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

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, i.e., each image being an independent slice that fails to form a coherent volume for diagnosis and analysis. bTo 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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Xu, Junshen

Advisor dc:contributor.advisor

• Adalsteinsson, Elfar

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151495

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151495