Multimodal fusion of tomographic sequences of medical images: MRE spatially enhanced by MRI. (August 2022)

• Record Type: Journal Article
• Authors: Santarelli, Chiara; Carfagni, Monica; Alparone, Luciano; Arienzo, Alberto; Argenti, Fabrizio
• Is Part Of: Computer methods and programs in biomedicine. Volume 223(2022)
• Publication Date: 2022-08
• Subjects: Biomedical images -- Image processing -- Magnetic resonance elastography -- Multimodal fusion -- Multiresolution analysis -- Tomographic sequences; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106964
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Reconstruction of 3D anatomical models is important in several biomedical applications. Medical image sequences may be characterized by a low axial resolution. 3D reconstructions affected by staircase effect. Axial interpolation allows accurate 3D models to be obtained Methods inspired by video processing are proposed and compared to solve the problem. Abstract: Background and Objective: In biomedical fields, image analysis is often necessary for an accurate diagnosis. In order to obtain all the information needed to form an in-depth clinical picture, it may be useful to combine the contents of images taken under different diagnostic modes. Multimodal medical image fusion techniques enable complementary information acquired by different imaging devices to be automatically combined into a unique image. Methods: In this paper, multimodal medical images fusion method based on multiresolution analysis (MRA) is proposed, with the aim to combine the high geometric content of magnetic resonance imaging (MRI) and the elasticity information of magnetic resonance elastography (MRE), simultaneously acquired on the same organs of a patient. First, the slices of MRE are volumetrically interpolated to exactly overlap, each with a slice of MRI. Then, the spatial details of MRI are extracted by means of MRA and injected into the corresponding slices of MRE. Due to the intrinsic dissimilarity between corresponding slices of MRE and MRI, the spatial details of MRI are modulated by local or global matching functions. Results: The performance of the proposed method is quantitatively assessed considering radiometric and geometric consistency of the fused images with respect to their originals, in a comparison with two popular methods from the literature. For a qualitative evaluation, a visual inspection is carried out. Conclusions: The results show that the proposed method enables an effective MRI-MRE fusion that allows the elasticity information and geometric details of the examined organs to be evaluated in a single image.