Original InvestigationValidation of Renal Artery Dimensions Measured by Magnetic Resonance Angiography in Patients Referred for Renal Sympathetic Denervation
Section snippets
Patient Selection
Nine consecutive patients underwent both MRA and preprocedural IVUS at the time of the renal denervation procedure. Because of an early trifurcation in the vessel in one patient, automated segmentation with MRA was not successful in one artery. For the MRA analyses, 17 vessels were used. For the correlation analyses between MRA and IVUS, MRA vessel length was determined and manually adjusted on the basis of the length of the IVUS pullback with either the ostium or bifurcation as landmark.
Segmentation Algorithm and Renal Artery Dimensions
In nine patients, 17 renal arteries were analyzed to study intraobserver and interobserver reproducibility. In three renal arteries by observer 1 and in 3 renal arteries by observer 2, the bifurcation was selected manually. The segmentation was not possible in two matched renal arteries. Minimum lumen diameter, mean lumen diameter, and total lumen volume measured by MRA and IVUS are summarized in Table 1 and Supplementary Data, .
Intraobserver Reproducibility MRA
Mean minimum lumen diameter was 5.1 ± 0.8 mm. Bias of minimum
Discussion
Our study demonstrates that quantitative MRA can measure the renal artery dimensions (vessel length, minimum lumen diameter, mean lumen diameter, and renal artery volumes) reproducibly and with good intraobserver and interobserver variability. Intraobserver variability using semiautomatic MRA analysis was excellent for minimum lumen diameter and mean lumen diameter (0.8% and 0.7%, respectively). For vessel, length and total lumen volume was slightly higher but were still well (2.5% and 2.9%,
Conclusions
Renal artery dimensions acquired with MRA can be quantified with good reproducibility, and these measurements correlated well with IVUS measurements. . The software is a promising tool to assess changes in renal artery dimension after renal artery intervention and for the selection of the proper device before the procedure.
Acknowledgments
The authors thank Jean-Paul Aben, Pie Medical Imaging, Maastricht, The Netherlands, for his help with the detailed description of the segmentation algorithm.
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Conflicts of Interest: A.K. has received research support from St Jude Medical; K.N. has received institutional research support from Siemens Medical Solutions, GE Healthcare, and Bayer Healthcare; W.N. is co-founder, scientific director, and share holder of Quantib BV; G.K. received institutional research support from GE Healthcare, Siemens AG, and Bayer Healthcare. He received payment for lectures from EISAI Japan. All other authors have reported that they have no relationships relevant to the contents of this article to disclose.