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Register a new userXCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
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Added by
Xiaobo Qu
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms. In this work, we develop an open-access, easy-to-use and high-performance medical intelligence cloud computing platform (XCloud-pFISTA) to reconstruct MRI images from undersampled k-space data. Two state-of-the-art approaches of the Projected Fast Iterative Soft-Thresholding Algorithm (pFISTA) family have been successfully implemented on the cloud. This work can be considered as a good example of cloud-based medical image reconstruction and may benefit the future development of integrated reconstruction and online diagnosis system.
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For accelerated multi-dimensional NMR spectroscopy, non-uniform sampling is a powerful approach but requires sophisticated algorithms to reconstruct undersampled data. Here, we first devise a high-performance deep learning framework (MoDern), which shows astonishing performance in robust and high-quality reconstruction of challenging multi-dimensional protein NMR spectra and reliable quantitative measure of the metabolite mixture. Remarkably, the few trainable parameters of MoDern allowed the neural network to be trained on solely synthetic data while generalizing well to experimental undersampled data in various scenarios. Then, we develop a novel artificial intelligence cloud computing platform (XCloud-MoDern), as a reliable, widely-available, ultra-fast, and easy-to-use technique for highly accelerated NMR. All results demonstrate that XCloud-MoDern contributes a promising platform for further development of spectra analysis.
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Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in tumor subregion analysis are discussed.
Purpose: To develop an improved self-supervised learning strategy that efficiently uses the acquired data for training a physics-guided reconstruction network without a database of fully-sampled data. Methods: Currently self-supervised learning for physics-guided reconstruction networks splits acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network and the other to define the training loss. The proposed multi-mask self-supervised learning via data undersampling (SSDU) splits acquired measurements into multiple pairs of disjoint sets for each training sample, while using one of these sets for DC units and the other for defining loss, thereby more efficiently using the undersampled data. Multi-mask SSDU is applied on fully-sampled 3D knee and prospectively undersampled 3D brain MRI datasets, which are retrospectively subsampled to acceleration rate (R)=8, and compared to CG-SENSE and single-mask SSDU DL-MRI, as well as supervised DL-MRI when fully-sampled data is available. Results: Results on knee MRI show that the proposed multi-mask SSDU outperforms SSDU and performs closely with supervised DL-MRI, while significantly outperforming CG-SENSE. A clinical reader study further ranks the multi-mask SSDU higher than supervised DL-MRI in terms of SNR and aliasing artifacts. Results on brain MRI show that multi-mask SSDU achieves better reconstruction quality compared to SSDU and CG-SENSE. Reader study demonstrates that multi-mask SSDU at R=8 significantly improves reconstruction compared to single-mask SSDU at R=8, as well as CG-SENSE at R=2. Conclusion: The proposed multi-mask SSDU approach enables improved training of physics-guided neural networks without fully-sampled data, by enabling efficient use of the undersampled data with multiple masks.
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured approaches demand considerable memory consumption and are time-consuming due to a noticeable number of matrix operations performed on the huge-size block Hankel-like matrix. In this work, we proposed a novel framework to utilize the low-rank property but meanwhile to achieve faster reconstructions and promising results. The framework allows us to enforce the low-rankness of Hankel matrices constructing from 1D vectors instead of 2D matrices from 1D vectors and thus avoid the construction of huge block Hankel matrix for 2D k-space matrices. Moreover, under this framework, we can easily incorporate other information, such as the smooth phase of the image and the low-rankness in the parameter dimension, to further improve the image quality. We built and validated two models for parallel and parameter magnetic resonance imaging experiments, respectively. Our retrospective in-vivo results indicate that the proposed approaches enable faster reconstructions than the state-of-the-art approaches, e.g., about 8x faster than STDLRSPIRiT, and faithful removal of undersampling artifacts.
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