Septal late enhancement by cardiac CT is associated with repeat ablation in nonischemic cardiomyopathy patients.

INTRODUCTION: Scar substrate in nonischemic cardiomyopathy (NICM) patients is often difficult to identify. Advances in cardiac imaging, especially using late iodine-enhanced computed tomography (LIE-CT), allow better characterization of scars giving rise to ventricular tachycardia (VT). Currently, there are limited data on clinical correlates of CT-derived scar substrates in NICM. We sought assess the relationship between scar location on LIE-CT and outcomes after radiofrequency catheter ablation (RFCA) in NICM patients with VT. METHODS: From 2020 to 2022, consecutive patients with NICM undergoing VT RFCA with integration of cardiac CT scar modeling (inHeart, Pessac, France) were included at two US tertiary care centers. The CT protocol included both arterial-enhanced imaging for anatomical modeling and LIE-CT for scar assessment. The distribution of substrate on CT was analyzed in relation to patient outcomes, with primary endpoints being VT recurrence and the need for repeat ablation procedure. RESULTS: Sixty patients were included (age 64 ± 12 years, 90% men). Over a median follow-up of 120 days (interquartile range [IQR]: 41-365), repeat ablation procedures were required in 32 (53%). VT recurrence occurred in 46 (77%), with a median time to recurrence of 40 days (IQR: 8-65). CT-derived total scar volume positively correlated with intrinsic QRS duration (r = .34, p = 0.008). Septal scar was found on CT in 34 (57%), and lateral scar in 40 (7%). On univariate logistic regression, septal scar was associated with increased odds of repeat ablation (odds ratio [OR]: 2.9 [1.0-8.4]; p = 0.046), while lateral scar was not (OR: 0.9 [0.3-2.7]; p = 0.855). Septal scar better predicted VT recurrence when compared to lateral scar, but neither were statistically significant (septal scar OR: 3.0 [0.9-10.7]; p = 0.078; lateral scar OR: 1.7 [0.5-5.9]; p = 0.391). CONCLUSION: In this tertiary care referral population, patients with NICM undergoing VT catheter ablation with septal LIE-CT have nearly threefold increased risk of need for repeat ablation.

Experience-based SEEG planning: from retrospective data to automated electrode trajectories suggestions.

StereoElectroEncephaloGraphy (SEEG) is a minimally invasive technique that consists of the insertion of multiple intracranial electrodes to precisely identify the epileptogenic focus. The planning of electrode trajectories is a cumbersome and time-consuming task. Current approaches to support the planning focus on electrode trajectory optimisation based on geometrical constraints but are not helpful to produce an initial electrode set to begin with the planning procedure. In this work, the authors propose a methodology that analyses retrospective planning data and builds a set of average trajectories, representing the practice of a clinical centre, which can be mapped to a new patient to initialise planning procedure. They collected and analysed the data from 75 anonymised patients, obtaining 30 exploratory patterns and 61 mean trajectories in an average brain space. A preliminary validation on a test set showed that they were able to correctly map 90% of those trajectories and, after optimisation, they have comparable or better values than manual trajectories in terms of distance from vessels and insertion angle. Finally, by detecting and analysing similar plans, they were able to identify eight planning strategies, which represent the main tailored sets of trajectories that neurosurgeons used to deal with the different patient cases.

Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.

Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant.

PURPOSE: Focal epilepsy is a neurological disease that can be surgically treated by removing area of the brain generating the seizures. The stereotactic electroencephalography (SEEG) procedure allows patient brain activity to be recorded in order to localize the onset of seizures through the placement of intracranial electrodes. The planning phase can be cumbersome and very time consuming, and no quantitative information is provided to neurosurgeons regarding the safety and efficacy of their trajectories. In this work, we present a novel architecture specifically designed to ease the SEEG trajectory planning using the 3D Slicer platform as a basis. METHODS: Trajectories are automatically optimized following criteria like vessel distance and insertion angle. Multi-trajectory optimization and conflict resolution are optimized through a selective brute force approach based on a conflict graph construction. Additionally, electrode-specific optimization constraints can be defined, and an advanced verification module allows neurosurgeons to evaluate the feasibility of the trajectory. RESULTS: A retrospective evaluation was performed using manually planned trajectories on 20 patients: the planning algorithm optimized and improved trajectories in 98% of cases. We were able to resolve and optimize the remaining 2% by applying electrode-specific constraints based on manual planning values. In addition, we found that the global parameters used discards 68% of the manual planned trajectories, even when they represent a safe clinical choice. CONCLUSIONS: Our approach improved manual planned trajectories in 98% of cases in terms of quantitative indexes, even when applying more conservative criteria with respect to actual clinical practice. The improved multi-trajectory strategy overcomes the previous work limitations and allows electrode optimization within a tolerable time span.

Standard and fenestrated endograft sizing in EVAR planning: Description and validation of a semi-automated 3D software.

An abdominal aortic aneurysm (AAA) is a pathological dilation of the abdominal aorta that may lead to a rupture with fatal consequences. Endovascular aneurysm repair (EVAR) is a minimally invasive surgical procedure consisting of the deployment and fixation of a stent-graft that isolates the damaged vessel wall from blood circulation. The technique requires adequate endovascular device sizing, which may be performed by vascular analysis and quantification on Computerized Tomography Angiography (CTA) scans. This paper presents a novel 3D CTA image-based software for AAA inspection and EVAR sizing, eVida Vascular, which allows fast and accurate 3D endograft sizing for standard and fenestrated endografts. We provide a description of the system and its innovations, including the underlying vascular image analysis and visualization technology, functional modules and user interaction. Furthermore, an experimental validation of the tool is described, assessing the degree of agreement with a commercial, clinically validated software, when comparing measurements obtained for standard endograft sizing in a group of 14 patients.

Activity Classification Using Mobile Phone based Motion Sensing and Distributed Computing.

In this work we present a system that uses the accelerometer embedded in a mobile phone to perform activity recognition, with the purpose of continuously and pervasively monitoring the users' level of physical activity in their everyday life. Several classification algorithms are analysed and their performance measured, based for 6 different activities, namely walking, running, climbing stairs, descending stairs, sitting and standing. Feature selection has also been explored in order to minimize computational load, which is one of the main concerns given the restrictions of smartphones in terms of processor capabilities and specially battery life.