Temporal Registration of Cardiac Multimodal Images Using Locally Linear Embedding Algorithm

Talayeh  Ghodsizad; Hamid  Behnam; Emad  Fatemizadeh; Taraneh  Faghihi Langroudi; Fariba  Bayat

doi:10.18502/fbt.v8i4.7757

Talayeh Ghodsizad Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Hamid Behnam Department of Biomedical Engineering, Iran University of Science and Technology, Tehran, Iran
Emad Fatemizadeh School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Taraneh Faghihi Langroudi Department of Radiology, Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Fariba Bayat Cardiovascular Research Center, Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v8i4.7757

Keywords: Multimodal Temporal Registration; Manifold Learning Algorithm; Locally Linear Embedding; Nonlinear Dimension Reduction.

Abstract

Purpose: Multimodal Cardiac Image (MCI) registration is one of the evolving fields in the diagnostic methods of Cardiovascular Diseases (CVDs). Since the heart has nonlinear and dynamic behavior, Temporal Registration (TR) is the fundamental step for the spatial registration and fusion of MCIs to integrate the heart's anatomical and functional information into a single and more informative display. Therefore, in this study, a TR framework is proposed to align MCIs in the same cardiac phase.

Materials and Methods: A manifold learning-based method is proposed for the TR of MCIs. The Euclidean distance among consecutive samples lying on the Locally Linear Embedding (LLE) of MCIs is computed. By considering cardiac volume pattern concepts from distance plots of LLEs, six cardiac phases (end-diastole, rapid-ejection, end-systole, rapid-filling, reduced-filling, and atrial-contraction) are temporally registered.

Results: The validation of the proposed method proceeds by collecting the data of Computed Tomography Coronary Angiography (CTCA) and Transthoracic Echocardiography (TTE) from ten patients in four acquisition views. The Correlation Coefficient (CC) between the frame number resulted from the proposed method and manually selected by an expert is analyzed. Results show that the average CC between two resulted frame numbers is about 0.82±0.08 for six cardiac phases. Moreover, the maximum Mean Absolute Error (MAE) value of two slice extraction methods is about 0.17 for four acquisition views.

Conclusion: By extracting the intrinsic parameters of MCIs, and finding the relationship among them in a lower-dimensional space, a fast, fully automatic, and user-independent framework for TR of MCIs is presented. The proposed method is more accurate compared to Electrocardiogram (ECG) signal labeling or time-series processing methods which can be helpful in different MCI fusion methods.