An in vivo validation dataset for dynamic volumetric MRI

TL;DR

This paper introduces a publicly available in vivo dataset with fully sampled and undersampled dynamic volumetric MRI data, enabling validation and development of reconstruction algorithms for motion imaging.

Contribution

It provides the first in vivo dataset with controlled deformations, fully sampled ground truth, and multiple subjects to facilitate validation of dynamic MRI reconstruction methods.

Findings

Reconstructed images matched fully sampled validation data.

Dataset includes data from nine volunteers with controlled muscle deformations.

Supports development of advanced reconstruction algorithms.

Abstract

Dynamic volumetric MRI provides valuable information on in vivo motion and biomechanics, with applications spanning cardiac, musculoskeletal, or pulmonary imaging, amongst others. Developing reconstruction methods for time-resolved volumetric MRI is challenging due to the inherently slow acquisition process of MRI, which makes it an active area of research. However, in vivo validation of these methods remains challenging due to the lack of publicly available datasets with fully sampled ground-truth images. Here, we present a publicly available in vivo dataset designed to facilitate the development and validation of dynamic volumetric MRI reconstruction algorithms. Controlled and repeatable deformations of the muscles in the thigh were induced using a pneumatic pressure cuff, enabling the acquisition of both undersampled dynamic data and fully sampled validation images. The dataset comprises multichannel undersampled k-space data from nine healthy volunteers across four different dynamic deformations, with fully sampled validation data for one deformation. Additionally, an anatomical reference scan and muscle segmentation masks are provided for each subject. To illustrate a possible image reconstruction and validation approach, a binning-based reconstruction was performed on the undersampled data from six dynamic repetitions. The resulting images were consistent with the corresponding fully sampled validation images. This dataset offers possibilities for validating and advancing time-resolved volumetric MRI reconstruction methods.