Enhanced MRI-PET fusion using Laplacian pyramid and empirical mode decomposition for improved oncology imaging

• Published in: PloS one Vol. 20; no. 5; p. e0322443
• Main Authors: Suryanarayana, Gunnam, Nimmagadda, Satyanarayana Murthy, Nageswara Rao, Sabbavarapu, Y. Mahnashi, Ali Mohammed, Kondamuri, Shri Ramtej, Hussain Ahmadini, Abdullah Ali, Zaagan, Abdullah Ahmed, Msmali, Ahmed Hussein
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.05.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Cancer; Computer and Information Sciences; Computer vision; Decomposition; Diagnosis; Engineering and Technology; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mean square errors; Medical imaging; Medicine and Health Sciences; Methods; Multimodal Imaging - methods; Neoplasms - diagnostic imaging; Oncology; PET imaging; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Research and Analysis Methods; Spatial discrimination; Spatial resolution; Wavelet transforms; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0322443

In the field of oncology imaging, the fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) modalities is crucial for enhancing diagnostic capabilities. This article introduces a novel fusion method that leverages the strengths of both modalities to overcome limitations associated with functional information in MRI and the spatial resolution in PET scans. Our approach integrates the Laplacian pyramid for extracting high and low-frequency components, along with empirical mode decomposition and phase congruency to preserve crucial structural details in the fused image. Additionally, a rolling guidance filter is employed to mitigate edge detail loss. Through extensive comparative experiments on multi-focus and multi-modal image datasets, our method consistently outperforms existing techniques in terms of visualization, objective metrics, and computational efficiency. The proposed fusion method demonstrates superior performance, establishing it as a compelling alternative for oncology imaging applications.