Oliver Taubmann

Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography

Purpose: The performance of many state‐of‐the‐art coronary artery centerline reconstruction algorithms in rotational angiography heavily depends on accurate two‐dimensional centerline information that, in practice, is not available due to segmentation errors. To alleviate the need for correct segmentations, we propose generic extensions to symbolic centerline reconstruction algorithms that target symmetrization, outlier rejection, and topology recovery on asymmetrically reconstructed point clouds. Methods: Epipolar geometry‐ and graph cut‐based reconstruction algorithms are used to reconstruct three‐dimensional point clouds from centerlines in reference views. These clouds are input to the proposed methods that consist of (a) merging of asymmetric reconstructions, (b) removal of inconsistent three‐dimensional points using the reprojection error, and (c) projection domain‐informed geodesic computation. We validate our extensions in a numerical phantom study and on two clinical datasets. Results: In the phantom study, the overlap measure between the reconstructed point clouds and the three‐dimensional ground truth increased from 68.4 ± 9.6% to 85.9 ± 3.3% when the proposed extensions were applied. In addition, the averaged mean and maximum reprojection error decreased from 4.32 ± 3.03 mm to 0.189 ± 0.182 mm and from 8.39 ± 6.08 mm to 0.392 ± 0.434 mm. For the clinical data, the mean and maximum reprojection error improved from 1.73 ± 0.97 mm to 0.882 ± 0.428 mm and from 3.83 ± 1.87 mm to 1.48 ± 0.61 mm, respectively. Conclusions: The application of the proposed extensions yielded superior reconstruction quality in all cases and effectively removed erroneously reconstructed points. Future work will investigate possibilities to integrate parts of the proposed extensions directly into reconstruction.

GPU-Accelerated Time-of-Flight Super-Resolution for Image-Guided Surgery

In the field of image-guided surgery, Time-of-Flight (ToF) sensors are of interest due to their fast acquisition of 3-D surfaces. However, the poor signal-to-noise ratio and low spatial resolution of today’s ToF sensors require preprocessing of the acquired range data. Superresolution is a technique for image restoration and resolution enhancement by utilizing information from successive raw frames of an image sequence. We propose a super-resolution framework using the graphics processing unit. Our framework enables interactive frame rates, computing an upsampled image from 10 noisy frames of 200 × 200 px with an upsampling factor of 2 in 109 ms. The root-mean-square error of the super-resolved surface with respect to ground truth data is improved by more than 20 % relative to a single raw frame.

Estimate, Compensate, Iterate: Joint Motion Estimation and Compensation in 4-D Cardiac C-arm Computed Tomography

C-arm computed tomography reconstruction of multiple cardiac phases could provide a highly useful tool to interventional cardiologists in the catheter laboratory. Today, however, for clinically reasonable acquisition protocols the achievable image quality is still severely limited due to undersampling artifacts. We propose an iterative optimization scheme combining image registration, motion compensation and spatio-temporal regularization to improve upon the state-of-the-art w.r.t. image quality and accuracy of motion estimation. Evaluation of clinical cases indicates an improved visual appearance and temporal consistency, evidenced by a strong decrease in temporal variance in uncontrasted regions accompanied by an increased sharpness of the contrasted left ventricular blood pool boundary. In a phantom study, the universal image quality index proposed by Wang et al. is raised from 0.80 to 0.95, with 1.0 corresponding to a perfect match with the ground truth. The results lay a promising foundation for interventional cardiac functional analysis.

Respiratory motion compensation in rotational angiography: Graphical model-based optimization of auto-focus measures

Non-recurrent intra-scan motion, such as respiration, corrupts rotational coronary angiography acquisitions and inhibits uncompensated 3D reconstruction. Therefore, state-of-the-art algorithms that rely on 3D/2D registration of initial reconstructions to the projection data are unfavorable as prior models of sufficient quality cannot be obtained. To overcome this limitation, we propose a compensation method that optimizes a task-based autofocus measure using graphical model-based optimization.

A kernel-based framework for intra-fractional respiratory motion estimation in radiation therapy

In radiation therapy, tumor tracking allows to adjust the beam such that it follows the respiration-induced tumor motion. However, most clinical approaches rely on implanted fiducial markers to locate the tumor and, thus, only provide sparse information. Motion models have been investigated to estimate dense internal displacement fields from an external surrogate signal, such as range imaging. With increasing surrogate complexity, we propose a respiratory motion estimation framework based on kernel ridge regression to cope with high-dimensional domains. This approach was validated on five patient datasets, consisting of a planning 4DCT and a follow-up 4DCT for each patient. Mean residual error was at best 2.73 ± 0.25 mm, but varied greatly between patients.

Sharp as a Tack

Organ motion occuring during acquisition of medical images can cause motion blur artifacts, thus posing a major problem for many commonly employed modalities. Therefore, compensating for that motion during image reconstruction has been a focus of research for several years. However, objectively comparing the quality of different motion compensated reconstructions is no easy task. Often, intensity profiles across image edges are utilized to compare their sharpness. Manually positioning such a profile line is highly subjective and prone to bias. Expanding on this notion, we propose a robust, semi-automatic scheme for comparing edge sharpness using an ensemble of profiles. We study the behavior of our approach, which was implemented as an open-source tool, for synthetic data in the presence of noise and artifacts and demonstrate its practical use in respiratory motion-compensated MRI as well as cardiac motion-compensated C-arm CT.

A Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy

This paper discusses a kernel ridge regression (KRR) model for motion estimation in radiotherapy. Using KRR, dense internal motion fields are estimated from high-dimensional surrogates without the need for prior dimensionality reduction. We compare the proposed model to a related approach with dimensionality reduction in the form of principal component analysis and principle component regression. Evaluation was performed in a simulation study based on nine 4D CT patient data sets achieving a mean estimation error of 0.84 ± 0.21mm for our approach.

Make the Most of Time Temporal Extension of the iTV Algorithm for 4D Cardiac C-Arm CT

Gated 4D cardiac imaging with C-arm CT scanners suffers from insufficient image quality due to strong angular undersampling. To deal with this problem, we suggest an iterative reconstruction method with spatial and temporal total variation regularization based on an established framework which controls the relative contributions of raw data error minimization and regularization. This new method is tested on a simulated heart phantom and on two clinical data sets. We show that the additional use of temporal regularization is advantageous compared to spatial regularization exclusively, with the relative root mean square error lowered from 11.75% to 8.24% in the phantom study.

Fast Interpolation of Dense Motion Fields from Synthetic Phantoms

Numerical phantoms are a common tool for the evaluation of registration and reconstruction algorithms. For applications concerning motion, dense deformation fields are of particular interest. Phantoms, however, are often described as surfaces and thus motion vectors can only be generated at these surfaces. In order to create dense motion fields, interpolation is required. A frequently used method for this purpose is the Parzen interpolator. However, with a high number of surface motion vectors and a high voxel count, its run time increases dramatically. In this paper, we investigate different methods to accelerate the creation of these motion fields using hierarchical sampling and the random ball cover. In the results, we show that a 64 3 volume can be sampled in less than one second with an error below 0.1 mm. Furthermore, we accelerate the interpolation of a 256 3 dense deformation field to only ˜. 5m inutes using the proposed methods from days with previous methods.

Traditional Machine Learning Techniques for Streak Artifact Reduction in Limited Angle Tomography

In this work, the application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to streak reduction in limited angle tomography is investigated. Specifically, linear regression (LR), multi-layer perceptron (MLP), and reduced-error pruning tree (REPTree) are investigated. When choosing the mean-variation-median (MVM), Laplacian, and Hessian features, REPTree learns streak artifacts best and reaches the smallest root-mean-square error (RMSE) of 29HU for the Shepp-Logan phantom. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. Preliminary experiments on clinical data suggests that further investigation of clinical applications using REPTree may be worthwhile.