Mean PSNR and SSIM of the unfiltered CT volumes.

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be utilized to reduce radiation without losing diagnosis capabilities. The objective in this work is to obtain an implementation of a filter capable of processing medical images in real-time. To achieve this we have developed several filter methods based on fuzzy logic, and their GPU implementations, to reduce mixed Gaussian-impulsive noise. These filters have been developed to work in attenuation coefficients so as to not lose any information from the CT scans. The testing volumes come from the Mayo clinic database and consist of CT volumes at full and at simulated low dose. The GPU parallelizations reach speedups of over 2700 and take less than 0.1 seconds to filter more than 300 slices. In terms of quality the filter is competitive with other state of the art algorithmic and AI filters. The proposed method obtains good performance in terms of quality and the parallelization results in real-time filtering.

在医学图像的采集与重建阶段，图像可能会产生噪声并丧失诊断质量。以计算机断层扫描（CT，Computed Tomography）为例，获取低噪声图像需要为患者施加更高的辐射剂量。滤波技术可在不损失诊断性能的前提下降低辐射剂量。本研究的目标是实现一种可实时处理医学图像的滤波器。为达成这一目标，我们开发了多种基于模糊逻辑的滤波方法及其图形处理器（GPU，Graphics Processing Unit）实现方案，用于抑制混合高斯-脉冲噪声。这些滤波器针对衰减系数设计，以确保不会丢失CT扫描中的任何信息。测试数据集源自梅奥诊所（Mayo Clinic）数据库，包含全剂量与模拟低剂量的CT体数据。该GPU并行方案的加速比超过2700，对超过300层的CT切片进行滤波仅需不到0.1秒。在成像质量方面，该滤波器可与当前前沿的经典算法滤波与人工智能（AI，Artificial Intelligence）滤波器相媲美。所提方法在成像质量上表现优异，且其并行化方案可实现实时滤波。