Martin Berger

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.

Marker‐free motion correction in weight‐bearing cone‐beam CT of the knee joint

PURPOSE To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint. METHODS Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6 × 4 × K motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration. RESULTS The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrected reconstructions. Compared to NGI and MB, the GC method showed increased streaking artifacts due to misregistrations in lateral projection images. NGI and MB showed comparable image quality at the bone regions. Because the markers are attached to the skin, the MB method performed better at the surface of the legs where the authors observed slight streaking of the NGI and GC methods. For a quantitative evaluation, the authors computed the universal quality index (UQI) for all bone regions with respect to the motion-free reconstruction. The authors quantitative evaluation over regions around the bones yielded a mean UQI of 18.4 for no correction, 53.3 and 56.1 for the proposed method using GC and NGI, respectively, and 53.7 for the MB reference approach. In contrast to the authors registration-based corrections, the MB reference method caused slight nonrigid deformations at bone outlines when compared to a motion-free reference scan. CONCLUSIONS The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.

Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT

PURPOSE Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. METHODS A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anterior-posterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. RESULTS The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. CONCLUSIONS The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.

Image-based compensation for involuntary motion in weight-bearing C-arm cone-beam CT scanning of knees

We previously introduced four fiducial marker-based strategies to compensate for involuntary knee-joint motion during weight-bearing C-arm CT scanning of the lower body. 2D methods showed significant reduction of motion- related artifacts, but 3D methods worked best. However, previous methods led to increased examination times and patient discomfort caused by the marker attachment process. Moreover, sub-optimal marker placement may lead to decreased marker detectability and therefore unstable motion estimates. In order to reduce overall patient discomfort, we developed a new image-based 2D projection shifting method. A C-arm cone-beam CT system was used to acquire projection images of five healthy volunteers at various flexion angles. Projection matrices for the horizontal scanning trajectory were calibrated using the Siemens standard PDS-2 phantom. The initial reconstruction was forward projected using maximum-intensity projections (MIP), yielding an estimate of a static scan. This estimate was then used to obtain the 2D projection shifts via registration. For the scan with the most motion, the proposed method reproduced the marker-based results with a mean error of 2.90 mm +/- 1.43 mm (compared to a mean error of 4.10 mm +/- 3.03 mm in the uncorrected case). Bone contour surrounding modeling clay layer was improved. The proposed method is a first step towards automatic image-based, marker-free motion-compensation.

Motion compensation for cone-beam CT using Fourier consistency conditions

In cone-beam CT, involuntary patient motion and inaccurate or irreproducible scanner motion substantially degrades image quality. To avoid artifacts this motion needs to be estimated and compensated during image reconstruction. In previous work we showed that Fourier consistency conditions (FCC) can be used in fan-beam CT to estimate motion in the sinogram domain. This work extends the FCC to [Formula: see text] cone-beam CT. We derive an efficient cost function to compensate for [Formula: see text] motion using [Formula: see text] detector translations. The extended FCC method have been tested with five translational motion patterns, using a challenging numerical phantom. We evaluated the root-mean-square-error and the structural-similarity-index between motion corrected and motion-free reconstructions. Additionally, we computed the mean-absolute-difference (MAD) between the estimated and the ground-truth motion. The practical applicability of the method is demonstrated by application to respiratory motion estimation in rotational angiography, but also to motion correction for weight-bearing imaging of knees. Where the latter makes use of a specifically modified FCC version which is robust to axial truncation. The results show a great reduction of motion artifacts. Accurate estimation results were achieved with a maximum MAD value of 708 μm and 1184 μm for motion along the vertical and horizontal detector direction, respectively. The image quality of reconstructions obtained with the proposed method is close to that of motion corrected reconstructions based on the ground-truth motion. Simulations using noise-free and noisy data demonstrate that FCC are robust to noise. Even high-frequency motion was accurately estimated leading to a considerable reduction of streaking artifacts. The method is purely image-based and therefore independent of any auxiliary data.

Dynamic detector offsets for field of view extension in C-arm computed tomography with application to weight-bearing imaging.

PURPOSE In C-arm computed tomography (CT), the field of view (FOV) is often not sufficient to acquire certain anatomical structures, e.g., a full hip or thorax. Proposed methods to extend the FOV use a fixed detector displacement and a 360° scan range to double the radius of the FOV. These trajectories are designed for circular FOVs. However, there are cases in which the required FOV is not circular but rather an ellipsoid. METHODS In this work, the authors show that in fan-beam CT, the use of a dynamically adjusting detector offset can reduce the required scan range when using a noncircular FOV. Furthermore, the authors present an analytic solution to determine the minimal required scan ranges for elliptic FOVs given a certain detector size and an algorithmic approach for arbitrary FOVs. RESULTS The authors show that the proposed method can result in a substantial reduction of the required scan range. Initial reconstructions of data sets acquired with our new minimal trajectory yielded image quality comparable to reconstructions of data acquired using a fixed detector offset and a full 360° rotation. CONCLUSIONS Our results show a promising reduction of the necessary scan range especially for ellipsoidal objects that extend the FOV. In noncircular FOVs, there exists a set of solutions that allow a trade-off between detector size and scan range.

Projection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT

A reduction of the radiation dose in computed tomography typically leads to more noise in the acquired projections. Here filtering methods can help to reduce the noise level and preserve the diagnostic value of the low-dose images. In this work, six variants of Gaussian and bilateral filters are applied in both projection and reconstruction domain. Our comparison of noise reduction and image resolution shows that 2D and 3D bilateral filtering in the projection domain can reduce the noise level, but must be applied carefully to avoid streaking artifacts. By smoothing homogeneous regions while preserving sharp edges, the 3D bilateral filter applied in the reconstruction domain yielded the best results in terms of noise reduction and image sharpness.

Automatic Removal of Externally Attached Fiducial Markers in Cone Beam C-Arm CT

In computed tomography fiducial markers are frequently used to obtain accurate point correspondences for further processing. These markers typically cause metal artefacts, decreasing image quality of the subsequent reconstruction and are therefore often removed from the pro- jection data. The placement of such markers is usually done on a surface, separating two materials, e.g. skin and air. Hence, a correct restoration of the occluded area is difficult. In this work six state-of-the-art interpo- lation techniques for the removal of high-density fiducial markers from cone-beam CT projection data are compared. We conducted a qualita- tive and quantitative evaluation for the removal of such markers and the ability to reconstruct the adjoining edge. Results indicate that an iter- ative spectral deconvolution is best suited for this application, showing promising results in terms of edge, as well as noise restoration.

Automatic Motion Estimation and Compensation Framework for Weight-bearing C-arm CT scans using Fiducial Markers

Cone-beam CT systems are widely used because of their high flexibility with respect to patient position and scan trajectory. In the last years, C-arm CT systems have been used to acquire images in weight-bearing conditions in order to expose, e.g. the knee joint under realistic loads. Straight standing or squatting patient positions lead to involuntary patient motion during the acquisition. In this paper, a fully-automatic motion estimation and compensation framework to mitigate knee-joint motion during weight-bearing C-arm scans is presented. Our framework consists of three major steps: marker detection with outlier removal, motion estimation and correction, and marker removal. The marker detection is based on an initial estimate of the marker position extracted from the motion-blurred filtered backprojection (FDK) reconstruction and on the fast radial symmetry transformed (FRST) 2-D projection images. The motion is estimated by the alignment of the forward projected 3-D initial marker positions with the actual detected 2-D marker positions. The motion is then corrected in the filtered backprojection step. Finally, the detected markers are removed in the 2-D projection images by simple interpolation. The framework was evaluated on three C-arm CT datasets from one volunteer in a straight standing, moderate squatted and deep squatted position. All 3-D reconstructions show a large improvement in image quality compared to the non-corrected 3-D reconstructions.

Over-Exposure Correction in CT Using Optimization-Based Multiple Cylinder Fitting

Flat-Panel Computed Tomography (CT) has found its commonly used applications in the healthcare field by providing an approach of examining 3D structural information of a human’s body. The popular CT reconstruction algorithms are based on a filtered backprojection (FBP) scheme, which would face challenges when imaging the knee. This because in some views, the X-rays are highly attenuated when traveling through both thigh bones. In the same view, X-rays also travel through soft tissue that absorbs much less energy with respect to bone. When these high intensity X-rays arrive at the detector they cause detector saturation and the generated sinogram suffers from overexposure. Reconstructing an overexposed sinogram results in images with streaking and cupping artifacts, which are unusable for diagnostics. In this paper we propose a method to correct overexposure artifacts using an optimization approach. Parameters describing a specific geometry are determined by thc optimization and then used to extrapolate the overexposed acquisition data.