A Joint Super-Resolution and Deformable Registration Network for 3D Brain Images

2021 ◽  
Author(s):  
Sheng Lan ◽  
Zhenhua Guo
Keyword(s):  
Super Resolution ◽  
Deformable Registration ◽  
Brain Images

A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research

2007 ◽  
Vol 26 (4) ◽  
pp. 452-461 ◽  
Author(s):  
Daniel Schwarz ◽  
Tomas Kasparek ◽  
Ivo Provaznik ◽  
Jiri Jarkovsky
Keyword(s):  
Deformable Registration ◽  
Brain Images ◽  
Registration Method ◽  
Mri Brain ◽  
Automated Morphometry ◽  
Mri Brain Images

Learning-based deformable registration of MR brain images

2006 ◽  
Vol 25 (9) ◽  
pp. 1145-1157 ◽  
Author(s):  
Guorong Wu ◽  
Feihu Qi ◽  
Dinggang Shen
Keyword(s):  
Deformable Registration ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

scholarly journals Arbitrary Scale Super-Resolution for Medical Images

2021 ◽  
pp. 2150037
Author(s):  
Jin Zhu ◽  
Chuan Tan ◽  
Junwei Yang ◽  
Guang Yang ◽  
Pietro Lio’
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Perceptual Quality ◽  
Clinical Image ◽  
Brain Images ◽  
Computed Tomography Images ◽  
Mr Brain Images ◽  
Mr Brain

Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discussed in medical image processing, because of their potential to achieve high-quality, high spatial resolution images without the cost of additional scans. However, most existing methods are designed for scale-specific SR tasks and are unable to generalize over magnification scales. In this paper, we propose an approach for medical image arbitrary-scale super-resolution (MIASSR), in which we couple meta-learning with generative adversarial networks (GANs) to super-resolve medical images at any scale of magnification in [Formula: see text]. Compared to state-of-the-art SISR algorithms on single-modal magnetic resonance (MR) brain images (OASIS-brains) and multi-modal MR brain images (BraTS), MIASSR achieves comparable fidelity performance and the best perceptual quality with the smallest model size. We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT). The source code of our work is also public. Thus, MIASSR has the potential to become a new foundational pre-/post-processing step in clinical image analysis tasks such as reconstruction, image quality enhancement, and segmentation.

scholarly journals Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaodan Sui ◽  
Yuanjie Zheng ◽  
Yunlong He ◽  
Weikuan Jia
Keyword(s):  
Anatomical Variations ◽  
Deformable Registration ◽  
Deformation Field ◽  
Image Pair ◽  
Imaging Analysis ◽  
Brain Images ◽  
Image Guided ◽  
Mr Brain Images ◽  
Image Pairs ◽  
Mr Brain

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

Deformable Registration-Based Super-resolution for Isotropic Reconstruction of 4-D MRI Volumes

2017 ◽  
Vol 21 (6) ◽  
pp. 1617-1624 ◽  
Author(s):  
Geetha Soujanya V. N. Chilla ◽  
Cher Heng Tan ◽  
Chueh Loo Poh
Keyword(s):  
Super Resolution ◽  
Deformable Registration
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