Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data.

Abstract

PURPOSE\nA rapid real-time 2D accelerated method was developed for MRI using principal component analysis (PCA) in the temporal domain. This method employs a moving window of previous dynamic frames to reconstruct the current, real-time frame within this window. This technique could be particularly useful in real-time tracking applications such as in MR-guided radiotherapy, where low latency real-time reconstructions are essential.\n\n\nMETHODS\nThe method was tested retrospectively on 15 fully-sampled data sets of lung patient data acquired on a 3T Philips Achieva system. High frequency data is incoherently undersampled while the central low-frequency data is always acquired to characterize the temporal fluctuations through PCA. The undersampling pattern is derived in such a way that all of k-space is acquired within a pre-determined number of frames. The missing data in the current frame is then filled in by fitting the temporal characterizations to the acquired undersampled data, using a pre-determined number of principal components. A subset of 6 patients was used to test the contour ability of the images. Various accelerations between 3x and 8x were tested along with the optimal number of principal components for fitting. A comparison was also performed with previous work from our group proposed by Dietz et\xa0al. as well as with a standard low resolution acquisition. In order to determine how the method would perform at lower SNR, noise levels of 2x, 4x and 6x were added to the 3T data. Metrics such as normalised mean square error and Dice coefficient were used to measure the reconstruction image quality and contour ability.\n\n\nRESULTS\nThe proposed method demonstrated good temporal robustness as consistent metrics were detected for the duration of the imaging session. It was found that the optimal number of principal components for temporal fitting was dependent on the acceleration rate. For the data tested, five principal components was found to be optimal at acceleration rates of 3x and 4x. This number decreases to three at accelerations of 5x and 6x and further decreases to two at an acceleration rate of 8x, likely due to greater instability with fewer acquired data points. The use of too many principal components for fitting increased the chances of noisy reconstruction which affected contourability.\n\n\nCONCLUSIONS\nThe proposed 2D real-time MR acceleration method demonstrated greater robustness in the metrics over time when compared with previous real-time PCA methods using metrics such as NMSE, PSNR and SSIM up to an acceleration of 8x. Improved temporal robustness of image structure contourability and accurate definition was also demonstrated using several metrics including the Dice coefficient. Reconstruction of raw acquired data can be performed at approximately 50 ms per frame using an Intel core i5 CPU. The method has the advantage of being very flexible in terms of hardware requirements as it can operate successfully on a single coil channel and does not require specialized computing power to implement in real-time. This article is protected by copyright. All rights reserved.