Subtractive NCE-MRA: Improved background suppression using robust regression-based weighted subtraction

To correct the intensity difference of static background signal between bright blood images and dark blood images in subtractive non-contrast-enhanced MR angiography using robust regression, thereby improving static background signal suppression on subtracted angiograms.

Robust regression (RR), using iteratively reweighted least squares, is used to calculate the regression coefficient of background tissues from a scatter plot showing the voxel intensity of bright blood images versus dark blood images. The weighting function is based on either the Euclidean distance from the estimated regression line or the deviation angle. Results from RR using the deviation angle (RRDA), conventional RR using the Euclidean distance, and ordinary leastsquares regression were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic flow-sensitive dephasing data sets, 13 iliac flow-sensitive dephasing data sets, and 26 femoral fresh blood imaging data sets.

RR deviation angle achieved robust and accurate performance in all types of images, with small bias, small mean absolute error, and high-correlation coefficients with reference values. Background tissues, such as muscle, veins, and bladder, were suppressed while the vascular signal was preserved. Euclidean distance gave good performance for thoracic and iliac flow-sensitive dephasing, but could not suppress background tissues in femoral fresh blood imaging. Ordinary least squares regression was sensitive to outliers and overestimated regression coefficients in thoracic flow-sensitive dephasing.

Weighted subtraction using RR was able to acquire the regression coefficients of background signal and improve background suppression of subtractive non-contrast-enhanced MR angiography techniques. RR deviation angle has the most robust and accurate overall performance among three regression methods.