Tissue motion estimation using dictionary learning: Application to cardiac amyloidosis
Cardiac strain estimation from ultrasound images is an efficient tool for the diagnosis of cardiac diseases. This study focuses on cardiac amyloidosis (CA), a pathology characterized by nonspecific early symptoms such as increased wall thickness. Recent clinical studies have demonstrated that patients with CA 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 (ME) methods belong to three categories: optical flow, speckle tracking and elastic registration. To overcome the ill-posedness of ME, they use local parametric models (e.g., affine) or global regularizations (e.g., B-splines). The objective of this study is twofold: i) to propose a novel ME method based on dictionary learning, ii) to evaluate this method on patients subjected to CA.