Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.

BACKGROUND: Protoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of using deep learning to enhance PA images. As the model was trained using a limited number of patients' data, its efficacy was limited when applied to individual patients. PURPOSE: In this study, we developed a patient-specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individual patients. METHODS: Our method consists of two stages: in the first stage, a group model is trained from a diverse training set containing all patients, where a novel deep learning network is employed to directly reconstruct the initial pressure maps from the radiofrequency (RF) signals; in the second stage, we apply transfer learning on the pre-trained group model using patient-specific dataset derived from a novel data augmentation method to tune it into a patient-specific model. Raw PA signals were simulated based on computed tomography (CT) images and the pressure map derived from the planned dose. The reconstructed PA images were evaluated against the ground truth by using the root mean squared errors (RMSE), structural similarity index measure (SSIM) and gamma index on 10 specific prostate cancer patients. The significance level was evaluated by t-test with the p-value threshold of 0.05 compared with the results from the group model. RESULTS: The patient-specific model achieved an average RMSE of 0.014 ( p < 0.05 ${{{p}}}<{0.05}$ ), and an average SSIM of 0.981 ( p < 0.05 ${{{p}}}<{0.05}$ ), out-performing the group model. Qualitative results also demonstrated that our patient-specific approach acquired better imaging quality with more details reconstructed when comparing with the group model. Dose verification achieved an average RMSE of 0.011 ( p < 0.05 ${{{p}}}<{0.05}$ ), and an average SSIM of 0.995 ( p < 0.05 ${{{p}}}<{0.05}$ ). Gamma index evaluation demonstrated a high agreement (97.4% [ p < 0.05 ${{{p}}}<{0.05}$ ] and 97.9% [ p < 0.05 ${{{p}}}<{0.05}$ ] for 1%/3  and 1%/5 mm) between the predicted and the ground truth dose maps. Our approach approximately took 6 s to reconstruct PA images for each patient, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. CONCLUSIONS: Our method demonstrated the feasibility of achieving 3D high-precision PA-based dose verification using patient-specific deep-learning approaches, which can potentially be used to guide the treatment to mitigate the impact of range uncertainty and improve the precision. Further studies are needed to validate the clinical impact of the technique.

Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.

Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by better and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modelling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models can predict clinical outcomes. We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression (TRD), across multiple coils and protocols. We then compared the predictive power of those models. LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52-0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Accuracy of patient-specific implants versus CAD/CAM splints with the mandible-first approach in bimaxillary orthognathic surgery for skeletal Class II malocclusion.

This retrospective study aimed to compare the accuracy of patient-specific implants (PSI) versus mandible-first computer-aided design and manufacturing (CAD/CAM) splints for maxilla repositioning in orthognathic surgery of skeletal Class II malocclusion patients. The main predictor was the surgical method (PSI vs. splints), with the primary outcome being the discrepancy in maxilla centroid position, and secondary outcomes being translation and orientation discrepancies. A total of 82 patients were enrolled (70 female, 12 male; mean age 25.5 years), 41 in each group. The PSI group exhibited a median maxillary position discrepancy of 1.25 mm (interquartile range (IQR) 1.03 mm), significantly lower than the splint group's 1.98 mm (IQR 1.64 mm) (P < 0.001). In the PSI group, the largest median translation discrepancy was 0.74 mm (IQR 1.17 mm) in the anteroposterior direction, while the largest orientation discrepancy was 1.83° (IQR 1.63°) in pitch. In the splint group, the largest median translation discrepancy was 1.14 mm (IQR 1.37 mm) in the anteroposterior direction, while the largest orientation discrepancy was 3.03° (IQR 2.11°) in pitch. In conclusion, among patients with skeletal Class II malocclusion, the application of PSI in orthognathic surgery yielded increased precision in maxillary positioning compared to mandible-first CAD/CAM splints.

Exploring CT pixel and voxel size effect on anatomic modeling in mandibular reconstruction.

BACKGROUND: Computer-aided modeling and design (CAM/CAD) of patient anatomy from computed tomography (CT) imaging and 3D printing technology enable the creation of tangible, patient-specific anatomic models that can be used for surgical guidance. These models have been associated with better patient outcomes; however, a lack of CT imaging guidelines risks the capture of unsuitable imaging for patient-specific modeling. This study aims to investigate how CT image pixel size (X-Y) and slice thickness (Z) impact the accuracy of mandibular models. METHODS: Six cadaver heads were CT scanned at varying slice thicknesses and pixel sizes and turned into CAD models of the mandible for each scan. The cadaveric mandibles were then dissected and surface scanned, producing a CAD model of the true anatomy to be used as the gold standard for digital comparison. The root mean square (RMS) value of these comparisons, and the percentage of points that deviated from the true cadaveric anatomy by over 2.00 mm were used to evaluate accuracy. Two-way ANOVA and Tukey-Kramer post-hoc tests were used to determine significant differences in accuracy. RESULTS: Two-way ANOVA demonstrated significant difference in RMS for slice thickness but not pixel size while post-hoc testing showed a significant difference in pixel size only between pixels of 0.32 mm and 1.32 mm. For slice thickness, post-hoc testing revealed significantly smaller RMS values for scans with slice thicknesses of 0.67 mm, 1.25 mm, and 3.00 mm compared to those with a slice thickness of 5.00 mm. No significant differences were found between 0.67 mm, 1.25 mm, and 3.00 mm slice thicknesses. Results for the percentage of points deviating from cadaveric anatomy greater than 2.00 mm agreed with those for RMS except when comparing pixel sizes of 0.75 mm and 0.818 mm against 1.32 mm in post-hoc testing, which showed a significant difference as well. CONCLUSION: This study suggests that slice thickness has a more significant impact on 3D model accuracy than pixel size, providing objective validation for guidelines favoring rigorous standards for slice thickness while recommending isotropic voxels. Additionally, our results indicate that CT scans up to 3.00 mm in slice thickness may provide an adequate 3D model for facial bony anatomy, such as the mandible, depending on the clinical indication.

Evaluation of a method to quantify posture and scapula position using biplanar radiography.

BACKGROUND: Recent studies have stated the relevance of having new parameters to quantify the position and orientation of the scapula with patients standing upright. Although biplanar radiography can provide 3D reconstructions of the scapula and the spine, it is not yet possible to acquire these images with patients in the same position. METHODS: Two pairs of images were acquired, one for the 3D reconstruction of the spine and ribcage and one for the 3D reconstruction of the scapula. Following 3D reconstructions, scapular alignment was performed in two stages, a coarse alignment based on manual annotations of landmarks on the clavicle and pelvis, and an adjusted alignment. Clinical parameters were computed: protraction, internal rotation, tilt and upward rotation. Reproducibility was assessed on an in vivo dataset of upright biplanar radiographs. Accuracy was assessed using supine cadaveric CT-scans and digitally reconstructed radiographs. FINDINGS: The mean error was less than 2° for all clinical parameters, and the 95 % confidence interval for reproducibility ranged from 2.5° to 5.3°. INTERPRETATION: The confidence intervals were lower than the variability measured between participants for the clinical parameters assessed, which indicates that this method has the potential to detect different patterns in pathological populations.

Pre-operative lung ablation prediction using deep learning.

OBJECTIVE: Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments. MATERIALS AND METHODS: Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from follow-up CT post-treatment. Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared against the applicator vendor's estimates. RESULTS: The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49-67); 41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes (p = 0.169) unlike the vendor's estimate (p < 0.001) and had smaller limits of agreement (p < 0.001). An 11% improvement was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest wall, heart, lung boundaries, and fissures was shown. CONCLUSIONS: We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to the procedure, with the potential for improved planning, achieving complete treatments, and reduce tumor recurrence. CLINICAL RELEVANCE STATEMENT: Our method addresses the current lack of reliable tools to estimate ablation extents, required for ensuring successful ablation treatments. The potential clinical implications include improved treatment planning, ensuring complete treatments, and reducing tumor recurrence.

Numerical Modeling of Flow in the Cerebral Vasculature: Understanding Changes in Collateral Flow Directions in the Circle of Willis for a Cohort of Vasospasm Patients Through Image-Based Computational Fluid Dynamics.

The Circle of Willis (CoW) is a ring-like network of blood vessels that perfuses the brain. Flow in the collateral pathways that connect major arterial inputs in the CoW change dynamically in response to vessel narrowing or occlusion. Vasospasm is an involuntary constriction of blood vessels following subarachnoid hemorrhage (SAH), which can lead to stroke. This study investigated interactions between localization of vasospasm in the CoW, vasospasm severity, anatomical variations, and changes in collateral flow directions. Patient-specific computational fluid dynamics (CFD) simulations were created for 25 vasospasm patients. Computed tomographic angiography scans were segmented capturing the anatomical variation and stenosis due to vasospasm. Transcranial Doppler ultrasound measurements of velocity were used to define boundary conditions. Digital subtraction angiography was analyzed to determine the directions and magnitudes of collateral flows as well as vasospasm severity in each vessel. Percent changes in resistance and viscous dissipation were analyzed to quantify vasospasm severity and localization of vasospasm in a specific region of the CoW. Angiographic severity correlated well with percent changes in resistance and viscous dissipation across all cerebral vessels. Changes in flow direction were observed in collateral pathways of some patients with localized vasospasm, while no significant changes in flow direction were observed in others. CFD simulations can be leveraged to quantify the localization and severity of vasospasm in SAH patients. These factors as well as anatomical variation may lead to changes in collateral flow directions. Future work could relate localization and vasospasm severity to clinical outcomes like the development of infarct.

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm.

PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources. MATERIALS AND METHODS: We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs) of thoracic organs using the DeepViewer software. For each patient, radiomics features of the thoracic ROIs were extracted via the Pyradiomics package. The support vector regression (SVR) model was trained based on the radiomics features and reference organ dose obtained by Monte Carlo (MC) simulation. The root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were evaluated. The robustness was verified by randomly assigning patients to the train and test sets of data and comparing regression metrics of different patient assignments. RESULTS: For the right lung, left lung, lungs, esophagus, heart, and trachea, results showed that the trained SVR model achieved the RMSEs of 2 mGy to 2.8 mGy on the test sets, 1.5 mGy to 2.5 mGy on the train sets. The calculated MAPE ranged from 0.1 to 0.18 on the test sets, and 0.08 to 0.15 on the train sets. The calculated R-squared was 0.75 to 0.89 on test sets. CONCLUSIONS: By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.

Fibrosis modeling choice affects morphology of ventricular arrhythmia in non-ischemic cardiomyopathy.

Introduction: Patients with non-ischemic cardiomyopathy (NICM) are at risk for ventricular arrhythmias, but diagnosis and treatment planning remain a serious clinical challenge. Although computational modeling has provided valuable insight into arrhythmic mechanisms, the optimal method for simulating reentry in NICM patients with structural disease is unknown. Methods: Here, we compare the effects of fibrotic representation on both reentry initiation and reentry morphology in patient-specific cardiac models. We investigate models with heterogeneous networks of non-conducting structures (cleft models) and models where fibrosis is represented as a dense core with a surrounding border zone (non-cleft models). Using segmented cardiac magnetic resonance with late gadolinium enhancement (LGE) of five NICM patients, we created 185 3D ventricular electrophysiological models with different fibrotic representations (clefts, reduced conductivity and ionic remodeling). Results: Reentry was induced by electrical pacing in 647 out of 3,145 simulations. Both cleft and non-cleft models can give rise to double-loop reentries meandering through fibrotic regions (Type 1-reentry). When accounting for fibrotic volume, the initiation sites of these reentries are associated with high local fibrotic density (mean LGE in cleft models: p< 0.001, core volume in non-cleft models: p = 0.018, negative binomial regression). In non-cleft models, Type 1-reentries required slow conduction in core tissue (non-cleftsc models) as opposed to total conduction block. Incorporating ionic remodeling in fibrotic regions can give rise to single- or double-loop rotors close to healthy-fibrotic interfaces (Type 2-reentry). Increasing the cleft density or core-to-border zone ratio in cleft and non-cleftc models, respectively, leads to increased inducibility and a change in reentry morphology from Type 2 to Type 1. Conclusions: By demonstrating how fibrotic representation affects reentry morphology and location, our findings can aid model selection for simulating arrhythmogenesis in NICM.

Hemodynamics and Wall Mechanics of Vascular Graft Failure.

Blood vessels are subjected to complex biomechanical loads, primarily from pressure-driven blood flow. Abnormal loading associated with vascular grafts, arising from altered hemodynamics or wall mechanics, can cause acute and progressive vascular failure and end-organ dysfunction. Perturbations to mechanobiological stimuli experienced by vascular cells contribute to remodeling of the vascular wall via activation of mechanosensitive signaling pathways and subsequent changes in gene expression and associated turnover of cells and extracellular matrix. In this review, we outline experimental and computational tools used to quantify metrics of biomechanical loading in vascular grafts and highlight those that show potential in predicting graft failure for diverse disease contexts. We include metrics derived from both fluid and solid mechanics that drive feedback loops between mechanobiological processes and changes in the biomechanical state that govern the natural history of vascular grafts. As illustrative examples, we consider application-specific coronary artery bypass grafts, peripheral vascular grafts, and tissue-engineered vascular grafts for congenital heart surgery as each of these involves unique circulatory environments, loading magnitudes, and graft materials.

A parametric study of the effect of 3D plaque shape on local hemodynamics and implications for plaque instability.

The vast majority of heart attacks occur when vulnerable plaques rupture, releasing their lipid content into the blood stream leading to thrombus formation and blockage of a coronary artery. Detection of these unstable plaques before they rupture remains a challenge. Hemodynamic features including wall shear stress (WSS) and wall shear stress gradient (WSSG) near the vulnerable plaque and local inflammation are known to affect plaque instability. In this work, a computational workflow has been developed to enable a comprehensive parametric study detailing the effects of 3D plaque shape on local hemodynamics and their implications for plaque instability. Parameterized geometric 3D plaque models are created within a patient-specific coronary artery tree using a NURBS (non-uniform rational B-splines)-based vascular modeling pipeline. Realistic blood flow features are simulated by using a Navier-Stokes solver within an isogeometric finite-element analysis framework. Near wall hemodynamic quantities such as WSS and WSSG are quantified, and vascular distribution of an inflammatory marker (VCAM-1) is estimated. Results show that proximally skewed eccentric plaques have the most vulnerable combination of high WSS and high positive spatial WSSG, and the presence of multiple lesions increases risk of rupture. The computational tool developed in this work, in conjunction with clinical data, -could help identify surrogate markers of plaque instability, potentially leading to a noninvasive clinical procedure for the detection of vulnerable plaques before rupture.

Patient-specific analysis of bicuspid aortic valve hemodynamics using a fully coupled fluid-structure interaction model.

Bicuspid aortic valve (BAV), the most common congenital heart disease, is prone to develop significant valvular dysfunction and aortic wall abnormalities such as ascending aortic aneurysm. Growing evidence has suggested that abnormal BAV hemodynamics could contribute to disease progression. In order to investigate BAV hemodynamics, we performed 3D patient-specific fluid-structure interaction (FSI) simulations with fully coupled blood flow dynamics and valve motion throughout the cardiac cycle. Results showed that the hemodynamics during systole can be characterized by a systolic jet and two counter-rotating recirculation vortices. At peak systole, the jet was usually eccentric, with asymmetric recirculation vortices and helical flow motion in the ascending aorta. The flow structure at peak systole was quantified using the vorticity, flow rate reversal ratio and local normalized helicity (LNH) at four locations from the aortic root to the ascending aorta. The systolic jet was evaluated with the peak velocity, normalized flow displacement, and jet angle. It was found that peak velocity and normalized flow displacement (rather than jet angle) gave a strong correlation with the vorticity and LNH in the ascending aorta, which suggests that these two metrics could be used for clinical noninvasive evaluation of abnormal blood flow patterns in BAV patients.

Validation of a patient-specific finite element analysis framework for identification of growing rod-failure regions in early onset scoliosis patients.

PURPOSE: Growing rods are the gold-standard for treatment of early onset scoliosis (EOS). However, these implanted rods experience frequent fractures, requiring additional surgery. A recent study by the U.S. Food and Drug Administration (FDA) identified four common rod fracture locations. Leveraging this data, Agarwal et al. were able to correlate these fractures to high-stress regions using a novel finite element analysis (FEA) framework for one patient. The current study aims to further validate this framework through FEA modeling extended to multiple patients. METHODS: Three patient-specific FEA models were developed to match the pre-operative patient data taken from both registry and biplanar radiographs. The surgical procedure was then simulated to match the post-operative deformity. Body weight and flexion bending (1 Nm) loads were then applied and the output stress data on the rods were analyzed. RESULTS: Radiographic data showed fracture locations at the mid-construct, adjacent to the distal and tandem connector across the patients. Stress analysis from the FEA showed these failure locations matched local high-stress regions for all fractures observed. These results qualitatively validate the efficacy of the FEA framework by showing a decent correlation between localized high-stress regions and the actual fracture sites in the patients. CONCLUSIONS: This patient-specific, in-silico framework has huge potential to be used as a surgical tool to predict sites prone to fracture in growing rod implants. This prospective information would therefore be vital for surgical planning, besides helping optimize implant design for reducing rod failures.

Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation.

INTRODUCTION: As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes. METHODS: Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios. RESULTS: The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04). CONCLUSIONS: By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.

On the feasibility of minimally invasive Le Fort I with patient-specific implants: Proof of concept.

A novel approach to Le Fort I osteotomy is presented, integrating patient-specific implants (PSIs), osteosynthesis and cutting guides within a minimally invasive surgical framework, and the accuracy of the procedure is assessed through 3D voxel-based superimposition. The technique was applied in 5 cases. Differences between the surgical plan and final outcome were evaluated as follows: a 2-mm color scale was established to assess the anterior surfaces of the maxilla, mandible and chin, as well as the condylar surfaces. Measurements were made at 8 specific landmarks, and all of them showed a mean difference of less than 1 mm. In conclusion, the described protocol allows for minimally invasive Le Fort I osteotomy using PSIs. Besides, although the accuracy of the results may be limited by the small sample size, the findings are consistent with those reported in the literature. A prospective comparative study is needed to obtain statistically significant results and draw meaningful conclusions.

A comparative study of altered hemodynamics in iliac vein compression syndrome.

Introduction: Iliac vein compression syndrome (IVCS) is present in over 20% of the population and is associated with left leg pain, swelling, and thrombosis. IVCS symptoms are thought to be induced by altered pelvic hemodynamics, however, there currently exists a knowledge gap on the hemodynamic differences between IVCS and healthy patients. To elucidate those differences, we carried out a patient-specific, computational modeling comparative study. Methods: Computed tomography and ultrasound velocity and area data were used to build and validate computational models for a cohort of IVCS (N = 4, Subject group) and control (N = 4, Control group) patients. Flow, cross-sectional area, and shear rate were compared between the right common iliac vein (RCIV) and left common iliac vein (LCIV) for each group and between the Subject and Control groups for the same vessel. Results: For the IVCS patients, LCIV mean shear rate was higher than RCIV mean shear rate (550 ± 103 s-1 vs. 113 ± 48 s-1, p = 0.0009). Furthermore, LCIV mean shear rate was higher in the Subject group than in the Control group (550 ± 103 s-1 vs. 75 ± 37 s-1, p = 0.0001). Lastly, the LCIV/RCIV shear rate ratio was 4.6 times greater in the Subject group than in the Control group (6.56 ± 0.9 vs. 1.43 ± 0.6, p = 0.00008). Discussion: Our analyses revealed that IVCS patients have elevated shear rates which may explain a higher thrombosis risk and suggest that their thrombus initiation process may share aspects of arterial thrombosis. We have identified hemodynamic metrics that revealed profound differences between IVCS patients and Controls, and between RCIV and LCIV in the IVCS patients. Based on these metrics, we propose that non-invasive measurement of shear rate may aid with stratification of patients with moderate compression in which treatment is highly variable. More investigation is needed to assess the prognostic value of shear rate and shear rate ratio as clinical metrics and to understand the mechanisms of thrombus formation in IVCS patients.

Predicting Lipid-Rich Plaque Progression in Coronary Arteries Using Multimodal Imaging and Wall Shear Stress Signatures.

BACKGROUND: Plaque composition and wall shear stress (WSS) magnitude act as well-established players in coronary plaque progression. However, WSS magnitude per se does not completely capture the mechanical stimulus to which the endothelium is subjected, since endothelial cells experience changes in the WSS spatiotemporal configuration on the luminal surface. This study explores WSS profile and lipid content signatures of plaque progression to identify novel biomarkers of coronary atherosclerosis. METHODS: Thirty-seven patients with acute coronary syndrome underwent coronary computed tomography angiography, near-infrared spectroscopy intravascular ultrasound, and optical coherence tomography of at least 1 nonculprit vessel at baseline and 1-year follow-up. Baseline coronary artery geometries were reconstructed from intravascular ultrasound and coronary computed tomography angiography and combined with flow information to perform computational fluid dynamics simulations to assess the time-averaged WSS magnitude (TAWSS) and the variability in the contraction/expansion action exerted by WSS on the endothelium, quantifiable in terms of topological shear variation index (TSVI). Plaque progression was measured as intravascular ultrasound-derived percentage plaque atheroma volume change at 1-year follow-up. Plaque composition information was extracted from near-infrared spectroscopy and optical coherence tomography. RESULTS: Exposure to high TSVI and low TAWSS was associated with higher plaque progression (4.00±0.69% and 3.60±0.62%, respectively). Plaque composition acted synergistically with TSVI or TAWSS, resulting in the highest plaque progression (≥5.90%) at locations where lipid-rich plaque is exposed to high TSVI or low TAWSS. CONCLUSIONS: Luminal exposure to high TSVI, solely or combined with a lipid-rich plaque phenotype, is associated with enhanced plaque progression at 1-year follow-up. Where plaque progression occurred, low TAWSS was also observed. These findings suggest TSVI, in addition to low TAWSS, as a potential biomechanical predictor for plaque progression, showing promise for clinical translation to improve patient prognosis.

Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.