Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data

TL;DR

This paper introduces a task-specific data preparation method for deep learning in CBCT imaging, enabling accurate reconstruction of structures of interest from severely truncated data, thus expanding clinical utility.

Contribution

It proposes a novel task-specific training approach for deep learning models to focus on structures of interest in truncated CBCT data, improving reconstruction accuracy.

Findings

Pix2pixGAN with task-specific training reliably reconstructs ribs.

Conventional training risks false positives and negatives.

Method enhances clinical applicability of CBCT.

Abstract

Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might be missing outside the limited field-of-view (FOV), which restricts the clinical applications of CBCT systems. Recently, deep learning methods have been proposed to extend the FOV for multi-slice CT systems. However, in mobile CBCT system with a smaller FOV size, projection data is severely truncated and it is challenging for a network to restore all missing structures outside the FOV. In some applications, only certain structures outside the FOV are of interest, e.g., ribs in needle path planning for liver/lung cancer diagnosis. Therefore, a task-specific data preparation method is proposed in this work, which automatically let the network focus on structures of interest instead of all the structures. Our preliminary experiment shows that Pix2pixGAN with a conventional training has the risk to reconstruct false positive and false negative rib structures from severely truncated CBCT data, whereas Pix2pixGAN with the proposed task-specific training can reconstruct all the ribs reliably. The proposed method is promising to empower CBCT with more clinical applications.