Modeling and correction of bolus dispersion effects in dynamic susceptibility contrast MRI

• Published in: Magnetic resonance in medicine Vol. 72; no. 6; pp. 1762 - 1774
• Main Authors: Mehndiratta, Amit, Calamante, Fernando, MacIntosh, Bradley J., Crane, David E., Payne, Stephen J., Chappell, Michael A.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.12.2014; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Algorithms; arterial input function; Bayesian Analysis; Carotid Arteries - metabolism; Carotid Arteries - pathology; Carotid Stenosis - metabolism; Carotid Stenosis - pathology; Computer Simulation; Contrast Media - pharmacokinetics; control point interpolation method; deconvolution; dispersion; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Angiography - methods; Male; Metabolic Clearance Rate; Models, Cardiovascular; Reproducibility of Results; residue function; Sensitivity and Specificity; Tissue Distribution; control point interpolation method; deconvolution; Bayesian Analysis; arterial input function; residue function; dispersion
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.25077

Purpose Bolus dispersion in DSC‐MRI can lead to errors in cerebral blood flow (CBF) estimation by up to 70% when using singular value decomposition analysis. However, it might be possible to correct for dispersion using two alternative methods: the vascular model (VM) and control point interpolation (CPI). Additionally, these approaches potentially provide a means to quantify the microvascular residue function. Methods VM and CPI were extended to correct for dispersion by means of a vascular transport function. Simulations were performed at multiple dispersion levels and an in vivo analysis was performed on a healthy subject and two patients with carotid atherosclerotic disease. Results Simulations showed that methods that could not address dispersion tended to underestimate CBF (ratio in CBF estimation, CBFratio = 0.57–0.77) in the presence of dispersion; whereas modified CPI showed the best performance at low‐to‐medium dispersion; CBFratio = 0.99 and 0.81, respectively. The in vivo data showed trends in CBF estimation and residue function that were consistent with the predictions from simulations. Conclusion In patients with atherosclerotic disease the estimated residue function showed considerable differences in the ipsilateral hemisphere. These differences could partly be attributed to dispersive effects arising from the stenosis when dispersion corrected CPI was used. It is thus beneficial to correct for dispersion in perfusion analysis using this method. Magn Reson Med 72:1762–1774, 2014. © 2014 Wiley Periodicals, Inc.