CONRAD—A software framework for cone‐beam imaging in radiology

Maier, Andreas; Hofmann, Hannes; Berger, Martin; Fischer, Peter; Schwemmer, Chris; Wu, Haibo; Müller, Kerstin; Hornegger, Joachim; Choi, Jang-Hwan; Rieß, Christian; Keil, Andreas; Fahrig, Rebecca

doi:10.1118/1.4824926

Cited by 116 publications

(68 citation statements)

References 28 publications

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“…The CONRAD [7] and ASTRA [8] toolkits allow flexible scanning geometries but present limitations. The simulation of non-standard geometries with CONRAD is less straightforward, as it is based on a projection matrix that needs to be previously obtained, and the ASTRA toolkit is limited to datasets that fit completely in the memory space of the GPU and to circular orbits, thus preventing simulation of new acquisition geometries such as those used in tomosynthesis.…”

Section: Discussionmentioning

confidence: 99%

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

et al. 2017

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The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden.The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms.In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures.A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.

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Section: Discussionmentioning

confidence: 99%

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

et al. 2017

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“…A ventricle dataset (Müller, Maier, Fischer, Bier, Lauritsch, Schwemmer, Fahrig & Hornegger 2013, Maier et al 2012, Maier et al 2013) of a similar design to the XCAT phantom (Segars et al 2008) was simulated. It was assumed that all materials have the same spectral absorption behaviour as water.…”

Section: Experiments and Evaluation Methodsmentioning

confidence: 99%

Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT

Müller¹,

Maier²,

Schwemmer³

et al. 2014

Phys. Med. Biol.

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The acquisition of data for cardiac imaging using a C-arm CT system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable 3-D/3-D registration performed on initial 3-D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3-D images. In this paper, the sensitivity of the 3-D/3-D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3-D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc.) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing (PICCS) combined with the improved total variation (iTV) algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3-D/3-D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. As conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm.

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“…The analysis presented here has been applied to LV surface models generated from a cardiac phantom (Maier et al 2012, Müller, Maier, Fischer, Bier, Lauritsch, Schwemmer, Fahrig & Hornegger 2013, Maier et al 2013), which is similarly designed to the widely used 4-D XCAT phantom (Segars et al 2008). The phantom is defined by cubic B-splines and can be tessellated to generate a triangulated mesh for every time point.…”

Section: Methodsmentioning

confidence: 99%

Interventional heart wall motion analysis with cardiac C-arm CT systems

Müller¹,

Maier²,

Zheng³

et al. 2014

Phys. Med. Biol.

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Today, quantitative analysis of 3-D dynamics of the left ventricle (LV) cannot be performed directly in the catheter lab using a current angiographic C-arm system, which is the workhorse imaging modality for cardiac interventions. Therefore, myocardial wall analysis is completely based on the 2-D angiographic images or pre-interventional 3-D/4-D imaging. In this paper, we present a complete framework to study the ventricular wall motion in 4-D (3-D+t) directly in the catheter lab. From the acquired 2-D projection images, a dynamic 3-D surface model of the LV is generated, which is then used to detect ventricular dyssynchrony. Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, MR) are transferred to the C-arm CT data. We use the ejection fraction (EF), the systolic dyssynchrony index (SDI), a 3-D fractional shortening (3DFSi) and the phase to maximal contraction (φi,max) to determine an indicator of LV dyssynchrony and to discriminate regionally pathological from normal myocardium. The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. The used LV data is publicly available online at https://conrad.stanford.edu/data/heart. In addition, the presented framework was tested on eight clinical patient data sets. The first clinical results demonstrate promising performance of the proposed analysis tool and encourage the application of the presented framework to a larger study in clinical practice.

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CONRAD—A software framework for cone‐beam imaging in radiology

Abstract: As a common software framework, CONRAD enables the medical physics community to share algorithms and develop new ideas. In particular this offers new opportunities for scientific collaboration and quantitative performance comparison between the methods of different groups.

Cited by 116 publications

References 28 publications

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT

Interventional heart wall motion analysis with cardiac C-arm CT systems

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