FPGA Acceleration for Simultaneous Medical Image Reconstruction and Segmentation

Peng Li, Thomas Page, Guojie Luo, Wentai Zhang, Pei Wang, Peng Zhang, P. Maass, M. Jiang, J. Cong
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引用次数: 6

Abstract

The conventional approach of computed tomography (CT) is to solve each image processing task individually in sequence. An obvious drawback is that the measured data is only used once at the first step, and the possible errors, from noises in the measured data, inappropriate modeling, or inappropriate parameters, are not easy to be corrected and will be propagated into the later steps. As a consequence, approaches that combine the reconstruction and the specific processing task have become popular. This work adopts an iterative algorithm with simultaneous reconstruction and segmentation using the Mumford-Shah model, which can be applied not only to regularize the ill-posedness of the tomographic reconstruction problem, but also to compute segmentation directly from the measured data. The Mumford-Shah model is both mathematically and computationally difficult. In this paper, we accelerated this computation and data intensive application by FPGA devices and achieved 9.24X speedup over the conventional CPU implementation.
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同时医学图像重建和分割的FPGA加速
计算机断层扫描(CT)的传统方法是按顺序单独解决每个图像处理任务。一个明显的缺点是,测量数据在第一步只使用一次,由于测量数据中的噪声、建模不当或参数不合适,可能产生的误差不容易纠正,并且会传播到后面的步骤中。因此,将重建和特定处理任务结合起来的方法变得流行起来。本文采用了一种基于Mumford-Shah模型的同时重建和分割的迭代算法,该算法不仅可以正则化层析重建问题的病态性,而且可以直接从测量数据中计算分割。Mumford-Shah模型在数学和计算上都很困难。在本文中,我们通过FPGA器件加速了这种计算和数据密集型应用,比传统的CPU实现速度提高了9.24倍。
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