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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Published on: November 23, 2019

Unsupervised deep learning-based 4DCT deformable image registration and carbon-ion 4D dose calculation.

Xiaoyan An^1,2,3,4,5, Jian Wang^1,2,3,6, Jun Zhang^1,4

¹Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.

Medical Physics

|March 15, 2026

Summary

Deep learning models like TransMatch and VoxelMorph accelerate deformable image registration for carbon-ion lung radiotherapy. These advanced methods achieve accurate dose calculations comparable to traditional techniques, enabling faster adaptive treatments.

Keywords:

4DCT carbon‐ion 4D dose calculation deformable image registration unsupervised deep learning

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Last Updated: Mar 17, 2026

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Published on: June 21, 2024

Area of Science:

Medical Physics
Radiotherapy
Image Analysis

Background:

Accurate four-dimensional dose calculation (4DDC) is critical for carbon-ion lung radiotherapy.
Conventional deformable image registration (DIR) is computationally intensive, limiting online adaptive workflows.

Purpose of the Study:

Investigate unsupervised deep learning DIR models (TransMatch, VoxelMorph) for accelerating lung 4DCT registration.
Assess their efficacy in facilitating accurate carbon-ion 4D dose calculations.

Main Methods:

Utilized 150 clinical lung 4DCT datasets for training, validation, and testing.
Evaluated registration accuracy using MAE, DSC, HD95, and Jacobian determinant.
Accumulated carbon-ion 4D dose distributions and quantified dosimetric impacts on GTV and OARs.

Main Results:

TransMatch and VoxelMorph demonstrated superior registration accuracy (mean DSC > 0.97, HD95 < 2.5 mm) and minimal folding.
Deep learning models achieved sub-second registration times, significantly faster than the conventional B-spline method (>10 min).
Dosimetric differences for GTV and OARs were within 2% of the prescription dose.

Conclusions:

Unsupervised deep learning DIR models (TransMatch, VoxelMorph) offer comparable geometric and dosimetric accuracy to conventional methods.
These models provide substantial computational speed improvements for carbon-ion lung radiotherapy.
Their efficiency highlights potential for real-time adaptive carbon-ion therapy.