Nabil Boutagy

Recent Advances and Clinical Applications of PET Cardiac Autonomic Nervous System Imaging

Purpose of ReviewThe purpose of this review was to summarize current advances in positron emission tomography (PET) cardiac autonomic nervous system (ANS) imaging, with a specific focus on clinical applications of novel and established tracers.Recent Findings[11C]-Meta-hydroxyephedrine (HED) has provided useful information in evaluation of normal and pathological cardiovascular function. Recently, [11C]-HED PET imaging was able to predict lethal arrhythmias, sudden cardiac death (SCD), and all-cause mortality in heart failure patients with reduced ejection fraction (HFrEF). In addition, initial [11C]-HED PET imaging studies have shown the potential of this agent in elucidating the relationship between impaired cardiac sympathetic nervous system (SNS) innervation and the severity of diastolic dysfunction in HF patients with preserved ejection fraction (HFpEF) and in predicting the response to cardiac resynchronization therapy (CRT) in HFrEF patients. Longer half-life 18F-labeled presynaptic SNS tracers (e.g., [18F]-LMI1195) have been developed to facilitate clinical imaging, although no PET radiotracers that target the ANS have gained wide clinical use in the cardiovascular system. Although the use of parasympathetic nervous system radiotracers in cardiac imaging is limited, the novel tracer, [11C]-donepezil, has shown potential utility in initial studies.SummaryMany ANS radioligands have been synthesized for PET cardiac imaging, but to date, the most clinically relevant PET tracer has been [11C]-HED. Recent studies have shown the utility of [11C]-HED in relevant clinical issues, such as in the elusive clinical syndrome of HFpEF. Conversely, tracers that target cardiac PNS innervation have been used less clinically, but novel tracers show potential utility for future work. The future application of [11C]-HED and newly designed 18F-labeled tracers for targeting the ANS hold promise for the evaluation and management of a wide range of cardiovascular diseases, including the prognostication of patients with HFpEF.

Resistance exercise training and in vitro skeletal muscle oxidative capacity in older adults.

Whether resistance exercise training (RET) improves skeletal muscle substrate oxidative capacity and reduces mitochondrial production of reactive oxygen species in older adults remains unclear. To address this, 19 older males (≥60 years) were randomized to a RET (n = 11) or to a waitlist control group (n = 8) that remained sedentary for 12 weeks. RET was comprised of three upper body and four lower body movements on resistance machines. One set of 8–12 repetitions to failure of each movement was performed on three nonconsecutive days/week. Improvements in chest press and leg press strength were assessed using a three‐repetition maximum (3 RM). Body composition was assessed via dual energy X‐ray absorptiometry. Muscle biopsies were obtained from the vastus lateralis muscle at baseline and at both 3 weeks and 12 weeks. Palmitate and pyruvate oxidation rates were measured from the 14CO2 produced from [1‐14C] palmitic acid and [U‐14C] pyruvate, respectively, during incubation of muscle homogenates. PGC‐1α, TFAM, and PPARδ levels were quantified using qRT‐PCR. Citrate synthase (CS) and β‐HAD activities were determined spectrophotometrically. Mitochondrial production of reactive oxygen species (ROS) were assessed using the Amplex Red Hydrogen Peroxide/Peroxidase assay. There were no significant changes in body weight or body composition following the intervention. Chest press and leg press strength (3RM) increased ~34% (both P < 0.01) with RET. There were no significant changes in pyruvate or fatty acid oxidation or in the expression of target genes with the intervention. There was a modest increase (P < 0.05) in βHAD activity with RET at 12 weeks but the change in CS enzyme activity was not significant. In addition, there were no significant changes in ROS production in either group following RET. Taken together, the findings of this study suggest that 12 weeks of low volume RET does not increase skeletal muscle oxidative capacity or reduce ROS production in older adults.

An extremely high dietary iodide supply forestalls severe hypothyroidism in Na + /I − symporter (NIS) knockout mice

The sodium/iodide symporter (NIS) mediates active iodide (I−) accumulation in the thyroid, the first step in thyroid hormone (TH) biosynthesis. Mutations in the SLC5A5 gene encoding NIS that result in a non-functional protein lead to congenital hypothyroidism due to I− transport defect (ITD). ITD is a rare autosomal disorder that, if not treated promptly in infancy, can cause mental retardation, as the TH decrease results in improper development of the nervous system. However, in some patients, hypothyroidism has been ameliorated by unusually large amounts of dietary I−. Here we report the first NIS knockout (KO) mouse model, obtained by targeting exons 6 and 7 of the Slc5a5 gene. In NIS KO mice, in the thyroid, stomach, and salivary gland, NIS is absent, and hence there is no active accumulation of the NIS substrate pertechnetate (99mTcO4−). NIS KO mice showed undetectable serum T4 and very low serum T3 levels when fed a diet supplying the minimum I− requirement for rodents. These hypothyroid mice displayed oxidative stress in the thyroid, but not in the brown adipose tissue or liver. Feeding the mice a high-I− diet partially rescued TH biosynthesis, demonstrating that, at high I− concentrations, I− enters the thyroid through routes other than NIS.

Integrated Dynamic Shape Tracking and RF Speckle Tracking for Cardiac Motion Analysis

We present a novel dynamic shape tracking (DST) method that solves for Lagrangian motion trajectories originating at the left ventricle (LV) boundary surfaces using a graphical structure and Dijkstra’s shortest path algorithm.

Optimized and Automated Radiosynthesis of [18F]DHMT for Translational Imaging of Reactive Oxygen Species with Positron Emission Tomography

Reactive oxygen species (ROS) play important roles in cell signaling and homeostasis. However, an abnormally high level of ROS is toxic, and is implicated in a number of diseases. Positron emission tomography (PET) imaging of ROS can assist in the detection of these diseases. For the purpose of clinical translation of [18F]6-(4-((1-(2-fluoroethyl)-1H-1,2,3-triazol-4-yl)methoxy)phenyl)-5-methyl-5,6-dihydrophenanthridine-3,8-diamine ([18F]DHMT), a promising ROS PET radiotracer, we first manually optimized the large-scale radiosynthesis conditions and then implemented them in an automated synthesis module. Our manual synthesis procedure afforded [18F]DHMT in 120 min with overall radiochemical yield (RCY) of 31.6% ± 9.3% (n = 2, decay-uncorrected) and specific activity of 426 ± 272 GBq/µmol (n = 2). Fully automated radiosynthesis of [18F]DHMT was achieved within 77 min with overall isolated RCY of 6.9% ± 2.8% (n = 7, decay-uncorrected) and specific activity of 155 ± 153 GBq/µmol (n = 7) at the end of synthesis. This study is the first demonstration of producing 2-[18F]fluoroethyl azide by an automated module, which can be used for a variety of PET tracers through click chemistry. It is also the first time that [18F]DHMT was successfully tested for PET imaging in a healthy beagle dog.

In Vivo Reactive Oxygen Species Detection With a Novel Positron Emission Tomography Tracer, 18F-DHMT, Allows for Early Detection of Anthracycline-Induced Cardiotoxicity in Rodents

Visual Abstract

Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching

We present a novel cardiac motion tracking method where motion is modeled as flow through a network. The motion is subject to physiologically consistent constraints and solved using linear programming. An additional important contribution of our work is the use of a Siamese neural network to generate edge weights that guide the flow through the network. The Siamese network learns to detect and quantify similarity and dissimilarity between pairs of image patches corresponding to the graph nodes. Despite cardiac motion tracking being an inherently spatiotemporal problem, few methods reliably address it as such. Furthermore, many tracking algorithms depend on tedious feature engineering and metric refining. Our approach provides solutions to both of these problems. We benchmark our method against a few other approaches using a synthetic 4D echocardiography dataset and compare the performance of neural network based feature matching with other features. We also present preliminary results on data from 5 canine cases.

Learning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis

Dense cardiac motion tracking and deformation analysis from echocardiography is important for detection and localization of myocardial dysfunction. However, tracking methods are often unreliable due to inherent ultrasound imaging properties. In this work, we propose a new data-driven spatiotemporal regularization strategy. We generate 4D Lagrangian displacement patches from different input sources as training data and learn the regularization procedure via a multi-layered perceptron (MLP) network. The learned regularization procedure is applied to initial noisy tracking results. We further propose a framework for integrating tracking methods to produce better overall estimations. We demonstrate the utility of this approach on block-matching, surface tracking, and free-form deformation-based methods. Finally, we quantitatively and qualitatively evaluate our performance on both tracking and strain accuracy using both synthetic and in vivo data.

Imaging of the Cardiac Sympathetic Nervous System Has Potential Value in the Evaluation of Patients with HFpEF

Heart failure (HF) is a major public health problem that affects more than 5.8 million people in the United States and 23 million people worldwide (1). HF is a clinical syndrome, rather than a disease, and can occur in patients with reduced and preserved left ventricular (LV) ejection fraction. Community-based studies indicate that approximately 50% of patients with the clinical diagnosis of HF have preserved EF (HFpEF), whereas the remaining patient population presents with HF with reduced EF (HFrEF) (2,3). The values used to define preserved EF range in the literature from 40% to 55%, but current guidelines recommend an EF of more than 50% with elevated natriuretic peptide (BNP or NTproBNP) levels and relevant structural heart disease, such as left atrial enlargement or LV diastolic dysfunction, as criteria for HFpEF (4,5). Although clinical symptoms and mortality are similar among patients with HFpEF and HErEF, there are pronounced differences between these HF phenotypes in patient demographics, responses to therapy, and underlying pathophysiology of LV remodeling (6,7). Importantly, the prevalence of HFpEF relative to HFrEF is rising at a rate of 1% per year and will thus dominate as the prevalent HF phenotype over the next decade (8). Despite the rising prevalence, there are limited data to support effective therapies for HFpEF, and the role of diagnostic strategies and prognostic biomarkers remain ambiguous (4,9). Excessive cardiac sympathetic nervous system (SNS) activation is a hallmark of HF progression in patients with HFrEF (10–12). A compensatory increase in adrenergic drive causes desensitization/ downregulation of the norepinephrine transporter (or uptake-1 mechanism) on the cardiac presynaptic nerve terminal due to excess norepinephrine in the synaptic cleft. The downregulation of uptake-1 exposes the heart and postsynaptic adrenergic receptors to greater concentrations of norepinephrine, which in turn causes desensitization/downregulation of b-adrenergic receptors, cardiac remodeling, and worsening of HF and prognosis (10). Importantly, in vivo noninvasive assessment of sympathetic innervation with SPECT and PET imaging using radiolabeled analogs of norepinephrine have provided valuable prognostic information in patients with HFrEF beyond currently available biomarkers, such as BNP and LV ejection fraction. The most commonly used SPECT tracer to assess cardiac sympathetic innervation is 123Imetaiodobenzylguanadine (123I-mIBG), whereas the most commonly used PET tracer is 11C-hydroxyephedrine (11C-HED). Both of these tracers are norepinephrine radioanalogs, and their uptake primarily represents presynaptic nerve function (or density) in the heart. LV diastolic dysfunction is a characteristic finding in patients with HFpEF, and increasing severity of diastolic dysfunction is related to HF progression and a worse prognosis in these patients (13,14). Several lines of evidence suggest that dysregulated SNS activity plays an important role in the pathophysiology of HFpEF, particularly in the development of diastolic dysfunction. Preclinical studies show that mimicking an elevation of SNS activity via isoproterenol administration leads to diastolic dysfunction, accompanied by increased myocardial stiffening, fibrosis, and LV hypertrophy (15). Grassi et al. (16) observed that patients with diastolic dysfunction and hypertension display higher SNS activity (e.g., muscle sympathetic nerve activity [MSNA]) and abnormal baroreflex modulation compared with hypertensive patients without diastolic dysfunction, and both these groups show higher MSNA than age-matched controls. In these studies, MSNA was significantly and inversely related to various transthoracic echocardiographic indices of diastolic dysfunction (e.g., E/A wave ratio, deceleration time, and isovolumic relaxation time). Other human studies have observed similar findings with respect to the relationship between SNS activity and HFpEF (15), although many of these studies are limited by the small sample sizes, and not all of these studies report on diastolic parameters. Interestingly, albeit in a limited number of studies, it has been shown that indices of sympathetic presynaptic nerve function derived from planar 123I-mIBG scintigraphy correlate with the severity of diastolic dysfunction, exercise capacity, LV remodeling, response to therapy, and HF severity and are able to predict adverse cardiovascular events in patients with HFpEF (17–19). More specifically, the 123I-mIBG heart-to-mediastinal ratio and 123I-mIBG washout rate have proved to be the most useful indices in these studies. Indeed, the small number of patients, the use of different definitions of HFpEF and diastolic dysfunction, and the application of only semiquantitative analyses of 123I-mIBG planar scintigraphy have limited the generalizability and strength of these findings. Received Dec. 16, 2016; revision accepted Feb. 15, 2017. For correspondence or reprints contact: Albert J. Sinusas, Section of Cardiovascular Medicine, Yale University School of Medicine, P.O. Box 208017, Dana 3, New Haven, CT 06520-8017. E-mail: albert.sinusas@yale.edu Published online Feb. 23, 2017. COPYRIGHT© 2017 by the Society of Nuclear Medicine and Molecular Imaging. DOI: 10.2967/jnumed.116.186130

Dictionary learning-based spatiotemporal regularization for 3D dense speckle tracking

Speckle tracking is a common method for non-rigid tissue motion analysis in 3D echocardiography, where unique texture patterns are tracked through the cardiac cycle. However, poor tracking often occurs due to inherent ultrasound issues, such as image artifacts and speckle decorrelation; thus regularization is required. Various methods, such as optical flow, elastic registration, and block matching techniques have been proposed to track speckle motion. Such methods typically apply spatial and temporal regularization in a separate manner. In this paper, we propose a joint spatiotemporal regularization method based on an adaptive dictionary representation of the dense 3D+time Lagrangian motion field. Sparse dictionaries have good signal adaptive and noise-reduction properties; however, they are prone to quantization errors. Our method takes advantage of the desirable noise suppression, while avoiding the undesirable quantization error. The idea is to enforce regularization only on the poorly tracked trajectories. Specifically, our method 1.) builds data-driven 4-dimensional dictionary of Lagrangian displacements using sparse learning, 2.) automatically identifies poorly tracked trajectories (outliers) based on sparse reconstruction errors, and 3.) performs sparse reconstruction of the outliers only. Our approach can be applied on dense Lagrangian motion fields calculated by any method. We demonstrate the effectiveness of our approach on a baseline block matching speckle tracking and evaluate performance of the proposed algorithm using tracking and strain accuracy analysis.