Simulation of random deformable motion in soft-tissue cone-beam CT with learned models

Abstract

Cone-beam CT (CBCT) is widely used for guidance in interventional radiology but it is susceptible to motion artifacts. Motion in interventional CBCT features a complex combination of diverse sources including quasi-periodic, consistent motion patterns such as respiratory motion, and aperiodic, quasi-random, motion such as peristalsis. Recent developments in image-based motion compensation methods include approaches that combine autofocus techniques with deep learning models for extraction of image features pertinent to CBCT motion. Training of such deep autofocus models requires the generation of large amounts of realistic, motion-corrupted CBCT. Previous works on motion simulation were mostly focused on quasi-periodic motion patterns, and reliable simulation of complex combined motion with quasi-random components remains an unaddressed challenge.This work presents a framework aimed at synthesis of realistic motion trajectories for simulation of deformable motion in soft-tissue CBCT. The approach leveraged the capability of conditional generative adversarial network (GAN) models to learn the complex underlying motion present in unlabeled, motion-corrupted, CBCT volumes. The approach is designed for training with unpaired clinical CBCT in an unsupervised fashion. This work presents a first feasibility study, in which the model was trained with simulated data featuring known motion, providing a controlled scenario for validation of the proposed approach prior to extension to clinical data. Our proof-of-concept study illustrated the potential of the model to generate realistic, variable simulation of CBCT deformable motion fields, consistent with three trends underlying the designed training data: i) the synthetic motion induced only diffeomorphic deformationswith Jacobian Determinant larger than zero; ii) the synthetic motion showed median displacement of 0.5 mm in regions predominantly static in the training (e.g., the posterior aspect of the patient laying supine), compared to a median displacement of 3.8 mm in regions more prone to motion in the training; and iii) the synthetic motion exhibited predominant directionality consistent with the training set, resulting in larger motion in the superior-inferior direction (median and maximum amplitude of 4.58 mm and 20 mm, > 2x larger than the two remaining direction). Together, the proposed framework shows the feasibility for realistic motion simulation and synthesis of variable CBCT data.

Deformable motion compensation in interventional cone‐beam CT with a context‐aware learned autofocus metric

“…Further integration of complex motion scenarios can be achieved via ongoing research on generative learned models for synthesis of clinically realistic motion, trained in an unsupervised fashion with large collections of unpaired motion corrupted clinical CBCT datasets. 71 A second limitation of the training strategy in this work is the lack of instances including high-attenuating metal instruments that might be present in interventional environments. Such instruments, even if not present inside the system field of view, might result in streak artifacts that can resemble motion-induced streaking, resulting in an artificial reduction in V IF DL not attributable to patient motion.…”

“…In the experimental studies, interfaces between the surrogate aorta model (rigid) and the surrounding interstitial soft tissue featured sliding motion components that were captured as distorted, yielding a lower $${\bm{VI}}{{\bm{F}}}_{{\bm{DL}}}$$ value (Figure 7d), pointing to generalization of the trained model to scenarios containing abrupt spatial variations in the motion field. Further integration of complex motion scenarios can be achieved via ongoing research on generative learned models for synthesis of clinically realistic motion, trained in an unsupervised fashion with large collections of unpaired motion corrupted clinical CBCT datasets 71 …”