Temporal Registration in Application to In-utero MRI Time Series

03/06/2019
by   Ruizhi Liao, et al.
0

We present a robust method to correct for motion in volumetric in-utero MRI time series. Time-course analysis for in-utero volumetric MRI time series often suffers from substantial and unpredictable fetal motion. Registration provides voxel correspondences between images and is commonly employed for motion correction. Current registration methods often fail when aligning images that are substantially different from a template (reference image). To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We evaluate the utility of the temporal model in the context of in-utero MRI time series alignment by examining the accuracy of propagated segmentation label maps. Our results suggest that the proposed model captures accurately the temporal dynamics of transformations in in-utero MRI time series.

READ FULL TEXT

page 2

page 3

page 5

research
08/12/2016

Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations for in...
research
08/22/2019

Motion correction of dynamic contrast enhanced MRI of the liver

Motion correction of dynamic contrast enhanced magnetic resonance images...
research
12/08/2020

Automatic Registration and Convex Clustering of Time Series

Clustering of time series data exhibits a number of challenges not prese...
research
08/04/2022

Automatic Segmentation of the Placenta in BOLD MRI Time Series

Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can asse...
research
11/21/2021

Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)

We introduce an unsupervised deep manifold learning algorithm for motion...
research
03/31/2023

Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI

Recent quantitative parameter mapping methods including MR fingerprintin...
research
06/15/2021

Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning

Adhesions are an important cause of chronic pain following abdominal sur...

Please sign up or login with your details

Forgot password? Click here to reset