An MRI framework for respiratory motion modelling validation

Abstract

Introduction: Respiratory motion models establish a correspondence between respiratory-correlated (RC) 4-dimensional (4D) imaging and respiratory surrogates, to estimate time-resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine-MRI and (ii) additional 4DMRI data for testing intra-/inter-fraction breathing motion variability.

Methods: Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter-fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine-MRI and 4DMRI respiratory phases, respectively.

Results: The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm³ ), with higher values in the presence of large inter-fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks' position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra- and inter-fraction motion, respectively. The range of motion described in the cine-MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter-fraction baseline variations occurred.

Conclusions: Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI-guided radiotherapy.

Keywords: 4DMRI; MRI-guidance; breathing motion; radiation oncology imaging; respiratory motion modelling.

Fig. 1

MRI‐based validation framework. Two 4DMRI (R1 and R2) and a cine‐MRI scan were acquired during the first (S1) and second (S2) session. These were used for 3D and 2D intra‐ and inter‐fraction motion testing. Arrows point from the building to the testing data set and labels indicates the corresponding test.

Fig. 2

Example of acquired data (P02): sagittal (a) and coronal (b) cine‐MRI frames; sagittal (c) and coronal (d) view of the overlay between the end‐exhale (green) and end‐inhale (violet) phases of the 4DMRI.

Fig. 3

DIR error for 4DMRI S1 (a), 4DMRI S2 (b) and baseline (c) registrations.

Fig. 4

Boxplots and circles represent the model estimation error and the range of motion to be compensated for by the model, respectively, as quantified by tumour COM distances in the (a) 3D intra‐S1, (b) 3D intra‐S2, (c) 3D inter‐R1 and (d) 3D inter‐R2 scenarios.