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Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning.

Saiprasad Ravishankar¹, Jong Chul Ye², Jeffrey A Fessler³

¹Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering at Michigan State University, East Lansing, MI, 48824 USA.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers

|February 26, 2020

Summary

This summary is machine-generated.

This study reviews advanced medical image reconstruction techniques. It focuses on sparsity/low-rank models and machine learning for improved imaging quality and efficiency.

Keywords:

Compressed sensing Deep learning Dictionary learning Efficient algorithms Image reconstruction MRI Machine learning Multi-layer models Nonconvex optimization PET SPECT Sparse and low-rank models Structured models Transform learning X-ray CT

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Area of Science:

Medical Imaging
Image Reconstruction
Computational Science

Background:

Analytical methods like FBP and inverse Fourier transform are fast but have limitations.
Iterative reconstruction methods improve image quality but often use simple models.
Recent methods address modified data acquisition and utilize machine learning.

Purpose of the Study:

To review and highlight recent advancements in medical image reconstruction.
To focus on sparsity/low-rank models and data-driven machine learning approaches.
To discuss the evolution from analytical to adaptive reconstruction techniques.

Main Methods:

Exploration of sparsity and low-rank models for image reconstruction.
Investigation of data-driven and machine learning-based adaptive models.
Comparison of modern techniques with traditional analytical and iterative methods.

Main Results:

Sparsity and low-rank models enable reconstruction with modified data acquisition (e.g., reduced sampling).
Machine learning-based methods offer adaptive models, potentially surpassing traditional approaches.
These advanced methods aim to improve resolution-noise trade-off and reduce artifacts.

Conclusions:

The paper emphasizes the significance of sparsity, low-rank, and machine learning in modern medical image reconstruction.
These techniques are crucial for enhancing image quality and enabling faster, lower-dose scans.
Future directions likely involve further integration of AI and adaptive modeling.