Evaluation of MRI-derived surrogate signals to model respiratory motion

Abstract

An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.

Real-time non-rigid 3D respiratory motion estimation for MR-guided radiotherapy using MR-MOTUS

“…Further reducing the dimensionality, [43] achieved 5 Hz with 2D input data, and [16] generated 3D volumes at 3.3 Hz (acquisition + reconstruction) with 1D input data. Slightly different type of methods are based on surrogate signal models [14], [15], [44]- [46] that -similarly to this work -also use a bi-linear motion model, but directly incorporate 1D surrogate signals in this model to infer 3D motion-fields from 1D input data. These methods can thus achieve high temporal resolution, but rely heavily on the quality of the motion surrogate.…”

“…In [13], a pre-trained 3D motion model was fit to incoming 2D cine-images to obtain fast motion estimates. Additionally, surrogate-driven motion models have been proposed that relate cine-MRI-derived surrogate signals to motion-fields [14], [15]. Another recent work [16] rapidly generates 3D MR-images by determining the best match between the current 1D motion state and 1D motion states in a 3D+t respiratory-resolved image reconstruction.…”

Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

“…The spatial resolution of a two-dimensional navigator can be at 1.8 × 1.8 mm 2 with an expected temporal resolution no better than around 185 ms [ 32 ]. One advantage of the two-dimensional navigators is that multiple one-dimensional navigators can be extracted from distant points providing multiple surrogate signals for motion tracking, or an input for a more complete fully three-dimensional respiratory motion model [ 33 ]. The disadvantage of a two-dimensional navigator lies in the out-of-plane motion which a three-dimensional navigator can resolve effectively.…”

Time‐resolved MRI for off‐line treatment robustness evaluation in carbon‐ion radiotherapy of pancreatic cancer

“…[13][14][15][16] TR 3D images still present insufficient spatiotemporal resolution 9 ; however, TR 2D cine-MRI can be acquired to describe cycle-to-cycle breathing variations. 17 To obtain 3D information on breathing organ motion based on 2D cine-MRI, different methods have been proposed in the literature, [18][19][20][21][22][23] generally relying on a global respiratory motion model 24 which establishes a correlation between a priori knowledge on the breathing motion (e.g., pretreatment 4D imaging) and a surrogate of respiration (e.g., image-based signals 25 ). The method by Paganelli et al, 26 recently validated on both computational and physical phantoms, 21,27 allows TR 3D reconstruction of respiratory motion without the need of previous RC (4D) imaging.…”