SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan.

PURPOSE: Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms. METHODS: A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D-Lung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-mean-squared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) - patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV. RESULTS: Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion-compensation, and deformation of the prior 4D-CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm. The RMS of the three-dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA-ROOSTER method, which utilizes a 4D-CT motion model for temporal regularization, had the best and most consistent image quality and accuracy. CONCLUSION: The SPARE Challenge represents the first framework for systematically evaluating state-of-the-art algorithms for 4D-CBCT reconstruction from a 1-min scan. Results suggest the potential for reducing scan time and dose for 4D-CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

Comparison of five one-step reconstruction algorithms for spectral CT.

Over the last decade, dual-energy CT scanners have gone from prototypes to clinically available machines, and spectral photon counting CT scanners are following. They require a specific reconstruction process, consisting of two steps: material decomposition and tomographic reconstruction. Image-based methods perform reconstruction, then decomposition, while projection-based methods perform decomposition first, and then reconstruction. As an alternative, 'one-step inversion' methods have been proposed, which perform decomposition and reconstruction simultaneously. Unfortunately, one-step methods are typically slower than their two-step counterparts, and in most CT applications, reconstruction time is critical. This paper therefore proposes to compare the convergence speeds of five one-step algorithms. We adapted all these algorithms to solve the same problem: spectral photon-counting CT reconstruction from five energy bins, using a three materials decomposition basis and spatial regularization. The paper compares a Bayesian method which uses non-linear conjugate gradient for minimization (Cai et al 2013 Med. Phys. 40 111916-31), three methods based on quadratic surrogates (Long and Fessler 2014 IEEE Trans. Med. Imaging 33 1614-26, Weidinger et al 2016 Int. J. Biomed. Imaging 2016 1-15, Mechlem et al 2018 IEEE Trans. Med. Imaging 37 68-80), and a primal-dual method based on MOCCA, a modified Chambolle-Pock algorithm (Barber et al 2016 Phys. Med. Biol. 61 3784). Some of these methods have been accelerated by using µ-preconditioning, i.e. by performing all internal computations not with the actual materials the object is made of, but with carefully chosen linear combinations of those. In this paper, we also evaluated the impact of three different µ-preconditioners on convergence speed. Our experiments on simulated data revealed vast differences in the number of iterations required to reach a common image quality objective: Mechlem et al (2018 IEEE Trans. Med. Imaging 37 68-80) needed ten iterations, Cai et al (2013 Med. Phys. 40 111916-31), Long and Fessler (2014 IEEE Trans. Med. Imaging 33 1614-26) and Weidinger et al (2016 Int. J. Biomed. Imaging 2016 1-15) several hundreds, and Barber et al (2016 Phys. Med. Biol. 61 3784) several thousands. We also sum up other practical aspects, like memory footprint and the need to tune extra parameters.

Motion-aware temporal regularization for improved 4D cone-beam computed tomography.

Four-dimensional cone-beam computed tomography (4D-CBCT) of the free-breathing thorax is a valuable tool in image-guided radiation therapy of the thorax and the upper abdomen. It allows the determination of the position of a tumor throughout the breathing cycle, while only its mean position can be extracted from three-dimensional CBCT. The classical approaches are not fully satisfactory: respiration-correlated methods allow one to accurately locate high-contrast structures in any frame, but contain strong streak artifacts unless the acquisition is significantly slowed down. Motion-compensated methods can yield streak-free, but static, reconstructions. This work proposes a 4D-CBCT method that can be seen as a trade-off between respiration-correlated and motion-compensated reconstruction. It builds upon the existing reconstruction using spatial and temporal regularization (ROOSTER) and is called motion-aware ROOSTER (MA-ROOSTER). It performs temporal regularization along curved trajectories, following the motion estimated on a prior 4D CT scan. MA-ROOSTER does not involve motion-compensated forward and back projections: the input motion is used only during temporal regularization. MA-ROOSTER is compared to ROOSTER, motion-compensated Feldkamp-Davis-Kress (MC-FDK), and two respiration-correlated methods, on CBCT acquisitions of one physical phantom and two patients. It yields streak-free reconstructions, visually similar to MC-FDK, and robust information on tumor location throughout the breathing cycle. MA-ROOSTER also allows a variation of the lung tissue density during the breathing cycle, similar to that of planning CT, which is required for quantitative post-processing.

Removing streak artifacts from ECG-gated reconstructions using deconvolution.

BACKGROUND: 4D cardiac computed tomography aims at reconstructing the beating heart from a series of 2D projections and the simultaneously acquired electrocardiogram. Each cardiac phase is reconstructed by exploiting the subset of projections acquired during this particular cardiac phase only. In these conditions, the Feldkamp, Davis and Kress method (FDK) generates large streak artifacts in the reconstructed volumes, hampering the medical interpretation. These artifacts can be substantially reduced by deconvolution methods. OBJECTIVE: The aim of this paper is to compare two 4D cardiac CT reconstruction methods based on deconvolution, and to evaluate their practical benefits on two applications: cardiac micro CT and human cardiac C-arm CT. METHODS: The first evaluated method builds upon inverse filtering. It has been proposed recently and demonstrated on 4D cardiac micro CT. The second one is an iterative deconvolution method, and turns out equivalent to an ECG-gated Iterative Filtered Back Projection (ECG-gated IFBP). RESULTS: Results are presented on simulated data in 2D parallel beam, 2D fan beam and 3D cone beam geometries. CONCLUSIONS: Both methods are efficient on the cardiac micro CT simulations, but insufficient to handle 4D human cardiac C-Arm CT simulations. Overall, ECG-gated IFPB largely outperforms the inverse filtering method.

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization.

PURPOSE: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. METHODS: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. RESULTS: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. CONCLUSIONS: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection.

Clinical and histological significance of gadolinium enhancement in carotid atherosclerotic plaque.

BACKGROUND AND PURPOSE: Although the ability of MRI to investigate carotid plaque composition is well established, the mechanism and the significance of plaque gadolinium (Gd) enhancement remain unknown. We evaluated clinical and histological significance of Gd enhancement of carotid plaque in patients undergoing endarterectomy for carotid stenosis. METHODS: Sixty-nine patients scheduled for a carotid endarterectomy prospectively underwent a 3-T MRI. Carotid plaque enhancement was assessed on T1-weighted images performed before and 5 minutes after Gd injection. Enhancement was recorded according to its localization. Histological analysis was performed of the entire plaque and of the area with matched contrast enhancement on MR images. RESULTS: Gd enhancement was observed in 59% patients. Three types of carotid plaques were identified depending on enhancement location (shoulder region, shoulder and fibrous cap, and central in the plaque). Fibrous cap rupture, intraplaque hemorrhage, and plaque Gd enhancement was significantly more frequent in symptomatic than in asymptomatic patients (P=0.043, P<0.0001, and P=0.034, respectively). After histological analysis, Gd enhancement was significantly associated with vulnerable plaque (American Heart Association VI, P=0.006), neovascularization (P<0.0001), macrophages (P=0.030), and loose fibrosis (P<0.0001). Prevalence of neovessels, macrophages, and loose fibrosis in the area of Gd enhancement was 97%, 87%, and 80%, respectively, and was different depending on the enhancement location in the plaque. Fibrous cap status and composition were different depending on the type of plaque. CONCLUSIONS: Gd enhancement of carotid plaque is associated with vulnerable plaque phenotypes and related to an inflammatory process.