PURPOSE: To evaluate the imaging of hemodialysis arteriovenous (AV) fistulas and grafts with use of magnetic resonance (MR) angiography with generalized autocalibrating partially parallel acquisition (GRAPPA) and time-resolved echo-sharing angiographic technique (TREAT) and compare the findings with those of digital subtraction angiography (DSA). MATERIALS AND METHODS: The vascular tree directly related to AV fistulas and grafts was divided into nine segments. Images of each segment obtained on GRAPPA MR angiography were evaluated for the presence of stenosis, occlusion, and any other disease (eg, pseudoaneurysm) by two independent observers and compared with a consensus reading of the same segments on DSA imaging. Sensitivity and specificity were calculated with use of DSA as the gold standard modality, and each image on MR angiography and DSA was rated for quality. Linear-weighted kappa scores were calculated as a measure of interobserver variability in the detection of pathologic processes. RESULTS: A total of 80 segments were evaluated by each observer. For both observers, sensitivity rates for the detection of stenosis, occlusion, and any disease were 100% (95% CI, 52%-100%), 100% (95% CI, 20%-100%), and 100% (95% CI, 60%-100%), respectively. For observer 1, specificity rates for the detection of stenosis, occlusion, and any disease were 96% (95% CI, 88%-99%), 100% (95% CI, 94%-100%), and 96% (95% CI, 88%-99%), respectively. For observer 2, the specificity rates for the detection of stenosis, occlusion, and any disease were 93% (95% CI, 84%-98%), 100% (95% CI, 94%-100%), and 93% (95% CI, 84%-97%), respectively. Linear-weighted kappa values for MR angiography and DSA were 0.78+/-0.084 and 0.62+/-0.152, respectively. CONCLUSION: Time-resolved MR angiography with GRAPPA and TREAT offers excellent image quality and provides an accurate and reliable modality for the detection of pathologic processes in hemodialysis AV fistulas and grafts.
PURPOSE: To evaluate the imaging of hemodialysis arteriovenous (AV) fistulas and grafts with use of magnetic resonance (MR) angiography with generalized autocalibrating partially parallel acquisition (GRAPPA) and time-resolved echo-sharing angiographic technique (TREAT) and compare the findings with those of digital subtraction angiography (DSA). MATERIALS AND METHODS: The vascular tree directly related to AV fistulas and grafts was divided into nine segments. Images of each segment obtained on GRAPPA MR angiography were evaluated for the presence of stenosis, occlusion, and any other disease (eg, pseudoaneurysm) by two independent observers and compared with a consensus reading of the same segments on DSA imaging. Sensitivity and specificity were calculated with use of DSA as the gold standard modality, and each image on MR angiography and DSA was rated for quality. Linear-weighted kappa scores were calculated as a measure of interobserver variability in the detection of pathologic processes. RESULTS: A total of 80 segments were evaluated by each observer. For both observers, sensitivity rates for the detection of stenosis, occlusion, and any disease were 100% (95% CI, 52%-100%), 100% (95% CI, 20%-100%), and 100% (95% CI, 60%-100%), respectively. For observer 1, specificity rates for the detection of stenosis, occlusion, and any disease were 96% (95% CI, 88%-99%), 100% (95% CI, 94%-100%), and 96% (95% CI, 88%-99%), respectively. For observer 2, the specificity rates for the detection of stenosis, occlusion, and any disease were 93% (95% CI, 84%-98%), 100% (95% CI, 94%-100%), and 93% (95% CI, 84%-97%), respectively. Linear-weighted kappa values for MR angiography and DSA were 0.78+/-0.084 and 0.62+/-0.152, respectively. CONCLUSION: Time-resolved MR angiography with GRAPPA and TREAT offers excellent image quality and provides an accurate and reliable modality for the detection of pathologic processes in hemodialysis AV fistulas and grafts.
Authors: R P Lim; M Shapiro; E Y Wang; M Law; J S Babb; L E Rueff; J S Jacob; S Kim; R H Carson; T P Mulholland; G Laub; E M Hecht Journal: AJNR Am J Neuroradiol Date: 2008-09-03 Impact factor: 3.825
Authors: Conor J MacDonald; Stephen Gandy; Eilidh C M Avison; Shona Matthew; Rose Ross; John G Houston Journal: MAGMA Date: 2018-08-21 Impact factor: 2.310
Authors: Tarek Zghaib; Adeel Shahid; Chiara Pozzessere; Kristin K Porter; Linda C Chu; John Eng; Hugh Calkins; Ihab R Kamel; Saman Nazarian; Stefan L Zimmerman Journal: Int J Cardiovasc Imaging Date: 2018-04-16 Impact factor: 2.357
Authors: Christopher J R Gallo; Joseph G Mammarappallil; David Y Johnson; Hamid Chalian; James Ronald; Mustafa R Bashir; Charles Y Kim Journal: Radiol Cardiothorac Imaging Date: 2020-11-19