Mul-material decomposition method for sandstone spectral CT images based on I-MultiEncFusion-Net

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Material analysis in sandstone is essential for oil and gas extraction. Energy spectrum Computed Tomography (CT) can acquire various spectrally distinct datasets and reconstruct energy-selective images. Additionally, deep learning significantly improves the accuracy of material decomposition by establishing a nonlinear mapping relationship between multi-energy channel reconstructed images and their corresponding multi-material reconstructed images. However, traditional convolutional neural networks (CNNs) demonstrate limited effectiveness in capturing non-local features. In this paper, we present a multi-encoder single-decoder network architecture named I-MultiEncFusion-Net, designed for material decomposition. In this framework, multiple encoders concentrate on the distinctive features of reconstructed images from different energy spectrum channels, while a single decoder enables feature fusion. The encoder incorporates Inception_B modules that utilize three parallel branches to comprehensively capture image features, while integrating a Local-Nonlocal Feature Aggregation (LNFA) module to fuse both local and global characteristics. The non-local feature extraction module constructs non-local neighborhood relationships and employs Euclidean distance metrics to extract global contextual features from images, thereby enhancing the material decomposition process. To further enhance model accuracy, the decoder computes Huber loss between each output and its corresponding label, while simultaneously incorporating correlations of base material images extracted by a High-Resolution Network (HRNet) as an auxiliary loss constraint for material decomposition. Validation experiments using spectral CT data of sandstone demonstrate the method’s efficacy. Both simulated and practical results indicate that I-MultiEncFusion-Net exhibits superior generalization capability, preserves internal image details, and produces decomposed images with sharper edges.

This publication has 28 references indexed in Scilit: