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1.
Med Phys ; 42(5): 2143-56, 2015 May.
Article in English | MEDLINE | ID: mdl-25979009

ABSTRACT

PURPOSE: Coarctation of the aorta (CoA) is a congenital heart disease characterized by an abnormal narrowing of the proximal descending aorta. Severity of this pathology is quantified by the blood pressure drop (△P) across the stenotic coarctation lesion. In order to evaluate the physiological significance of the preoperative coarctation and to assess the postoperative results, the hemodynamic analysis is routinely performed by measuring the △P across the coarctation site via invasive cardiac catheterization. The focus of this work is to present an alternative, noninvasive measurement of blood pressure drop △P through the introduction of a fast, image-based workflow for personalized computational modeling of the CoA hemodynamics. METHODS: The authors propose an end-to-end system comprised of shape and computational models, their personalization setup using MR imaging, and a fast, noninvasive method based on computational fluid dynamics (CFD) to estimate the pre- and postoperative hemodynamics for coarctation patients. A virtual treatment method is investigated to assess the predictive power of our approach. RESULTS: Automatic thoracic aorta segmentation was applied on a population of 212 3D MR volumes, with mean symmetric point-to-mesh error of 3.00 ± 1.58 mm and average computation time of 8 s. Through quantitative evaluation of 6 CoA patients, good agreement between computed blood pressure drop and catheter measurements is shown: average differences are 2.38 ± 0.82 mm Hg (pre-), 1.10 ± 0.63 mm Hg (postoperative), and 4.99 ± 3.00 mm Hg (virtual stenting), respectively. CONCLUSIONS: The complete workflow is realized in a fast, mostly-automated system that is integrable in the clinical setting. To the best of our knowledge, this is the first time that three different settings (preoperative--severity assessment, poststenting--follow-up, and virtual stenting--treatment outcome prediction) of CoA are investigated on multiple subjects. We believe that in future-given wider clinical validation-our noninvasive in-silico method could replace invasive pressure catheterization for CoA.


Subject(s)
Aortic Coarctation/pathology , Aortic Coarctation/physiopathology , Blood Pressure , Hemodynamics , Magnetic Resonance Imaging/methods , Precision Medicine/methods , Aorta/pathology , Aorta/physiopathology , Aorta/surgery , Aortic Coarctation/diagnosis , Aortic Coarctation/surgery , Computer Simulation , Follow-Up Studies , Humans , Imaging, Three-Dimensional/methods , Magnetic Resonance Angiography/methods , Models, Cardiovascular , Pattern Recognition, Automated , Prognosis , Stents , Time Factors , Treatment Outcome
2.
Med Image Comput Comput Assist Interv ; 15(Pt 2): 486-93, 2012.
Article in English | MEDLINE | ID: mdl-23286084

ABSTRACT

Coarctation of the aorta (CoA), is a congenital defect characterized by a severe narrowing of the aorta, usually distal to the aortic arch. The treatment options include surgical repair, stent implantation, and balloon angioplasty. In order to evaluate the physiological significance of the pre-operative coarctation and to assess the post-operative results, the hemodynamic analysis is usually performed by measuring the pressure gradient (deltaP) across the coarctation site via invasive cardiac catheterization. The measure of success is reduction of the (deltaP > 20 mmHg) systolic blood pressure gradient. In this paper, we propose a non-invasive method based on Computational Fluid Dynamics and MR imaging to estimate the pre- and post-operative hemodynamics for both native and recurrent coarctation patients. High correlation of our results and catheter measurements is shown on corresponding pre- and post-operative examination of 5 CoA patients.


Subject(s)
Aorta/physiopathology , Aortic Coarctation/physiopathology , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Angiography/methods , Myocardial Perfusion Imaging/methods , Aorta/pathology , Aortic Coarctation/pathology , Blood Flow Velocity , Humans , Image Enhancement/methods , Reproducibility of Results , Sensitivity and Specificity
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