All-in-One Deep Learning Framework for MR Image Reconstruction
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916260900503552 |
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| author | Jeong, Geunu Kim, Hyeonsoo Yang, Joonyoung Jang, Kyungeun Kim, Jeewook |
| author_facet | Jeong, Geunu Kim, Hyeonsoo Yang, Joonyoung Jang, Kyungeun Kim, Jeewook |
| contents | We introduce a novel, all-in-one deep learning framework for MR image reconstruction, enabling a single model to enhance image quality across multiple aspects of k-space sampling and to be effective across a wide range of clinical and technical scenarios. This DICOM-based algorithm serves as the core of SwiftMR (AIRS Medical, Seoul, Korea), which is FDA-cleared, CE-certified, and commercially available. We first detail the comprehensive development process of the model, including data collection, training pair preparation, model architecture design, and DICOM inference. We then assess the model's capability to enhance image quality in a multi-dimensional manner, specifically across various aspects of k-space sampling. Subsequently, we evaluate several features of the multi-dimensional enhancement: the accuracy of tunable denoising, the effectiveness of super-resolution in each encoding direction, and the reduction of artifacts that become more prominent at lower spatial resolutions. Additionally, we assess its compatibility with various scan parameter sets and its generalizability across scanner vendors not seen during training. Finally, we present specific cases demonstrating the model's utility in reducing scan time across anatomical regions in conjunction with protocol optimization. The proposed model is compatible with a broad spectrum of scenarios, including various vendors, pulse sequences, scan parameters, and anatomical regions. Its DICOM-based operation particularly enhances its applicability for real-world applications. Given its demonstrated effectiveness and versatility, we expect its use to expand in the field of clinical MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03684 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | All-in-One Deep Learning Framework for MR Image Reconstruction Jeong, Geunu Kim, Hyeonsoo Yang, Joonyoung Jang, Kyungeun Kim, Jeewook Image and Video Processing We introduce a novel, all-in-one deep learning framework for MR image reconstruction, enabling a single model to enhance image quality across multiple aspects of k-space sampling and to be effective across a wide range of clinical and technical scenarios. This DICOM-based algorithm serves as the core of SwiftMR (AIRS Medical, Seoul, Korea), which is FDA-cleared, CE-certified, and commercially available. We first detail the comprehensive development process of the model, including data collection, training pair preparation, model architecture design, and DICOM inference. We then assess the model's capability to enhance image quality in a multi-dimensional manner, specifically across various aspects of k-space sampling. Subsequently, we evaluate several features of the multi-dimensional enhancement: the accuracy of tunable denoising, the effectiveness of super-resolution in each encoding direction, and the reduction of artifacts that become more prominent at lower spatial resolutions. Additionally, we assess its compatibility with various scan parameter sets and its generalizability across scanner vendors not seen during training. Finally, we present specific cases demonstrating the model's utility in reducing scan time across anatomical regions in conjunction with protocol optimization. The proposed model is compatible with a broad spectrum of scenarios, including various vendors, pulse sequences, scan parameters, and anatomical regions. Its DICOM-based operation particularly enhances its applicability for real-world applications. Given its demonstrated effectiveness and versatility, we expect its use to expand in the field of clinical MRI. |
| title | All-in-One Deep Learning Framework for MR Image Reconstruction |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2405.03684 |