Towards Automatic Assessment of the Mitral Valve Coaptation Zone from 4D Ultrasound

Engelhardt, Sandy; Lichtenberg, Nils; Al-Maisary, Sameer; Simone, Raffaele De; Rauch, Helmut; Roggenbach, J.; Kretzler, Matthias; Meinzer, Hans Peter; Wolf, Ivo

doi:10.1007/978-3-319-20309-6_16

Cited by 11 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We target patient-specific maps and rely on an already existing semiautomatic method to extract MV surface models from 4D ultrasound scans [4], consisting of annulus and leaflets. This segmentation algorithm provides separate triangulations for both MV leaflets and the annulus.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mitral valve flattening and parameter mapping for patient-specific valve diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Purpose Intensive planning and analysis from echocardiography are a crucial step before reconstructive surgeries are applied to malfunctioning mitral valves. Volume visualizations of echocardiographic data are often used in clinical routine. However, they lack a clear visualization of the crucial factors for decision making. Methods We build upon patient-specific mitral valve surface models segmented from echocardiography that represent the valve's geometry, but suffer from self-occlusions due to complex 3D shape. We transfer these to 2D maps by unfolding their geometry, resulting in a novel 2D representation that maintains anatomical resemblance to the 3D geometry. It can be visualized together with color mappings and presented to physicians to diagnose the pathology in one gaze without the need for further scene interaction. Furthermore, it facilitates the computation of a Pathology Score, which can be used for diagnosis support. Results Quality and effectiveness of the proposed methods were evaluated through a user survey conducted with domain experts. We assessed pathology detection accuracy using 3D valve models in comparison with the novel visualizations. Classification accuracy increased by 5.3% across all tested valves and by 10.0% for prolapsed valves. Further, the participants' understanding of the relation between 3D and 2D views was evaluated. The Pathology Score is found to have potential to support discriminating pathologic valves from normal valves. Conclusions In summary, our survey shows that pathology detection can be improved in comparison with simple 3D surface visualizations of the mitral valve. The correspondence between the 2D and 3D representations is comprehensible, and colorcoded pathophysiological magnitudes further support the clinical assessment.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The flattening technique proposed in this paper (Fig. 2, right) builds upon a MV segmentation based on Engelhardt et al [4] (Fig. 2, center) and provides a novel, supportive view…”

Section: Introductionmentioning

confidence: 99%

Mitral valve flattening and parameter mapping for patient-specific valve diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Potential error sources of the computer-based mitral valve analysis are the optical tracking system per se and the performance of the user (eg, hand tremor). Tailored phantom experiments in a realistic environment [9] revealed a high accuracy (mean precision: 0.12 AE 0.09 mm; mean trueness: 0.77 AE 0.39 mm) of the optical tracking system and of the measurements conducted by the user (mean precision: 0.18 AE 0.10 mm; mean trueness: 0.81 AE 0.36 mm).…”

Section: Commentmentioning

confidence: 99%

“…The 3D visualization, along with geometric parameters of the valve, including annulus dimensions and areas, chordal length indices, and leaflets regional areas, supported the surgeon's understanding of the complex valve anatomy and the spatial relationship between the different valve components. Figure 5 presents the measured geometric parameters along with values obtained from the 3D preoperative echocardiographic data [9]. In addition, the measured annular areas are presented together with the areas of the selected prosthetic ring to give further insight into the extension of annular remodeling.…”

Section: Clinical Experiencesmentioning

confidence: 99%

Intraoperative Quantitative Mitral Valve Analysis Using Optical Tracking Technology

Engelhardt

Wolf

Al-Maisary

et al. 2016

The Annals of Thoracic Surgery

Self Cite

View full text Add to dashboard Cite

show abstract

“…We satisfy these two essential requirements using our comprehensive MVR preprocessing workflow [14] , which we integrated into the Medical Simulation Markup Language (MSML) [15] , and which is wrapped as a cognitive application, too: For this workflow, on the one hand, we employ the information derived by the above depicted deductive system for MVR ring selection (see 2.2), and on the other hand, we use patient-individual ultrasound-imaging-based segmentations of the mitral valve [16] that were obtained by means of the open-source MITK software [17] . Thereon-based, we execute a series of analytics tools and simulation preprocessing operators, in order to automatically set up a comprehensive patientspecific MVR simulation scenario with the corresponding ring prosthesis, cf.…”

Section: Msml-based Simulation Preprocessing For Mvrmentioning

confidence: 99%

Cognitive tools pipeline for assistance of mitral valve surgery

et al. 2016

Self Cite

View full text Add to dashboard Cite

For cardiac surgeons, mitral valve reconstruction (MVR) surgery is a highly demanding procedure, where an artificial annuloplasty ring is implanted onto the mitral valve annulus to re-enable the valve's proper closing functionality. For a successful operation the surgeon has to keep track of a variety of relevant impact factors, such as patient-individual medical history records, valve geometries, or tissue properties of the surgical target, and thereon-based deduce type and size of the bestsuitable ring prosthesis according to practical surgery experience. With this work, we aim at supporting the surgeon in selecting this ring prosthesis by means of a comprehensive information processing pipeline. It gathers all available patient-individual information, and mines this data according to 'surgical rules', that represent published MVR expert knowledge and recommended best practices, in order to suggest a set of potentially suitable annuloplasty rings. Subsequently, these rings are employed in biomechanical MVR simulation scenarios, which simulate the behavior of the patient-specific mitral valve subjected to the respective virtual ring implantation. We present the implementation of our deductive system for MVR ring selection and how it is integrated into a cognitive data processing pipeline architecture, which is built under consideration of Linked Data principles in order to facilitate holistic information processing of heterogeneous medical data. By the example of MVR surgery, we demonstrate the ease of use and the applicability of our development. We expect to essentially support patient-specific decision making in MVR surgery by means of this holistic information processing approach.

show abstract

Towards Automatic Assessment of the Mitral Valve Coaptation Zone from 4D Ultrasound

Cited by 11 publications

References 11 publications

Mitral valve flattening and parameter mapping for patient-specific valve diagnosis

Mitral valve flattening and parameter mapping for patient-specific valve diagnosis

Intraoperative Quantitative Mitral Valve Analysis Using Optical Tracking Technology

Cognitive tools pipeline for assistance of mitral valve surgery

Contact Info

Product

Resources

About