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Cormier P, Poree J, Bourquin C, Provost J. Dynamic Myocardial Ultrasound Localization Angiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3379-3388. [PMID: 34086566 DOI: 10.1109/tmi.2021.3086115] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Dynamic Myocardial Ultrasound Localization Angiography (MULA) is an ultrasound-based imaging modality destined to enhance the diagnosis and treatment monitoring of coronary pathologies. Current diagnosis methods of coronary artery disease focus on the observation of vessel narrowing in the coronary vasculature to assess the organ's condition. However, we would strongly benefit from mapping and measuring flow from intramyocardial arterioles and capillaries as they are the direct vehicle of the myocardium blood income. With the advent of ultrafast ultrasound scanners, imaging modalities based on the localization and tracking of injected microbubbles allow for the subwavelength resolution imaging of an organ's vasculature. Yet, the application of these vascular imaging modalities relies on an accumulation of cine loops of a region of interest undergoing no or minimal tissue motion. This work introduces the MULA framework that combines 1) the mapping of the dynamics of the microvascular flow using an ultrasound sequence triggered by the electrocardiogram with a 2) novel Lagrangian beamformer based on non-rigid motion registration algorithm to form images directly in the myocardium's material coordinates and thus correcting for the large myocardial motion and deformation. Specifically, we show that this framework enables the non-invasive imaging of the angioarchitecture and dynamics of intramyocardial flow in vessels as small as a few tens of microns in the rat's beating heart in vivo.
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Queirós S, Morais P, Fehske W, Papachristidis A, Voigt JU, Fonseca JC, D'hooge J, Vilaça JL. Assessment of aortic valve tract dynamics using automatic tracking of 3D transesophageal echocardiographic images. Int J Cardiovasc Imaging 2019; 35:881-895. [PMID: 30701439 DOI: 10.1007/s10554-019-01532-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 01/08/2019] [Indexed: 12/13/2022]
Abstract
The assessment of aortic valve (AV) morphology is paramount for planning transcatheter AV implantation (TAVI). Nowadays, pre-TAVI sizing is routinely performed at one cardiac phase only, usually at mid-systole. Nonetheless, the AV is a dynamic structure that undergoes changes in size and shape throughout the cardiac cycle, which may be relevant for prosthesis selection. Thus, the aim of this study was to present and evaluate a novel software tool enabling the automatic sizing of the AV dynamically in three-dimensional (3D) transesophageal echocardiography (TEE) images. Forty-two patients who underwent preoperative 3D-TEE images were retrospectively analyzed using the software. Dynamic measurements were automatically extracted at four levels, including the aortic annulus. These measures were used to assess the software's ability to accurately and reproducibly quantify the conformational changes of the aortic root and were validated against automated sizing measurements independently extracted at distinct time points. The software extracted physiological dynamic measurements in less than 2 min, that were shown to be accurate (error 2.2 ± 26.3 mm2 and 0.0 ± 2.53 mm for annular area and perimeter, respectively) and highly reproducible (0.85 ± 6.18 and 0.65 ± 7.90 mm2 of intra- and interobserver variability, respectively, in annular area). Using the maximum or minimum measured values rather than mid-systolic ones for device sizing resulted in a potential change of recommended size in 7% and 60% of the cases, respectively. The presented software tool allows a fast, automatic and reproducible dynamic assessment of the AV morphology from 3D-TEE images, with the extracted measures influencing the device selection depending on the cardiac moment used to perform its sizing. This novel tool may thus ease and potentially increase the observer's confidence during prosthesis' size selection at the preoperative TAVI planning.
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Affiliation(s)
- Sandro Queirós
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal. .,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal. .,Lab on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium. .,Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal.
| | - Pedro Morais
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Lab on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium.,2Ai-Polytechnic Institute of Cávado and Ave, Barcelos, Portugal
| | - Wolfgang Fehske
- Department of Cardiology, St Vinzenz-Hospital, Cologne, Germany
| | | | - Jens-Uwe Voigt
- Department of Cardiology, University Hospital Leuven, Leuven, Belgium
| | - Jaime C Fonseca
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Jan D'hooge
- Lab on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium
| | - João L Vilaça
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,2Ai-Polytechnic Institute of Cávado and Ave, Barcelos, Portugal
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Queiros S, Morais P, Barbosa D, Fonseca JC, Vilaca JL, D'Hooge J. MITT: Medical Image Tracking Toolbox. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2547-2557. [PMID: 29993570 DOI: 10.1109/tmi.2018.2840820] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Over the years, medical image tracking has gained considerable attention from both medical and research communities due to its widespread utility in a multitude of clinical applications, from functional assessment during diagnosis and therapy planning to structure tracking or image fusion during image-guided interventions. Despite the ever-increasing number of image tracking methods available, most still consist of independent implementations with specific target applications, lacking the versatility to deal with distinct end-goals without the need for methodological tailoring and/or exhaustive tuning of numerous parameters. With this in mind, we have developed the medical image tracking toolbox (MITT)-a software package designed to ease customization of image tracking solutions in the medical field. While its workflow principles make it suitable to work with 2-D or 3-D image sequences, its modules offer versatility to set up computationally efficient tracking solutions, even for users with limited programming skills. MITT is implemented in both C/C++ and MATLAB, including several variants of an object-based image tracking algorithm and allowing to track multiple types of objects (i.e., contours, multi-contours, surfaces, and multi-surfaces) with several customization features. In this paper, the toolbox is presented, its features discussed, and illustrative examples of its usage in the cardiology field provided, demonstrating its versatility, simplicity, and time efficiency.
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Barbosa D, Pedrosa J, Heyde B, Dietenbeck T, Friboulet D, Bernard O, D’hooge J. heartBEATS: A hybrid energy approach for real-time B-spline explicit active tracking of surfaces. Comput Med Imaging Graph 2017; 62:26-33. [DOI: 10.1016/j.compmedimag.2017.07.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 07/24/2017] [Accepted: 07/25/2017] [Indexed: 11/29/2022]
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Queirós S, Barbosa D, Heyde B, Morais P, Vilaça JL, Friboulet D, Bernard O, D’hooge J. Fast automatic myocardial segmentation in 4D cine CMR datasets. Med Image Anal 2014; 18:1115-31. [DOI: 10.1016/j.media.2014.06.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Revised: 05/05/2014] [Accepted: 06/06/2014] [Indexed: 10/25/2022]
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