4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings

10/04/2019
by   Gino Gulamhussene, et al.
0

Purpose: We aim to develop a robust 4D MRI method for large FOVs enabling the extraction of irregular respiratory motion that is readily usable with all MRI machines and thus applicable to support a wide range of interventional settings. Method: We propose a 4D MRI reconstruction method to capture an arbitrary number of breathing states. It uses template updates in navigator slices and search regions for fast and robust vessel cross-section tracking. It captures FOVs of 255 mm x 320 mm x 228 mm at a spatial resolution of 1.82 mm x 1.82 mm x 4mm and temporal resolution of 200ms. To validate the method, a total of 38 4D MRIs of 13 healthy subjects were reconstructed. A quantitative evaluation of the reconstruction rate and speed of both the new and baseline method was performed. Additionally, a study with ten radiologists was conducted to assess the subjective reconstruction quality of both methods. Results: Our results indicate improved mean reconstruction rates compared to the baseline method (79.4% vs. 45.5%) and improved mean reconstruction times (24s vs. 73s) per subject. Interventional radiologists perceive the reconstruction quality of our method as higher compared to the baseline (262.5 points vs. 217.5 points, p=0.02). Conclusions: Template updates are an effective and efficient way to increase 4D MRI reconstruction rates and to achieve better reconstruction quality. Search regions reduce reconstruction time. These improvements increase the applicability of 4D MRI as base for seamless support of interventional image guidance in percutaneous interventions.

READ FULL TEXT

page 7

page 8

research
02/25/2022

Predicting 4D Liver MRI for MR-guided Interventions

Organ motion poses an unresolved challenge in image-guided interventions...
research
01/18/2023

Three-dimensional reconstruction and characterization of bladder deformations

Background and Objective: Pelvic floor disorders are prevalent diseases ...
research
11/04/2020

Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems

Purpose: A fast data-driven optimization approach, named bias-accelerate...
research
06/23/2020

Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness

The performance of traditional compressive sensing-based MRI (CS-MRI) re...
research
05/18/2017

Model-based Catheter Segmentation in MRI-images

Accurate and reliable segmentation of catheters in MR-gui- ded intervent...
research
08/28/2019

SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions

Purpose: This study demonstrates a proof of concept of a method for simu...
research
01/31/2019

Automated brain extraction of multi-sequence MRI using artificial neural networks

Brain extraction is a critical preprocessing step in the analysis of MRI...

Please sign up or login with your details

Forgot password? Click here to reset