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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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Updated: May 9, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Deep learning-based MRI reconstruction with Artificial Fourier Transform Network (AFTNet).

Yanting Yang¹, Yiren Zhang¹, Zongyu Li¹

¹Department of Biomedical Engineering, Columbia University, 500 W. 120th Street #351, New York, 10027, NY, United States.

Computers in Biology and Medicine

|May 6, 2025

Summary

This summary is machine-generated.

Artificial Fourier Transform Network (AFTNet) uses complex-valued deep learning to directly process frequency-domain data for superior accelerated MRI reconstruction. This innovative approach enhances image reconstruction and offers solutions for various imaging and spectroscopy inverse problems.

Keywords:

Deep learning MRI Reconstruction

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

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

Published on: November 8, 2012

Area of Science:

Medical Imaging
Deep Learning
Signal Processing

Background:

Deep complex-valued neural networks (CVNNs) excel in phase-based applications but haven't fully explored frequency-domain impacts.
Conventional accelerated MRI reconstruction often uses magnitude images or separates real/imaginary k-space data.

Purpose of the Study:

Introduce a unified complex-valued deep learning framework, Artificial Fourier Transform Network (AFTNet).
Enable direct processing of raw k-space data in the frequency domain using complex-valued operations.
Develop a cross-domain learning approach for frequency and image domains.

Main Methods:

Developed AFTNet, combining domain-manifold learning and CVNNs.
Processed raw k-space data directly in the frequency domain.
Utilized complex-valued operations for cross-domain mapping between frequency and image domains.

Main Results:

AFTNet achieved superior accelerated MRI reconstruction compared to existing methods.
Demonstrated effectiveness in denoised magnetic resonance spectroscopy (MRS) reconstruction.
Showcased applicability to datasets with various contrasts and preclinical studies.

Conclusions:

AFTNet offers an innovative alternative for solving inverse problems in imaging and spectroscopy.
The framework provides a valuable preprocessing component for preclinical studies.
Directly processing frequency-domain data with CVNNs enhances reconstruction quality.