Abstract

Motivation: Water exchange measured by filter-exchange imaging (FEXI) is expected to serve as an important biomarker for several brain diseases. However, its estimation accuracy is easily affected by noise. Goal(s): To develop an approach for reconstruction of FEXI parameters from noisy signals. Approach: An end-to-end framework was constructed to achieve parameter reconstruction without corresponding labels. An adaptative deformable convolutional network was introduced to explore structural information. A loss function was designed to enhance network denoising performance. Results: Simulation results under SNR=30~50 showed that the S2P achieved optimal results in the reconstruction of apparent water exchange rate, with PSNR of 27.44 and SSIM of 0.9050. Impact: The S2P, an end-to-end framework, reconstructs high-quality FEXI parameter maps from only a single scan when it has been trained with noisy pairs, which can provide efficient and reliable medical images for clinical diagnosis.