Structural Abnormality Detection of ARVC Patients via Localised Distance-to-Average Mapping

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• 作者：Kristin McLeod (19) (20) (23)
  Marcus Noack (19) (23) (24)
  J酶rg Saberniak (21) (22) (23)
  Kristina Haugaa (21) (22) (23)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8896
• 期：1
• 页码：177-186
• 全文大小：529 KB
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• 作者单位：Kristin McLeod (19) (20) (23)
  Marcus Noack (19) (23) (24)
  J酶rg Saberniak (21) (22) (23)
  Kristina Haugaa (21) (22) (23)
  
  19. Simula Research Laboratory, Oslo, Norway
  20. INRIA M茅diterran茅e, ASCLEPIOS Project, Sophia Antipolis, France
  23. Centre for Cardiological Innovation, Lysaker, Norway
  24. Kalkulo AS, Oslo, Norway
  21. Oslo University Hospital Rikshospitalet, Oslo, Norway
  22. University of Oslo, Oslo, Norway
  
• 丛书名：Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges
• ISBN：978-3-319-14678-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Many heart conditions result in irregular ventricular shape caused by, for example, increased ventricular pressure, regurgitated blood and poor electrical conduction, which affect the overall function of the heart. Structural abnormalities can be characteristic of a disease. Therefore, identifying structurally abnormal regions can give indicators for diagnosis and can provide useful information to guide long-term therapy planning. Given the difficulty in quantitatively measuring structural abnormalities in patients where the ventricular structure is significantly affected by the pathology, such as patients with arrhythmogenic right ventricular cardiomyopathy (ARVC), a method for computing the distance between a normal geometry and patient-specific geometries is presented. The proposed method involves computing distance maps that can visually emphasise regions with high variation from a normal geometry. A consistent parameterisation of the ventricular shape is imposed using an open-source implementation of the LDDMM algorithm on currents to deform patient-specific geometries to a mean surface, which is also computed using the LDDMM algorithm. The chosen shape parameterisation can be applied to meshes extracted from any segmentation algorithm, allowing a wide range of data to be analysed from different hospitals, different scanners and different imaging modalities. Given a consistent shape parameterisation of all meshes, distance maps can be generated by plotting the Euclidean distance point-wise on a triangulated mesh to visualise regions of high shape variability. The proposed method was applied to 10 ARVC patients to highlight patient-specific shape features.