Browsing by Author "Rollins, Dave M."

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    A Mock Circulation Loop to Characterize In Vitro Hemodynamics in Human Systemic Arteries with Stenosis
    (MDPI, 2023) Hong, Weichen; Yu, Huidan; Chen, Jun; Talamantes, John; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuck, Alan P.; Surgery, School of Medicine
    Vascular disease is the leading cause of morbidity and mortality and a major cause of disability for Americans, and arterial stenosis is its most common form in systemic arteries. Hemodynamic characterization in a stenosed arterial system plays a crucial role in the diagnosis of its lesion severity and the decision-making process for revascularization, but it is not readily available in the current clinical measurements. The newly emerged image-based computational hemodynamics (ICHD) technique provides great potential to characterize the hemodynamics with fine temporospatial resolutions in realistic human vessels, but medical data is rather limited for validation requirements. We present an image-based experimental hemodynamics (IEHD) technique through a mock circulation loop (MCL) to bridge this critical gap. The MCL mimics blood circulation in human stenosed systemic arterial systems that can be either 3D-printed silicone, artificial, or cadaver arteries and thus enables in vitro measurement of hemodynamics. In this work, we focus on the development and validation of the MCL for the in vitro measurement of blood pressure in stenosed silicone arteries anatomically extracted from medical imaging data. Five renal and six iliac patient cases are studied. The pressure data from IEHD were compared with those from ICHD and medical measurement. The good agreements demonstrate the reliability of IEHD. We also conducted two parametric studies to demonstrate the medical applicability of IEHD. One was the cardiovascular response to MCL parameters. We found that blood pressure has a linear correlation with stroke volume and heart rate. Another was the effect of arterial stenosis, characterized by the volumetric reduction (VR) of the arterial lumen, on the trans-stenotic pressure gradient (TSPG). We parametrically varied the stenosis degree and measured the corresponding TSPG. The TSPG-VR curve provides a critical VR that can be used to assess the true hemodynamic severity of the stenosis. Meanwhile, the TSPG at VR = 0 can predict the potential pressure improvement after revascularization. Unlike the majority of existing MCLs that are mainly used to test medical devices involving heart function, this MCL is unique in its specific focus on pressure measurement in stenosed human systemic arteries. Meanwhile, rigorous hemodynamic characterization through concurrent IEHD and ICHD will significantly enhance our current understanding of the pathophysiology of stenosis and contribute to advancements in the medical treatment of arterial stenosis.
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    A new noninvasive and patient-specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
    (Wiley, 2022-07) Yu, Huidan; Khan, Monsurul; Wu, Hao; Du, Xiaoping; Chen, Rou; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuk, Alan P.; Surgery, School of Medicine
    Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with graphic processing unit parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S = 0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p < .001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient-specific hemodynamic index has the potential to aid in the clinical decision-making of interventional treatment with reduced medical cost and patient risks.
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    A new noninvasive and patient-specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
    (Wiley, 2022) Yu, Huidan; Khan, Monsurul; Wu, Hao; Du, Xiaoping; Chen, Rou; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuk, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with GPU parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S=0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in-vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p<0.001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient-specific hemodynamic index has the potential to aid in the clinical decision-making of interventional treatment with reduced medical cost and patient risks.
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    Predicting future occlusion or stenosis of lower extremity bypass grafts using artificial intelligence to simultaneously analyze all flow velocities collected in current and previous ultrasound examinations
    (Elsevier, 2024-02-05) Luo, Xiao; Tahabi, Fattah Muhammad; Rollins, Dave M.; Sawchuk, Alan P.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Objective: Routine surveillance with duplex ultrasound (DUS) examination is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts with various intervals postoperatively. The presently used methodology to analyze bypass graft DUS examination does not use all the available data and has been shown to have a significant rate for missing impending bypass graft failure. The objective of this research is to investigate recurrent neural networks (RNNs) to predict future bypass graft occlusion or stenosis. Methods: This study includes DUS examinations of 663 patients who had bypass graft operations done between January 2009 and June 2022. Only examinations without missing values were included. We developed two RNNs (a bidirectional long short-term memory unit and a bidirectional gated recurrent unit) to predict bypass graft occlusion and stenosis based on peak systolic velocities collected in the 2 to 5 previous DUS examinations. We excluded the examinations with missing values and split our data into training and test sets. Then, we applied 10-fold cross-validation on training to optimize the hyperparameters and compared models using the test data. Results: The bidirectional long short-term memory unit model can gain an overall sensitivity of 0.939, specificity of 0.963, and area under the curve of 0.950 on the prediction of bypass graft occlusion, and an overall sensitivity of 0.915, specificity of 0.909, and area under the curve of 0.912 predicting the development of a future critical stenosis. The results on different bypass types show that the system performs differently on different types. The results on subcohorts based on gender, smoking status, and comorbidities show that the performance on current smokers is lower than the never smoker. Conclusions: We found that RNNs can gain good sensitivity, specificity, and accuracy for the detection of impending bypass graft occlusion or the future development of a critical bypass graft stenosis using all the available peak systolic velocity data in the present and previous bypass graft DUS examinations. Integrating clinical data, including demographics, social determinants, medication, and other risk factors, together with the DUS examination may result in further improvements. Clinical relevance: Detecting bypass graft failure before it occurs is important clinically to prevent amputations, salvage limbs, and save lives. Current methods evaluating screening duplex ultrasound examinations have a significant failure rate for detecting a bypass graft at risk for failure. Artificial intelligence using recurrent neural networks has the potential to improve the detection of at-risk bypass graft before they fail. Additionally, artificial intelligence is in the news and is being applied to many fields. Vascular surgeons need to know its potential to improve vascular outcomes.