def sdt(training, category, snr, denoised, odeconv, tv, method, weight=0.1): data, affine, gtab, mask, evals, S0, prefix = prepare(training, category, snr, denoised, odeconv, tv, method) if category == 'dti': csdt_model = ConstrainedSDTModel(gtab, ratio = evals[1] / evals[0], sh_order=6) if category == 'hardi': csdt_model = ConstrainedSDTModel(gtab, ratio = evals[1] / evals[0], sh_order=8) csdt_fit = csdt_model.fit(data, mask) sphere = get_sphere('symmetric724') odf = csdt_fit.odf(sphere) if tv == True: odf = tv_denoise_4d(odf, weight=weight) save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def test_odfdeconv(): SNR = 100 S0 = 1 _, fbvals, fbvecs = get_data('small_64D') bvals = np.load(fbvals) bvecs = np.load(fbvecs) gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) angles = [(0, 0), (90, 0)] S, sticks = multi_tensor(gtab, mevals, S0, angles=angles, fractions=[50, 50], snr=SNR) sphere = get_sphere('symmetric362') odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) e1 = 15.0 e2 = 3.0 ratio = e2 / e1 csd = ConstrainedSDTModel(gtab, ratio, None) csd_fit = csd.fit(S) fodf = csd_fit.odf(sphere) directions, _, _ = peak_directions(odf_gt, sphere) directions2, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions, directions2) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) assert_equal(directions2.shape[0], 2) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=10) assert_equal(len(w) > 0, True) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=8) assert_equal(len(w) > 0, False) csd_fit = csd.fit(np.zeros_like(S)) fodf = csd_fit.odf(sphere) assert_array_equal(fodf, np.zeros_like(fodf)) odf_sh = np.zeros_like(fodf) odf_sh[1] = np.nan fodf, it = odf_deconv(odf_sh, csd.R, csd.B_reg) assert_array_equal(fodf, np.zeros_like(fodf))
def test_odfdeconv(): SNR = 100 S0 = 1 _, fbvals, fbvecs = get_data('small_64D') bvals = np.load(fbvals) bvecs = np.load(fbvecs) gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) S, sticks = multi_tensor(gtab, mevals, S0, angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=SNR) sphere = get_sphere('symmetric724') mevecs = [all_tensor_evecs(sticks[0]).T, all_tensor_evecs(sticks[1]).T] odf_gt = multi_tensor_odf(sphere.vertices, [0.5, 0.5], mevals, mevecs) e1 = 15.0 e2 = 3.0 ratio = e2 / e1 csd = ConstrainedSDTModel(gtab, ratio, None) csd_fit = csd.fit(S) fodf = csd_fit.odf(sphere) directions, _, _ = peak_directions(odf_gt, sphere) directions2, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions, directions2) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) assert_equal(directions2.shape[0], 2) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=10) assert_equal(len(w) > 0, True) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=8) assert_equal(len(w) > 0, False)
def test_r2_term_odf_sharp(): SNR = None S0 = 1 angle = 45 # 45 degrees is a very tight angle to disentangle _, fbvals, fbvecs = get_data('small_64D') # get_data('small_64D') bvals = np.load(fbvals) bvecs = np.load(fbvecs) sphere = get_sphere('symmetric724') gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) angles = [(0, 0), (angle, 0)] S, sticks = multi_tensor(gtab, mevals, S0, angles=angles, fractions=[50, 50], snr=SNR) odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None) fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15., sh_order=8, lambda_=1., tau=0.1, r2_term=True) fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None) directions_gt, _, _ = peak_directions(odf_gt, sphere) directions, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions_gt, directions) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) # This should pass as well sdt_model = ConstrainedSDTModel(gtab, ratio=3 / 15., sh_order=8) sdt_fit = sdt_model.fit(S) fodf = sdt_fit.odf(sphere) directions_gt, _, _ = peak_directions(odf_gt, sphere) directions, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions_gt, directions) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2)
def test_r2_term_odf_sharp(): SNR = None S0 = 1 angle = 45 #45 degrees is a very tight angle to disentangle _, fbvals, fbvecs = get_data('small_64D') #get_data('small_64D') bvals = np.load(fbvals) bvecs = np.load(fbvecs) sphere = get_sphere('symmetric724') gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) angles = [(0, 0), (angle, 0)] S, sticks = multi_tensor(gtab, mevals, S0, angles=angles, fractions=[50, 50], snr=SNR) odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None) fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15., sh_order=8, lambda_=1., tau=0.1, r2_term=True) fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None) directions_gt, _, _ = peak_directions(odf_gt, sphere) directions, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions_gt, directions) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) # This should pass as well sdt_model = ConstrainedSDTModel(gtab, ratio=3/15., sh_order=8) sdt_fit = sdt_model.fit(S) fodf = sdt_fit.odf(sphere) directions_gt, _, _ = peak_directions(odf_gt, sphere) directions, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions_gt, directions) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2)
def test_odfdeconv(): SNR = 100 S0 = 1 _, fbvals, fbvecs = get_fnames('small_64D') bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs) gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) angles = [(0, 0), (90, 0)] S, _ = multi_tensor(gtab, mevals, S0, angles=angles, fractions=[50, 50], snr=SNR) sphere = get_sphere('symmetric362') odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) e1 = 15.0 e2 = 3.0 ratio = e2 / e1 csd = ConstrainedSDTModel(gtab, ratio, None) csd_fit = csd.fit(S) fodf = csd_fit.odf(sphere) directions, _, _ = peak_directions(odf_gt, sphere) directions2, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions, directions2) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) assert_equal(directions2.shape[0], 2) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=10) assert_equal(len(w) > 0, True) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=8) assert_equal(len(w) > 0, False) csd_fit = csd.fit(np.zeros_like(S)) fodf = csd_fit.odf(sphere) assert_array_equal(fodf, np.zeros_like(fodf)) odf_sh = np.zeros_like(fodf) odf_sh[1] = np.nan fodf, _ = odf_deconv(odf_sh, csd.R, csd.B_reg) assert_array_equal(fodf, np.zeros_like(fodf))
def test_odfdeconv(): SNR = 100 S0 = 1 _, fbvals, fbvecs = get_fnames('small_64D') bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs) gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) angles = [(0, 0), (90, 0)] S, _ = multi_tensor(gtab, mevals, S0, angles=angles, fractions=[50, 50], snr=SNR) sphere = get_sphere('symmetric362') odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) e1 = 15.0 e2 = 3.0 ratio = e2 / e1 csd = ConstrainedSDTModel(gtab, ratio, None) csd_fit = csd.fit(S) fodf = csd_fit.odf(sphere) directions, _, _ = peak_directions(odf_gt, sphere) directions2, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions, directions2) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) assert_equal(directions2.shape[0], 2) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=10) w_count = len(w) # A warning is expected from the ConstrainedSDTModel constructor # and additionnal warnings should be raised where legacy SH bases # are used assert_equal(w_count > 1, True) with warnings.catch_warnings(record=True) as w: ConstrainedSDTModel(gtab, ratio, sh_order=8) # Test that the warning from ConstrainedSDTModel # constructor is no more raised assert_equal(len(w) == w_count - 1, True) csd_fit = csd.fit(np.zeros_like(S)) fodf = csd_fit.odf(sphere) assert_array_equal(fodf, np.zeros_like(fodf)) odf_sh = np.zeros_like(fodf) odf_sh[1] = np.nan fodf, _ = odf_deconv(odf_sh, csd.R, csd.B_reg) assert_array_equal(fodf, np.zeros_like(fodf))
def test_r2_term_odf_sharp(): SNR = None S0 = 1 angle = 45 # 45 degrees is a very tight angle to disentangle _, fbvals, fbvecs = get_fnames('small_64D') # get_fnames('small_64D') bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs) sphere = default_sphere gtab = gradient_table(bvals, bvecs) mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003])) angles = [(0, 0), (angle, 0)] S, _ = multi_tensor(gtab, mevals, S0, angles=angles, fractions=[50, 50], snr=SNR) odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) with warnings.catch_warnings(): warnings.filterwarnings("ignore", message=descoteaux07_legacy_msg, category=PendingDeprecationWarning) odfs_sh = sf_to_sh(odf_gt, sphere, sh_order=8, basis_type=None) with warnings.catch_warnings(): warnings.filterwarnings("ignore", message=descoteaux07_legacy_msg, category=PendingDeprecationWarning) fodf_sh = odf_sh_to_sharp(odfs_sh, sphere, basis=None, ratio=3 / 15., sh_order=8, lambda_=1., tau=0.1, r2_term=True) with warnings.catch_warnings(): warnings.filterwarnings("ignore", message=descoteaux07_legacy_msg, category=PendingDeprecationWarning) fodf = sh_to_sf(fodf_sh, sphere, sh_order=8, basis_type=None) directions_gt, _, _ = peak_directions(odf_gt, sphere) directions, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions_gt, directions) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2) # This should pass as well with warnings.catch_warnings(): warnings.filterwarnings("ignore", message=descoteaux07_legacy_msg, category=PendingDeprecationWarning) sdt_model = ConstrainedSDTModel(gtab, ratio=3 / 15., sh_order=8) sdt_fit = sdt_model.fit(S) with warnings.catch_warnings(): warnings.filterwarnings("ignore", message=descoteaux07_legacy_msg, category=PendingDeprecationWarning) fodf = sdt_fit.odf(sphere) directions_gt, _, _ = peak_directions(odf_gt, sphere) directions, _, _ = peak_directions(fodf, sphere) ang_sim = angular_similarity(directions_gt, directions) assert_equal(ang_sim > 1.9, True) assert_equal(directions.shape[0], 2)
ratio = l01[1] / l01[0] #ratio = 0.2 from dipy.data import get_sphere sphere = get_sphere('symmetric724') # csd_model = ConstrainedSphericalDeconvModel(gtab, (evals, S0), regul_sphere = sphere) # csd_fit = csd_model.fit(data[25 - 10:25 + 10, 25 - 10:25 + 10, 25]) # csd_odf = csd_fit.odf(sphere) # from dipy.viz import fvtk # r = fvtk.ren() # fvtk.add(r, fvtk.sphere_funcs(csd_odf, sphere)) # fvtk.show(r) csdt_model = ConstrainedSDTModel(gtab, ratio, regul_sphere = sphere) csdt_fit = csdt_model.fit(data[25 - 10:25 + 10, 25 - 10:25 + 10, 25]) csdt_odf = csdt_fit.odf(sphere) from dipy.viz import fvtk r = fvtk.ren() fvtk.clear(r) fvtk.add(r, fvtk.sphere_funcs(csdt_odf, sphere)) fvtk.show(r)