# Signal representation

Laplace inversion of the diffusion signal typically relies on regularization in order to guide the search for a suitable solution to the inversion problem [@Provencher:1982 ; @Kroeker:1986 ; @Whittall:1989 ; @Mitchell:2012]. However, the memory requirements of these algorithms make them impractical for high dimensions [@Sun:2005], imposing a need for data compression [@Venkataramanan:2002]. Besides, the choice of regularization term inherently influences the characteristics of the solution, potentially causing a mismatch between these characteristics and those of the underlying microstructure.

To circumvent these problems, diffusion tensor distribution imaging (DTD) [@Topgaard:2019] explores the space of diffusion tensors using a quasi-genetic approach that does not rely on regularization nor on assumptions regarding the nature of the voxel content (other than those of the $$\mathcal{P}(\mathbf{D})$$ description).

## Quasi-genetic inversion algorithm

### Discretization

To facilitate the numerical inversion of the above signal, the continuous distribution $$\mathcal{P}(\mathbf{D})$$ can be approximated by a discrete vector $$\mathbf{w}$$ containing the weights of $$\mathit{N}$$ distinct diffusion tensors. This allows us to rewrite the above multidimensional Laplace transform as

$\mathcal{S}_m = \sum_{n=1}^{N} w_n\, \exp(-\mathbf{b}_m:\mathbf{D}_n) \, ,$

where $$\mathcal{S}_\mathit{m}$$ is the $$\mathit{m}$$-th signal amplitude measured with the encoding tensor $$\mathbf{b}_\mathit{m}$$, and $$\mathit{w}_\mathit{n}$$ is the weight of the $$\mathit{n}$$-th component of the discretized DTD. The weights $$w_n$$ are normalized so that $$\sum_{n=1}^{N} w_n = \mathcal{S}_0$$, implying that $$w_n/\sum_{m=1}^{N} w_m$$ corresponds to the signal fraction of the $$\mathit{n}$$-th component of $$\mathcal{P}(\mathbf{D})$$.

### Non-negative least-squares minimization

Let us denote by $$M$$ the number of acquisition points. Since $$\mathit{M}$$ is typically larger than the number $$\mathit{N}$$ of unknowns, inverting the above equation is an overdetermined system. The estimation of $$\mathbf{w}$$ is thus formulated as a non-negative linear least squares (NNLS) problem [@Lawson_book:1974]

$\mathbf{w} = \underset{\mathbf{w}^\prime\geq 0}{\mathrm{argmin}} \, \Vert \mathbf{s}-\mathbf{K}\cdot\mathbf{w}^\prime \Vert_2 \, ,$

where $$\Vert \cdot \Vert_2$$ denotes the Euclidian norm, $$\mathbf{w}$$ is the $$\mathit{N} \times 1$$ sought-for probability vector, $$\mathbf{s}$$ is the $$\mathit{M} \times 1$$ vector containing the signal amplitude measurements, and $$\mathbf{K}$$ is the $$\mathit{M} \times \mathit{N}$$ matrix whose elements correpond to the various signal decays. While the above non-negativity constraint could be seen as a form of regularization, it merely enforces the fact that the weights $$w_n$$ are positive numbers.

### Monte-Carlo inversion

Overall, the signal inversion is performed using a quasi-genetic process called “Monte Carlo inversion”, introduced in the field of physical chemistry [@Prange:2009] and detailed in previous works [@Topgaard:2019 ; @Reymbaut_accuracy_precision:2020]. Its main steps read as follows:

• Proliferation - The algorithm selects a random set $$\{\mathbf{D}_{\mathit{n}}\}_{1\leq \mathit{n} \leq \mathit{N}_\mathrm{in}}$$ consisting of $$\mathit{N}_\mathrm{in}$$ axisymmetric diffusion tensors (diffusion components) within the following bounds:

• $$-11 \leq \mathrm{log}_{10}(\mathit{D}_{\parallel} / \mathrm{m}^{2}\mathrm{s}^{-1}) \leq -8.3$$.
• $$-11 \leq \mathrm{log}_{10}(\mathit{D}_{\perp} / \mathrm{m}^{2}\mathrm{s}^{-1}) \leq -8.3$$.
• $$\ 0 \leq \mathrm{cos}(\theta) \leq 1$$.
• $$\ 0 \leq \phi \leq 2\pi$$.

Note that generating random values of $$\cos\theta$$ instead of $$\theta$$ enables to actually consider random orientations on the unit sphere.

The algorithm then estimates the associated set of weights $$\{\mathit{w}_\mathit{n}\}_{1\leq\mathit{n}\leq\mathit{N}_\mathrm{in}}$$ via a NNLS minimization routine, storing the components with non-zero weights $$\mathit{w}_\mathit{n}$$. This process, called “proliferation”, is repeated for $$\mathit{N}_\mathrm{p}$$ distinct rounds. At each new round, the new set of random components is appended to the previous set of "surviving" components before the NNLS minimization.

• Mutation/extinction - The set of components $$\{\mathbf{D}_{\mathit{n}}\}$$ obtained at the end of all proliferation rounds is subjected to a small random perturbation, called “mutation”. In other words, the characteristics $$D_{\parallel}$$, $$D_{\perp}$$, $$\theta$$ and $$\phi$$ of each component is randomly altered by a small amount. This marks the beginning of the "extinction" step, wherein diffusion components compete with their respective mutations on the basis of lowest residual sums of squares. The winning components are kept for the next mutation/extinction round. The mutation/extinction steps are repeated $$\mathit{N}_\mathrm{m}$$ times.

• Final trimming - Finally, the $$\mathit{N}_\mathrm{out}$$ components with highest weights are taken as a solution $$\{ (\mathbf{D}_n, w_n) \}_{1\leq \mathit{n}\leq \mathit{N}_\mathrm{out}} \equiv \{(\mathit{D}_{\parallel,\mathit{n}}, \mathit{D}_{\perp,\mathit{n}} , \theta_\mathit{n}, \phi_\mathit{n}, \mathit{w}_\mathit{n})\}_{1\leq \mathit{n}\leq \mathit{N}_\mathrm{out}}$$ of the inversion problem.

Typically, one uses $$\mathit{N}_\mathrm{in} = 200$$, $$\mathit{N}_\mathrm{p} = 20$$, $$\mathit{N}_\mathrm{m} = 20$$ and $$\mathit{N}_\mathrm{out} = 50$$. Such a large value of $$\mathit{N}_\mathrm{out}$$ ensures that voxel contents with low orientational order remain properly captured (as each relevant sub-voxel diffusion direction should be represented by at least one component).

While the final trimming of $$\mathit{N}_\mathrm{out}$$ components might seem restrictive, it concludes the rather random exploration of around $$\mathit{N}_\mathrm{in}(\mathit{N}_\mathrm{p}+\mathit{N}_\mathrm{m})$$ components. As an order of magnitude, using the previous numbers gives $$\mathit{N}_\mathrm{in}(\mathit{N}_\mathrm{p}+\mathit{N}_\mathrm{m}) = 8\,000$$ explored components.

## Bootstrapping

Embracing the inherent ill-conditioning of Laplace inversion problems, the DTD method is usually performed using bootstrapping with replacement [@Efron:1979 ; @Efron:1997 ; @de_Kort:2014] on the acquired data. For each voxel, this results in estimating an ensemble of $$\mathit{N}_\mathrm{b}$$ plausible sets of components, also called “bootstrap solutions”, each denoted by $$\{(\mathit{D}_{\parallel,\mathit{n}}, \mathit{D}_{\perp,\mathit{n}} , \theta_\mathit{n}, \phi_\mathit{n}, \mathit{w}_\mathit{n})\}_{1\leq \mathit{n}\leq \mathit{N}_\mathrm{out}}$$.