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

arXiv: https://arxiv.org/abs/2409.08800 · HTML: https://mlsanity.com/paper/2409.08800/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective](https://mlsanity.com/paper/2207.06551/) (2022)
- [One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection](https://mlsanity.com/paper/1810.00500/) (2018)
- [Field of View Extension in Computed Tomography Using Deep Learning Prior](https://mlsanity.com/paper/1911.01178/) (2019)
- [Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery](https://mlsanity.com/paper/2108.13844/) (2021)

## Abstract (arXiv)

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.