A Resolution Enhancement Plug-in for Deformable Registration of Medical Images
Kaicong Sun, Sven Simon

TL;DR
This paper introduces a CNN-based resolution enhancement plug-in (REM) for deformable medical image registration, improving accuracy and image quality by integrating super-resolution as a preprocessing step.
Contribution
The paper presents a novel CNN-based REM module that can be integrated into registration networks, enhancing resolution and registration accuracy in medical imaging.
Findings
REM improves registration accuracy on brain MRI datasets.
REM generates higher-resolution images useful for diagnosis.
Effective across different upscaling factors.
Abstract
Image registration is a fundamental task for medical imaging. Resampling of the intensity values is required during registration and better spatial resolution with finer and sharper structures can improve the resampling performance and hence the registration accuracy. Super-resolution (SR) is an algorithmic technique targeting at spatial resolution enhancement which can achieve an image resolution beyond the hardware limitation. In this work, we consider SR as a preprocessing technique and present a CNN-based resolution enhancement module (REM) which can be easily plugged into the registration network in a cascaded manner. Different residual schemes and network configurations of REM are investigated to obtain an effective architecture design of REM. In fact, REM is not confined to image registration, it can also be straightforwardly integrated into other vision tasks for enhanced…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Advanced Image Processing Techniques · Medical Image Segmentation Techniques
MethodsDense Connections · Q-Learning · Convolution · Deep Q-Network · Random Ensemble Mixture
