ReviewDiagnostics (Basel, Switzerland)2023
A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A comprehensive survey of multi-modality medical image fusion: methods, challenges, and applications.BMC medical imaging · 2026Review
- Hybrid Imaging in Industrial Applications: A Review of Principles and Deployment.Journal of imaging · 2026Review
- CAFrontiers in artificial intelligence · 2026Article
- Comprehensive evaluation of optimization algorithms for medical image segmentation.Scientific reports · 2025Article
- A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study.BMC medical imaging · 2024Article
- Tensor Methods in Biomedical Image Analysis.Journal of medical signals and sensors · 2024Review
- An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19.PloS one · 2024Article
- Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.