An analysis of reconstruction noise from undersampled 4D flow MRI

• DOI: 10.1016/j.bspc.2023.104800
• Publication Date: 2023-03-15T19:19:35Z
• Authors: Lauren Partin; Daniele E. Schiav...; Carlos A. Sing Long
• Keywords: 4D flow MRI; FOS: Computer and information sciences; MRI noise characterization; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; 610; FOS: Physical sciences; Electrical Engineering and Systems Science - Image and Video Processing; 03 Good health and well-being; Physics - Medical Physics; Statistics - Applications; Methodology (stat.ME); 03 medical and health sciences; 0302 clinical medicine; Compressed Sensing; Uncertainty propagation; 03 Salud y bienestar; FOS: Electrical engineering, electronic engineering, information engineering; Applications (stat.AP); Medical Physics (physics.med-ph); Medicina y salud; Statistics - Methodology

Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular disease. To reduce the acquisition times, reconstruction methods from undersampled measurements are routinely used, that leverage representations designed to increase image compressibility. Reconstructed anatomical and hemodynamic images may present visual artifacts. Although some of these artifact are essentially reconstruction errors, and thus a consequence of undersampling, others may be due to measurement noise or the random choice of the sampled frequencies. Said otherwise, a reconstructed image becomes a random variable, and both its bias and its covariance can lead to visual artifacts; the latter leads to spatial correlations that may be misconstrued for visual information. Although the nature of the former has been studied in the literature, the latter has not received as much attention. In this study, we investigate the theoretical properties of the random perturbations arising from the reconstruction process, and perform a number of numerical experiments on simulated and MR aortic flow. Our results show that the correlation length remains limited to two to three pixels when a Gaussian undersampling pattern is combined with recovery algorithms based on $\ell_1$-norm minimization. However, the correlation length may increase significantly for other undersampling patterns, higher undersampling factors (i.e., 8x or 16x compression), and different reconstruction methods.