Search

This report reviews the image acquisition and reconstruction characteristics of C‐arm Cone Beam Computed Tomography (C‐arm CBCT) systems and provides guidance on quality control of C‐arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image‐Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C‐arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C‐arm fluoroscopy quality control (QC) in relation to 3D C‐arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C‐arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C‐arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free‐in‐air and in‐phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6. [ABSTRACT FROM AUTHOR]

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. Index Terms--Cone-beam tomography, iterative reconstruction, rotational angiography, transmission reconstruction.

We have recently proposed a regularized least square criterion for adaptive regularization of single photon emission computed tomography (SPECT) reconstruction with nonuniform attenuation correction. In the present study, we show that this regularization is closely related to a diffusion scheme used for Gaussian filtering. For a given value of the regularization parameter, the amount of smoothing is independent from the patient attenuation map, and it is mathematically related to the full-width at half-maximum (FWHM) of a Gaussian filter. A second regularized least square criterion is then derived for which regularization also behaves as a diffusion scheme. The new penalty is then shown to be also applicable to the weighted least square criterion, and to the Poisson maximum-likelihood criterion for positron emission tomography (PET) data (i.e., without attenuation) solved by the expectation maximization (EM) algorithm. For all these criteria, the regularization level can thus be set as the FWHM of a Gaussian filter. Index Terms--Bayesian reconstruction, least square, positron emission tomography (PET), regularization, single photon emission computed tomography (SPECT), tomography.

We have recently proposed a regularized least square criterion for adaptive regularization of SPECT reconstruction with nonuniform attenuation correction. In the present study, we show that this regularization is closely related to a diffusion scheme used for Gaussian filtering. For a given value of the regularization parameter, the amount of smoothing is independent from the patient attenuation map, and it is mathematically related to the full width at half maximum (FWHM) of a Gaussian filter. A second regularized least square criterion is then derived for which regularization also behaves as a diffusion scheme. The new penalty is then shown to be also applicable to the weighted least square criterion, and to the Poisson maximum likelihood criterion for PET data (i.e., without attenuation) solved by the EM algorithm. For all these criteria, the regularization level can thus be set as the FWHM of a Gaussian filter. Index Terms--Diffusion equations, least squares methods, OSEM, regularization, tomography.

This paper 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 practitioner's point of view and advantages and limitations of the method are discussed. Index Terms--Filtering, James-Stein estimator, kinetic analysis, positron emission tomography (PET), wavelets.

This work addresses the resolution of penalized least-squares problems using the proximal gradient algorithm (PGA). PGA can be accelerated by preconditioning strategies. However, typical effective choices of preconditioners may correspond to intricate matrices that are not easily inverted, leading to increased complexity in the computation of the proximity step. To relax these requirements, we propose an unmatched preconditioning approach where two metrics are used in the gradient step and the proximity step. We provide convergence conditions for this new iterative scheme and characterize its limit point. Simulations for tomographic image reconstruction from undersampled measurements show the benefits of our approach for various simple choices of metrics. [ABSTRACT FROM AUTHOR]

A nonlinear regularizing least-square reconstruction criterion is proposed for simultaneously estimating a single-photon emission computed tomography (SPECT) emission distribution corrected for attenuation together with its degree of regularization. Only a regularization trend has to be defined and tuned once for all on a reference study. Given this regularization trend, the precise regularization weight, which is usually fixed a priori, is automatically computed for each data set to adapt to the noise content of the data. We demonstrate that this adaptive process yields better results when the noise conditions change than when the regularization weight is kept constant. This adaptation is illustrated on simulated cardiac data for noise variations due to changes in the acquisition duration, background intensity, and attenuation map. Index Terms--Attenuation, biomedical image processing, image reconstruction, inverse problems, regularization, single photon emission computed tomography (SPECT).

Truncation artifacts can occur in simultaneous emission transmission SPECT imaging even with parallel geometry, especially when the acquisition geometry is optimized for emission at the expense of the transmission data. We hypothesized that the addition of only a few projections sampling truncated areas (by shifting the bed/camera) would permit a significant improvement in image quality with only a small increase in imaging time. In parallel geometry, data are preprocessed and the additional projections are merged into the original sinogram, thereby partially completing it. For fan-beam data, the projector routine is modified to take into account different bed positions. Both cases require the use of an iterative reconstruction algorithm. Improvements due to partial completion are shown for different increases of total imaging time for both geometries. On simulated and true data, a 15% increase of imaging time led to a better recovery of the truncated area than obtained from a priori knowledge of the body contour.

A least squares technique for reconstructing truncated data is presented. The method involves including the ramp filter into the system matrix. The sinogram is extrapolated with zeros for filtering but extrapolated values are not considered during backward and forward projections. The new matrix leads to a frequency weighted least squares criterion. Tikhonov regularization and a priori information are considered. For the O-order regularization, the resulting images are biased but it is shown that this bias is recovered by a simple scaling process. First and second order regularizations provide true restoration. The technique is applied to simulated and real transmission SPECT data. Results show that the frequency least squares criterion is a valuable approach for efficiently reconstructing truncated sinograms.

Purpose: Discretizing tomographic forward and backward operations is a crucial step in the design of model‐based reconstruction algorithms. Standard projectors rely on linear interpolation, whose adjoint introduces discretization errors during backprojection. More advanced techniques are obtained through geometric footprint models that may present a high computational cost and an inner logic that is not suitable for implementation on massively parallel computing architectures. In this work, we take a fresh look at the discretization of resampling transforms and focus on the issue of magnification‐induced local sampling variations by introducing a new magnification‐driven interpolation approach for tomography. Methods: Starting from the existing literature on spline interpolation for magnification purposes, we provide a mathematical formulation for discretizing a one‐dimensional homography. We then extend our approach to two‐dimensional representations in order to account for the geometry of cone‐beam computed tomography with a flat panel detector. Our new method relies on the decomposition of signals onto a space generated by nonuniform B‐splines so as to capture the spatially varying magnification that locally affects sampling. We propose various degrees of approximations for a rapid implementation of the proposed approach. Our framework allows us to define a novel family of projector/backprojector pairs parameterized by the order of the employed B‐splines. The state‐of‐the‐art distance‐driven interpolation appears to fit into this family thus providing new insight and computational layout for this scheme. The question of data resampling at the detector level is handled and integrated with reconstruction in a single framework. Results: Results on both synthetic data and real data using a quality assurance phantom, were performed to validate our approach. We show experimentally that our approximate implementations are associated with reduced complexity while achieving a near‐optimal performance. In contrast with linear interpolation, B‐splines guarantee full usage of all data samples, and thus the X‐ray dose, leading to more uniform noise properties. In addition, higher‐order B‐splines allow analytical and iterative reconstruction to reach higher resolution. These benefits appear more significant when downsampling frames acquired by X‐ray flat‐panel detectors with small pixels. Conclusions: Magnification‐driven B‐spline interpolation is shown to provide high‐accuracy projection operators with good‐quality adjoints for iterative reconstruction. It equally applies to backprojection for analytical reconstruction and detector data downsampling. [ABSTRACT FROM AUTHOR]

We consider the proximal gradient algorithm for solving penalized least-squares minimization problems arising in data science. This first-order algorithm is attractive due to its flexibility and minimal memory requirements allowing to tackle large-scale minimization problems involving non-smooth penalties. However, for problems such as x-ray computed tomography, the applicability of the algorithm is dominated by the cost of applying the forward linear operator and its adjoint at each iteration. In practice, the adjoint operator is thus often replaced by an alternative operator with the aim to reduce the overall computation burden and potentially improve conditioning issues. In this paper, we propose to analyze the effect of such an adjoint mismatch on the convergence of the proximal gradient algorithm in an infinite-dimensional setting, thus generalizing the existing results on PGA. We derive conditions on the step-size and on the gradient of the smooth part of the objective function under which convergence of the algorithm to a fixed point is guaranteed. We also derive bounds on the error between this point and the solution to the original minimization problem. We illustrate our theoretical findings with two image reconstruction tasks in computed tomography. [ABSTRACT FROM AUTHOR]

Quantitative measurement of tumor blood flow with [[sup.15]O]water can be used to evaluate the effects of tumor treatment over time. Since quantitative flow measurements require an input function, we developed the profile fitting method (PFM) to measure the input function from positron emission tomography images of the aorta. First, a [[sup.11]C]CO scan was acquired and the aorta region was analyzed. The aorta diameter was determined by fitting the image data with a model that includes scanner resolution, the measured venous blood radioactivity concentration, and the spillover of counts from the background. The diameter was used in subsequent fitting of [[sup.15]O]water dynamic images to estimate the aorta and background radioactivity concentrations. Phantom experiments were performed to test the model. Image quantification biases (up to 15%) were found for small objects, particularly for those in a large elliptical phantom. However, the bias in the PFM concentration estimates was much smaller (2%-6%). A simulation study showed that PFM had less bias and/or variability in flow parameter estimates than an ROI method. PFM was applied to human [[sup.11]C]CO and [[sup.15]O]water dynamic studies with left ventricle input functions used as the gold standard. PFM parameter estimates had higher variability than found in the simulation but with minimal bias. These studies suggest that PFM is a promising technique for the noninvasive measurement of the aorta [[sup.15]O]water input function. Index Terms--Aorta, input function, tumor blood flow, [[sup.15]O]water.

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. [ABSTRACT FROM AUTHOR]

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.

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.

The current x-ray source trajectory for C-arm based cone-beam CT is a single arc. Reconstruction from data acquired with this trajectory yields cone-beam artifacts for regions other than the central slice. In this work we present the preliminary evaluation of reconstruction from a source trajectory of two concentric arcs using a flat-panel detector equipped C-arm gantry (GE Healthcare Innova 4100 system, Waukesha, Wisconsin). The reconstruction method employed is a summation of FDK-type reconstructions from the two individual arcs. For the angle between arcs studied here, 30°, this method offers a significant reduction in the visibility of cone-beam artifacts, with the additional advantages of simplicity and ease of implementation due to the fact that it is a direct extension of the reconstruction method currently implemented on commercial systems. Reconstructed images from data acquired from the two arc trajectory are compared to those reconstructed from a single arc trajectory and evaluated in terms of spatial resolution, low contrast resolution, noise, and artifact level.