What question did this study set out to answer?

To validate a respiratory motion model using electromagnetic surface tracking and multi-cycle 4D MRI.

February 16, 2026

Surface Tracking-Assisted Multi-Cycle 4D MRI Motion Modeling for Lung Radiotherapy: A Preliminary Validation Study

Key Points

To validate a respiratory motion model using electromagnetic surface tracking and multi-cycle 4D MRI.
Conducted TRMC-MRI on four volunteers with EM sensors for surface motion capture.
Generated deformation vector fields (DVFs) through image registration and temporal alignment.
Developed PCA and ICA machine learning models to map surface motion to internal DVFs.
Evaluated DVF estimates by reconstructing dynamic MR images against ground truth using mutual information and RMSE.
Both PCA and ICA models achieved comparable mean mutual information scores of 62%.
Adaptive median filtering improved MI scores to approximately 66%.
Mean RMSE for diaphragm profile analysis was 3.77-4.71 mm, showing close agreement with ground truth.

Abstract

Abstract Objective: To validate a respiratory motion model that uses real-time electromagnetic (EM) surface tracking acquired concurrently with time-resolved multi-cycle 4D MRI (TRMC-MRI) to estimate respiration-induced changes within the entire irradiated volume.Approach: Four volunteer participants with no self-reported history of lung cancer or other pulmonary disease underwent TRMC-MRI using a golden-angle stack-of-stars 3D GRE sequence while breathing freely for 2 minutes. Concurrently, real-time thoracoabdominal surface motion was recorded using four MR-compatible electromagnetic (EM) sensors (EndoScout II). Each MR volume was temporally aligned with corresponding EM data, resulting in 2,000 paired samples per participant. Deformation vector fields (DVFs) were generated through deformable image registration between a selected reference volume and all subsequent volumes. To capture temporal anatomical changes, additional DVFs were computed via consecutive volume-to-volume registration (e.g., volume 2 to 1, 3 to 2, and so on). Two machine learning models were developed to map surface motion to internal DVFs using dimensionality reduction: one employing Principal Component Analysis (PCA), and the other using Independent Component Analysis (ICA). Estimated DVFs were applied to the reference volume to reconstruct dynamic MR images, which were evaluated against ground truth using mutual information (MI) and an image-derived diaphragm profile–based root mean squared error (RMSE).Main results: Our preliminary results demonstrated that both PCA- and ICA-based models achieved comparable MI scores (mean 62%; one-way ANOVA, p > 0.05). Adaptive median filtering significantly improved MI to approximately 66% on average (one-way ANOVA, p < 0.001), outperforming no filtering across all participants (Tukey’s HSD, p < 0.05). Diaphragm profile analysis showed close agreement with ground truth, with mean RMSE of 3.77–4.71 mm (SD: 1.07–1.63 mm).Significance: This proof-of-concept study demonstrates the feasibility of a non-invasive respiratory motion model derived from EM-based surface tracking combined with TRMC-MRI, with potential applications in MR-guided and conventional radiotherapy.

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