Virtual medicine: Utilization of the advanced cardiac imaging patient avatar for procedural planning and facilitation

A geometry-based finite element tool for evaluating mitral valve biomechanics

Mitral valve function depends on its complex geometry and tissue health, with alterations in shape and tissue response affecting the long-term restorarion of function. Previous computational frameworks for biomechanical assessment are mostly based on patient-specific geometries; however, these are not flexible enough to yield a variety of models and assess mitral closure for individually tuned morphological parameters or material property representations. This study details the finite element approach implemented in our previously developed toolbox to assess mitral valve biomechanics and showcases its flexibility through the generation and biomechanical evaluation of different models. A healthy valve geometry was generated and its computational predictions for biomechanics validated against data in the literature. Moreover, two mitral valve models including geometric alterations associated with disease were generated and analysed. The healthy mitral valve model yielded biomechanical predictions in terms of valve closure dynamics, leaflet stresses and papillary muscle and chordae forces comparable to previous computational and experimental studies. Mitral valve function was compromised in geometries representing disease, expressed by the presence of regurgitating areas, elevated stress on the leaflets and unbalanced subvalvular apparatus forces. This showcases the flexibility of the toolbox concerning the generation of a range of mitral valve models with varying geometric definitions and material properties and the evaluation of their biomechanics.

Pediatric patient-specific three-dimensional virtual models for surgical decision making in resection of hepatic and retroperitoneal tumors

PURPOSE

Typically, preoperative imaging is viewed in two dimensions (2D) only, but three-dimensional (3D) virtual models may improve viewers' anatomical perspective by permitting them to interact with the imaging through manipulating it in space. Research into the utility of these models in most surgical specialties is growing rapidly. This study investigates the utility of 3D virtual models of complex pediatric abdominal tumors for clinical decision making, particularly the decision to proceed with surgical resection or not.

METHODS

3D virtual models of tumors and adjacent anatomy were created from CT images of pediatric patients scanned for Wilms tumor, neuroblastoma or hepatoblastoma. Pediatric surgeons individually assessed the resectability of the tumors. First, they assessed resectability using the standard protocol of viewing imaging on conventional screens and then reassessed resectability after being presented with the 3D virtual models. Inter-physician agreement on resectability for each patient was analyzed using Krippendorff's alpha. Inter-physician agreement was used as a surrogate for correct interpretation. Participants were also surveyed afterward on the utility and practicality of the 3D virtual models for clinical decision making.

RESULTS

Inter-physician agreement when using CT imaging alone was "fair" (Krippendorff's alpha α = 0.399), while inter-physician agreement when using 3D virtual models increased to "moderate" (Krippendorff's alpha α = 0.532). When surveyed about model utility, all 5 participants considered them helpful. Two participants felt the models would be practical for clinical use in most cases, while 3 felt they would be practical for select cases only.

CONCLUSION

This study demonstrates the subjective utility of 3D virtual models of pediatric abdominal tumors for clinical decision making. The models are an adjunct that can be particularly useful in complicated tumors that efface or displace critical structures that may impact resectability. Statistical analysis demonstrates the improved inter-rater agreement with the 3D stereoscopic display over the 2D display. The use of 3D displays of medical images will increase over time, and evaluation of their potential usefulness in various clinical settings is necessary.

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.

From CT to artificial intelligence for complex assessment of plaque-associated risk

The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.

Left Atrial Appendage Mechanical Exclusion: Procedural Planning Using Cardiovascular Computed Tomographic Angiography

Left atrial appendage (LAA) mechanical exclusion is being investigated for nonpharmacologic stroke risk reduction in selected patients with atrial fibrillation. There are multiple potential approaches in various stages of development and clinical application, each of which depends on specific cardiothoracic anatomic characteristics for optimal performance. Multiple imaging modalities can be utilized for application of this technology, with transesophageal echocardiography used for intraprocedural guidance. Cardiovascular computed tomographic angiography can act as a virtual patient avatar, allowing for the assessment of cardiac structures in the context of surrounding cardiac, coronary vascular, thoracic vascular, and visceral and skeletal anatomy, aiding preprocedural decision-making, planning, and follow-up. Although transesophageal echocardiography is used for intraprocedural guidance, computed tomographic angiography may be a useful adjunct for preprocedure assessment of LAA sizing and anatomic obstacles or contraindications to deployment, aiding in the assessment of optimal approaches. Potential approaches to LAA exclusion include endovascular occlusion, epicardial ligation, primary minimally invasive intercostal thoracotomy with thoracoscopic LAA ligation or appendectomy, and minimally invasive or open closure as part of cardiothoracic surgery for other indications. The goals of these procedures are complete isolation or exclusion of the entire appendage without leaving a residual appendage stump or residual flow with avoidance of acute or chronic damage to surrounding cardiovascular structures. The cardiovascular imager plays an important role in the preprocedural and postprocedural assessment of the patient undergoing LAA exclusion.

3D printing with MRI in pediatric applications

3D printing (3DP) applications for clinical evaluation, preoperative planning, patient and trainee education, and simulation has increased in the past decade. Most of the applications are found in cardiovascular, head and neck, orthopedic, neurological, urological, and oncological surgical cases. This review has three parts. The first part discusses the technical pathway to realizing a physical model, 3DP considerations in pediatric MRI image acquisition, data and resolution requirements, and related structural segmentation and postprocessing steps needed to generalize both virtual and physical models. Standard practices and processing software used in these processes will be assessed. The second part discusses complementary examples in pediatric applications, including cases from cardiology, neuroradiology, neurology, and neurosurgery, head and neck, orthopedics, pelvic and urological applications, oncological applications, and fetal imaging. The third part explores other 3D printing applications and considerations such as using 3DP to develop tissue-specific phantoms and devices for testing in the MR environment, to educate patients and their families, to train clinicians and students, and facility requirements for building a 3DP program. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2020;51:1641-1658.

Cardiovascular Computed Tomographic Angiography as a Virtual Patient Avatar for Individualized Surgical Planning of Complex Anomalous Coronary Artery Anatomy

Cardiovascular computed tomographic angiography (CCTA) 3-D thoracic reconstruction can serve as a "virtual patient avatar" providing surgical views for approach to complex anomalous coronary artery anatomy. Images demonstrated a single coronary artery ostium arising from the right aortic sinus with trifurcation into a prepulmonic left anterior descending coronary artery (LAD), an interarterial circumflex with a subsequent intraseptal course, and normal course of the right coronary artery. Virtual 3-D CCTA reconstructions were important to planning an incisional plane for surgical correction.