Tissue motion estimation using dictionary learning: application to cardiac amyloidosis

Nora Leïla Ouzir (1) , Olivier Lairez (2) , Adrian Basarab (1, 3) , Jean-Yves Tourneret (4, 5)

1 IRIT-TCI - Traitement et Compréhension d’Images
2 I2MC - Institut des Maladies Métaboliques et Cardiovasculaires
3 UT3 - Université Toulouse III - Paul Sabatier
4 IRIT-MINDS - CoMputational imagINg anD viSion
5 Toulouse INP - Institut National Polytechnique (Toulouse)

Cardiac strain estimation from ultrasound images is an efficient tool for the diagnosis of cardiac diseases. This study focuses on cardiac amyloidosis, a pathology characterized by non-specific early symptoms such as the increased wall thickness. Recent clinical studies have demonstrated that patients with cardiac amyloidosis present an apex-to-base gradient longitudinal strain pattern, i.e., a normal strain in apex and abnormally lower values for base segments. Existing cardiac motion estimation methods belong to three categories based on optical flow, speckle tracking and elastic registration. To overcome the ill-posedness of motion estimation, they use local parametric models (e.g., affine) or global regularizations (e.g., B-splines). The objective of this study is to evaluate a recently proposed cardiac motion estimation method based on dictionary learning on patients subjected to cardiac amyloidosis.

Mots clés: Cardiac motion estimation; Dictionary learning; Cardiac amyloidosis

Domaines: Apprentissage [cs.LG]

Nora Leïla Ouzir, Olivier Lairez, Adrian Basarab, Jean-Yves Tourneret. Tissue motion estimation using dictionary learning: application to cardiac amyloidosis. IEEE International Ultrasonics Symposium (IUS 2017), Sep 2017, Washington, United States. pp.1-4, ⟨10.1109/ULTSYM.2017.8092152⟩. ⟨hal-02871331⟩