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Preparation and In Vitro Characterization of Dendrimer-based Contrast Agents for Magnetic Resonance Imaging

Preparation and In Vitro Characterization of Dendrimer-based Contrast Agents for Magnetic Resonance Imaging

Published on: December 4, 2016

A compressed sensing approach for MR tissue contrast synthesis.

Snehashis Roy¹, Aaron Carass, Jerry Prince

¹Image Analysis and Communication Laboratory, Dept. of Electrical and Computer Engg., The Johns Hopkins University, USA. snehashisr@jhu.edu

Information Processing in Medical Imaging : Proceedings of the ... Conference

|July 19, 2011

Summary

Synthesize missing magnetic resonance (MR) neuroimaging contrasts using a novel atlas-based method. This approach improves image analysis consistency across diverse scanners and studies, aiding multi-site research.

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Area of Science:

Medical Imaging
Neuroimaging
Computer Vision

Background:

Magnetic resonance (MR) neuroimaging data set tissue contrast significantly impacts image analysis tasks like registration and segmentation.
Inconsistent tissue contrast in MR data, caused by operator, software, or hardware variations, hinders reliable image analysis.
Missing desired MR tissue contrasts in datasets due to cost, time, or patient factors limits comprehensive study analysis.

Purpose of the Study:

To develop a method for synthesizing missing MR tissue contrasts from available acquired images.
To normalize multi-site, multi-scanner neuroimaging data into a common intensity space for improved analysis.
To enhance the consistency of image analysis tasks, such as segmentation, across diverse MR datasets.

Main Methods:

A novel method utilizing an atlas with the desired contrast and a patch-based compressed sensing strategy is described.
The approach synthesizes missing MR tissue contrasts from existing acquired images.
Application includes synthesizing specific tissue contrasts from multiple studies using a single atlas for data normalization.

Main Results:

Experiments on real-world data from different scanners and pulse sequences demonstrate improved segmentation consistency.
The method successfully synthesizes missing MR tissue contrasts, normalizing data into a common intensity space.
Demonstrated value in pooling multi-site, multi-scanner neuroimaging studies.

Conclusions:

The described method effectively synthesizes missing MR tissue contrasts, addressing a key challenge in neuroimaging.
This technique normalizes data across different acquisition parameters, enhancing consistency for large-scale studies.
The approach offers significant potential for improving the reliability and comparability of multi-site neuroimaging research.