K-space weighted image average (KWIA) for ASL-based dynamic MR angiography and perfusion imaging

Abstract

A novel denoising algorithm termed k-space weighted image average (KWIA) was proposed to improve the signal-to-noise ratio (SNR) of dynamic MRI, such as arterial spin labeling (ASL)-based dynamic magnetic resonance angiography (dMRA) and perfusion imaging. KWIA divides the k-space of each time frame into multiple rings, the central ring of the k-space remains intact to preserve the image contrast and temporal resolution, while outer rings are progressively averaged with neighboring time frames to increase SNR. Simulations and in-vivo dMRA and multi-delay ASL studies were performed to evaluate the performance of KWIA under various MRI acquisition conditions. SNR ratios and temporal signal errors between KWIA-processed and the original data were measured. Visualization of dynamic blood flow signals as well as quantitative parametric maps were evaluated for KWIA-processed images as compared to the original images. KWIA achieved a SNR ratio of 1.73 for dMRA and 2.0 for multi-delay ASL respectively, which were in accordance with the theoretical predictions. Improved visualization of dynamic blood flow signals was demonstrated using KWIA in distal small vessels in dMRA and small brain structures in multi-delay ASL. Approximately 5% temporal errors were observed in both KWIA-processed dMRA and ASL signals. Fine anatomical features were revealed in the quantitative parametric maps of dMRA, and the residuals of model fitting were reduced for multi-delay ASL. Compared to other conventional denoising methods, KWIA is a flexible denoising algorithm that improves the SNR of ASL-based dMRA and perfusion MRI by up to 2-fold without compromising spatial and temporal resolution or quantification accuracy.