Non-contrast-enhanced magnetic resonance angiography: a reliable clinical tool for evaluating transplant renal artery stenosis
To evaluate image quality of non-contrast-enhanced magnetic resonance angiography (MRA) and compare transplant renal artery stenosis (TRAS) seen by non-contrast-enhanced MRA with digital subtraction angiography (DSA) as the gold standard.
Materials and methods
330 patients receiving 369 non-contrast-enhanced MRA examinations from July 2014 to June 2017 were included. Thirty patients received at least two MRA examinations. Image quality was independently assessed by two radiologists. Inter-observer agreement was analyzed. Transplant renal artery anatomy and complications were evaluated and compared with DSA. If possible, accuracy was calculated on a per-artery basis.
Good or excellent image quality was found in 95.4 % (352/369) of examinations with good inter-observer agreement (K=0.760). Twenty-two patients with DSA had 28 non-contrast-enhanced MRA examinations within a 2-month period. Of these, 19 patients had TRAS, two patients had pseudoaneurysms, and one patient had a normal transplant renal artery but an occluded external iliac artery. Non-contrast-enhanced MRA correctly detected 19 TRAS and nine normal arteries, giving 96.6 % accuracy on a per-artery basis.
Non-contrast-enhanced MRA demonstrates a good depiction of the transplanted renal artery and shows good correlation with DSA in cases where there was TRAS.
• Good or excellent image quality was found in 95.4 % of examinations.
• Non-contrast-enhanced MRA can clearly map transplant renal artery anatomy.
• Non-contrast-enhanced MRA is a reliable tool to detect TRAS.
KeywordsAngiography, digital subtraction Renal artery obstruction Kidney transplantation Magnetic resonance angiography Magnetic resonance imaging
Chronic kidney disease
Digital subtraction angiography
Inflow inversion recovery
Magnetic resonance angiography
Steady-state free precession
Transplant renal artery stenosis
Compliance with ethical standards
The scientific guarantor of this publication is Long Jiang Zhang.
Conflict of interest
UJS is a consultant for and/or receives research support from Astellas, Bayer, General Electric, Guerbet and Siemens Healthineers. AVS received research support from Siemens Healthineers. All other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• performed at one institution
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