Automated Left Ventricle Ischemic Scar Detection in CT Using Deep Neural Networks.

Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows. Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar. Methods: A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes, which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA dataset (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts. Results: Note that 84.7% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA-derived data, with no further training, where it achieved an 88.3% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians. Conclusion: Automatic ischemic scar detection can be performed from a routine cardiac CTA, without any scar-specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times, and guide clinical decision-making.

Mechanical Activation Computation from Fluoroscopy for Guided Cardiac Resynchronization Therapy.

Congestive heart failure is associated with significant morbidity and mortality, as first line treatments are not always effective in improving symptoms and quality of life. Furthermore, 30-50% of patients who are treated with cardiac resynchronization therapy (CRT), a minimally invasive intervention, do not respond when assessed by objective criteria such as cardiac remodeling. Positioning of the left ventricular lead in the latest activating myocardial region is associated with the best outcome. Cardiac magnetic resonance (CMR) imaging can detect scar tissue and interventricular dyssynchrony; improving the outcome of CRT. However, MR is currently not standard modality for CRT due to its cost and limited availability. This paper explores a novel method to exploit interventional X-ray fluoroscopy set up in CRT procedures to gain information on mechanical activation of the myocardium by tracking the movement of vessels overlying to left ventricular myocardium. Fluoroscopic images were labelled, to track branch movement and determine the motion along the main principal component associated with cardiac motion, to optimize lead placement in CRT. A comparison between MR- and fluoroscopy-derived mechanical activation was performed on 9 datasets, showing more than 66% agreement in 8 cases.

Image Data Analysis for Quantifying Scar Transmurality in MRI phantoms for Cardiac Resynchronisation Therapy.

The use of implantable cardiac devices has increased in the last 30 years. Cardiac resynchronisation therapy (CRT) is a procedure which involves implanting a coin sized pacemaker for reversing heart failure. The pacemaker electrode leads are implanted into cardiac myocardial tissue. The optimal site for implantation is highly patient-specific. Most implanters use empirical placement of the lead. One region identified to have a poor response rate are myocardial tissue with transmural scar. Studies that precisely measure transmurality of scar tissue in the left ventricle (LV) are few. Most studies lack proper validation of their transmurality measurement technique. This study presents an image analysis technique for computing scar transmurality from late-gadolinium enhancement MRI. The technique is validated using phantoms under a CRT image guidance system. The study concludes that scar transmurality can be accurately measured in certain situations and validation with phantoms is important.

3D/2D model-to-image registration by imitation learning for cardiac procedures.

PURPOSE: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. METHODS: This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. RESULTS: Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases. CONCLUSION: Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair.

PURPOSE: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible. METHOD: We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation. RESULTS: We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively. CONCLUSION: The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.

LV function validation of computer-assisted interventional system for cardiac resyncronisation therapy.

PURPOSE: Cardiac resynchronisation therapy (CRT) is an established treatment for symptomatic patients with heart failure, a prolonged QRS duration, and impaired left ventricular (LV) function; however, non-response rates remain high. Recently proposed computer-assisted interventional platforms for CRT provide new routes to improving outcomes. Interventional systems must process information in an accurate, fast and highly automated way that is easy for the interventional cardiologists to use. In this paper, an interventional CRT platform is validated against two offline diagnostic tools to demonstrate that accurate information processing is possible in the time critical interventional setting. METHODS: The study consisted of 3 healthy volunteers and 16 patients with heart failure and conventional criteria for CRT. Data analysis included the calculation of end-diastolic volume, end-systolic volume, stroke volume and ejection fraction; computation of global volume over the cardiac cycle as well as time to maximal contraction expressed as a percentage of the total cardiac cycle. RESULTS: The results showed excellent correlation ([Formula: see text] values of [Formula: see text] and Pearson correlation coefficient of [Formula: see text]) with comparable offline diagnostic tools. CONCLUSION: Results confirm that our interventional system has good accuracy in everyday clinical practice and can be of clinical utility in identification of CRT responders and LV function assessment.

Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI.

The current challenge for electrophysiology procedures, targeting the left ventricle, is the localization and qualification of myocardial scar. Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the current gold standard to visualize regions of myocardial infarction. Commonly, a stack of 2-D images is acquired of the left ventricle in short-axis orientation. Recently, 3-D LGE-MRI methods were proposed that continuously cover the whole heart with a high resolution within a single acquisition. The acquisition promises an accurate quantification of the myocardium to the extent of myocardial scarring. The major challenge arises in the analysis of the resulting images, as the accurate segmentation of the myocardium is a requirement for a precise scar tissue quantification. In this work, we propose a novel approach for fully automatic left ventricle segmentation in 3-D whole-heart LGE-MRI, to address this limitation. First, a two-step registration is performed to initialize the left ventricle. In the next step, the principal components are computed and a pseudo short axis view of the left ventricle is estimated. The refinement of the endocardium and epicardium is performed in polar space. Prior knowledge for shape and inter-slice smoothness is used during segmentation. The proposed method was evaluated on 30 clinical 3-D LGE-MRI datasets from individual patients obtained at two different clinical sites and were compared to gold standard segmentations of two clinical experts. This comparison resulted in a Dice coefficient of 0.83 for the endocardium and 0.80 for the epicardium.

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach.

Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00 mm±0.82 mm in plane A and 1.13 mm±0.24 mm in plane B. The localization in 3-D was associated with an average error of 0.36 mm±0.86 mm.