title: Computed Tomography Fractional Flow Reserve Can Identify Culprit Lesions in Aortoiliac Occlusive Disease Using Minimally Invasive Techniques.
abstract: BACKGROUND
  Currently, the gold standard diagnostic examination for significant aortoiliac lesions is angiography. Fractional flow reserve (FFR) has a growing body of literature in coronary artery disease as a minimally invasive diagnostic procedure. Improvements in numerical hemodynamics have allowed for an accurate and minimally invasive approach to estimating FFR, utilizing cross-sectional imaging. We aim to demonstrate a similar approach to aortoiliac occlusive disease (AIOD).
  
  
  METHODS
  A retrospective review evaluated 7 patients with claudication and cross-sectional imaging showing AIOD. FFR was subsequently measured during conventional angiogram with pull-back pressures in a retrograde fashion. To estimate computed tomography (CT) FFR, CT angiography (CTA) image data were analyzed using the SimVascular software suite to create a computational fluid dynamics model of the aortoiliac system. Inlet flow conditions were derived based on cardiac output, while 3-element Windkessel outlet boundary conditions were optimized to match the expected systolic and diastolic pressures, with outlet resistance distributed based on Murray's law. The data were evaluated with a Student's t-test and receiver operating characteristic curve.
  
  
  RESULTS
  All patients had evidence of AIOD on CT and FFR was successfully measured during angiography. The modeled data were found to have high sensitivity and specificity between the measured and CT FFR (P = 0.986, area under the curve = 1). The average difference between the measured and calculated FFRs was 0.136, with a range from 0.03 to 0.30.
  
  
  CONCLUSIONS
  CT FFR successfully identified aortoiliac lesions with significant pressure drops that were identified with angiographically measured FFR. CT FFR has the potential to provide a minimally invasive approach to identify flow-limiting stenosis for AIOD.
authors: Department of Vascular Surgery, University of California, San Diego, San Diego, CA. Electronic address: epward@ucsd.edu.
authors: Erin P
authors: EP
authors: Ward
authors: Department of Pediatrics, Stanford University, Stanford, CA; Department of Bioengineering, Stanford University, Stanford, CA.
authors: Daniele
authors: D
authors: Shiavazzi
authors: Department of Vascular Surgery, University of California, San Diego, San Diego, CA.
authors: Divya
authors: D
authors: Sood
authors: Department of Pediatrics, Stanford University, Stanford, CA; Department of Bioengineering, Stanford University, Stanford, CA.
authors: Allison
authors: A
authors: Marsden
authors: Department of Vascular Surgery, University of California, San Diego, San Diego, CA.
authors: John
authors: J
authors: Lane
authors: Department of Vascular Surgery, University of California, San Diego, San Diego, CA.
authors: Erik
authors: E
authors: Owens
authors: Department of Vascular Surgery, University of California, San Diego, San Diego, CA.
authors: Andrew
authors: A
authors: Barleben
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001018:Aortic Diseases / Q000000981:diagnostic imaging* / Q000503:physiopathology; D001027:Aortography / Q000379:methods*; D019540:Area Under Curve; D001157:Arterial Occlusive Diseases / Q000000981:diagnostic imaging* / Q000503:physiopathology; D062186:Arterial Pressure; D001783:Blood Flow Velocity; D000072226:Computed Tomography Angiography; D003198:Computer Simulation; D003251:Constriction, Pathologic; D005240:Feasibility Studies; D005260:Female; D006439:Hemodynamics; D006801:Humans; D007083:Iliac Artery / Q000000981:diagnostic imaging* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D011237:Predictive Value of Tests; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D012039:Regional Blood Flow; D015203:Reproducibility of Results; D012189:Retrospective Studies