A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the dynamic sequence by interpolating the volumes between the acquired image volumes. However, these methods are limited to either 2D images and/or are unable to support large variations in the motion between the image volume sequences. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation methods that have been extended to support volumetric images. Our ablation study further exemplified that our motion network was able to better represent the large functional motion compared with the state-of-the-art unsupervised medical registration methods.Comment: 10 pages, 8 figures, Conference on Computer Vision and Pattern Recognition (CVPR) 202

Performance assessment of displacement-field estimation of the human left atrium from 4D-CT images using the coherent point drift algorithm

Background: Cardiac four-dimensional computed tomography (4D-CT) imaging is a standard approach used to visualize left atrium (LA) deformation for clinical diagnosis. However, the quantitative evaluation of LA deformation from 4D-CT images is still a challenging task. We assess the performance of LA displacement-field estimation from 4D-CT images using the coherent point drift (CPD) algorithm, which is a robust point set alignment method based on the expectation–maximization (EM) algorithm. Method: Subject-specific LA surfaces at 20 phases/cardiac cycles were reconstructed from 4D-CT images and expressed as sets of triangular elements. The LA surface at the phase that maximized the LA surface area was assigned as the control LA surface and those at the other 19 phases were assigned as observed LA surfaces. The LA displacement-field was estimated by solving the alignment between the control and observation LA surfaces using CPD. Results: Global correspondences between the estimated and observed LA surfaces were successfully confirmed by quantitative evaluations using the Dice similarity coefficient and differences of surface area for all phases. The surface distances between the estimated and observed LA surfaces ranged within 2 mm, except at the left atrial appendage and boundaries, where incomplete data, such as missing or false detections, were included on the observed LA surface. We confirmed that the estimated LA surface displacement and its spatial distribution were anisotropic, which is consistent with existing clinical observations. Conclusion: These results highlight that the LA displacement field estimated by CPD robustly tracks global LA surface deformation observed in 4D-CT images

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset