UCSF

Introduction

The aortic wall and intraluminal thrombus (ILT) have been increasingly studied as potential markers of progressive disease with abdominal aortic aneurysms (AAAs). Our goal was to develop a high resolution, 3D black blood MR technique for AAA wall and ILT imaging within a clinically acceptable scan time.

Methods

Twenty two patients with AAAs (maximal diameter 4.3±1.0cm), along with five healthy volunteers, were imaged at 3T with a 3D T1-weighted fast-spin-echo sequence using variable flip angle trains (SPACE) with a preparation pulse (DANTE) for suppressing blood signal. Volunteers and ten patients were also scanned with SPACE alone for comparison purposes. The signal to noise ratio (SNR) and the aortic wall/ILT to lumen contrast to noise ratio (CNR) were measured. Qualitative image scores (1-4 scale) assessing the inner lumen and outer wall boundaries of AAA were performed by two blinded reviewers. In patients with ILT, the ratio of ILT signal intensity (ILTSI) over psoas muscle SI (MuscleSI) was calculated, and the signal heterogeneity of ILT was quantified as standard deviation (SD) over the mean.

Results

All subjects were imaged successfully with an average scan time of 7.8±0.7minutes. The DANTE preparation pulse for blood suppression substantially reduced flow artifacts in SPACE with lower lumen SNR (8.8 vs. 21.4, p<0.001) and improved the wall/ILT to lumen CNR (9.9 vs. 6.3, p<0.001) in patients. Qualitative assessment showed improved visualization of lumen boundaries (73% higher scores on average, p=0.01) and comparable visualization of outer wall boundary (p>0.05). ILT was present in ten patients, with relatively high signal and a wide SD (average ILTSI/MuscleSI 1.42±0.48 (range 0.75-2.11)) and with SD/mean of 27.7%±6.6% (range 19.6%-39.4%).

Conclusion

High resolution, 3D black blood MRI of AAAs can be achieved in a clinical accepted scan time with reduction of flow artifacts using the DANTE preparation pulse. Signal characteristics of ILT can be quantified and may be used for improved patient-specific risk stratification.