4D MR imaging of respiratory organ motion and its variability

Abstract

This paper describes a method for 4D imaging, which is used to study respiratory organ motion, a key problem in various treatments. Whilst the commonly used imaging methods rely on simplified breathing patterns to acquire one breathing cycle, the proposed method was developed to study irregularities in organ motion during free breathing over tens of minutes. The method does not assume a constant breathing depth or even strict periodicity and does not depend on an external respiratory signal. Time-resolved 3D image sequences were reconstructed by retrospective stacking of dynamic 2D images using internal image-based sorting. The generic method is demonstrated for the liver and for the lung. Quantitative evaluations of the volume consistency show the advantages over one-dimensional measurements for image sorting. Dense deformation fields describing the respiratory motion were estimated from the reconstructed volumes using non-rigid 3D registration. All obtained motion fields showed variations in the range of minutes such as drifts and deformations, which changed both the exhalation position of the liver and the breathing pattern. The obtained motion data are used in proton therapy planning to evaluate dose delivery methodologies with respect to their motion sensitivity. Besides this application, the new possibilities of studying respiratory motion are valuable for other applications such as the evaluation of gating techniques with respect to residual motion.

Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-Dependent Regularization

“…the mean Euclidean distance between reference landmarks and target landmarks a) before registration, b) after a diffusive registration, c) after a masked [5,7] by defining f D (x) := 0 for all x ∈ Ω/Γ . In order to quantify the improvement of the directional-dependent approach with relation to the standard diffusive registration, we further perform a two-sample t-test following [14].…”

“…This is especially the case when two objects slip along each other, which can be observed for example in the case of lung- [4] or liver motion [5].…”

“…While these allow to explicitly or implicitly incorporate boundary conditions, inner-organ information like bronchial or vessel trees are dismissed which results in an inaccurate registration of those structures. In [5,7] the problem of slipping organs is dressed implicitly by masking the background in order to prevent it from affecting the force calculation. However, this limits motion estimation to the object and provides no information about background motion.…”

Technology preview: X‐ray fused with magnetic resonance during invasive cardiovascular procedures

“…In the heart, volume and loading alterations may cause significant geometric variation between current and roadmapped historical representations of chamber size and position, especially if there is significant time disparity. Finally, uncorrected periodic and nonperiodic respiratory (speech, sighs, and deep breaths) and nonperiodic cardiac motion (irregular rhythm) also can contribute to the registration error [15][16][17][18]. Coordinating images during voluntary or controlled end expiration may reduce this error.…”

Temporal Groupwise Registration for Motion Modeling

“…Thanks to dynamic imaging, which provides time-resolved images of the anatomy, the modeling of organ motion becomes feasible. Time-resolved images are extensively used to study cardiac [10,11,16], lung [3,4,8] and liver [17,26] motion.…”