Andreas Keil

THz Active Imaging Systems With Real-Time Capabilities

This paper presents a survey of the status of five active THz imaging modalities which we have developed and investigated during the last few years with the goal to explore their potential for real-time imaging. We start out by introducing a novel waveguide-based all-electronic imaging system which operates at 812 GHz. Its salient feature is a 32-pixel linear detector array heterodyne-operated at the eighth subharmonic. This array in combination with a telescope optics for object distances of 2-6 m reaches a data acquisition speed suited for real-time imaging. The second system described then is again an all-electronic scanner (now for around 300 GHz ), designed for object distances of ≥ 8 m , which combines mechanical scanning in vertical direction, synthetic-aperture image generation in horizontal direction, and frequency-modulated continuous-wave sweeping for the depth information. The third and fourth systems follow an optoelectronic approach by relying on several- to multi-pixel parallel electrooptic detection. One imager is based on a pulsed THz-OPO and homodyne detection with a CCD camera, the other on either continuous-wave electronic or femtosecond optoelectronic THz sources and a photonic-mixing device (PMD) camera. The article concludes with a description of the state of the art of imaging with focal-plane arrays based on CMOS field-effect transistors.

Joint Reconstruction of Image and Motion in Gated Positron Emission Tomography

We present a novel intrinsic method for joint reconstruction of both image and motion in positron emission tomography (PET). Intrinsic motion compensation methods exclusively work on the measured data, without any external motion measurements. Most of these methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually reconstructed gates. Then, the image is estimated based on this motion information. With these methods, a main problem lies in the motion estimation step, which is based on the noisy gated frames. The more noise is present, the more inaccurate the image registration becomes. As we show both visually and quantitatively, joint reconstruction using a simple deformation field motion model can compete with state-of-the-art image registration methods which use robust multilevel B-spline motion models.

CONRAD--a software framework for cone-beam imaging in radiology.

PURPOSE In the community of x-ray imaging, there is a multitude of tools and applications that are used in scientific practice. Many of these tools are proprietary and can only be used within a certain lab. Often the same algorithm is implemented multiple times by different groups in order to enable comparison. In an effort to tackle this problem, the authors created CONRAD, a software framework that provides many of the tools that are required to simulate basic processes in x-ray imaging and perform image reconstruction with consideration of nonlinear physical effects. METHODS CONRAD is a Java-based state-of-the-art software platform with extensive documentation. It is based on platform-independent technologies. Special libraries offer access to hardware acceleration such as OpenCL. There is an easy-to-use interface for parallel processing. The software package includes different simulation tools that are able to generate up to 4D projection and volume data and respective vector motion fields. Well known reconstruction algorithms such as FBP, DBP, and ART are included. All algorithms in the package are referenced to a scientific source. RESULTS A total of 13 different phantoms and 30 processing steps have already been integrated into the platform at the time of writing. The platform comprises 74.000 nonblank lines of code out of which 19% are used for documentation. The software package is available for download at http://conrad.stanford.edu. To demonstrate the use of the package, the authors reconstructed images from two different scanners, a table top system and a clinical C-arm system. Runtimes were evaluated using the RabbitCT platform and demonstrate state-of-the-art runtimes with 2.5 s for the 256 problem size and 12.4 s for the 512 problem size. CONCLUSIONS 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.

Fast simulation of x-ray projections of spline-based surfaces using an append buffer

Many scientists in the field of x-ray imaging rely on the simulation of x-ray images. As the phantom models become more and more realistic, their projection requires high computational effort. Since x-ray images are based on transmission, many standard graphics acceleration algorithms cannot be applied to this task. However, if adapted properly, the simulation speed can be increased dramatically using state-of-the-art graphics hardware. A custom graphics pipeline that simulates transmission projections for tomographic reconstruction was implemented based on moving spline surface models. All steps from tessellation of the splines, projection onto the detector and drawing are implemented in OpenCL. We introduced a special append buffer for increased performance in order to store the intersections with the scene for every ray. Intersections are then sorted and resolved to materials. Lastly, an absorption model is evaluated to yield an absorption value for each projection pixel. Projection of a moving spline structure is fast and accurate. Projections of size 640 × 480 can be generated within 254 ms. Reconstructions using the projections show errors below 1 HU with a sharp reconstruction kernel. Traditional GPU-based acceleration schemes are not suitable for our reconstruction task. Even in the absence of noise, they result in errors up to 9 HU on average, although projection images appear to be correct under visual examination. Projections generated with our new method are suitable for the validation of novel CT reconstruction algorithms. For complex simulations, such as the evaluation of motion-compensated reconstruction algorithms, this kind of x-ray simulation will reduce the computation time dramatically.

CAVAREV?an open platform for evaluating 3D and 4D cardiac vasculature reconstruction

The 3D reconstruction of cardiac vasculature, e.g. the coronary arteries, using C-arm CT (rotational angiography) is an active and challenging field of research. There are numerous publications on different reconstruction techniques. However, there is still a lack of comparability of achieved results for several reasons: foremost, datasets used in publications are not open to public and thus experiments are not reproducible by other researchers. Further, the results highly depend on the vasculature motion, i.e. cardiac and breathing motion patterns which are also not comparable across publications. We aim to close this gap by providing an open platform, called CAVAREV (CArdiac VAsculature Reconstruction EValuation). It features two simulated dynamic projection datasets based on the 4D XCAT phantom with contrasted coronary arteries which was derived from patient data. In the first dataset, the vasculature undergoes a continuous periodic motion. The second dataset contains aperiodic heart motion by including additional breathing motion. The geometry calibration and acquisition protocol were obtained from a real-world C-arm system. For qualitative evaluation of the reconstruction results, the correlation of the morphology is used. Two segmentation-based quality measures are introduced which allow us to assess the 3D and 4D reconstruction quality. They are based on the spatial overlap of the vasculature reconstruction with the ground truth. The measures enable a comprehensive analysis and comparison of reconstruction results independent from the utilized reconstruction algorithm. An online platform (www.cavarev.com) is provided where the datasets can be downloaded, researchers can manage and publish algorithm results and download a reference C++ and Matlab implementation.

Metal artifact correction for x-ray computed tomography using kV and selective MV imaging.

PURPOSE The overall goal of this work is to improve the computed tomography (CT) image quality for patients with metal implants or fillings by completing the missing kilovoltage (kV) projection data with selectively acquired megavoltage (MV) data that do not suffer from photon starvation. When both of these imaging systems, which are available on current radiotherapy devices, are used, metal streak artifacts are avoided, and the soft-tissue contrast is restored, even for regions in which the kV data cannot contribute any information. METHODS Three image-reconstruction methods, including two filtered back-projection (FBP)-based analytic methods and one iterative method, for combining kV and MV projection data from the two on-board imaging systems of a radiotherapy device are presented in this work. The analytic reconstruction methods modify the MV data based on the information in the projection or image domains and then patch the data onto the kV projections for a FBP reconstruction. In the iterative reconstruction, the authors used dual-energy (DE) penalized weighted least-squares (PWLS) methods to simultaneously combine the kV/MV data and perform the reconstruction. RESULTS The authors compared kV/MV reconstructions to kV-only reconstructions using a dental phantom with fillings and a hip-implant numerical phantom. Simulation results indicated that dual-energy sinogram patch FBP and the modified dual-energy PWLS method can successfully suppress metal streak artifacts and restore information lost due to photon starvation in the kV projections. The root-mean-square errors of soft-tissue patterns obtained using combined kV/MV data are 10-15 Hounsfield units smaller than those of the kV-only images, and the structural similarity index measure also indicates a 5%-10% improvement in the image quality. The added dose from the MV scan is much less than the dose from the kV scan if a high efficiency MV detector is assumed. CONCLUSIONS The authors have shown that it is possible to improve the image quality of kV CTs for patients with metal implants or fillings by completing the missing kV projection data with selectively acquired MV data that do not suffer from photon starvation. Numerical simulations demonstrated that dual-energy sinogram patch FBP and a modified kV/MV PWLS method can successfully suppress metal streak artifacts and restore information lost due to photon starvation in kV projections. Combined kV/MV images may permit the improved delineation of structures of interest in CT images for patients with metal implants or fillings.

Dynamic Cone Beam Reconstruction Using a New Level Set Formulation

This paper addresses an approach toward tomographic reconstruction from rotational angiography data as it is generated by C-arms in cardiac imaging. Since the rotational acquisition scheme forces a trade-off between consistency of the scene and reasonable baselines, most existing reconstruction techniques fail at recovering the 3D + t scene. We propose a new reconstruction framework based on variational level sets including a new data term for symbolic reconstruction as well as a novel incorporation of motion into the level set formalism. The resulting simultaneous estimation of shape and motion proves feasible in the presented experiments. Since the proposed formulation offers a great flexibility in incorporating other data terms as well as hard or soft constraints, it allows an adaption to a wider range of problems and could be of interest to other reconstruction settings as well.

Semi-Automatic Segmentation of the Patellar Cartilage in MRI

A software system for semi-automatic segmentation of the patellar cartilage is introduced. Providing tools for sub-pixel accurate edge tracing, automatic contour completion, and adequate visualization we achieve a remarkable speed-up of the physician’s segmentation process. The exactness for cartilage segmentation can be reached if expertise and automation are merged in a meaningful way.

Manifold learning for patient position detection in MRI

Magnetic resonance imaging is performed without ionizing radiation, however, the applied radio frequency power leads to heating, which is dependent on the body part being imaged. Determining the patient position in the scanner allows to better monitor the absorbed power and therefore optimize the image acquisition. Low-resolution images, acquired during the initial placement of the patient in the scanner, are exploited for detecting the patient position. We use Laplacian eigenmaps, a manifold learning technique, to learn the low-dimensional manifold embedded in the high-dimensional image space. Our experiments clearly show that the presumption of the slices lying on a low dimensional manifold is justified and that the proposed integration of neighborhood slices and image normalization improves the method. We obtain very good classification results with a nearest neighbor classifier operating on the low-dimensional embedding.

A high performance AR system for medical applications

We report on a new single PC based stereoscopic video-see-through AR system which we developed for medical applications. Recent advances in graphics hardware, memory bandwidth, and computing power of standard PCs made it possible that this system outperforms an earlier version which included 3 networked SGI workstations. We designed and implemented a new AR software platform. It is component based and - in conjunction with XML configuration files - provides efficiency, modularity, and extensibility for fast and robust prototyping of AR applications. The system has a compelling real-time performance with 30 frames/second, displaying stereoscopic augmented video views with XGA resolution.