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Super-resolution Imaging of Neuronal Dense-core Vesicles

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NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution.

Kaifeng Pang¹, Kai Zhao², Alex Ling Yu Hung³

¹Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, United States; Department of Radiological Sciences, University of California, Los Angeles, CA, 90095, United States.

Computers in Biology and Medicine

|November 27, 2024

Summary

Neural Explicit Representation (NExpR) offers fast, arbitrary-scale medical image super-resolution (SR) by using explicit analytical functions. This method achieves over 100x speedup compared to implicit neural representation techniques without compromising image quality.

Keywords:

Arbitrary-scale super-resolution Artificial intelligence Deep learning Medical image super-resolution Neural implicit representation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Medical image rescaling is crucial for interpretation at various resolutions.
Conventional deep learning super-resolution (SR) is limited to fixed scales.
Implicit Neural Representation (INR) enables arbitrary-scale SR but is computationally slow.

Purpose of the Study:

To introduce Neural Explicit Representation (NExpR) for efficient arbitrary-scale medical image SR.
To overcome the speed limitations of existing INR-based SR methods.
To achieve high-quality medical image rescaling with significantly reduced processing time.

Main Methods:

NExpR represents medical images using explicit analytical functions derived from low-resolution inputs.
A single neural network (NN) inference generates the analytical function parameters.
Arbitrary-scale SR images are obtained by evaluating these explicit functions.

Main Results:

NExpR achieves significant speedups, reducing rescaling time by over 100x (from 1 ms to 0.01 ms).
The method maintains or surpasses image quality compared to existing SR techniques.
Experiments on diverse datasets (MRI, CT) validate NExpR's effectiveness.

Conclusions:

NExpR provides a fast and effective solution for arbitrary-scale medical image super-resolution.
The explicit analytical representation offers a substantial advantage in processing speed.
NExpR demonstrates strong performance across various medical imaging modalities and datasets.