Frakes, David

INTRODUCTION: Despite more than a decade of endovascular coil treatment, the effects of coils on cerebral aneurysm (CA) hemodynamics are still poorly understood. Coils present several challenges to in vivo and in vitro flow measurement techniques and previous in silico methods have suffered from large assumptions on coil geometry. Here we present the first fluid dynamic simulations of coiled CAs that consider the structure and deployment mechanics of embolic coils. We also investigate the influence of coil packing density, design, and configuration on CA fluid dynamics. Methods: Coil deployment was modeled using a novel finite element approach that realistically simulates coil dynamics during deployment. Two coil designs were investigated: helical and 3D. Coil design and material properties were matched to manufacturer specifications. Five deployment sequences of each coil design, at different microcatheter placements, were simulated in two idealized CA models with variable neck sizes. Blood flow was simulated using computational fluid dynamics. Simulated deployments and fluid dynamics were then compared to deployments of actual coils in identical physical CA models and in vitro particle image velocimetry flow measurements. Results: Simulated results closely matched in vitro data. Reductions in aneurysmal velocity magnitudes were largest for 3D coils and in a narrow-neck model. In that model, 3D coil deployments reduced average aneurysmal velocity magnitudes by a 51% - 69% range at packing densities less than 20% and by a 74% - 84% range at packing densities greater than 30%. Linear regression results showed reductions were strongly dependent on the spacing between coil loops within the aneurysm and packing density, with correlations of 0.6 and 0.7 respectively. Conclusion: Coil design and packing density may play equally important roles in determining CA hemodynamics. Results in an anatomical model will also be presented. The proposed virtual coiling approach represents a novel and effective method for realistically simulating coiled CAs, and is an important step towards clinical preoperative planning of coil treatment.

Abstract Background Listed pediatric heart transplant patients have the highest solid‐organ waitlist mortality rate. The donor‐recipient body weight ( DRBW ) ratio is the clinical standard for allograft size matching but may unnecessarily limit a patient's donor pool. To overcome DRBW ratio limitations, two methods of performing virtual heart transplant fit assessments were developed that account for patient‐specific nuances. Method 1 uses an allograft total cardiac volume ( TCV ) prediction model informed by patient data wherein a matched allograft 3‐D reconstruction is selected from a virtual library for assessment. Method 2 uses donor images for a direct virtual transplant assessment. Methods Assessments were performed in medical image reconstruction software. The allograft model was developed using allometric/isometric scaling assumptions and cross‐validation. Results The final predictive model included gender, height, and weight. The 25th‐, 50th‐, and 75th‐percentiles for TCV percentage errors were −13% (over‐prediction), −1%, and 8% (under‐prediction), respectively. Two examples illustrating the potential of virtual assessments are presented. Conclusion Transplant centers can apply these methods to perform their virtual assessments using existing technology. These techniques have potential to improve organ allocation. With additional experience and refinement, virtual transplants may become standard of care for determining suitability of donor organ size for an identified recipient.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Embolic coiling is one of the most effective treatments for cerebral aneurysms (CAs), largely due to the hemodynamic modifications that the treatment effects in the aneurysmal environment. However, coiling can have very different hemodynamic outcomes in aneurysms with different geometries. Previous work in the field of biofluid mechanics has demonstrated on a general level that geometry is a driving factor behind aneurysmal hemodynamics. The goal of this study was to relate two specific geometric factors that describe CAs (i.e., dome size (DS) and parent-vessel contact-angle (PV-CA)) and one factor that describes treatment (i.e., coil packing density (PD)) to three clinically relevant hemodynamic responses (i.e., aneurysmal root-mean-square velocity (Vrms), aneurysmal wall shear stress (WSS), and cross-neck flow (CNF)). Idealized models of basilar tip aneurysms were created in both virtual and physical forms to satisfy two-level multifactorial experimental designs. Steady and pulsatile flow hemodynamics were then evaluated in the virtual models using computational fluid dynamics (CFD) (before and after virtual treatment with finite element (FE) embolic coil models), and hemodynamics were also evaluated in the physical models using particle image velocimetry (PIV) (before and after treatment with actual embolic coils). Results showed that among the factors considered, PD made the greatest contributions to effects on hemodynamic responses in and around the aneurysmal sac (i.e., Vrms and WSS), while DS made the greatest contributions to effects on hemodynamics at the neck (i.e., CNF). Results also showed that while a geometric factor (e.g., PV-CA) may play a relatively minor role in dictating hemodynamics in the untreated case, the same factor can play a much greater role after coiling. We consider the significance of these findings in the context of aneurysmal recurrence and rupture, and explore potential roles for the proposed methods in endovascular treatment planning.

Endovascular coiling is the most common treatment for cerebral aneurysms. The treatment consists of deploying coils of different shapes and sizes into the aneurysm with the intent of reducing blood flow therein. While the treatment has been shown to be more effective than conventional surgical methods, it is successful in only 50–80% of cases, depending on the type of aneurysm [1]. Treatment success has been correlated with favorable post-treatment hemodynamics [2]. Understanding of the effects of coils on post-treatment hemodynamics is limited, however, due to limited in-vivo and in-vitro flow measurement techniques. Previous computational studies have also suffered from drastic assumptions about coil geometry.

Mechanical circulatory support emerged for the pediatric population in the late 1980s as a bridge to cardiac transplantation. The Total Artificial Heart (TAH-t) (SynCardia Systems Inc., Tuscon, AZ) has been approved for compassionate use by the Food and Drug Administration for patients with end-stage biventricular heart failure as a bridge to heart transplantation since 1985 and has had FDA approval since 2004. However, of the 1,061 patients placed on the TAH-t, only 21 (2%) were under the age 18. SynCardia Systems, Inc. recommends a minimum patient body surface area (BSA) of 1.7 m 2 , thus, limiting pediatric application of this device. This unique case report shares this pediatric institution’s first experience with the TAH-t. A 14-year-old male was admitted with dilated cardiomyopathy and severe biventricular heart failure. The patient rapidly decompensated, requiring extracorporeal life support. An echocardiogram revealed severe biventricular dysfunction and diffuse clot formation in the left ventricle and outflow tract. The decision was made to transition to biventricular assist device. The biventricular failure and clot formation helped guide the team to the TAH-t, in spite of a BSA (1.5 m 2 ) below the recommendation of 1.7m 2 . A computed tomography (CT) scan of the thorax, in conjunction with a novel three-dimensional (3D) modeling system and team, assisted in determining appropriate fit. Chest CT and 3D modeling following implantation were utilized to determine all major vascular structures were unobstructed and the bronchi were open. The virtual 3D model confirmed appropriate device fit with no evidence of compression to the left pulmonary veins. The postoperative course was complicated by a left lung opacification. The left lung anomalies proved to be atelectasis and improved with aggressive recruitment maneuvers. The patient was supported for 11 days prior to transplantation. Chest CT and 3D modeling were crucial in assessing whether the device would fit, as well as postoperative complications in this smaller pediatric patient.