Evaluation of an optimized metal artifact reduction algorithm for flat-detector angiography compared to DSA imaging in follow-up after neurovascular procedures.

BACKGROUND: Flat detector CT - angiography (FDCTA) has become a valuable imaging tool in post- and peri-interventional imaging after neurovascular procedures. Metal artifacts produced by radiopaque implants like clips or coils still impair image quality. METHODS: FDCTA was performed in periprocedural or follow-up imaging of 21 patients, who had received neurovascular treatment. Raw data was sent to a dedicated workstation and subsequently a metal artifact reduction algorithm (MARA) was applied. Two neuroradiologists examined the images. RESULTS: Application of MARA improved image appearance and led to a significant reduction of metal artifacts. After application of MARA only 8 datasets (34% of the images) were rated as having many or extensive artifacts, before MARA 15 (65%) of the images had extensive or many artifacts. Twenty percent more cases of reperfusion were diagnosed after application of MARA, congruent to the results of digital subtraction angiography (DSA) imaging. Also 3 (13% of datasets) images, which could not be evaluated before application of MARA, could be analyzed after metal artifact reduction and reperfusion could be excluded. CONCLUSION: Application of MARA improved image evaluation, reduced the extent of metal artifacts, and more cases of reperfusion could be detected or excluded, congruent to DSA imaging.

Evaluation of noninvasive follow-up methods for the detection of intracranial in-stent restenosis: a phantom study.

OBJECTIVES: Intra-arterial digital subtraction angiography (IA-DSA), an invasive procedure, is the current reference examination after percutaneous transluminal angioplasty and stenting for the detection of in-stent restenosis (ISR). In this phantom study, we evaluated flat-panel angiographic computed tomography after intravenous contrast agent application (IV-ACT) and multidetector computed tomographic angiography (MDCTA) as potential noninvasive follow-up alternatives after intracranial percutaneous transluminal angioplasty and stenting. MATERIALS AND METHODS: We simulated an intracranial vessel using a silicon tube placed inside a human skull. Three different stent systems were deployed inside the silicon tubes, each with diameters of 3 or 4 mm. Three grades of ISR (25%, 50%, and 75%) were simulated. The IA-DSA and IV-ACT examinations were performed on a flat-panel detector angiography system. The MDCTA images were acquired with a 128-slice computed tomographic scanner. The mean stenosis diameters, measured with each technique, were compared using the Bland-Altman plot. The difference between the known stenosis diameter and the measured stenosis diameter was calculated for each examination. RESULTS: Stenosis measurements on the IA-DSA images showed no statistically significant differences compared with the known stenosis diameters (P = 0.19). In the 3-mm stent category, when compared with the known stenosis diameter, mean (SD) differences of 0.01 (0.15) mm, 0.03 (0.24) mm and 0.16 (0.5) mm were calculated for the IA-DSA, IV-ACT, and MDCTA stenosis measurements, respectively. As for the 4-mm stents, IA-DSA and IV-ACT were again very accurate, with mean (SD) differences of -0.03 (0.11) mm and 0.07 (0.19) mm, respectively, compared with the known stenosis diameters, whereas MDCTA overestimated ISR, with a mean (SD) difference of 0.49 (0.53) mm. The Bland-Altman plots show a mean (SD) difference of 0.08 (0.2) mm between IA-DSA and IV-ACT (95% confidence interval, 0.05-0.11) and a mean (SD) difference of 0.34 (0.56) mm between IA-DSA and MDCTA measurements (95% confidence interval, 0.25-0.42). CONCLUSIONS: In our phantom study, IA-DSA was the only examination to predict accurately degrees of stenosis compared with the known stenosis diameters. The results of the IV-ACT measurements were comparable with those of IA-DSA. Multidetector computed tomographic angiography was less accurate in the quantification of stenosis, usually overestimating ISR.