From this post, Confidence regions on bivariate normal distributions using $\hat{\Sigma}_{MLE}$ or $\mathbf{S}$, the equation
$(\mathbf{x} - \boldsymbol{\mu})^{T} \mathbf{\Sigma}^{-1} (\mathbf{x} - \boldsymbol{\mu}) \leq \chi_{p, \alpha}^2$
can be described by the parametric curve
$ \mathbf{x} = \boldsymbol{\mu} + \sqrt{\chi_{p, \alpha}^2} \mathbf{L} \begin{bmatrix} \cos(\theta)\\ \sin(\theta) \end{bmatrix} $
for $ 0 < \theta < 2 \pi $
Is it possible to generalize to larger dimensions?
For example, for three dimensions
$ \mathbf{x} = \boldsymbol{\mu} + \sqrt{\chi_{p, \alpha}^2} \mathbf{L} \begin{bmatrix} \cos(\theta) \cos(\phi)\\ \cos(\theta) \sin(\phi)\\ \sin(\theta) \end{bmatrix} $
for $ 0 < \theta < \pi $ and $ 0 < \phi < 2 \pi $
And so on?