.voxel.multi_tensor(gtab, mevals, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=20)

Simulate a Multi-Tensor signal.

Parameters: gtab : GradientTable mevals : array (K, 3) each tensor’s eigenvalues in each row S0 : float Unweighted signal value (b0 signal). angles : array (K,2) or (K,3) List of K tensor directions in polar angles (in degrees) or unit vectors fractions : float Percentage of the contribution of each tensor. The sum of fractions should be equal to 100%. snr : float Signal to noise ratio, assuming Rician noise. If set to None, no noise is added. S : (N,) ndarray Simulated signal. sticks : (M,3) Sticks in cartesian coordinates.

Examples

>>> import numpy as np
>>> from dipy.sims.voxel import multi_tensor
>>> from dipy.data import get_fnames
>>> fimg, fbvals, fbvecs = get_fnames('small_101D')
>>> bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
>>> mevals=np.array(([0.0015, 0.0003, 0.0003],[0.0015, 0.0003, 0.0003]))
>>> e0 = np.array([1, 0, 0.])
>>> e1 = np.array([0., 1, 0])
>>> S = multi_tensor(gtab, mevals)


### multi_tensor_dki

dipy.sims.voxel.multi_tensor_dki(gtab, mevals, S0=1.0, angles=[(90.0, 0.0), (90.0, 0.0)], fractions=[50, 50], snr=20)

Simulate the diffusion-weight signal, diffusion and kurtosis tensors based on the DKI model

Parameters: gtab : GradientTable mevals : array (K, 3) eigenvalues of the diffusion tensor for each individual compartment S0 : float (optional) Unweighted signal value (b0 signal). angles : array (K,2) or (K,3) (optional) List of K tensor directions of the diffusion tensor of each compartment in polar angles (in degrees) or unit vectors fractions : float (K,) (optional) Percentage of the contribution of each tensor. The sum of fractions should be equal to 100%. snr : float (optional) Signal to noise ratio, assuming Rician noise. If set to None, no noise is added. S : (N,) ndarray Simulated signal based on the DKI model. dt : (6,) elements of the diffusion tensor. kt : (15,) elements of the kurtosis tensor.

Notes

Simulations are based on multicompartmental models which assumes that tissue is well described by impermeable diffusion compartments characterized by their only diffusion tensor. Since simulations are based on the DKI model, coefficients larger than the fourth order of the signal’s taylor expansion approximation are neglected.

References

 [1] R. Neto Henriques et al., “Exploring the 3D geometry of the diffusion kurtosis tensor - Impact on the development of robust tractography procedures and novel biomarkers”, NeuroImage (2015) 111, 85-99.

Examples

>>> import numpy as np
>>> from dipy.sims.voxel import multi_tensor_dki
>>> from dipy.data import get_fnames
>>> fimg, fbvals, fbvecs = get_fnames('small_64D')
>>> bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
>>> bvals_2s = np.concatenate((bvals, bvals * 2), axis=0)
>>> bvecs_2s = np.concatenate((bvecs, bvecs), axis=0)
>>> mevals = np.array([[0.00099, 0, 0],[0.00226, 0.00087, 0.00087]])
>>> S, dt, kt =  multi_tensor_dki(gtab, mevals)


### multi_tensor_msd

dipy.sims.voxel.multi_tensor_msd(mf, mevals=None, tau=0.025330295910584444)

Simulate a Multi-Tensor rtop.

Parameters: mf : sequence of floats, bounded [0,1] Percentages of the fractions for each tensor. mevals : sequence of 1D arrays, Eigen-values for each tensor. By default, values typical for prolate white matter are used. tau : float, diffusion time. By default the value that makes q=sqrt(b). msd : float, Mean square displacement.

References

 [1] Cheng J., “Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI”, PhD Thesis, 2012.

### multi_tensor_odf

dipy.sims.voxel.multi_tensor_odf(odf_verts, mevals, angles, fractions)

Simulate a Multi-Tensor ODF.

Parameters: odf_verts : (N,3) ndarray Vertices of the reconstruction sphere. mevals : sequence of 1D arrays, Eigen-values for each tensor. angles : sequence of 2d tuples, Sequence of principal directions for each tensor in polar angles or cartesian unit coordinates. fractions : sequence of floats, Percentages of the fractions for each tensor. ODF : (N,) ndarray Orientation distribution function.

Examples

Simulate a MultiTensor ODF with two peaks and calculate its exact ODF.

>>> import numpy as np
>>> from dipy.sims.voxel import multi_tensor_odf, all_tensor_evecs
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric724')
>>> vertices, faces = sphere.vertices, sphere.faces
>>> mevals = np.array(([0.0015, 0.0003, 0.0003],[0.0015, 0.0003, 0.0003]))
>>> angles = [(0, 0), (90, 0)]
>>> odf = multi_tensor_odf(vertices, mevals, angles, [50, 50])


### multi_tensor_pdf

dipy.sims.voxel.multi_tensor_pdf(pdf_points, mevals, angles, fractions, tau=0.025330295910584444)

Simulate a Multi-Tensor ODF.

Parameters: pdf_points : (N, 3) ndarray Points to evaluate the PDF. mevals : sequence of 1D arrays, Eigen-values for each tensor. By default, values typical for prolate white matter are used. angles : sequence, Sequence of principal directions for each tensor in polar angles or cartesian unit coordinates. fractions : sequence of floats, Percentages of the fractions for each tensor. tau : float, diffusion time. By default the value that makes q=sqrt(b). pdf : (N,) ndarray, Probability density function of the water displacement.

References

 [1] Cheng J., “Estimation and Processing of Ensemble Average Propagator and its Features in Diffusion MRI”, PhD Thesis, 2012.

### multi_tensor_rtop

dipy.sims.voxel.multi_tensor_rtop(mf, mevals=None, tau=0.025330295910584444)

Simulate a Multi-Tensor rtop.

Parameters: mf : sequence of floats, bounded [0,1] Percentages of the fractions for each tensor. mevals : sequence of 1D arrays, Eigen-values for each tensor. By default, values typical for prolate white matter are used. tau : float, diffusion time. By default the value that makes q=sqrt(b). rtop : float, Return to origin probability.

References

 [1] Cheng J., “Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI”, PhD Thesis, 2012.

### single_tensor

dipy.sims.voxel.single_tensor(gtab, S0=1, evals=None, evecs=None, snr=None)

Simulated Q-space signal with a single tensor.

Parameters: gtab : GradientTable Measurement directions. S0 : double, Strength of signal in the presence of no diffusion gradient (also called the b=0 value). evals : (3,) ndarray Eigenvalues of the diffusion tensor. By default, values typical for prolate white matter are used. evecs : (3, 3) ndarray Eigenvectors of the tensor. You can also think of this as a rotation matrix that transforms the direction of the tensor. The eigenvectors need to be column wise. snr : float Signal to noise ratio, assuming Rician noise. None implies no noise. S : (N,) ndarray Simulated signal: S(q, tau) = S_0 e^(-b g^T R D R.T g).

References

 [1] M. Descoteaux, “High Angular Resolution Diffusion MRI: from Local Estimation to Segmentation and Tractography”, PhD thesis, University of Nice-Sophia Antipolis, p. 42, 2008.
 [2] E. Stejskal and J. Tanner, “Spin diffusion measurements: spin echos in the presence of a time-dependent field gradient”, Journal of Chemical Physics, nr. 42, pp. 288–292, 1965.

### single_tensor_msd

dipy.sims.voxel.single_tensor_msd(evals=None, tau=0.025330295910584444)

Simulate a Multi-Tensor rtop.

Parameters: evals : 1D arrays, Eigen-values for the tensor. By default, values typical for prolate white matter are used. tau : float, diffusion time. By default the value that makes q=sqrt(b). msd : float, Mean square displacement.

References

 [1] Cheng J., “Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI”, PhD Thesis, 2012.

### single_tensor_odf

dipy.sims.voxel.single_tensor_odf(r, evals=None, evecs=None)

Simulated ODF with a single tensor.

Parameters: r : (N,3) or (M,N,3) ndarray Measurement positions in (x, y, z), either as a list or on a grid. evals : (3,) Eigenvalues of diffusion tensor. By default, use values typical for prolate white matter. evecs : (3, 3) ndarray Eigenvectors of the tensor, written column-wise. You can also think of these as the rotation matrix that determines the orientation of the diffusion tensor. ODF : (N,) ndarray The diffusion probability at r after time tau.

References

 [1] Aganj et al., “Reconstruction of the Orientation Distribution Function in Single- and Multiple-Shell q-Ball Imaging Within Constant Solid Angle”, Magnetic Resonance in Medicine, nr. 64, pp. 554–566, 2010.

### single_tensor_pdf

dipy.sims.voxel.single_tensor_pdf(r, evals=None, evecs=None, tau=0.025330295910584444)

Simulated ODF with a single tensor.

Parameters: r : (N,3) or (M,N,3) ndarray Measurement positions in (x, y, z), either as a list or on a grid. evals : (3,) Eigenvalues of diffusion tensor. By default, use values typical for prolate white matter. evecs : (3, 3) ndarray Eigenvectors of the tensor. You can also think of these as the rotation matrix that determines the orientation of the diffusion tensor. tau : float, diffusion time. By default the value that makes q=sqrt(b). pdf : (N,) ndarray The diffusion probability at r after time tau.

References

 [1] Cheng J., “Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI”, PhD Thesis, 2012.

### single_tensor_rtop

dipy.sims.voxel.single_tensor_rtop(evals=None, tau=0.025330295910584444)

Simulate a Single-Tensor rtop.

Parameters: evals : 1D arrays, Eigen-values for the tensor. By default, values typical for prolate white matter are used. tau : float, diffusion time. By default the value that makes q=sqrt(b). rtop : float, Return to origin probability.

References

 [1] Cheng J., “Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI”, PhD Thesis, 2012.

### sphere2cart

dipy.sims.voxel.sphere2cart(r, theta, phi)

Spherical to Cartesian coordinates

This is the standard physics convention where theta is the inclination (polar) angle, and phi is the azimuth angle.

Imagine a sphere with center (0,0,0). Orient it with the z axis running south-north, the y axis running west-east and the x axis from posterior to anterior. theta (the inclination angle) is the angle to rotate from the z-axis (the zenith) around the y-axis, towards the x axis. Thus the rotation is counter-clockwise from the point of view of positive y. phi (azimuth) gives the angle of rotation around the z-axis towards the y axis. The rotation is counter-clockwise from the point of view of positive z.

Equivalently, given a point P on the sphere, with coordinates x, y, z, theta is the angle between P and the z-axis, and phi is the angle between the projection of P onto the XY plane, and the X axis.

Geographical nomenclature designates theta as ‘co-latitude’, and phi as ‘longitude’

Parameters: r : array_like radius theta : array_like inclination or polar angle phi : array_like azimuth angle x : array x coordinate(s) in Cartesion space y : array y coordinate(s) in Cartesian space z : array z coordinate

Notes

See these pages:

for excellent discussion of the many different conventions possible. Here we use the physics conventions, used in the wikipedia page.

Derivations of the formulae are simple. Consider a vector x, y, z of length r (norm of x, y, z). The inclination angle (theta) can be found from: cos(theta) == z / r -> z == r * cos(theta). This gives the hypotenuse of the projection onto the XY plane, which we will call Q. Q == r*sin(theta). Now x / Q == cos(phi) -> x == r * sin(theta) * cos(phi) and so on.

We have deliberately named this function sphere2cart rather than sph2cart to distinguish it from the Matlab function of that name, because the Matlab function uses an unusual convention for the angles that we did not want to replicate. The Matlab function is trivial to implement with the formulae given in the Matlab help.

### sticks_and_ball

dipy.sims.voxel.sticks_and_ball(gtab, d=0.0015, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[35, 35], snr=20)

Simulate the signal for a Sticks & Ball model.

Parameters: gtab : GradientTable Signal measurement directions. d : float Diffusivity value. S0 : float Unweighted signal value. angles : array (K,2) or (K, 3) List of K polar angles (in degrees) for the sticks or array of K sticks as unit vectors. fractions : float Percentage of each stick. Remainder to 100 specifies isotropic component. snr : float Signal to noise ratio, assuming Rician noise. If set to None, no noise is added. S : (N,) ndarray Simulated signal. sticks : (M,3) Sticks in cartesian coordinates.

References

 [1] Behrens et al., “Probabilistic diffusion tractography with multiple fiber orientations: what can we gain?”, Neuroimage, 2007.

### vec2vec_rotmat

dipy.sims.voxel.vec2vec_rotmat(u, v)

rotation matrix from 2 unit vectors

u, v being unit 3d vectors return a 3x3 rotation matrix R than aligns u to v.

In general there are many rotations that will map u to v. If S is any rotation using v as an axis then R.S will also map u to v since (S.R)u = S(Ru) = Sv = v. The rotation R returned by vec2vec_rotmat leaves fixed the perpendicular to the plane spanned by u and v.

The transpose of R will align v to u.

Parameters: u : array, shape(3,) v : array, shape(3,) R : array, shape(3,3)

Examples

>>> import numpy as np
>>> from dipy.core.geometry import vec2vec_rotmat
>>> u=np.array([1,0,0])
>>> v=np.array([0,1,0])
>>> R=vec2vec_rotmat(u,v)
>>> np.dot(R,u)
array([ 0.,  1.,  0.])
>>> np.dot(R.T,v)
array([ 1.,  0.,  0.])
`