Purpose 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. Methods 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. Results 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. Conclusion 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.

The proposed technique showed accurate IMT measurement results. Furthermore, benefiting from the CBOS filter, the robustness to noise of the algorithm was substantially improved. Therefore, CGACO could provide a reliable way to segment the carotid artery from ultrasound images and could be used in clinical practice of IMT measurement, particularly in early atherosclerotic stages.

Purpose To evaluate the performance of acceleration‐dependent vascular anatomy for non‐contrast‐enhanced MR venography (ADVANCE‐MRV) in femoral veins and to investigate whether venous signal uniformity can be improved by applying multiple acquisitions with different flow suppressions or multiple flow suppressions in 1 acquisition. Methods The ADVANCE‐MRV method uses flow‐sensitized modules to acquire a dark‐artery image set and a dark‐artery vein set, which are subsequently subtracted. Ten healthy volunteers were imaged using the ADVANCE‐MRV sequence with improved venous suppression uniformity in the dark‐artery vein images achieved by applying multiple flow suppressions in the same acquisition or by combining multiple images acquired with different flow suppressions. The performance of the improved technique was also evaluated in 13 patients with lower‐limb deep venous thrombosis. Results Multiple‐preparation and multiple‐acquisition approaches all improved venous signal uniformity and reduced the signal void artifacts observed in the original ADVANCE‐MRV images. The multiple‐acquisition approaches achieved excellent blood signal uniformity and intensity, albeit at the cost of an increase in the total acquisition time. The double‐preparation approach demonstrated good performance in all measurements, providing a good compromise between signal uniformity and acquisition time. The blood signal spatial variation and its variation using different gradient amplitudes were reduced by 20% and 29%. All patient images showed uniform and bright venous signal in nonoccluded sections of vein. Conclusion The enhanced ADVANCE‐MRV methods substantially improved signal uniformity in healthy volunteers and patients with known deep venous thrombosis. The double‐preparation approach gave good‐quality femoral vein images, providing improved venous signal uniformity without increasing acquisition time in comparison to the original sequence.

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