Source code for sigpy.mri.util

# -*- coding: utf-8 -*-
"""MRI utilities.
import numpy as np
import sigpy as sp

__all__ = ['get_cov', 'whiten', 'tseg_off_res_b_ct', 'apply_tseg']

[docs]def get_cov(noise): """Get covariance matrix from noise measurements. Args: noise (array): Noise measurements of shape [num_coils, ...] Returns: array: num_coils x num_coils covariance matrix. """ num_coils = noise.shape[0] X = noise.reshape([num_coils, -1]) X -= np.mean(X, axis=-1, keepdims=True) cov = np.matmul(X, X.T.conjugate()) return cov
[docs]def whiten(ksp, cov): """Whitens k-space measurements. Args: ksp (array): k-space measurements of shape [num_coils, ...] cov (array): num_coils x num_coils covariance matrix. Returns: array: whitened k-space array. """ num_coils = ksp.shape[0] x = ksp.reshape([num_coils, -1]) L = np.linalg.cholesky(cov) x_w = np.linalg.solve(L, x) ksp_w = x_w.reshape(ksp.shape) return ksp_w
def tseg_off_res_b_ct(b0, bins, lseg, dt, T): """ Creates B and Ct matrices needed for time-segmented off-resonance compensation. Args: b0 (array): inhomogeneity matrix. bins (int): number of histogram bins to use. lseg (int): number of time segments. dt (float): hardware dwell time (ms). T (float): length of pulse (ms). Returns: 2-element tuple containing - **B** (*array*): temporal interpolator. - **Ct** (*array*): off-resonance phase at each time segment center. """ # create time vector t = np.linspace(0, T, hist_wt, bin_edges = np.histogram(np.imag(2j * np.pi * np.concatenate(b0)), bins) # Build B and Ct bin_centers = bin_edges[1:] - bin_edges[1]/2 zk = 0 + 1j * bin_centers tl = np.linspace(0, lseg, lseg) / lseg * T / 1000 # time seg centers # calculate off-resonance phase @ each time seg, for hist bins ch = np.exp(-np.expand_dims(tl, axis=1) @ np.expand_dims(zk, axis=0)) w = np.diag(np.sqrt(hist_wt)) p = np.linalg.pinv(w @ np.transpose(ch)) @ w b = p @ np.exp(-np.expand_dims(zk, axis=1) @ np.expand_dims(t, axis=0) / 1000) b = np.transpose(b) b0_v = np.expand_dims(2j * np.pi * np.concatenate(b0), axis=0) ct = np.transpose(np.exp(-np.expand_dims(tl, axis=1) @ b0_v)) return b, ct def apply_tseg(array_in, coord, b, ct, fwd=True): """Apply the temporal interpolator and phase shift maps calculated Args: array_in (array): array to apply correction to. coord (array): coordinates for noncartesian trajectories. [Nt 2]. b (array): temporal interpolator. ct (array): off-resonance phase at each time segment center. fwd (Boolean): indicates forward direction (img -> kspace) or backward (kspace->img) Returns: out (array): array with correction applied. """ # get number of time segments from B input. lseg = b.shape[1] dim = array_in.shape[0] out = 0 if fwd: for ii in range(lseg): ctd = np.reshape(ct[:, ii] * array_in.flatten(), (dim, dim)) out = out + b[:, ii] * sp.fourier.nufft(ctd, coord * 20) else: for ii in range(lseg): ctd = np.reshape(np.conj(ct[:, ii]) * array_in.flatten(), (dim, dim)) out = out + sp.fourier.nufft(ctd, coord * 20) * np.conj(b[:, ii]) return np.expand_dims(out, 1)