def sdt_ms(gtab, data, affine, mask, ratio, sphere, min_angle=25.0, relative_peak_th=0.1):
gd1, gd2, gd3 = prepare_data_for_multi_shell(gtab, data, mask)
coeffs = []
invBs = []
for gd in [gd1, gd2, gd3]:
response, ratio = estimate_response(gd[0], gd[1],
affine, mask, fa_thr=0.7)
model = ConstrainedSDTModel(gd[0], ratio, sh_order=8)
peaks = pfm(model, gd[1], mask, sphere, False, min_angle, relative_peak_th)
coeffs.append(peaks.shm_coeff)
invBs.append(peaks.invB)
#print(peaks.invB)
coeffs3 = np.array(coeffs)
best_coeffs = max_abs(coeffs3)
odf = np.dot(best_coeffs, peaks.invB)
new_peaks = PeaksAndMetrics()
new_peaks.peak_dirs = dirs_from_odf(odf, sphere, min_separation_angle = min_angle, relative_peak_threshold = relative_peak_th)
new_peaks.invB = peaks.invB
new_peaks.shm_coeff = best_coeffs
return new_peaks
def csd_ms(gtab, data, affine, mask, response, sphere):
gd1, gd2, gd3 = prepare_data_for_multi_shell(gtab, data, mask)
coeffs = []
invBs = []
for gd in [gd1, gd2, gd3]:
response, ratio = estimate_response(gd[0], gd[1],
affine, mask, fa_thr=0.7)
model = ConstrainedSphericalDeconvModel(gd[0], response, sh_order=8)
peaks = pfm(model, gd[1], mask, sphere)
coeffs.append(peaks.shm_coeff)
invBs.append(peaks.invB)
#print(peaks.invB)
coeffs3 = np.array(coeffs)
best_coeffs = max_abs(coeffs3)
odf = np.dot(best_coeffs, peaks.invB)
new_peaks = PeaksAndMetrics()
new_peaks.peak_dirs = dirs_from_odf(odf, sphere)
new_peaks.invB = peaks.invB
new_peaks.shm_coeff = best_coeffs
return new_peaks
