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Related Experiment Video

Updated: May 15, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Deep-Motion-Net: GNN-based volumetric liver shape reconstruction from single-view 2D projections.

Isuru Wijesinghe¹, Michael Nix², Arezoo Zakeri³

¹Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Mechanical Engineering, University of Leeds, Leeds, UK. isurusuranga.wijesinghe@gmail.com.

International Journal of Computer Assisted Radiology and Surgery

|May 13, 2026

Summary

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Gross Anatomy of the Liver

Gross Anatomy of the Liver

The liver, the largest gland within the human body, is a firm and reddish-brown organ. This wedge-shaped structure weighs approximately 1.5 kg and occupies a significant portion of the right hypochondriac and epigastric regions. It extends more to the right of the body's midline than to the left.
Located under the diaphragm, the liver is almost entirely ensconced within the rib cage, providing it with substantial protection. Except for the superior most bare area, the liver's surface is covered...

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This study introduces Deep-Motion-Net, a novel graph neural network that accurately predicts internal organ motion using only standard X-ray images during radiotherapy. This method enhances radiation delivery precision without needing extra equipment or markers.

Area of Science:

Medical Imaging
Radiotherapy Physics
Artificial Intelligence in Medicine

Background:

Internal anatomical motion during radiotherapy complicates precise dose delivery to tumors and increases risk to surrounding healthy tissues.
Accurate estimation and compensation for organ motion are critical for improving treatment efficacy and patient safety in external beam radiotherapy.
Current methods often rely on surrogate signals or invasive markers, which have limitations in accuracy and patient comfort.

Purpose of the Study:

To develop a patient-specific deep learning framework for accurate 3D organ motion prediction using only standard in-treatment planar X-ray images.
To enable precise radiation delivery by compensating for internal anatomical motion without additional imaging modalities or invasive procedures.
To reconstruct volumetric 3D organ models from single-view X-ray images at arbitrary angles during radiotherapy sessions.

Keywords:

Adaptive radiotherapy Graph attention Graph neural network Motion modelling Synthetic data X-ray image

Related Experiment Videos

Last Updated: May 15, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Main Methods:

Proposed Deep-Motion-Net, an end-to-end graph neural network (GNN) for 3D organ reconstruction from kV X-ray images.
Utilized a 2D CNN encoder for feature extraction, followed by feature pooling and a ResNet-based graph attention network for mesh deformation.
Trained the model using synthetically generated motion instances and kV images, incorporating digitally reconstructed radiographs (DRRs) and conditional CycleGAN for style transfer.

Main Results:

Achieved sub-millimetre accuracy with overall mean prediction errors as low as 0.12 ± 0.11 mm on synthetic data.
Demonstrated clinical feasibility on in-treatment kV images from four liver cancer patients, with mean peak prediction errors up to 3.29 mm.
Validated the model's performance across various synthetic respiratory motion scenarios.

Conclusions:

Deep-Motion-Net successfully reconstructs volumetric 3D organ models from single-view X-ray images, achieving high accuracy.
The approach leverages accessible in-treatment imaging, offering a cost-effective alternative to MRI or invasive markers.
This represents the first deep learning framework capable of volumetric 3D organ reconstruction from single-view images throughout an entire treatment scan series, demonstrating clinical potential.