Spherical Harmonics (SH) are functions defined on the sphere. A collection of SH can be used as a basis function to represent and reconstruct any function on the surface of a unit sphere.

Spherical harmonics are orthonormal functions defined by:

\[Y_l^m(\theta, \phi) = \sqrt{\frac{2l + 1}{4 \pi} \frac{(l - m)!}{(l + m)!}} P_l^m( cos \theta) e^{i m \phi}\]

where \(l\) is the order, \(m\) is the degree, \(P_l^m\) is an associated \(l\)-th order, \(m\)-th degree Legendre polynomial, and \((\theta, \phi)\) is the representation of the direction vector in spherical coordinates. The relation between \(Y_l^{m}\) and \(Y_l^{-m}\) is given by:

\[Y_l^{-m}(\theta, \phi) = (-1)^m \overline{Y_l^m}\]

where \(\overline{Y_l^m}\) is the complex conjugate of \(Y_l^m\) defined as \(\overline{Y_l^m} = \Re(Y_l^m) - \Im(Y_l^m)\).

A function \(f(\theta, \phi)\) can be represented using a spherical harmonics basis using the spherical harmonics coefficients \(a_l^m\), which can be computed using the expression:

\[a_l^m = \int_S f(\theta, \phi) Y_l^m(\theta, \phi) ds\]

Once the coefficients are computed, the function \(f(\theta, \phi)\) can be computed as:

\[f(\theta, \phi) = \sum_{l = 0}^{\infty} \sum_{m = -l}^{l} a^m_l Y_l^m(\theta, \phi)\]

In HARDI, the Orientation Distribution Function (ODF) is a function on the
sphere. Therefore, SH functions offer the ideal framework for reconstructing
the ODF. Descoteaux *et al.* 1 use the Q-Ball Imaging (QBI) formalization
to recover the ODF, while Tournier *et al.* 2 use the Spherical Deconvolution
(SD) framework.

Several modified SH bases have been proposed in the diffusion imaging literature
for the computation of the ODF. DIPY implements two of these in the
`shm`

module. Below are the formal definitions taken
directly from the literature.

The basis proposed by Descoteaux

*et al.*1:

\[\begin{split}Y_i(\theta, \phi) =
\begin{cases}
\sqrt{2} \Re(Y_l^{m}(\theta, \phi)) & -l \leq m < 0, \\
Y_l^0(\theta, \phi) & m = 0, \\
\sqrt{2} \Im(Y_l^m(\theta, \phi)) & 0 < m \leq l
\end{cases}\end{split}\]

The basis proposed by Tournier

*et al.*2:

\[\begin{split}Y_i(\theta, \phi) =
\begin{cases}
\Im(Y_l^{m}(\theta, \phi)) & -l \leq m < 0, \\
Y_l^0(\theta, \phi) & m = 0, \\
\Re(Y_{l}^m(\theta, \phi)) & 0 < m \leq l
\end{cases}\end{split}\]

In both cases, \(\Re\) denotes the real part of the spherical harmonic basis, and \(\Im\) denotes the imaginary part. The SH bases are both orthogonal and real. Moreover, the descoteaux07 basis is orthonormal.

In both cases, \(\Re\) denotes the real part of the SH basis, and \(\Im\) denotes
the imaginary part. By alternately selecting the real or imaginary part of the
original SH basis, the modified SH bases have the properties of being both
orthogonal and real. Moreover, due to the presence of the \(\sqrt{2}\) factor,
the basis proposed by Descoteaux *et al.* is orthonormal.

The SH bases implemented in DIPY for versions 1.2 and below differ slighly from the litterature. Their implementation is given below.

The

`descoteaux07`

basis is based on the one proposed by Descoteaux*et al.*1 and is given by:

\[\begin{split}Y_i(\theta, \phi) =
\begin{cases}
\sqrt{2} \Re(Y_l^{|m|}(\theta, \phi)) & -l \leq m < 0, \\
Y_l^0(\theta, \phi) & m = 0, \\
\sqrt{2} \Im(Y_l^m(\theta, \phi)) & 0 < m \leq l
\end{cases}\end{split}\]

The

`tournier07`

basis is based on the one proposed by Tournier*et al.*2 and is given by:

\[\begin{split}Y_i(\theta, \phi) =
\begin{cases}
\Im(Y_l^{|m|}(\theta, \phi)) & -l \leq m < 0, \\
Y_l^0(\theta, \phi) & m = 0, \\
\Re(Y_{l}^m(\theta, \phi)) & 0 < m \leq l
\end{cases}\end{split}\]

These bases differ from the literature by the presence of an absolute value around \(m\) when \(m < 0\). Due to relations \(-p = |p| ; \forall p < 0\) and \(Y_l^{-m}(\theta, \phi) = (-1)^m \overline{Y_l^m}\), the effect of this change is a sign flip for the SH functions of even degree \(m < 0\). This has no effect on the mathematical properties of each basis.

The `tournier07`

SH basis defined above is the basis used in MRtrix 0.2 3.
However, the omission of the \(\sqrt{2}\) factor seen in the basis from Descoteaux
*et al.* 1 makes it non-orthonormal. For this reason, the MRtrix3 4 SH
basis uses a new basis including the normalization factor.

Since DIPY 1.3, the `descoteaux07`

and `tournier07`

SH bases have been
updated in order to agree with the literature and the latest MRtrix3
implementation. While previous bases are still available as *legacy* bases,
the `descoteaux07`

and `tournier07`

bases now default to:

\[\begin{split}Y_i(\theta, \phi) =
\begin{cases}
\sqrt{2} \Re(Y_l^{m}(\theta, \phi)) & -l \leq m < 0, \\
Y_l^0(\theta, \phi) & m = 0, \\
\sqrt{2} \Im(Y_l^m(\theta, \phi)) & 0 < m \leq l
\end{cases}\end{split}\]

for the `descoteaux07`

basis and

\[\begin{split}Y_i(\theta, \phi) =
\begin{cases}
\sqrt{2} \Im(Y_l^{|m|}(\theta, \phi)) & -l \leq m < 0, \\
Y_l^0(\theta, \phi) & m = 0, \\
\sqrt{2} \Re(Y_{l}^m(\theta, \phi)) & 0 < m \leq l
\end{cases}\end{split}\]

for the `tournier07`

basis. Both bases are very similar, with their only
difference being the sign of \(m\) for which the imaginary and real parts of
the spherical harmonic \(Y_{l}^m\) are used.

In practice, a maximum order \(k\) is used to truncate the SH series. By only taking into account even order SH functions, the above bases can be used to reconstruct symmetric spherical functions. The choice of an even order is motivated by the symmetry of the diffusion process around the origin.

Both bases are also available as full SH bases, where odd order SH functions are also taken into account when reconstructing a spherical function. These full bases can successfully reconstruct asymmetric signals as well as symmetric signals.

- 1(1,2,3,4)
Descoteaux, M., Angelino, E., Fitzgibbons, S. and Deriche, R. Regularized, Fast, and Robust Analytical Q‐ball Imaging. Magn. Reson. Med. 2007;58:497-510.

- 2(1,2,3)
Tournier J.D., Calamante F. and Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007;35(4):1459–1472.

- 3
Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage. 2019 Nov 15;202:116-137.

- 4
Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage. 2019 Nov 15;202:116-137.