John C. Stendahl

Nanoparticles for cardiovascular imaging and therapeutic delivery, Part 1: Compositions and features

Imaging agents made from nanoparticles are functionally versatile and have unique properties that may translate to clinical utility in several key cardiovascular imaging niches. Nanoparticles exhibit size-based circulation, biodistribution, and elimination properties different from those of small molecules and microparticles. In addition, nanoparticles provide versatile platforms that can be engineered to create both multimodal and multifunctional imaging agents with tunable properties. With these features, nanoparticulate imaging agents can facilitate fusion of high-sensitivity and high-resolution imaging modalities and selectively bind tissues for targeted molecular imaging and therapeutic delivery. Despite their intriguing attributes, nanoparticulate imaging agents have thus far achieved only limited clinical use. The reasons for this restricted advancement include an evolving scope of applications, the simplicity and effectiveness of existing small-molecule agents, pharmacokinetic limitations, safety concerns, and a complex regulatory environment. This review describes general features of nanoparticulate imaging agents and therapeutics and discusses challenges associated with clinical translation. A second, related review to appear in a subsequent issue of JNM highlights nuclear-based nanoparticulate probes in preclinical cardiovascular imaging.

Nanoparticles for Cardiovascular Imaging and Therapeutic Delivery, Part 2: Radiolabeled Probes

Nanoparticulate imaging agents and therapeutics have proven to be valuable tools in preclinical cardiovascular disease research. Because of their distinct properties and significant functional versatility, nanoparticulate imaging agents afford certain capabilities that are typically not provided by traditional small molecule agents. This review is the second in a two-part series covering nanoparticulate imaging agents and theranostics. It highlights current examples of radiolabeled nanoparticulate probes in preclinical cardiovascular research and demonstrates their utility in applications such as blood pool imaging and molecular imaging of ischemia, angiogenesis, atherosclerosis, and inflammation. These agents provide valuable insight into the molecular and cellular mechanisms of cardiovascular disease and illustrate both the limitations and the significant potential of nanoparticles in diagnostic and therapeutic applications. Further technologic development to improve performance, address safety concerns, and fulfil regulatory obligations is required for clinical translation of these emergent technologies.

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.

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.

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.

Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT

Anatomical-based partial volume correction (PVC) has been shown to improve image quality and quantitative accuracy in cardiac SPECT/CT. However, this method requires manual segmentation of various organs from contrast-enhanced computed tomography angiography (CTA) data. In order to achieve fully automatic CTA segmentation for clinical translation, we investigated the most common multi-atlas segmentation methods. We also modified the multi-atlas segmentation method by introducing a novel label fusion algorithm for multiple organ segmentation to eliminate overlap and gap voxels. To evaluate our proposed automatic segmentation, eight canine 99mTc-labeled red blood cell SPECT/CT datasets that incorporated PVC were analyzed, using the leave-one-out approach. The Dice similarity coefficient of each organ was computed. Compared to the conventional label fusion method, our proposed label fusion method effectively eliminated gaps and overlaps and improved the CTA segmentation accuracy. The anatomical-based PVC of cardiac SPECT images with automatic multi-atlas segmentation provided consistent image quality and quantitative estimation of intramyocardial blood volume, as compared to those derived using manual segmentation. In conclusion, our proposed automatic multi-atlas segmentation method of CTAs is feasible, practical, and facilitates anatomical-based PVC of cardiac SPECT/CT images.