用FPGA加速CNN重建低功率散射阵列超声波成像
概括
这项研究在FPGA上引入了一种新的深度学习算法,用于重建超声波 (US) 通道,从而实现可穿戴膀成像. 这种方法将硬件需求和功耗减半,同时保持图像质量,用于早期发现疾病.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 用于实时膀监测的可穿戴超声波 (US) 成像在功率,成本和分辨率方面面临挑战.
- 高频道数量的美国系统产生大量数据流,使计算效率和无线传输复杂化.
- 现有的可穿戴的美国传感器往往缺乏有效的医疗保健应用所需的临床准确性和效率.
研究的目的:
- 开发一种以算法为中心的方法,使用现场可编程网关阵列 (FPGA) 加速的深度学习来重建缺失的超声波通道.
- 为了减少可穿戴超声波系统的模拟前端要求和功耗.
- 为了实现高质量的实时超声波成像,用于像尿膀这样的目标器官.
主要方法:
- 开发了一种轻量级的U-Net卷积神经网络 (L-UNET),优化用于稀疏阵列射频 (RF) 数据重建.
- 在深度学习处理单元 (DPU) 上部署了L-UNET,使用混合量子化意识训练 (Mixed-QAT) 以8位整数和16位浮点精度.
- 实现了用于实时推断的单核FPGA加速器,仅处理奇数索引物理通道,并推断偶数索引通道.
主要成果:
- 实现了1.48 × 10-2的平均平方误差 (MSE),明显低于32位浮点精度.
- 维护B模式图像质量,峰值信号噪声比 (PSNR) >18dB,结构相似度指数 (SSIM) >0.5.
- 在32通道配置中,平均功耗为0.918W,确定性延迟为221ms/frame.
结论:
- 在嵌入式FPGA上对缺失的美国频道进行算法中心的重建是可穿戴成像的可行策略.
- 这种方法有效地将成像光圈翻一番,同时将模拟前端要求和功耗减半.
- 开发的系统为预防性医疗保健和早期疾病诊断提供了一条通往完全集成的低功率超声监测系统的途径.
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