# Anisotropic functional Laplace deconvolution

## TL;DR

This paper develops an adaptive wavelet-Laguerre estimator for three-dimensional functions from noisy Laplace convolution data, with applications to DCE imaging, demonstrating near-optimal theoretical performance and promising finite sample results.

## Contribution

It introduces a novel adaptive wavelet-Laguerre estimator for 3D Laplace deconvolution and establishes its near-optimality in Laguerre-Sobolev spaces.

## Key findings

- Estimator performs well in finite samples
- Achieves minimax optimality in theory
- Effective in DCE imaging applications

## Abstract

In the present paper we consider the problem of estimating a three-dimensional function $f$ based on observations from its noisy Laplace convolution. Our study is motivated by the analysis of Dynamic Contrast Enhanced (DCE) imaging data. We construct an adaptive wavelet-Laguerre estimator of $f$, derive minimax lower bounds for the $L^2$-risk when $f$ belongs to a three-dimensional Laguerre-Sobolev ball and demonstrate that the wavelet-Laguerre estimator is adaptive and asymptotically near-optimal in a wide range of Laguerre-Sobolev spaces. We carry out a limited simulations study and show that the estimator performs well in a finite sample setting. Finally, we use the technique for the solution of the Laplace deconvolution problem on the basis of DCE Computerized Tomography data.