A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion

Diwakar, Manoj; Singh, Prabhishek; Ravi, Vinayakumar; Maurya, Ankur

doi:10.3390/diagnostics13050820

Cited by 11 publications

(2 citation statements)

References 93 publications

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“…Tensor-based fusing of biomedical images has been widely studied in different literatures. [ 11 12 13 38 100 101 102 103 104 105 106 107 108 109 110 111 ] The methods are “ usually ” based of coupled decomposition of different datasets ( Figure 4 ). However, some of fusion methods used the information of one modality for tensor decomposition of another modality, or applying tensor decomposition on a dataset which is composed of all modalities together.…”

Section: Tensor-based Biomedical Image Analysismentioning

confidence: 99%

Tensor Methods in Biomedical Image Analysis

Sedighin

2024

Journal of Medical Signals &Amp; Sensors

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In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrices cannot preserve the multidimensional correlation of elements in higher-order datasets and this highly reduces the effectiveness of matrix-based approaches in analyzing multidimensional datasets. Besides this, tensor-based approaches have demonstrated promising performances. These together, encouraged researchers to move from matrices to tensors. Among different signal and image processing applications, analyzing biomedical signals and images is of particular importance. This is due to the need for extracting accurate information from biomedical datasets which directly affects patient’s health. In addition, in many cases, several datasets have been recorded simultaneously from a patient. A common example is recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) of a patient with schizophrenia. In such a situation, tensors seem to be among the most effective methods for the simultaneous exploitation of two (or more) datasets. Therefore, several tensor-based methods have been developed for analyzing biomedical datasets. Considering this reality, in this paper, we aim to have a comprehensive review on tensor-based methods in biomedical image analysis. The presented study and classification between different methods and applications can show the importance of tensors in biomedical image enhancement and open new ways for future studies.

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Section: Tensor-based Biomedical Image Analysismentioning

confidence: 99%

Tensor Methods in Biomedical Image Analysis

Sedighin

2024

Journal of Medical Signals &Amp; Sensors

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“…Each imaging modality has its strengths and weaknesses and may be unable to provide interventionalists with complete information during treatment. Thus, multimodal image integration is a logical solution and has received increasing attention [ 44 ].…”

Section: Review Of Existing Multimodal Image Integration Techniquesmentioning

confidence: 99%

Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease

Zhang,

Gosnell,

Nainamalai

et al. 2023

Diagnostics

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Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and anatomical variability demand a high-resolution special understanding for intraprocedural image guidance. During the last decade, three-dimensional (3D) transesophageal echocardiography (TEE) has become one of the most accessed imaging methods for structural interventions. Although 3D-TEE can assess cardiac structures and functions in real-time, its limitations (e.g., limited field of view, image quality at a large depth, etc.) must be addressed for its universal adaptation, as well as to improve the quality of its imaging and interventions. This review aims to present the role of TEE in the intraprocedural guidance of percutaneous structural interventions. We also focus on the current and future developments required in a multimodal image integration process when using TEE to enhance the management of congenital and SHD treatments.

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