Cyril Riddell

A linear wavelet filter for parametric imaging with dynamic PET

Describes a new filter for parametric images obtained from dynamic positron emission tomography (PET) studies. The filter is based on the wavelet transform following the heuristics of a previously published method that are here developed into a rigorous theoretical framework. It is shown that the space-time problem of modeling a dynamic PET sequence reduces to the classical one of estimation of a normal multivariate vector of independent wavelet coefficients that, under least-squares risk, can be solved by straightforward application of well established theory. From the study of the distribution of wavelet coefficients of PET images, it is inferred that a James-Stein linear estimator is more suitable for the problem than traditional nonlinear procedures that are incorporated in standard wavelet filters. This is confirmed by the superior performance of the James-Stein filter in simulation studies compared to a state-of-the-art nonlinear wavelet filter and a nonstationary filter selected from literature. Finally, the formal framework is interpreted for the practitioners point of view and advantages and limitations of the method are discussed.

Design and development of C-arm based cone-beam CT for image-guided interventions : Initial results

X-ray cone-beam computed tomography (CBCT) is of importance in image-guided intervention (IGI) and image-guided radiation therapy (IGRT). In this paper, we present a cone-beam CT data acquisition system using a GE INNOVA 4100 (GE Healthcare Technologies, Waukesha, Wisconsin) clinical system. This new cone-beam data acquisition mode was developed for research purposes without interfering with any clinical function of the system. It provides us a basic imaging pipeline for more advanced cone-beam data acquisition methods. It also provides us a platform to study and overcome the limiting factors such as cone-beam artifacts and limiting low contrast resolution in current C-arm based cone-beam CT systems. A geometrical calibration method was developed to experimentally determine parameters of the scanning geometry to correct the image reconstruction for geometric non-idealities. Extensive phantom studies and some small animal studies have been conducted to evaluate the performance of our cone-beam CT data acquisition system.

Rectification for cone-beam projection and backprojection

The purpose of this paper is to derive a technique for accelerating the computation of cone-beam forward and backward projections that are the basic steps of tomographic reconstruction. The cone-beam geometry of C-arm systems is commonly described with projection matrices. Such matrices provide a continuous framework for analyzing the flow of operations needed to compute backprojection for analytical reconstruction, as well as the combination of forward and backward projections for iterative reconstruction. The proposed rectification technique resamples the original data to planes that are aligned with two of the reconstructed volume main axes, so that the original cone-beam geometry can be replaced by a simpler geometry, where succession of plane magnifications are involved only. Rectification generalizes previous independent results to the cone-beam backprojection of preprocessed data as well as to cone-beam iterative reconstruction. The memory access pattern of simple magnifications provides superior predictability and is, therefore, easier to optimize, independently of the choice of the interpolation technique. Rectification is also shown to provide control over interpolation errors through oversampling, allowing tradeoffs between computation speed and precision to be set. Experimental results are provided for linear and nearest neighbor interpolations, based on simulations, as well as phantom and patient data acquired on a digital C-arm system

Compressed sensing based 3d tomographic reconstruction for rotational angiography

In this paper, we address three-dimensional tomographic reconstruction of rotational angiography acquisitions. In clinical routine, angular subsampling commonly occurs, due to the technical limitations of C-arm systems or possible improper injection. Standard methods such as filtered backprojection yield a reconstruction that is deteriorated by sampling artifacts, which potentially hampers medical interpretation. Recent developments of compressed sensing have demonstrated that it is possible to significantly improve reconstruction of subsampled datasets by generating sparse approximations through l1-penalized minimization. Based on these results, we present an extension of the iterative filtered backprojection that includes a sparsity constraint called soft background subtraction. This approach is shown to provide sampling artifact reduction when reconstructing sparse objects, and more interestingly, when reconstructing sparse objects over a non-sparse background. The relevance of our approach is evaluated in cone-beam geometry on real clinical data.

Novel C-arm based cone-beam CT using a source trajectory of two concentric arcs

The first results from an interventional C-arm based computed tomography system where a complete source trajectory was used are presented. A scan with two arcs which are joined approximately at the center of their paths (CC trajectory) is utilized here. This trajectory satisfies Tuys sufficiency condition for a large volume, but is not well populated with PI-lines. Therefore, a non-PI-line based reconstruction method is required. The desire for high dose efficiency led to the selection of an equal weighting based method. An FBP type reconstruction algorithm which was derived for two orthogonal concentric circles was utilized for reconstruction. The concept of a virtual image object was used to relate the projections from the two acquired non-orthogonal arcs to projections of a virtual object from two orthogonal arcs. Geometrical calibration is vital when performing tomography from an interventional system, and was incorporated here with the use of a homogeneous virtual projection matrix. The results demonstrate a significant reduction in cone-beam artifacts when the complete source trajectory is utilized.

Circular tomosynthesis implemented with a clinical interventional flat-panel based C-Arm: initial performance study

There exists a strong desire for a platform in which researchers may investigate planar tomosynthesis (i.e. all source positions reside in a single plane that is parallel to the reconstructed image planes) trajectories directly on an interventional C-arm system. In this work we describe an experimental system designed to accomplish this aim, as well as the potential of this system for testing multiple aspects of the tomosynthetic image acquisition process. The system enables one to evaluate the effect of the physical imaging parameters on the image quality, as well as the effect of the reconstruction algorithm utilized. The experimental data collection for this work is from the Innova 4100 (Flat-panel based interventional C-arm system manufactured by GE Healthcare). The system is calibrated using a phantom with known geometrical placement of multiple small metallic spheres. Initial performance was assessed with three physical phantoms and performance was assessed by varying: the reconstruction algorithm (backprojection, filtered backprojection), the half tomographic angle (15°, 25°, 35°), and the angular sampling (20,40,80 views / acquisition). Initial results demonstrate the ability to well differentiate simulated vessels separated by 1 cm, even with the modest half tomographic angle of 15° and modest sampling of 20 views/acquisition.

Compressed sensing dynamic reconstruction in rotational angiography

This work tackles three-dimensional reconstruction of tomographic acquisitions in C-arm-based rotational angiography. The relatively slow rotation speed of C-arm systems involves motion artifacts that limit the use of three-dimensional imaging in interventional procedures. The main contribution of this paper is a reconstruction algorithm that deals with the temporal variations due to intra-arterial injections. Based on a compressed-sensing approach, we propose a multiple phase reconstruction with spatio-temporal constraints. The algorithm was evaluated by qualitative and quantitative assessment of image quality on both numerical phantom experiments and clinical data from vascular C-arm systems. In this latter case, motion artifacts reduction was obtained in spite of the cone-beam geometry, the short-scan acquisition, and the truncated and subsampled data.

Three-dimensional curvilinear device reconstruction from two fluoroscopic views

In interventional radiology, navigating devices under the sole guidance of fluoroscopic images inside a complex architecture of tortuous and narrow vessels like the cerebral vascular tree is a difficult task. Visualizing the device in 3D could facilitate this navigation. For curvilinear devices such as guide-wires and catheters, a 3D reconstruction may be achieved using two simultaneous fluoroscopic views, as available on a biplane acquisition system. The purpose of this paper is to present a new automatic three-dimensional curve reconstruction method that has the potential to reconstruct complex 3D curves and does not require a perfect segmentation of the endovascular device. Using epipolar geometry, our algorithm translates the point correspondence problem into a segment correspondence problem. Candidate 3D curves can be formed and evaluated independently after identifying all possible combinations of compatible 3D segments. Correspondence is then inherently solved by looking in 3D space for the most coherent curve in terms of continuity and curvature. This problem can be cast into a graph problem where the most coherent curve corresponds to the shortest path of a weighted graph. We present quantitative results of curve reconstructions performed from numerically simulated projections of tortuous 3D curves extracted from cerebral vascular trees affected with brain arteriovenous malformations as well as fluoroscopic image pairs of a guide-wire from both phantom and clinical sets. Our method was able to select the correct 3D segments in 97.5% of simulated cases thus demonstrating its ability to handle complex 3D curves and can deal with imperfect 2D segmentation.

Interventional C-arm tomosynthesis for vascular imaging: initial results

As percutaneous endovascular procedures address more complex and broader disease states, there is an increasing need for intra-procedure 3D vascular imaging. In this paper, we investigate C-Arm 2-axis tomosynthesis (“Tomo”) as an alternative to C-Arm Cone Beam Computed Tomography (CBCT) for workflow situations in which the CBCT acquisition may be inconvenient or prohibited. We report on our experience in performing tomosynthesis acquisitions with a digital angiographic imaging system (GE Healthcare Innova 4100 Angiographic Imaging System, Milwaukee, WI). During a tomo acquisition the detector and tube each orbit on a plane above and below the table respectively. The tomo orbit may be circular or elliptical, and the tomographic half-angle in our studies varied from approximately 16 to 28 degrees as a function of orbit period. The trajectory, geometric calibration, and gantry performance are presented. We overview a multi-resolution iterative reconstruction employing compressed sensing techniques to mitigate artifacts associated with incomplete data reconstructions. In this work, we focus on the reconstruction of small high contrast objects such as iodinated vasculature and interventional devices. We evaluate the overall performance of the acquisition and reconstruction through phantom acquisitions and a swine study. Both tomo and comparable CBCT acquisitions were performed during the swine study thereby enabling the use of CBCT as a reference in the evaluation of tomo vascular imaging. We close with a discussion of potential clinical applications for tomo, reflecting on the imaging and workflow results achieved.

Compressed‐sensing‐based content‐driven hierarchical reconstruction: Theory and application to C‐arm cone‐beam tomography

PURPOSE This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing. METHODS The central idea is to proceed with a hierarchical method where the most salient features (high intensities or gradients) are reconstructed first to reduce the artifacts these features induce. These artifacts are addressed first because their presence contaminates less salient features. Several hierarchical schemes aiming at streak artifacts reduction are introduced for C-arm CBCT: the empirical orthogonal matching pursuit approach with the ℓ0 pseudonorm for reconstructing sparse vessels; a convex variant using homotopy with the ℓ1-norm constraint of compressed sensing, for reconstructing sparse vessels over a nonsparse background; homotopy with total variation (TV); and a novel empirical extension to nonlinear diffusion (NLD). Such principles are implemented with penalized iterative filtered backprojection algorithms. For soft-tissue imaging, the authors compare the use of TV and NLD filters as sparsity constraints, both optimized with the alternating direction method of multipliers, using a threshold for TV and a nonlinear weighting for NLD. RESULTS The authors show on simulated data that their approach provides fast convergence to good approximations of the solution of the TV-constrained minimization problem introduced by the compressed sensing theory. Using C-arm CBCT clinical data, the authors show that both TV and NLD can deliver improved image quality by reducing streaks. CONCLUSIONS A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.