Abstract
In Compressed Sensing (CS) of MRI, optimization of the regularization parameters is not a trivial task. We aimed to establish a method that could determine the optimal weights for regularization parameters in CS of time-of-flight MR angiography (TOF-MRA) by comparing various image metrics with radiologists’ visual evaluation. TOF-MRA of a healthy volunteer was scanned using a 3T-MR system. Images were reconstructed by CS from retrospectively under-sampled data by varying the weights for the L1 norm of wavelet coefficients and that of total variation. The reconstructed images were evaluated both quantitatively by statistical image metrics including structural similarity (SSIM), scale invariant feature transform (SIFT) and contrast-to-noise ratio (CNR), and qualitatively by radiologists’ scoring. The results of quantitative metrics and qualitative scorings were compared. SSIM and SIFT in conjunction with brain masks and CNR of artery-to-parenchyma correlated very well with radiologists’ visual evaluation. By carefully selecting a region to measure, we have shown that statistical image metrics can reflect radiologists’ visual evaluation, thus enabling an appropriate optimization of regularization parameters for CS.
Highlights
Time—of—flight (TOF) magnetic resonance angiography (MRA) has held its established position in routine brain MRI examinations owing to its non-invasiveness and excellent diagnostic ability of arterial stenoses and aneurysms[1]
Optimization of regularization parameters in Compressed Sensing (CS) cannot be automated because it requires knowledge about the optimal results
The optimal parameter highly depends on the observation model, as well as the object of interest itself
Summary
Time—of—flight (TOF) magnetic resonance angiography (MRA) has held its established position in routine brain MRI examinations owing to its non-invasiveness and excellent diagnostic ability of arterial stenoses and aneurysms[1]. Its diagnostic ability has been shown to be comparable to computed tomography angiography (CTA)[2,3] as early as 2001, its major weaknesses include long scan time and limited spatial resolution relative to CTA[1,4]. Ever since the first magnetic resonance (MR) images were acquired in the early 1970s, the demand for faster acquisition and higher resolution of MR images has never ceased. In the past decades we have seen dramatic advancements in hardware such as higher magnetic fields, faster switching gradients and phased-array coils, as well as progression.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call