PET-MRI Sequence Fusion using Convolution Neural Network
H. Lakhani, Devang Undaviya, Harsh Dave, Sheshang Degadwala, Dhairya Vyas
Abstract
Combining positron emission tomography (PET) with magnetic resonance imaging (MRI) yields information that is complimentary from both a functional and anatomical standpoint. However, owing to the disparities in imaging physics and acquisition techniques, the integration of different modalities continues to be a difficult endeavor is challenge. Within the scope of this research, a deep learning-based strategy is presented in this study for PET-MRI sequence fusion that makes use of convolutional neural networks (CNNs). The proposed approach trains a CNN model to discover a mapping between the two modalities by capitalizing on the similarities that exist between the spatial and temporal characteristics of the two sequences. The proposed technique was tested using a dataset consisting of fifty PET-MRI scans. The findings illustrate the ability of our method to properly fuse the two sequences and increase picture quality in comparison to registration-based approaches that have been used traditionally. The CNN-based fusion strategy offers promise for enabling the clinical integration of PET-MRI, which would ultimately result in more accurate diagnosis and treatment planning for a variety of disorders.