Data Fusion of Left Ventricle Electro-Anatomic Mapping and Multislice Computerized Tomography for Cardiac Resynchronisation Therapy Optimization

Cardiac Resynchronization Therapy is a treatment for bi-ventricular asynchronism. It can be optimized by the identification of the most effective pacing sites. The aim of this study is to provide a helpful tool to perform this identification by the fusion of electrical and anatomical information resulting from Electro-Anatomic Mapping (EAM) data and Multislice Computerized Tomography (MSCT) imaging. EAM data provide an approximation of the left ventricle (LV) 3D-surface (SEAM). Left cardiac chambers are segmented from MSCT imaging and surfaces are reconstructed (SCT). In order to represent this information in a unified framework, a three steps method is proposed: (1) the LV is separated from the left auricle on SCT providing S ′ CT; (2) a semi-automatic rigid registration method; (3) activation time delays is applied to SEAM and S ′ CT are estimated on S ′ CT from the EAM data. This method results in a graphical interface offering to clinicians means to identify abnormal electrical activity sites

Data fusion of left ventricle Electro-Anatomic Mapping and Multislice Computerized Tomography for Cardiac Resynchronisation Therapy optimization

International audienceCardiac Resynchronization Therapy is a treatment for bi-ventricular asynchronism. It can be optimized by the identification of the most effective pacing sites. The aim of this study is to provide a helpful tool to perform this identification by the fusion of electrical and anatomical information resulting from Electro-Anatomic Mapping (EAM) data and Multislice Computerized Tomography (MSCT) imaging. EAM data provide an approximation of the left ventricle (LV) 3D-surface (SEAM). Left cardiac chambers are segmented from MSCT imaging and surfaces are reconstructed (SCT). In order to represent this information in a unified framework, a three steps method is proposed: (1) the LV is separated from the left auricle on SCT providing S'CT; (2) a semi-automatic rigid registration method is applied to SEAM and S'CT; (3) activation time delays are estimated on S'CT from the EAM data. This method results in a graphical interface offering to clinicians means to identify abnormal electrical activity sites

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

3D/2D Registration with Superabundant Vessel Reconstruction for Cardiac Resynchronization Therapy

<p>Miscellaneous classes: consistent/single studies of pregnancy associated pharmacokinetic changes (percent calculated as pregnant/nonpregnant values).</p

Rapid Segmentation Techniques for Cardiac and Neuroimage Analysis

Recent technological advances in medical imaging have allowed for the quick acquisition of highly resolved data to aid in diagnosis and characterization of diseases or to guide interventions. In order to to be integrated into a clinical work flow, accurate and robust methods of analysis must be developed which manage this increase in data. Recent improvements in in- expensive commercially available graphics hardware and General-Purpose Programming on Graphics Processing Units (GPGPU) have allowed for many large scale data analysis problems to be addressed in meaningful time and will continue to as parallel computing technology improves. In this thesis we propose methods to tackle two clinically relevant image segmentation problems: a user-guided segmentation of myocardial scar from Late-Enhancement Magnetic Resonance Images (LE-MRI) and a multi-atlas segmentation pipeline to automatically segment and partition brain tissue from multi-channel MRI. Both methods are based on recent advances in computer vision, in particular max-flow optimization that aims at solving the segmentation problem in continuous space. This allows for (approximately) globally optimal solvers to be employed in multi-region segmentation problems, without the particular drawbacks of their discrete counterparts, graph cuts, which typically present with metrication artefacts. Max-flow solvers are generally able to produce robust results, but are known for being computationally expensive, especially with large datasets, such as volume images. Additionally, we propose two new deformable registration methods based on Gauss-Newton optimization and smooth the resulting deformation fields via total-variation regularization to guarantee the problem is mathematically well-posed. We compare the performance of these two methods against four highly ranked and well-known deformable registration methods on four publicly available databases and are able to demonstrate a highly accurate performance with low run times. The best performing variant is subsequently used in a multi-atlas segmentation pipeline for the segmentation of brain tissue and facilitates fast run times for this computationally expensive approach. All proposed methods are implemented using GPGPU for a substantial increase in computational performance and so facilitate deployment into clinical work flows. We evaluate all proposed algorithms in terms of run times, accuracy, repeatability and errors arising from user interactions and we demonstrate that these methods are able to outperform established methods. The presented approaches demonstrate high performance in comparison with established methods in terms of accuracy and repeatability while largely reducing run times due to the employment of GPU hardware

Knowledge discovery on the integrative analysis of electrical and mechanical dyssynchrony to improve cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) is a standard method of treating heart failure by coordinating the function of the left and right ventricles. However, up to 40% of CRT recipients do not experience clinical symptoms or cardiac function improvements. The main reasons for CRT non-response include: (1) suboptimal patient selection based on electrical dyssynchrony measured by electrocardiogram (ECG) in current guidelines; (2) mechanical dyssynchrony has been shown to be effective but has not been fully explored; and (3) inappropriate placement of the CRT left ventricular (LV) lead in a significant number of patients. In terms of mechanical dyssynchrony, we utilize an autoencoder to extract new predictive features from nuclear medicine images, characterizing local mechanical dyssynchrony and improving the CRT response rate. Although machine learning can identify complex patterns and make accurate predictions from large datasets, the low interpretability of these black box methods makes it difficult to integrate them with clinical decisions made by physicians in the healthcare setting. Therefore, we use visualization techniques to enable physicians to understand the physical meaning of new features and the reasoning behind the clinical decisions made by the artificial intelligent model. For electrical dyssynchrony, we use short-time Fourier transform (STFT) to transform one-dimensional waveforms into two-dimensional frequency-time spectra. And transfer learning is used to leverage the knowledge learned from a large arrhythmia ECG dataset of related medical conditions to improve patient selection for CRT with limited data. This improves prediction accuracy, reduces the time and resources required, and potentially leads to better patient outcomes. Furthermore, an innovative approach is proposed for using three-dimensional spatial VCG information to describe the characteristics of electrical dyssynchrony, locate the latest activation site, and combine it with the latest mechanical contraction site to select the optimal LV lead position. In addition, we apply deep reinforcement learning to the decision-making problem of CRT patients. We investigate discrete state space/specific action space models to find the best treatment strategy, improve the reward equation based on the physician\u27s experience, and learn the approximation of the best action-value function that can improve the treatment policy used by clinicians and provide interpretability

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future