Modeling and incorporating cardiac‐induced lung tissue motion in a breathing motion model

Abstract

Purpose:The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model. Methods:The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement γ ( X 0 ) of each voxel ( X 0 ) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow. Results: The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement. Conclusions:The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

Bioreactor Development for Lung Tissue Engineering

“…Such measurements have already been incorporated into whole lung bioreactors. Although cardiac-induced lung motion has been considered in breathing models, the displacement is relatively small, and the costs/efforts of incorporating this parameter would not be justified at this early stage of bioreactor development[34]. …”

Modelling the effects of lung cancer motion due to respiration

“…In contrast, the target detected in the apex of the lung is characterized by strongly reduced motion [23]. For mediastinal tumours, the breathing impact may be more complicated to predict due to the heartbeat effect, detected when PTV encompasses the heart wall [8,23,33]. Another aspect of breathing impact should be linked to the possible dependence between tumour size and its motion.…”

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

“…Thin-plate splines are often used to determine the transformation [ 170 – 179 ]. Using intensity-based algorithms, the nonrigid component of the transformation can be determined using a linear combination of polynomial terms [ 120 , 180 ], basis functions, or B-spline [ 27 , 49 , 80 , 181 – 184 ] surfaces defined by a regular grid of control points. As an alternative, pseudophysical models, such as elastic deformation or fluid flow [ 78 , 169 , 185 – 187 ], can be used, wherein the deformation between the images is modeled as a physical process.…”

“…However, the limitation is that this assumption is not applicable in many cases. To reduce the blurriness of edges, more recent approaches have been studied, including the polynomial curve fitting [ 120 , 180 ] method.…”