Comparison of stenosis models for usage in the estimation of pressure gradient across aortic coarctation

IF 1.8 4区 生物学 Q3 BIOPHYSICS
Yubing Shi, Israel Valverde, Patricia V. Lawford, Heynric B. Grotenhuis, Philipp Beerbaum, D. Rodney Hose
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引用次数: 0

Abstract

Non-invasive estimation of the pressure gradient in cardiovascular stenosis has much clinical importance in assisting the diagnosis and treatment of stenotic diseases. In this research, a systematic comparison is conducted to investigate the accuracy of a group of stenosis models against the MRI- and catheter-measured patient data under the aortic coarctation condition. Eight analytical stenosis models, including six from the literature and two proposed in this study, are investigated to examine their prediction accuracy against the clinical data. The two improved models proposed in this study consider comprehensively the Poiseuille loss, the Bernoulli loss in its exact form, and the entrance effect, of the blood flow. Comparison of the results shows that one of the proposed models demonstrates a cycle-averaged mean prediction error of −0.15 ± 3.03 mmHg, a peak-to-peak prediction error of −1.8 ± 6.89 mmHg, which is the best among the models studied.

Abstract Image

用于估计主动脉缩窄压力梯度的狭窄模型的比较
无创评估心血管狭窄的压力梯度对辅助狭窄性疾病的诊断和治疗具有重要的临床意义。在本研究中,我们对主动脉缩窄情况下一组狭窄模型与MRI和导管测量患者数据的准确性进行了系统的比较。研究了8个分析性狭窄模型,其中6个来自文献,2个来自本研究,以检验其对临床数据的预测准确性。本研究提出的两种改进模型综合考虑了泊泽维尔损失、伯努利损失的精确形式以及血流的入口效应。结果表明,其中一个模型的周期平均预测误差为- 0.15±3.03 mmHg,峰间预测误差为- 1.8±6.89 mmHg,是所研究模型中最好的。
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来源期刊
Journal of Biological Physics
Journal of Biological Physics 生物-生物物理
CiteScore
3.00
自引率
5.60%
发文量
20
审稿时长
>12 weeks
期刊介绍: Many physicists are turning their attention to domains that were not traditionally part of physics and are applying the sophisticated tools of theoretical, computational and experimental physics to investigate biological processes, systems and materials. The Journal of Biological Physics provides a medium where this growing community of scientists can publish its results and discuss its aims and methods. It welcomes papers which use the tools of physics in an innovative way to study biological problems, as well as research aimed at providing a better understanding of the physical principles underlying biological processes.
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