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Jianfeng Zhu

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Proceedings Article | 20 November 2024 Poster + Paper

Effective super-resolution reconstruction for multiprotocol low-field MRI

Rihan Hu, Qi Liu, Yang Liu, JianFeng Zhu, Yikun Zhang, Yang Chen

Proceedings Volume 13242, 132421S (2024) https://doi.org/10.1117/12.3035871

KEYWORDS: Magnetic resonance imaging, Super resolution, Adversarial training, Prior knowledge

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Due to the high cost of high-field MRI equipment, low-field MRI systems are still widely used in small and medium-sized hospitals. Compared to high-field MRI, images acquired from low-field MRI often suffer from lower resolution and lower signal-to-noise ratios. And the analysis of clinical data reveals that noise levels can vary significantly across different low-field MRI protocols. In this study, we propose an effective super-resolution reconstruction model based on generative adversarial networks (GAN). The proposed model can implicitly differentiate between various sequence types, allowing it to adapt to different scan protocols during reconstruction process. To further enhance image detail, a one-to-many supervision strategy is employed during the training process, utilizing similar patches within a single image. Additionally, the number of basic blocks in the model is reduced through knowledge distillation to meet the speed requirements for clinical use. The experimental results on actual 0.35T low-field MR images suggest that the proposed method holds substantial potential for clinical application.

Proceedings Article | 20 November 2024 Poster + Paper

Speed up implicit neural representation for parallel MRI reconstruction

Rihan Hu, Qi Liu, JianFeng Zhu, Yikun Zhang, Yang Chen

Proceedings Volume 13242, 132421Q (2024) https://doi.org/10.1117/12.3035767

KEYWORDS: Image restoration, Magnetic resonance imaging, Neural networks

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Parallel imaging is widely used in the clinic to accelerate magnetic resonance imaging (MRI) data collection. However, conventional reconstruction techniques for parallel imaging still face significant challenges in achieving satisfactory performance at high acceleration rates. It results in artifacts and noise that affect the subsequent diagnosis. Recently, implicit neural representation (INR) has emerged as a new deep learning paradigm that represents an object as a continuous function of spatial coordinates. INR’s continuity in representation enhances the model’s capacity to capture redundant information within the object. However, it usually needs thousands of training iterations to reconstruct the image. In this work, we proposed a method to speed up INR for parallel MRI reconstruction using hash-mapping and a pre-trained encoder. It enables INR to achieve better results with fewer training iterations. Benefiting from INR’s powerful representations, the proposed method outperforms existing methods in removing the aliasing artifacts and noise. The experimental results on simulated and real undersampled data demonstrate the model’s potential for further accelerating parallel MRI.

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