Cardiac Arrythmias: Multimodal Assessment Integrating Body Surface ECG Mapping into Cardiac Imaging

Cochet, Hubert; Dubois, Rémi; Sacher, Frédéric; Derval, Nicolas; Sermesant, Maxime; Hocini, Mélèze; Montaudon, Michel; Haı̈ssaguerre, Michel; Laurent, François; Jaı̈s, Pierre

doi:10.1148/radiol.13131331

Cited by 52 publications

(58 citation statements)

References 30 publications

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“…Step 6 and 7 Currently most steps are implemented in MUSIC software (multimodality software for specific imaging in cardiology, developed by IHU Liryc, University of Bordeaux and Inria Sophia Antipolis) [11].…”

Section: Region Growing Endocardium Segmentationmentioning

confidence: 99%

STACOM-SLAWT Challenge: Left Atrial Wall Segmentation and Thickness Measurement Using Region Growing and Marker-Controlled Geodesic Active Contour

Jia

Cadour

Cochet

et al. 2017

Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges

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Abstract. Analyzing the structure of the left atrium can provide precious insights into the pathology of atrial fibrillation, eventually resulting in optimization of treatment plans. In this paper, an interactive and patient-specific method is presented to segment the left atrial endocardium 3 , the left atrial epicardium and measure the left atrial wall thickness from cardiac computed tomography images. A region growing algorithm was adapted to segment the left atrial endocardium, whereas the left atrial epicardium was segmented indirectly: a marker-controlled geodesic active contour model was defined on its surrounding environment. The results of the left atrial wall thickness were then mapped onto meshes generated from the endocardium segmentation. We tested our pipeline on 10 datasets as a part of the STACOM 2016 Left Atrial Wall Segmentation Challenge and we compared our method with manual segmentation. Aimed at facilitating the segmentation of the left atrial thin-wall structure, this pipeline is partially implemented in MUSIC software for clinical use. The expertise of clinicians can be added through the choice of specific parameters for each patient, although this remains optional owing to the robustness of the approach.

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Section: Region Growing Endocardium Segmentationmentioning

confidence: 99%

STACOM-SLAWT Challenge: Left Atrial Wall Segmentation and Thickness Measurement Using Region Growing and Marker-Controlled Geodesic Active Contour

Jia

Cadour

Cochet

et al. 2017

Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges

Self Cite

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“…Delayed contrast-enhanced MRI can be used to assess focal myocardial fibrosis, which is the substrate involved in most cardiac arrhythmias. This can also be combined with body surface ECG mapping resulting in a patient-specific multimodal 3D model to assist in the diagnosis, prognosis and intracardiac catheter navigation for ablation targeting [11]. Multimodality imaging, combining cardiac MRI/CT with real time x-rays during cardiac resynchronization therapy implantation serves as an invaluable adjunct to physicians to accurately deliver pacemaker leads into optimal position to deliver cardiac resynchronization therapy [12].…”

Section: • Image Fusion In Cardiac Electrophysiologymentioning

confidence: 99%

Multimodality Medical Image Fusion: Applications in Congenital Cardiology

Narayan

Qureshi

2017

Future Cardiol.

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KEYWORDS• congenital cardiology • image fusion • image registration • medical image computing Image fusion: definition Image fusion consists of registering and visualizing different sets of images of a given object or scene. In medical disorders, pathology affects different tissues in a variable manner. These are best imaged in different modalities using diverse technologies. Image fusion helps to integrate all of the information and aids in accurate diagnosis and treatment. The implicit image fusion composed by the treating physician can be subjective and prone to error, which image fusion systems can help to mitigate. Medical image fusion is a very broad field covering aspects of computer vision, pattern recognition, machine learning and artificial intelligence in addition to image processing. Brief historyThe earliest mention of the word 'image fusion' in the clinical context was in 1992 by Swayne, who reported on computer-assisted fusion of single-photon emission computed tomographic (SPECT) and computed tomographic (CT) images in localizing inflammatory processes in 10 patients [1]. A combination of standard scaling, rotation, translation and stretching transformations were used to fuse images with anatomic and external landmarks. Stereotactic neurosurgery [2] was the next major application for image fusion, closely followed by oncology [3]. Rapid improvements in computers and their processing power led to increasing development and use of image fusion, particularly in imageguided surgery in a variety of medical specialties. The problems with software-based posthoc image fusion have led to the development of combined scanners, which acquire both anatomical and functional images during a single imaging session. This is achieved by fusing of technologies such as PET/CT scanner and PET/MRI scanner [4]. This also stimulated the development of co-location of complementary imaging modalities such

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“…Cardiac potential and myocardial activation sequences are currently the most common source models used in forward and inverse studies. [24][25][26][27] Potential-based models represent the cardiac source using extracellular potentials on the heart surface, whereas myocardial activation time models use the time of activation. Activation times are described as the time of arrival of the depolarization phase of an action potential and can be defined either through the 3D myocardial wall or on the heart surface including both the epicardium and endocardium.…”

Section: Cardiac Source Modelmentioning

confidence: 99%