Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)
This addresses motion artifacts in pulmonary MRI for medical imaging applications, representing an incremental improvement over existing methods.
The paper tackles the problem of high-resolution free-breathing pulmonary MRI reconstruction by introducing an unsupervised motion-compensated scheme that models image frames as deformed versions of a 3D template, with deformation maps on a smooth manifold driven by low-dimensional latent vectors. The result shows improved reconstructions compared to state-of-the-art methods, particularly in handling bulk motion during scans.
We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary MRI. We model the image frames in the time series as the deformed version of the 3D template image volume. We assume the deformation maps to be points on a smooth manifold in high-dimensional space. Specifically, we model the deformation map at each time instant as the output of a CNN-based generator that has the same weight for all time-frames, driven by a low-dimensional latent vector. The time series of latent vectors account for the dynamics in the dataset, including respiratory motion and bulk motion. The template image volume, the parameters of the generator, and the latent vectors are learned directly from the k-t space data in an unsupervised fashion. Our experimental results show improved reconstructions compared to state-of-the-art methods, especially in the context of bulk motion during the scans.