Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data

Author:

Legaz-Aparicio Alvar-Ginés1,Verdú-Monedero Rafael1,Larrey-Ruiz Jorge1,Morales-Sánchez Juan1,López-Mir Fernando2,Naranjo Valery2,Bernabéu Ángela3

Affiliation:

1. Universidad Politécnica de Cartagena, Plaza del Hospital, 1. Cartagena, 30202, Spain

2. Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, 46022, Spain

3. Inscanner S.L., Unidad de Resonancia Magnética, Avenida de Dénia, 78. Alicante, 03016, Spain

Abstract

This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Neonatal White Matter Damage Analysis Using DTI Super-Resolution and Multi-Modality Image Registration;International Journal of Neural Systems;2023-11-17

2. Brain Shape Correspondence Analysis Using Variational Mixtures for Gaussian Process Latent Variable Models;Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications;2022

3. Ensemble learning of multiview CNN models for survival time prediction of brain tumor patients using multimodal MRI scans;TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES;2021-03-30

4. Convolutional neural networks for multi-class brain disease detection using MRI images;Computerized Medical Imaging and Graphics;2019-12

5. Efficient combined SSIM- and landmark-driven image registration in a variational framework;Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería;2019-03-21

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