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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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In materials that exhibit elastic and plastic behavior, known as elastoplastic materials, residual stresses can accumulate when these materials experience plastic deformation. This deformation arises from either high levels of shearing stress or significant strains. Residual stresses are internal stresses that persist within a material after removing the external force causing deformation. This phenomenon is demonstrated when observing the behavior of a shaft under torque; notably, the...
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Related Experiment Video

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Contrast Enhanced Vessel Imaging using MicroCT
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Undersampled MR image reconstruction using an enhanced recursive residual network.

Lijun Bao1, Fuze Ye1, Congbo Cai1

  • 1Department of Electronic Science, Xiamen University, Xiamen 361000, China.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|July 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an Enhanced Recursive Residual Network (ERRN) for superior MRI reconstruction from undersampled data. ERRN significantly improves image quality and restores fine features, especially at high undersampling rates.

Keywords:
Convolutional neural networkError-correctionFeature guidanceRecursive residual learningUndersampled MRI reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Aggressive undersampling in Magnetic Resonance Imaging (MRI) hinders the recovery of high-quality images with fine details.
  • Existing methods struggle to reconstruct diagnostically useful images when significant undersampling is applied.

Purpose of the Study:

  • To propose an Enhanced Recursive Residual Network (ERRN) for improved MRI reconstruction.
  • To adapt ERRN for Compressed Sensing (CS) MRI and Super Resolution (SR) MRI applications.
  • To enhance image quality and feature recovery in undersampled MRI.

Main Methods:

  • Developed ERRN by integrating high-frequency feature guidance, an error-correction unit, and dense connections into a basic recursive residual network.
  • Incorporated application-specific error-correction units: data consistency for CS-MRI and back projection for SR-MRI.
  • Evaluated ERRN on diverse datasets including real-valued brain, complex-valued knee, pathological brain, and in vivo rat brain data.

Main Results:

  • ERRN demonstrated superior image reconstruction performance across all tested datasets and undersampling schemes.
  • The network effectively restored structural features and achieved the highest image quality metrics compared to state-of-the-art methods.
  • Performance was particularly notable at undersampling rates exceeding 5-fold.

Conclusions:

  • The proposed ERRN framework offers a flexible architecture with fewer parameters, leading to outstanding performance in various undersampling scenarios.
  • ERRN facilitates real-time reconstruction on MRI scanners by reducing overfitting and improving generalization.
  • This approach significantly enhances the potential for high-quality MRI reconstruction even with aggressive undersampling.