Super resolution dual-layer CBCT imaging with model-guided deep learning

TL;DR

This paper introduces a novel deep learning method called suRi-Net for enhancing the spatial resolution of dual-layer CBCT images, effectively improving image quality by leveraging a mathematical model and neural network to retrieve high-resolution information.

Contribution

The study presents a new super resolution CBCT imaging technique using a model-guided recurrent neural network tailored for dual-layer flat panel detectors.

Findings

Spatial resolution increased by 45% and 54% in top and bottom detector layers.

suRi-Net accurately retrieves high-resolution dual-energy information.

Method validated through phantom experiments showing improved image quality.

Abstract

Objective: This study aims at investigating a novel super resolution CBCT imaging technique with the dual-layer flat panel detector (DL-FPD). Approach: In DL-FPD based CBCT imaging, the low-energy and high-energy projections acquired from the top and bottom detector layers contain intrinsically mismatched spatial information, from which super resolution CBCT images can be generated. To explain, a simple mathematical model is established according to the signal formation procedure in DL-FPD. Next, a dedicated recurrent neural network (RNN), named as suRi-Net, is designed by referring to the above imaging model to retrieve the high resolution dual-energy information. Different phantom experiments are conducted to validate the performance of this newly developed super resolution CBCT imaging method. Main Results: Results show that the proposed suRi-Net can retrieve high spatial resolution information accurately from the low-energy and high-energy projections having lower spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images of the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance: In future, suRi-Net provides a new approach to achieve high spatial resolution dual-energy imaging in DL-FPD based CBCT systems.