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.

medical imaging is currently revolutionizing the diagnosis and treatment of a variety of diseases. Several imaging modalities have been developed based on advances in science and engineering. The impact of these imaging tools has been further improved with the advent of various modern chemistries, leading to the development of contrast agents that serve further to localize the detection of diseased tissues. Several researchers are recently involved in engineering contrast agents that can generate contrast differences between tissues in multiple imaging modalities, enabling cross-referenced determination of anomalies. To establish these Multimodal imaging agents, nanovectors have gained significance due to their key physicochemical properties. The major focus of this review is on the engineering strategies of nanovectors for Multimodal medical imaging. The review conceives the basic principles, major parameters, and limitations of imaging modalities, namely, magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging at the beginning. Drawbacks of traditional contrast agents and the demand for new contrast agents are established. The importance of Multimodal imaging and the need for a single contrast agent for these imaging applications are elaborated. Finally, the advantages, limitations, and design considerations of nanovectors based on magnetic and metallic nanoparticles with surface modifications to reduce toxicity and enable targeted delivery as Multimodal imaging agents are also emphasized.

Purpose: Recent Progress in multimooal imaging makes it possible to acquire several images from different physical properties of individual subject. Data fusion, image registration and especially image warping of these different images and sequential images have wide range of applications in medical diagnosis and treatment. Many registration techniques use interpolating function as a basis to map one image to another. Because of space variant and nonlinearity properties of imaging system, local warping algorithm improves performance of mapping.Materials and Methods: In this research we have developed a new method for local warping based on voronoi images.This method employs weighted average of partially warping function of distinct sets of landmark points.The method was tested on a large database including real as well as simulated magretic resonance images and its" results were compared with a standard benchmark sotware (AIR). Results and Discussions: Results indicated that our method has an excellent ability to compensate several geometrical distortions compare with the global warping methos.

The main purpose in various methods of image registration is to find the transformation parameters for accurate mapping an image onto another image coordinates. In medical sciences creating a precise mapping between medical images data is very important in application such as diagnosis and treatment. Accordingly, several approaches have been proposed for image registration. The compression of results and performance between different image registration algorithms was the main motivation for this research to design and implement a new hybrid algorithm so that provide high accuracy in Multimodal image registration. Automating the image registration process by using machine learning approach is the innovation of this method compared to previous ones.To this end, the proposed method which is named multi resolution learning is composed of multi resolution decomposition and a hierarchical neural network which it learn the transformation parameters by using global properties of the image and uses learned transformation parameter for image registration. The proposed method is implemented and tested on the medical images of Vanderbilt university database. Experiment result show acceptable accuracy for the proposed method compared with other methods.