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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI.

Luyi Han¹, Tao Tan², Tianyu Zhang³

¹Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.

Medical Image Analysis

|December 3, 2023

Summary

This summary is machine-generated.

This study introduces a novel sequence-to-sequence (Seq2Seq) framework for magnetic resonance imaging (MRI) to learn efficient representations by identifying unique information in each sequence. This approach enables using top-ranking sequences for diagnosis with non-inferior performance.

Keywords:

Imaging differentiation MRI synthesis Multi-sequence MRI

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Medical Imaging
Machine Learning
Radiomics

Background:

Multi-sequence MRI provides complementary diagnostic information but contains redundant data, hindering efficient representation learning.
Existing methods struggle to extract unique information from individual MRI sequences for improved clinical tasks.

Purpose of the Study:

To develop a sequence-to-sequence (Seq2Seq) generation framework for imaging-differentiation representation learning in MRI.
To enable arbitrary 3D/4D MRI sequence generation and rank the importance of each sequence.
To identify unique information regions within MRI sequences for enhanced clinical utility.

Main Methods:

Proposed a Seq2Seq framework capable of generating arbitrary 3D/4D MRI sequences.
Developed a novel metric to rank sequence importance based on generation difficulty.
Utilized model's generation inability to extract unique information regions per sequence.
Validated on simulated, brain MRI, and breast MRI datasets.

Main Results:

Top-ranking MRI sequences can replace complete sets with comparable diagnostic performance.
Integrating MRI with the proposed imaging-differentiation map improves clinical prediction tasks.
Demonstrated enhanced performance in glioblastoma and breast cancer status prediction.

Conclusions:

The Seq2Seq framework effectively learns imaging-differentiation representations from multi-sequence MRI.
This approach optimizes MRI data utilization, potentially reducing scan time and improving diagnostic accuracy.
The method shows promise for advancing AI-driven clinical decision support in radiology.