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Bracco MI, Broda M, Lorenzen US, Florkow MC, Somphone O, Avril S, Biancolini ME, Rouet L. Fast strain mapping in abdominal aortic aneurysm wall reveals heterogeneous patterns. Front Physiol 2023; 14:1163204. [PMID: 37362444 PMCID: PMC10285457 DOI: 10.3389/fphys.2023.1163204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/17/2023] [Indexed: 06/28/2023] Open
Abstract
Abdominal aortic aneurysm patients are regularly monitored to assess aneurysm development and risk of rupture. A preventive surgical procedure is recommended when the maximum aortic antero-posterior diameter, periodically assessed on two-dimensional abdominal ultrasound scans, reaches 5.5 mm. Although the maximum diameter criterion has limited ability to predict aneurysm rupture, no clinically relevant tool that could complement the current guidelines has emerged so far. In vivo cyclic strains in the aneurysm wall are related to the wall response to blood pressure pulse, and therefore, they can be linked to wall mechanical properties, which in turn contribute to determining the risk of rupture. This work aimed to enable biomechanical estimations in the aneurysm wall by providing a fast and semi-automatic method to post-process dynamic clinical ultrasound sequences and by mapping the cross-sectional strains on the B-mode image. Specifically, the Sparse Demons algorithm was employed to track the wall motion throughout multiple cardiac cycles. Then, the cyclic strains were mapped by means of radial basis function interpolation and differentiation. We applied our method to two-dimensional sequences from eight patients. The automatic part of the analysis took under 1.5 min per cardiac cycle. The tracking method was validated against simulated ultrasound sequences, and a maximum root mean square error of 0.22 mm was found. The strain was calculated both with our method and with the established finite-element method, and a very good agreement was found, with mean differences of one order of magnitude smaller than the image spatial resolution. Most patients exhibited a strain pattern that suggests interaction with the spine. To conclude, our method is a promising tool for investigating abdominal aortic aneurysm wall biomechanics as it can provide a fast and accurate measurement of the cyclic wall strains from clinical ultrasound sequences.
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Affiliation(s)
- Marta Irene Bracco
- Mines Saint-Étienne, University Jean Monnet, INSERM, Sainbiose, Saint-Étienne, France
- Philips Research Paris, Suresnes, France
| | - Magdalena Broda
- Department of Vascular Surgery, Rigshospitalet, Copenhagen, Denmark
| | | | | | | | - Stephane Avril
- Mines Saint-Étienne, University Jean Monnet, INSERM, Sainbiose, Saint-Étienne, France
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Mariscal-Harana J, Charlton PH, Vennin S, Aramburu J, Florkow MC, van Engelen A, Schneider T, de Bliek H, Ruijsink B, Valverde I, Beerbaum P, Grotenhuis H, Charakida M, Chowienczyk P, Sherwin SJ, Alastruey J. Estimating central blood pressure from aortic flow: development and assessment of algorithms. Am J Physiol Heart Circ Physiol 2020; 320:H494-H510. [PMID: 33064563 PMCID: PMC7612539 DOI: 10.1152/ajpheart.00241.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors≤2.1 ± 9.7mmHg and root-mean-square errors (RMSEs)≤6.4 ± 2.8mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7mmHg and RMSEs ≤ 5.9 ± 2.4mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data.
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Affiliation(s)
- Jorge Mariscal-Harana
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Peter H Charlton
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Samuel Vennin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Department of Clinical Pharmacology, King's College London, King's Health Partners, London , United Kingdom
| | - Jorge Aramburu
- TECNUN Escuela de Ingenieros, Universidad de Navarra, Donostia-San Sebastián, Spain
| | - Mateusz Cezary Florkow
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Philips Research, Cambridge, United Kingdom
| | - Arna van Engelen
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Torben Schneider
- Philips Healthcare UK, Philips Centre, Guildford Business Park, Guildford, Surrey, United Kingdom
| | - Hubrecht de Bliek
- HSDP Clinical Platforms, Philips Healthcare, Eindhoven, The Netherlands
| | - Bram Ruijsink
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Israel Valverde
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Cardiovascular Pathophysiology, Institute of Biomedicine of Seville, University Hospital of Virgen del Rocío, University of Seville, CIBERCV, CSIC, Seville, Spain
| | - Philipp Beerbaum
- Department of Pediatric Cardiology and Intensive Care, Hannover Medical School, Hannover, Germany
| | - Heynric Grotenhuis
- Department of Pediatric Cardiology, University Medical Center Utrecht/Wilhelmina Children's Hospital, Utrecht, The Netherlands
| | - Marietta Charakida
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Phil Chowienczyk
- Department of Clinical Pharmacology, King's College London, King's Health Partners, London , United Kingdom
| | - Spencer J Sherwin
- Department of Aeronautics, South Kensington Campus, Imperial College London, London, United Kingdom
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Institute of Personalized Medicine, Sechenov University, Moscow, Russia
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