ArticlePloS one2025
Enhanced MRI-PET fusion using Laplacian pyramid and empirical mode decomposition for improved oncology imaging.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
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.