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Deep Neural Networks for Image-Based Dietary Assessment

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SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.

Fang Liu¹, Alexey Samsonov¹, Lihua Chen²

¹Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.

Magnetic Resonance in Medicine

|June 6, 2019

Summary

A new deep learning framework, SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure), enhances magnetic resonance imaging (MRI) reconstruction. Its sampling-augmented training improves robustness against undersampling pattern variations, leading to better artifact removal and image quality.

Keywords:

data augmentation data cycle-consistent adversarial network deep learning image reconstruction reconstruction robustness sampling discrepancy

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Accelerated MRI acquisition relies on undersampling, which introduces aliasing artifacts.
Robust reconstruction methods are crucial for maintaining image quality despite variations in undersampling patterns.

Purpose of the Study:

To develop and evaluate SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure), a novel deep learning framework for efficient and robust MR image reconstruction.
To improve the network's ability to handle discrepancies between training and inference sampling patterns.

Main Methods:

SANTIS combines a data cycle-consistent adversarial network, end-to-end convolutional neural network mapping, and data fidelity enforcement.
A key feature is the sampling-augmented training strategy, involving extensive variation of undersampling patterns during training.
The framework was evaluated on accelerated knee and liver imaging using Cartesian and golden-angle radial trajectories, respectively, and tested for dynamic contrast-enhanced imaging via transfer learning.

Main Results:

SANTIS demonstrated superior reconstruction performance compared to conventional methods, achieving lower errors and greater image sharpness.
It showed comparable performance to standard learning-based methods but with significantly improved robustness against sampling pattern discrepancies.
Encouraging results were obtained for reconstructing liver images across different contrast phases.

Conclusions:

The sampling-augmented training strategy in SANTIS effectively removes undersampling artifacts, enhancing robustness.
The framework's novel approach addresses the critical challenge of deep learning-based reconstruction robustness against training-inference discrepancies.