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Related Experiment Video

Updated: Jan 20, 2026

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network

Shanshan Wang¹, Ziwen Ke^1,2, Huitao Cheng¹

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

NMR in Biomedicine

|September 5, 2019

Summary

This study introduces DIMENSION, a new dynamic MRI method that uses k-space and spatial prior knowledge for faster, improved image reconstruction. It overcomes limitations of current techniques by integrating multi-supervised network training for better results.

Keywords:

compressed sensing deep learning dynamic MR imaging k-space prior multi-supervised

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Related Experiment Videos

Last Updated: Jan 20, 2026

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

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Published on: February 19, 2018

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Published on: March 3, 2023

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Published on: October 13, 2023

Area of Science:

Medical Imaging
Magnetic Resonance Imaging (MRI)
Computational Imaging

Background:

Dynamic MR imaging accelerates scan times by reconstructing images from incomplete k-space data.
Reconstruction is ill-posed, leading to challenges with existing methods like long processing times or limited prior knowledge utilization.

Purpose of the Study:

To develop an advanced dynamic MR imaging method for improved reconstruction quality and reduced scan time.
To integrate both k-space and spatial prior knowledge effectively using multi-supervised network training.

Main Methods:

Proposed a novel method named DIMENSION, incorporating a frequential prior network and a spatial prior network.
Implemented a multi-supervised network training strategy to constrain both frequency and spatial domain information.
Compared DIMENSION against classical and state-of-the-art methods on in vivo datasets.

Main Results:

DIMENSION achieved superior reconstruction results compared to existing methods.
The proposed method demonstrated a significant reduction in reconstruction time.
Improved image quality with better structural details was observed.

Conclusions:

DIMENSION offers an effective approach for dynamic MR image reconstruction by leveraging multi-supervised learning.
The method successfully integrates diverse prior knowledge for enhanced performance.
DIMENSION presents a promising advancement for accelerating dynamic MRI acquisition and analysis.