Echocardiography Provides a Reliable Estimate of Total Cardiac Volume for Pediatric Heart Transplantation.

BACKGROUND: Donor-to-recipient size matching for heart transplantation typically involves comparing donor and recipient body weight; however, weight is not linearly related to cardiac size. Attention has shifted toward the use of computed tomography- (CT-) derived total cardiac volume (TCV), that is, CT-TCV, to compare donor and recipient heart organ size. At this time, TCV size matching is near impossible for most centers due to logistical limitations. To overcome this impediment, echocardiogram-derived TCV (ECHO-TCV) is an attractive, alternative option to estimate CT-TCV. The goal of this study is to test whether ECHO-TCV is an accurate and reliable surrogate for TCV measurement compared with the gold standard CT-TCV. METHODS: ECHO-TCV and CT-TCV were measured in a cohort spanning the neonatal to young adult age range with the intention to simulate the pediatric heart transplant donor pool. ECHO-TCV was measured using a modified Simpson's summation-of-discs method from the apical 4-chamber (A4C) view. The gold standard of CT-TCV was measured from CT scans using three-dimensional reconstruction software. The relationship between ECHO-TCV and CT-TCV was evaluated and compared with other anthropometric and image-based markers that may predict CT-TCV. Inter-rater reliability of ECHO-TCV was tested among 4 independent observers. Subanalyses were performed to identify imaging views and timing that enable greater accuracy of ECHO-TCV. RESULTS: Banked imaging data of 136 subjects with both echocardiogram and CT were identified. ECHO-TCV demonstrated a linear relationship to CT-TCV with a Pearson correlation coefficient of r = 0.96 (95% CI, 0.95-0.97; P < .0001) and mean absolute percent error of 8.6%. ECHO-TCV correlated most strongly with CT-TCV in the subset of subjects <4 years of age (n = 33; r = 0.98; 95% CI, 0.96-0.99; P < .0001). The single-score intraclass correlation coefficient across all 4 raters is 0.96 (interquartile range, 0.93-0.98). ECHO-TCV measured from a standard A4C view at end diastole with the atria in the plane of view had the strongest correlation to CT-TCV. CONCLUSIONS: ECHO-TCV by the A4C view was found to be both an accurate and reliable alternative measurement of CT-TCV and is derived from readily available donor ECHO images. The ECHO-TCV findings in this study make the ECHO method an attractive means of direct donor-to-recipient TCV size matching in pediatric heart transplantation.

Four-dimensional fetal cardiac imaging in a cohort of fetuses with suspected congenital heart disease.

BACKGROUND: Fetal cardiac magnetic resonance imaging (MRI) requires high spatial and temporal resolution and robustness to random fetal motion to capture the dynamics of the beating fetal heart. Slice-to-volume reconstruction techniques can produce high-resolution isotropic images while compensating for random fetal motion. OBJECTIVE: The objective of this study was to evaluate image quality for slice-to-volume reconstruction of four-dimensional balanced steady-state free precession (bSSFP) imaging of the fetal heart. MATERIALS AND METHODS: A cohort of 13 women carrying fetuses with congenital heart disease were imaged with real-time bSSFP sequences. Real-time bSSFP sequences were post-processed using a slice-to-volume reconstruction algorithm to produce retrospectively gated 4-D sequences with isotropic spatial resolution. Two radiologists evaluated slice-to-volume reconstruction image quality on a scale from 0 to 4 using 11 categories based on a segmental approach to defining cardiac anatomy and pathology. A score of 0 corresponded to cardiac structures not visualized at all and four corresponded to high quality and distinct appearance of structures. RESULTS: In 11 out of 13 cases, the average radiologist score of image quality across all categories was 3.0 or greater. In the remaining two cases, slice-to-volume reconstruction was not possible due to insufficient image quality in the acquisition. CONCLUSION: Slice-to-volume reconstruction has the potential to produce isotropic images with high spatial and temporal resolution that can display the anatomy of the fetal heart in arbitrary imaging planes retrospectively. More rapid, motion-robust acquisitions may be necessary to successfully reconstruct the fetal heart in all patients.

Comparing donor and recipient total cardiac volume predicts risk of short-term adverse outcomes following heart transplantation.

INTRODUCTION: In pediatric heart transplantation, donor: recipient weight ratio (DRWR) has long been the sole metric for size matching. Total cardiac volume (TCV)-based size matching has emerged as a novel method to precisely identify an upper limit of donor organ size of a heart transplant recipient while minimizing the risk of complications from oversizing. The clinical adoption of donor: recipient volume ratio (DRVR) to prevent short-term adverse outcomes of oversizing is unknown. The purpose of this single-center study is to determine the relationship of DRWR and DRVR to the risk of post-operative complications from allograft oversizing. METHODS: Recipient TCV was measured from imaging studies and donor TCV was calculated from published TCV prediction models. DRVR was defined as donor TCV divided by recipient TCV. The primary outcome was short-term post-transplant complications (SPTC), a composite outcome of delayed chest closure and prolonged intubation > 7 days. A multivariable logistic regression model of DRWR (cubic spline), DRVR (linear) and linear interaction between DRWR and DRVR was used to examine the probability of experiencing a SPTC over follow-up as a function of DRWR and DRVR. RESULTS: A total of 106 transplant patients' records were reviewed. Risk of the SPTC increased as DRVR increased. Both low and high DRWR was associated with the SPTC. A logistic regression model including DRWR and DRVR predicted SPTC with an AUROC curve of 0.74. [95% CI 0.62 0.85]. The predictive model identified a "low-risk zone" of donor-recipient size match between a weight ratio of 0.8 and 2.0 and a TCV ratio less than 1.0. CONCLUSION: DRVR in combination with DRWR predicts short-term post-transplant adverse events. Accepting donors with high DRWR may be safely performed when DRVR is considered.

Computational Investigation of Anastomosis Options of a Right-Heart Pump to Patient Specific Pulmonary Arteries.

Patients with Fontan circulation have increased risk of heart failure, but are not always candidates for heart transplant, leading to the development of the subpulmonic Penn State Fontan Circulation Assist Device. The aim of this study was to use patient-specific computational fluid dynamics simulations to evaluate anastomosis options for implanting this device. Simulations were performed of the pre-surgical anatomy as well as four surgical options: a T-junction and three Y-grafts. Cases were evaluated based on several fluid-dynamic quantities. The impact of imbalanced left-right pulmonary flow distribution was also investigated. Results showed that a 12-mm Y-graft was the most energy efficient. However, an 8-mm graft showed more favorable wall shear stress distribution, indicating lower risk of thrombosis and endothelial damage. The 8-mm Y-grafts also showed a more balanced pulmonary flow split, and lower residence time, also indicating lower thrombosis risk. The relative performance of the surgical options was largely unchanged whether or not the pulmonary vascular resistance remained imbalanced post-implantation.

Volumetrics and fit assessments for donor to recipient size matching in pediatric heart transplantation: Is it time for a new paradigm?

Pediatric heart transplant patients face the highest waitlist mortality in solid organ transplantation. Given the relatively fixed number of donor organs becoming available each year, improving donor organ utilization could potentially have significant impact on reducing waitlist mortality. Donor to recipient weight ratio has historically been used to identify suitable donors; however, this method does not take into account the potential for significant variance in heart size due to complex congenital heart disease or underlying cardiomyopathy. We believe, based on our experience to date, that donor matching based upon weight ratios should be augmented by improved methodologies that provide a more accurate assessment of heart volumes. Herein we describe the rationale for these methodologies and our single-center experience using volumetrics as an alternative for donor fit assessments.

Corrigendum to "Structural alterations of the brainstem in migraine" (Neuroimage Clin. 2017; 13: 223-227).

[This corrects the article DOI: 10.1016/j.nicl.2016.10.023.].

Alternative methods for virtual heart transplant-Size matching for pediatric heart transplantation with and without donor medical images available.

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.

3D printing for congenital heart disease: a single site's initial three-yearexperience.

BACKGROUND: 3D printing is an ideal manufacturing process for creating patient-matched models (anatomical models) for surgical and interventional planning. Cardiac anatomical models have been described in numerous case studies and journal publications. However, few studies attempt to describe wider impact of the novel planning augmentation tool. The work here presents the evolution of an institution's first 3 full years of 3D prints following consistent integration of the technology into clinical workflow (2012-2014) - a center which produced 79 models for surgical planning (within that time frame). Patient outcomes and technology acceptance following implementation of 3D printing were reviewed. METHODS: A retrospective analysis was designed to investigate the anatomical model's impact on time-based surgical metrics. A contemporaneous cohort of standard-of-care pre-procedural planning (no anatomical models) was identified for comparative analysis. A post-surgery technology acceptance assessment was also employed in a smaller subset to measure perceived efficacy of the anatomical models. The data was examined. RESULTS: Within the timeframe of the study, 928 primary-case cardiothoracic surgeries (encompassing both CHD and non-CHD surgeries) took place at the practicing pediatric hospital. One hundred sixty four anatomical models had been generated for various purposes. An inclusion criterion based on lesion type limited those with anatomic models to 33; there were 113 cases matching the same criterion that received no anatomical model. Time-based metrics such as case length-of-time showed a mean reduction in overall time for anatomical models. These reductions were not statistically significant. The technology acceptance survey did demonstrate strong perceived efficacy. Anecdotal vignettes further support the technology acceptance. DISCUSSION & CONCLUSION: The anatomical models demonstrate trends for reduced operating room and case length of time when compared with similar surgeries in the same time-period; in turn, these reductions could have significant impact on patient outcomes and operating room economics. While analysis did not yield robust statistical powering, strong Cohen's d values suggest poor powering may be more related to sample size than non-ideal outcomes. The utility of planning with an anatomical model is further supported by the technology acceptance study which demonstrated that surgeons perceive the anatomical models to be an effective tool in surgical planning for a complex CHD repair. A prospective multi-center trial is currently in progress to further validate or reject these findings.

Mathematical Analysis of Glioma Growth in a Murine Model.

Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.

Structural alterations of the brainstem in migraine.

Atypical brainstem modulation of pain might contribute to changes in sensory processing typical of migraine. The study objective was to investigate whether migraine is associated with brainstem structural alterations that correlate with this altered pain processing. MRI T1-weighted images of 55 migraine patients and 58 healthy controls were used to: (1) create deformable mesh models of the brainstem that allow for shape analyses; (2) calculate volumes of the midbrain, pons, medulla and the superior cerebellar peduncles; (3) interrogate correlations between regional brainstem volumes, cutaneous heat pain thresholds, and allodynia symptoms. Migraineurs had smaller midbrain volumes (healthy controls = 61.28 mm3, SD = 5.89; migraineurs = 58.80 mm3, SD = 6.64; p = 0.038), and significant (p < 0.05) inward deformations in the ventral midbrain and pons, and outward deformations in the lateral medulla and dorsolateral pons relative to healthy controls. Migraineurs had a negative correlation between ASC-12 allodynia symptom severity with midbrain volume (r = - 0.32; p = 0.019) and a positive correlation between cutaneous heat pain thresholds with medulla (r = 0.337; p = 0.012) and cerebellar peduncle volumes (r = 0.435; p = 0.001). Migraineurs with greater symptoms of allodynia have smaller midbrain volumes and migraineurs with lower heat pain thresholds have smaller medulla and cerebellar peduncles. The brainstem likely plays a role in altered sensory processing in migraine and brainstem structure might reflect severity of allodynia and hypersensitivity to pain in migraine.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

BACKGROUND: Glioblastoma (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. METHODS: We 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). RESULTS: We 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). CONCLUSION: MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

BACKGROUND: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. METHODS: We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. RESULTS: We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). CONCLUSION: Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Spatiotemporal quantification of local drug delivery using MRI.

Controlled release formulations for local, in vivo drug delivery are of growing interest to device manufacturers, research scientists, and clinicians; however, most research characterizing controlled release formulations occurs in vitro because the spatial and temporal distribution of drug delivery is difficult to measure in vivo. In this work, in vivo magnetic resonance imaging (MRI) of local drug delivery was performed to visualize and quantify the time resolved distribution of MRI contrast agents. Three-dimensional T1 maps (generated from T1-weighted images with varied TR) were processed using noise-reducing filtering. A segmented region of contrast, from a thresholded image, was converted to concentration maps using the equation 1/T1=1/T1,0+R1C, where T1,0 and T1 are the precontrast and postcontrast T1 map values, respectively. In this technique, a uniform estimated value for T 1,0 was used. Error estimations were performed for each step. The practical usefulness of this method was assessed using comparisons between devices located in different locations both with and without contrast. The method using a uniform T1,0, requiring no registration of pre- and postcontrast image volumes, was compared to a method using either affine or deformation registrations.