Doctoral Thesis: A Robust and Efficient Framework for Slice-to-Volume Reconstruction: Application to Fetal MRI

Junshen Xu

Abstract: 

Volumetric reconstruction in presence of motion is a challenging problem in medical imaging. When imaging moving targets, many modalities are limited to fast 2D imaging techniques that provide cross-sectional snapshots (2D images) of the subject with an attempt to freeze in-plane motion. However, inter-slice movement results in slice misalignment in 3D space, \ie, each image being an independent slice that fails to form a coherent volume for diagnosis and analysis. To this end, slice-to-volume reconstruction (SVR) has been proposed to reconstruct a high-quality 3D volume from misaligned 2D observations by performing inter-slice motion correction and super-resolution reconstruction. Existing SVR algorithms, however, have a limited capture range of slice motion and are time-consuming, particularly when producing high-resolution volumes.

This thesis proposes a motion-robust and efficient machine learning framework for SVR, motivated by the application of magnetic resonance imaging (MRI) in assessing fetal brain development. We first introduce a slice-to-volume registration transformer that models input slices as a sequence and performs inter-slice motion correction by simultaneously predicting rigid transformations of all images in 3D space. We then reformulate the reconstruction problem using implicit neural representation, where the underlying volume is represented by a continuous function of 3D coordinates. This resolution-agnostic approach allows efficient reconstruction of high-resolution volumes. Finally, we extend this method to data that suffer from non-rigid motion by introducing an implicit motion field that captures slice-dependent deformation. These advances together enable robust and efficient 3D reconstruction and visualization in fetal MRI, benefiting diagnosis and downstream analysis. Additionally, the proposed framework has the potential for broader clinical implications in various applications that involve similar volumetric reconstruction problems.

Details

• Date: Wednesday, April 19
• Time: 10:00 am - 11:30 am
• Category: Thesis Defense
• Location: 34-401A (Grier A)

Additional Location Details:

Thesis Supervisor: Prof. Elfar Adalsteinsson