Deep-SENSE: Learning Coil Sensitivity Functions for SENSE Reconstruction Using Deep Learning
文献类型：会议论文
| 作者 | Xi Peng; Kevin Perkins; Bryan Clifford; Brad Sutton; Zhi-Pei Liang |
| 出版日期 | 2018 |
| 会议日期 | 2018年 |
| 会议地点 | 巴黎 |
| 英文摘要 | Parallel imaging is an essential tool for accelerating image acquisition by exploiting the spatial encoding effects of RF receiver coil sensitivity functions. In practice, the coil sensitivity functions are often estimated from low-resolution auto-calibration signals (ACS) which limits estimation accuracy and in turn results in aliasing artifacts in the final reconstructions. This paper presents a novel deep learning based method for coil sensitivity estimation which exploits empirical and physics-based prior information to produce high-accuracy estimates of coil sensitivity functions from low-resolution ACS. Results are given which demonstrate the proposed method provides a significant reduction in aliasing over standard methods. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14568]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Xi Peng,Kevin Perkins,Bryan Clifford,et al. Deep-SENSE: Learning Coil Sensitivity Functions for SENSE Reconstruction Using Deep Learning[C]. 见:. 巴黎. 2018年. |
入库方式： OAI收割
来源：深圳先进技术研究院
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