Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting

Abstract

Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to approximate the nonlinear inverse of a diffeomorphic deformation composed with radiographic projection. This network recovers subspace coordinates to define the patient-specific deformation of the lungs from a baseline anatomic position. The geometric accuracy of the subspace deformations on real patient data is similar to accuracy attained by original image registration between individual respiratory-phase image volumes.

References in corpus (6)

• PCA-based lung motion model
• Memory-Efficient Implementation of DenseNets
• Diffeomorphic density matching by optimal information transport
• Generalised Hunter-Saxton equations, optimal information transport, and factorisation of diffeomorphisms
• Weighted Diffeomorphic Density Matching with Applications to Thoracic Image Registration
• Rank Constrained Diffeomorphic Density Motion Estimation for Respiratory Correlated Computed Tomography

Cited by in corpus (8)

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• Medical Image Registration Using Deep Neural Networks: A Comprehensive Review
• One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
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• Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey
• A Conditional Point Cloud Diffusion Model for Deformable Liver Motion Tracking Via a Single Arbitrarily-Angled X-ray Projection
• CNN-based real-time 2D-3D deformable registration from a single X-ray projection
• GuidedRec: Guiding Ill-Posed Unsupervised Volumetric Recovery