Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding (1705.02596v1)

Abstract: Magnetic Resonance Imaging (MRI) offers high-resolution \emph{in vivo} imaging and rich functional and anatomical multimodality tissue contrast. In practice, however, there are challenges associated with considerations of scanning costs, patient comfort, and scanning time that constrain how much data can be acquired in clinical or research studies. In this paper, we explore the possibility of generating high-resolution and multimodal images from low-resolution single-modality imagery. We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis. The learning process requires only a few registered multimodal image pairs as the training set. Additionally, the quality of the joint dictionary learning can be improved using a larger set of unpaired images. To combine unpaired data from different image resolutions/modalities, a hetero-domain image alignment term is proposed. Local image neighborhoods are naturally preserved by operating on the whole image domain (as opposed to image patches) and using joint convolutional sparse coding. The paired images are enhanced in the joint learning process with unpaired data and an additional maximum mean discrepancy term, which minimizes the dissimilarity between their feature distributions. Experiments show that the proposed method outperforms state-of-the-art techniques on both SR reconstruction and simultaneous SR and cross-modality synthesis.

Overview of WEENIE: Simultaneous Super-Resolution and Cross-Modality Synthesis in MRI

The paper "Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding," co-authored by Yawen Huang, Ling Shao, and Alejandro F. Frangi, introduces an innovative approach in the field of medical imaging, particularly focusing on MRI. This research addresses two major challenges: the enhancement of image resolution (super-resolution, SR) and the synthesis of images across different modalities (cross-modality synthesis, CMS). The proposed method, named WEENIE, leverages weakly-supervised joint convolutional sparse coding to address these challenges concurrently, providing significant advancements over existing methods.

Methodology

The WEENIE algorithm is a seminal application of joint convolutional sparse coding in a weakly-supervised setting. This approach enables the simultaneous processing of SR and CMS tasks by employing a unified learning model. The methodology involves generating high-resolution and multimodal images from low-resolution single-modality images, an endeavor critical for optimizing clinical routines constrained by scanner availability, costs, and patient comfort.

Key Aspects:

• Weakly-Supervised Learning: WEENIE utilizes a small set of paired high-resolution/low-resolution multimodal image pairs, augmented by a larger dataset of unpaired images, to enhance the joint learning process. This diminishes the requirement for a vast array of fully aligned image pairs, thus overcoming common limitations in medical imaging applications.
• Joint Convolutional Sparse Coding: This method avoids the pitfalls of conventional patch-based sparse coding by considering whole images, ensuring consistency in local neighborhoods and reducing shift variance issues typically associated with patch-based approaches.
• Hetero-Domain Image Alignment: By incorporating an alignment term that bridges different modalities and resolutions, WEENIE ensures more reliable correspondences across diverse image data sources.

Results

The WEENIE framework demonstrates superior performance in both SR reconstruction and SRCMS tasks compared to existing state-of-the-art techniques, including sparse coding-based methods and convolutional neural network approaches. Quantitatively, the algorithm achieves higher PSNR and SSIM metrics, indicating enhanced reconstruction accuracy and visual fidelity. The experimental validation spans datasets such as IXI and NAMIC, emphasizing the method's robustness across different resolutions and modalities.

Implications and Future Directions

The implications of this research are wide-ranging both theoretically and practically. Theoretically, WEENIE challenges conventional paradigms in image synthesis by integrating sparse coding with a weakly-supervised model, pushing the boundaries of joint learning techniques in heterogeneous image domains. Practically, this approach promises more efficient utilization of multispectral MRI data, enhancing diagnostic capabilities and patient management through improved image resolution and modality synthesis.

Looking forward, the method's principles could inspire analogous applications in other medical imaging modalities or interdisciplinary fields requiring efficient data synthesis across heterogeneous sources. In the domain of artificial intelligence, further exploration of convolutional sparse coding in weakly-supervised contexts may yield novel insights, particularly in enhancing model generalization and reducing data dependency.

Conclusion

In summation, WEENIE makes a substantial contribution in the medical imaging domain by concurrently addressing SR and CMS challenges through an innovative weakly-supervised joint convolutional sparse coding framework. Its potential to revolutionize clinical practices offers a promising pathway for future research and development in both medical diagnostics and artificial intelligence applications.