nibabel.nicom.dwiparams

Process diffusion imaging parameters

• q is a vector in Q space

• b is a b value

• g is the unit vector along the direction of q (the gradient direction)

Thus:

b = norm(q)

g = q / norm(q)

(norm(q) is the Euclidean norm of q)

The B matrix B is a symmetric positive semi-definite matrix. If q_est is the closest q vector equivalent to the B matrix, then:

B ~ (q_est . q_est.T) / norm(q_est)

Functions

B2q(B[, tol])	Estimate q vector from input B matrix B
nearest_pos_semi_def(B)	Least squares positive semi-definite tensor estimation
q2bg(q_vector[, tol])	Return b value and q unit vector from q vector q_vector