eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-05-12 19 2 71753 article Meenu Bala 1 Ayush Dogra 1 Priya Sadana 1 Bhawna Goyal 2 Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology Chitkara University, Punjab, India Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology,Chitkara University, Punjab, India, Department of Engineering, Marwadi University Research Centre,Marwadi University, Rajkot, Gujarat, India, Multimodal medical image fusion (MMIF) has emerged as a prominentparadigm in clinical research and therapeutic practice. However, a single imaging modality has its diagnostic accuracy limitation, since different modalities reflect different levels of anatomical or functional details. While MMIF integrates complementary information from modalities like MRI, CT, PET, and SPECT to improve portrayal of the pathological features, it can provide more precise and informed clinical decision support. The process of merging the complementary spatial and functional features derived from diverse medical imaging sources is referred to as multimodal medical image fusion. The central goal of image fusion is to unify spatial and functional information to enhance the disease visualization and support reliable disease evaluation, facilitating interpretation for clinicians and automated systems. This article discusses the key aspects and methodological framework required for effective multimodal medical image fusion, including comparison of key contributions of existing review papers, radiographic and tomographic imaging methods, medical image fusion framework, medical image databases, fusion techniques, performance metrics, and challenges. The experiment and comparisons of some existing methods are carried out in this paper.  FDCT method outperformed standard algorithms in most fusion metrics. A comparative evaluation of state-of-the-art fusion approaches, including CNP-NSST, ADCPNN, GoD, NSST-AGPCNN, and FDCT, is carried out on two sets of multimodal medical images, MRI/CT and T-1 MRI/CT. The comparisons demonstrate notable performance divergencies among the methods, examined through standard quantitative metrics comprising SSIM, EN, SD, Q_AB/F, and Mutual Information (MI).The reviewpresents a structured analysis of MMIF techniques, facilitating the directions for future breakthroughs in medical imaging and clinical decision making.Although advanced technologies are introduced, the issues of contrast enhancement, inadequate retention of fine-grained details, and spectral distortion remain to challenge the overall performance. The advantages and limitations of the existing methods are outlined, providing information to guide future progress in various clinical processes. https://biomedpharmajournal.org/vol19no2/multimodal-medical-image-fusion-a-review-of-imaging-modalities-databases-fusion-strategies-and-performance-validation/ Deep Learning Domain Frequency Domain Fusion Quality Metrics Hybrid Domain Pixel-Level Domain Radiographic Modalities Sparse Representation