Dynamic, patient-specific mitral valve modelling for planning transcatheter repairs

Ginty, Olivia K.; Moore, John; Eskandari, Mehdi; Carnahan, Patrick; Lassó, András; Jolley, Matthew A.; Monaghan, Mark; Peters, Terry M.

doi:10.1007/s11548-019-01998-y

Cited by 19 publications

(17 citation statements)

References 19 publications

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“…Another 24 studies (11.3%) applied a mixture of modalities across included patients (CCT and CMR, n = 21; CCT and 3D TEE, n = 2; and CCT, CMR, and 3D TEE, n = 1), while 2 studies (<1%) merged data from several modalities; 4 papers (1.9%) did not provide information on the sources of images. Echocardiography-derived 3DPSPs were limited to the depiction of cardiac valves (n = 16), 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ventricular or atrial septal defects (n = 3), 26 , 35 , 36 the left atrial appendage (n = 3), 17 , 37 , 38 or fetal hearts (n = 1). 39 …”

Section: Resultsmentioning

confidence: 99%

Translating Imaging Into 3D Printed Cardiovascular Phantoms

Illi

Bernhard

Nguyen

et al. 2022

JACC: Basic to Translational Science

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Section: Resultsmentioning

confidence: 99%

Translating Imaging Into 3D Printed Cardiovascular Phantoms

Illi

Bernhard

Nguyen

et al. 2022

JACC: Basic to Translational Science

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“…Preoperative planning may include 3-D reconstruction of a patient’s anatomy, rapid prototyping and computational modeling [ 52 , 53 , 54 ] for both MIVS and TMVR, including transcutaneous mitral valve replacement [ 55 ]. The availability of transcutaneous solutions has spurred efforts by biomedical engineers to simulate the complexity of the mitral valve by means of a blueprint [ 56 , 57 ], as well as the impact of the intervention [ 58 , 59 ] including determining the length of the neochordae [ 60 , 61 ].…”

Section: Resultsmentioning

confidence: 99%

At the Crossroads of Minimally Invasive Mitral Valve Surgery—Benching Single Hospital Experience to a National Registry: A Plea for Risk Management Technology

Cocchieri

Wetering

Tuijl

et al. 2022

JCDD

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Almost 30 years after the first endoscopic mitral valve repair, Minimally Invasive Mitral Valve Surgery (MIMVS) has become the standard at many institutions due to optimal clinical results and fast recovery. The question that arises is can already good results be further improved by an Institutional Risk Management Performance (IRMP) system in decreasing risks in minimally invasive mitral valve surgery (MIMVS)? As of yet, there are no reports on IRMP and learning systems in the literature. (2) Methods: We described and appraised our five-year single institutional experience with MIMVS in isolated valve surgery included in the Netherlands Heart Registry (NHR) and investigated root causes of high-impact complications. (3) Results: The 120-day and 12-month mortality were 1.1% and 1.9%, respectively, compared to the average of 4.3% and 5.3% reported in the NHR. The regurgitation rate was 1.4% compared to 5.2% nationwide. The few high-impact complications appeared not to be preventable. (4) Discussion: In MIMVS, freedom from major and minor complications is a strong indicator of an effective IRMP but remains concealed from physicians and patients, despite its relevance to shared decision making. Innovation adds to the complexity of MIMVS and challenges surgical competence. An IRMP system may detect and control new risks earlier. (5) Conclusion: An IRMP system contributes to an effective reduction of risks, pain and discomfort; provides relevant input for shared decision making; and warrants the safe introduction of new technology. Crossroads conclusions: investment in machine learning and AI for an effective IRMP system is recommended and the roles for commanding and operating surgeons should be considered.

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“…However, until SlicerHeart, 3D Slicer did not have the ability to import or visualize 3DE data, volume render 3DE data, nor have the workflows dedicated to the evaluation and modeling of cardiovascular structures such as cardiac valves. Although the initial development of these tools was focused on pediatrics and 3DE images, we have now demonstrated application to multiple cardiovascular diseases with clear relevance to all ages ( 7 , 21 – 29 ). Despite not being formally released until now, at the time of writing, SlicerHeart has been downloaded over 43,000 times since inception in 2015.…”

Section: Introductionmentioning

confidence: 99%

SlicerHeart: An open-source computing platform for cardiac image analysis and modeling

Lassó

Herz

Nam

et al. 2022

Front. Cardiovasc. Med.

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Cardiovascular disease is a significant cause of morbidity and mortality in the developed world. 3D imaging of the heart's structure is critical to the understanding and treatment of cardiovascular disease. However, open-source tools for image analysis of cardiac images, particularly 3D echocardiographic (3DE) data, are limited. We describe the rationale, development, implementation, and application of SlicerHeart, a cardiac-focused toolkit for image analysis built upon 3D Slicer, an open-source image computing platform. We designed and implemented multiple Python scripted modules within 3D Slicer to import, register, and view 3DE data, including new code to volume render and crop 3DE. In addition, we developed dedicated workflows for the modeling and quantitative analysis of multi-modality image-derived heart models, including heart valves. Finally, we created and integrated new functionality to facilitate the planning of cardiac interventions and surgery. We demonstrate application of SlicerHeart to a diverse range of cardiovascular modeling and simulation including volume rendering of 3DE images, mitral valve modeling, transcatheter device modeling, and planning of complex surgical intervention such as cardiac baffle creation. SlicerHeart is an evolving open-source image processing platform based on 3D Slicer initiated to support the investigation and treatment of congenital heart disease. The technology in SlicerHeart provides a robust foundation for 3D image-based investigation in cardiovascular medicine.

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Dynamic, patient-specific mitral valve modelling for planning transcatheter repairs

Cited by 19 publications

References 19 publications

Translating Imaging Into 3D Printed Cardiovascular Phantoms

Translating Imaging Into 3D Printed Cardiovascular Phantoms

At the Crossroads of Minimally Invasive Mitral Valve Surgery—Benching Single Hospital Experience to a National Registry: A Plea for Risk Management Technology

SlicerHeart: An open-source computing platform for cardiac image analysis and modeling

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