Optimal experimental design with k-space data: application to inverse hemodynamics

TL;DR

This paper introduces an optimal experimental design framework for selecting k-space sampling masks in MRI, significantly improving parameter estimation accuracy and reducing acquisition time in inverse hemodynamics applications.

Contribution

It is the first application of Optimal Experimental Design to k-space sampling pattern selection for MRI-based inverse problems, enhancing data efficiency.

Findings

Optimized masks outperform conventional sampling patterns.

Achieve 10x faster acquisition without loss of accuracy.

Improved parameter estimation accuracy and variance.

Abstract

Subject-specific cardiovascular models rely on parameter estimation using measurements such as 4D Flow MRI data. However, acquiring high-resolution, high-fidelity functional flow data is costly and taxing for the patient. As a result, there is growing interest in using highly undersampled MRI data to reduce acquisition time and thus the cost, while maximizing the information gain from the data. Examples of such recent work include inverse problems to estimate boundary conditions of aortic blood flow from highly undersampled k-space data. The undersampled data is selected based on a predefined sampling mask which can significantly influences the performance and the quality of the solution of the inverse problem. While there are many established sampling patterns to collect undersampled data, it remains unclear how to select the best sampling pattern for a given set of inference parameters. In this paper we propose an Optimal Experimental Design (OED) framework for MRI measurements in k-space, aiming to find optimal masks for estimating specific parameters directly from k-space. As OED is typically applied to sensor placement problems in spatial locations, this is, to our knowledge, the first time the technique is used in this context. We demonstrate that the masks optimized by employing OED consistently outperform conventional sampling patterns in terms of parameter estimation accuracy and variance, facilitating a speed-up of 10x of the acquisition time while maintaining accuracy.