记忆效率深度端到端后台网络 (深度) 逆向问题
Jyothi Rikhab Chand1, Mathews Jacob1
1Department of Electrical and Computer Engineering, University of Iowa, IA, USA.
Proceedings. IEEE International Symposium on Biomedical Imaging
|August 29, 2025
概括
我们开发了一种高效的深度学习方法来重建磁共振图像. 这种方法可以学习后部分布,改善图像恢复和提供不确定性地图.
科学领域:
- 医学成像
- 计算神经科学
- 机器学习
背景情况:
- 端到端 (E2E) 解卷优化框架对磁共振 (MR) 图像恢复具有前景.
- 这些确定性方法在训练过程中面临着高内存使用的挑战,并且缺乏后部分布采样能力.
研究的目的:
- 在MR图像重建中引入后部分布的E2E学习的记忆效率方法.
- 为了在图像恢复的同时实现不确定性量化.
主要方法:
- 这是一个新的框架, 结合了数据一致性概率术语和CNN参数化的先前能量模型.
- 通过最大概率优化对CNN权重进行E2E学习.
- 从低采样MR数据中进行图像恢复的最大后期优化 (MAP).
主要成果:
- 拟议的方法实现了与内存密集型E2E解卷算法相匹配的性能.
- 它在MRI图像重建方面表现优于现有的存储效率高的同行.
- 该框架成功生成了从后期分布抽样中得出的不确定性图.
结论:
- 这种具有记忆效率的E2E学习框架有助于MR图像重建.
- 它为高维 (3D+) 磁共振成像提供了可行的解决方案.
- 能够采样后部分布提供了有价值的不确定性信息.
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