Markus Oelhafen

Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT

PURPOSE Kilovoltage cone-beam computed tomography (CBCT) is widely used in image-guided radiation therapy for exact patient positioning prior to the treatment. However, producing time series of volumetric images (4D CBCT) of moving anatomical structures remains challenging. The presented work introduces a novel method, combining high temporal resolution inside anatomical regions with strong motion and image quality improvement in regions with little motion. METHODS In the proposed method, the projections are divided into regions that are subject to motion and regions at rest. The latter ones will be shared among phase bins, leading thus to an overall reduction in artifacts and noise. An algorithm based on the concept of optical flow was developed to analyze motion-induced changes between projections. The technique was optimized to distinguish patient motion and motion deriving from gantry rotation. The effectiveness of the method is shown in numerical simulations and patient data. RESULTS The images reconstructed from the presented method yield an almost the same temporal resolution in the moving volume segments as a conventional phase-correlated reconstruction, while reducing the noise in the motionless regions down to the level of a standard reconstruction without phase correlation. The proposed simple motion segmentation scheme is yet limited to rotation speeds of less than3°∕s. CONCLUSIONS The method reduces the noise in the reconstruction and increases the image quality. More data are introduced for each phase-correlated reconstruction, and therefore the applied dose is used more efficiently.

Efficient scatter correction using asymmetric kernels

X-ray cone-beam (CB) projection data often contain high amounts of scattered radiation, which must be properly modeled in order to produce accurate computed tomography (CT) reconstructions. A well known correction technique is the scatter kernel superposition (SKS) method that involves deconvolving projection data with kernels derived from pencil beam-generated scatter point-spread functions. The method has the advantages of being practical and computationally efficient but can suffer from inaccuracies. We show that the accuracy of the SKS algorithm can be significantly improved by replacing the symmetric kernels that traditionally have been used with nonstationary asymmetric kernels. We also show these kernels can be well approximated by combinations of stationary kernels thus allowing for efficient implementation of convolution via FFT. To test the new algorithm, Monte Carlo simulations and phantom experiments were performed using a table-top system with geometry and components matching those of the Varian On-Board Imager (OBI). The results show that asymmetric kernels produced substantially improved scatter estimates. For large objects with scatter-to-primary ratios up to 2.0, scatter profiles were estimated to within 10% of measured values. With all corrections applied, including beam hardening and lag, the resulting accuracies of the CBCT reconstructions were within ±25 Hounsfield Units (±2.5%).

Self‐adapting cyclic registration for motion‐compensated cone‐beam CT in image‐guided radiation therapy

PURPOSE In image-guided radiation therapy an additional kV imaging system next to the linear particle accelerator provides information for an accurate patient positioning. However, the acquisition time of the system is much longer than the patients breathing cycle due to the low gantry rotation speed. Our purpose is a cyclic registration in the context of motion-compensated image reconstruction that provides high quality respiratory-correlated 4D volumes for on-board flat panel detector cone-beam CT scans. METHODS Based on the small motion assumption, widely used within registration algorithms, a strategy is developed for motion estimation. In this strategy temporal restrictions are incorporated, for example, the cyclic motion patterns of respiration. The resultant cyclic registration method is to show less sensitivity on image artifacts, in particular on artifacts due to projection data sparsification. Using a new cyclic registration method a motion estimation is performed on respiratory-correlated reconstructions, and the obtained motion vector fields are used for motion compensation. RESULTS The proposed cyclic registration is evaluated in the context of motion-compensated image reconstruction using simulated data and patient data. Motion artifacts of 3D standard reconstructions can be significantly reduced by the resulting cyclic motion compensation. The method outperforms the respiratory-correlated reconstructions regarding sparse-view artifacts and maintains the high temporal resolution at the same time. Image artifacts show only minor to almost no effect on the motion estimation using the cyclic registration. CONCLUSIONS The cyclic motion compensation approach provides respiratory-correlated volumes with high image quality. The cyclic motion estimation is of such low sensitivity to sparse-view artifacts, that it is capable to determine high quality motion vector fields based on reconstructions of low sampled data.

Artifact‐resistant motion estimation with a patient‐specific artifact model for motion‐compensated cone‐beam CT

PURPOSE In image-guided radiation therapy (IGRT) valuable information for patient positioning, dose verification, and adaptive treatment planning is provided by an additional kV imaging unit. However, due to the limited gantry rotation speed during treatment the typical acquisition time is quite long. Tomographic images of the thorax suffer from motion blurring or, if a gated 4D reconstruction is performed, from significant streak artifacts. Our purpose is to provide a method that reliably estimates respiratory motion in presence of severe artifacts. The estimated motion vector fields are then used for motion-compensated image reconstruction to provide high quality respiratory-correlated 4D volumes for on-board cone-beam CT (CBCT) scans. METHODS The proposed motion estimation method consists of a model that explicitly addresses image artifacts because in presence of severe artifacts state-of-the-art registration methods tend to register artifacts rather than anatomy. Our artifact model, e.g., generates streak artifacts very similar to those included in the gated 4D CBCT images, but it does not include respiratory motion. In combination with a registration strategy, the model gives an error estimate that is used to compensate the corresponding errors of the motion vector fields that are estimated from the gated 4D CBCT images. The algorithm is tested in combination with a cyclic registration approach using temporal constraints and with a standard 3D-3D registration approach. A qualitative and quantitative evaluation of the motion-compensated results was performed using simulated rawdata created on basis of clinical CT data. Further evaluation includes patient data which were scanned with an on-board CBCT system. RESULTS The model-based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling. The motion is accurately estimated and our motion-compensated image reconstruction algorithm can correct for it. Motion artifacts of 3D standard reconstruction are significantly reduced, while almost no new artifacts are introduced. CONCLUSIONS Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.

Cone-beam CT image reconstruction with extended z range.

In circular cone-beam CT the Feldkamp [Feldkamp-Davis-Kress (FDK)] algorithm is the most prominent image reconstruction algorithm. For example, in radiation oncology images reconstructed with the Feldkamp algorithm are used for accurate patient positioning. The scan and reconstruction volumes are limited by the size of the flat panel detector. Flat panel detectors, however, are expensive and difficult to manufacture in large size. For numerous treatment techniques, extending this scan volume would be very beneficial. In most applications, data from 360° or more are available. However, usually only those slices are reconstructed where each pixel is seen under the full 360° range. Yet for a 360° scan there are regions that are seen by less than 360°, namely, those that lie further off the plane of the circular source trajectory. Performing a reconstruction also for those slices where all voxels are seen at least by 180° will extend the z range and therefore increase the dose usage. In this work a new method is presented that reconstructs also those slices where some or all pixels receive less than 360° but at least 180° of the data. The procedure significantly increases the longitudinal range of the reconstructed volume. As opposed to the existing techniques, the proposed method does not necessitate any multiple convolutions or multiple backprojections, lending itself therefore for a very efficient implementation. To validate the abilities of the extended reconstruction, the authors performed an evaluation of the image quality by using simulated and measured CT data. The method shows good image quality on simulated phantom data as well as on clinical patient scans. Image noise and spatial resolution behave as expected. This means that the noise equals FDK values in the normal region and increases in the extended region due to reduced data redundancies. The extended Feldkamp demonstrates its ability to extend the reconstructable z range and appears to be useful in clinical practice.

SU‐FF‐I‐18: Optimization of FDK Reconstruction Parameters to Minimize Aliasing and Reduce Metal Artifacts

Purpose: To maximize SNR, suppress metal artifacts and minimize backprojection times for FDK‐based cone‐beam CT(CBCT)reconstructions by optimizing binning, filtering and backprojection parameters, and to investigate the performance of new multi‐core CPUs. Methods and Materials:CBCTreconstruction times can be reduced by filtering and downsampling high resolution flat panel projection data to match the reconstruction matrix pitch, and by using nearest neighbor interpolation (NN) for backprojection. However, metal artifacts and noise aliasing may result. To investigate the tradeoffs involved, a frequency‐domain noise power spectrum (NPS) model was developed. Phantom and clinical CBCT data were acquired using the On‐Board Imager and reconstructed with a range of pre‐processing and backprojection parameters while keeping imageMTF constant. Imagenoise, including the amount of noise aliasing, and metal artifacts were evaluated. Reconstruction times were measured on Xeon workstations comprising either two single‐core, dual‐core, or quad‐core CPUs. Results: Two types of metal artifacts emerged. A moire pattern is produced if insufficient projection data densities in the transaxial direction are maintained, while radial streaks are produced by insufficiently dense axial data. For best image quality aliased noise should be less than 5% of the total imagenoise, and projection data should be 1.5–2.0× denser than the reconstructed image matrix pitch for backprojection with bilinear interpolation. NN interpolation is not preferred. Although backprojection times increased by ∼50% with these higher projection data densities, overall reconstruction times for relatively large image matrices (512×512×188, 675 projections), were <50 seconds using the quad‐core workstation which is sufficiently fast for IGRT applications. Conclusions: Optimal use of high data densities coupled with bilinear interpolation for backprojection can suppress some metal artifacts and minimize noise aliasing. New multi‐core CPU architectures provide sufficient speed to make such reconstructions clinically practical. Conflict of Interest: Employees of Varian Medical Systems.

A comparison of 4D cone-beam CT algorithms for slowly rotating scanners

The study compares several algorithms for the 4D reconstruction of cone-beam computed tomography (CBCT) data that were recently proposed and which can be used from slowly rotating devices. In our case the imaging units are mounted to linear particle accelerators (LINAC). The algorithms are the conventional phase-correlated reconstruction (PC), the McKinnon/Bates-Algorithm, the prior image constrained compressed sensing (PICCS) algorithm, the total-variation minimization (TV) algorithm, and our auto-adaptive phase-correlation (AAPC) algorithm. For each algorithm the same motion-affected rawdata are used and the reconstruction results compared to each other regarding their noise and artifact levels, as well as temporal resolution, and computational complexity and convergence. These criteria result in a discussion of the advantages and disadvantages of each algorithm. The temporal resolution is best in the algorithms which exclusively use data from a single motion phase only. The iterative algorithms show lower noise and artifact levels but are computationally complex and therefore may have a limited usage in the clinical application. Algorithms which include image enhancements beside a faster reconstruction represent a suitable trade-off for the clinical workflow.

TU‐A‐204B‐01: The CBCT Performance of a New Treatment Platform

Purpose: To characterize the CBCT performance of a new radiation therapy platform (Trilogy MX). Methods: An entirely new CBCT system has been developed for Trilogy MX. The new CBCT system differs from that of the On‐Board Imager® (OBI) in the use of a beam hardening filter, which reduces patient dose, and an improved reconstructor, which uses scatter correction algorithms to account for the x‐ray scatter caused by the cone‐beam geometry. Scans of Catphan® and electron density (Model 062A, CIRS) phantoms have been compared with OBI scans. Hounsfield unit (HU) accuracy was checked by changing the z scan length and the phantom diameters for pelvis (125kVp, 680mAs, 45cm dia.) acquisitions. The Catphan was imaged using doses ranging between 5–20mGy (CTDIw). Projections acquired using clinical OBI units were also reconstructed for comparison using the new reconstructor.Results: The new CBCT system has higher dose efficiency and higher HU accuracy. When the volume length is reduced from 160mm to 90mm or when the phantom diameter is reduced from 330mm to 180mm, the HU values measured for the same inserts differ by −190 to +80HU for OBI scans and by –60 to +50HU for Trilogy MX scans (for electron densities between 0.2 and 1.2). The contrast detectability of the 1% contrast objects in the Catphan phantom improves from 9mm to 4mm diameter when using the same CTDIwdose as OBI. Clinical images exhibit much better uniformity, elimination of streaks and better definition of the skin surface. Conclusions: The new reconstruction algorithm makes substantial improvements in CBCTimage quality, reduces patient dose and increases HU accuracy. The new system produces CBCTimages, which are much better suited to image guidance and which may be suitable for other tasks such as adaptive RT planning.

Voxel-based reconstruction combined with motion detection for slow rotating 4D CBCT

Flat panel detector (FPD) cone-beam computed tomography (CBCT) systems, such as C-arm CT scanners or onboard imaging systems, rotate far slower than a typical motion cycle of the heart or lung. Therefore 4D CBCT is more complicated with flat panel detectors than with clinical CT. Recent approaches for 4D imaging from FPD CBCT either use multiple scans over the same angular range or a single slow rotation, and they perform a motion phase-dependent weighting of complete projections (e.g. using a respiratory monitor). This leads to a relatively high temporal resolution but also to high image noise and in the second approach to artifacts due to sparse angular sampling. Our proposed method also uses the data from several scans without causing streak artifacts. But instead of weighting the whole projections the weighting is applied to the motion affected areas in the projection only. These regions are automatically estimated using motion detection techniques between consecutive projections. Thus, projection data are used in our algorithm that are completely ignored in conventional approaches. To evaluate our method simulated data of a breathing thorax phantom and measurements acquired with the OBI scanner (Varian Medical Systems, Palo Alto, CA) were reconstructed. Image quality was compared with the projection-weighting approach for whole projections and standard reconstructions without phase-correlation.

SU‐E‐I‐15: Comparison of State‐Of‐The‐Art Interpolation‐Based Metal Artifact Reduction (MAR) Algorithms for Cone‐Beam Computed Tomography (CBCT)

PURPOSE To compare four metal-artifact-reduction (MAR) algorithms in their ability to correct the typical streaking artifacts that appear in cone- beam computed tomography (CBCT) images. METHODS The goal was to compare the strengths and weaknesses of four MAR algorithms, Basic; Wei; Mazin and Meyer, using typical clinical situations where metal is present. Three clinical situations were evaluated: fiducial markers in the abdomen; hip implants and multiple dental fillings. The algorithms take original CBCT projections as input and produce a corrected image. The location of the metal is identified in the CBCT images and a forward projection identifies which pixels in the projections need to be replaced by interpolation of neighboring pixels. The three advanced algorithms extend the Basic technique with more sophisticated interpolation schemes. Wei and Meyer identify the high contrast structures using image segmentation in order to reduce their appearance in the projections before interpolation. Mazin corrects the original projections using a forward projection of the Basic correction. RESULTS All the algorithms reduced the streak artifacts typical of metal structures. Nevertheless, depending upon the clinical task, the algorithms also added shading and streaks which reduced the overall visual impression. Images containing fiducial markers in the abdomen showed obvious improvements; images containing hip implants were improved but also showed distracting shading artifacts; and, images with multiple dental fillings all appeared visually worse than the uncorrected images. In almost all cases, Mazin outperformed the other approaches and introduced the fewest additional streaks and shading artifacts. CONCLUSIONS This work indicates that the Mazin algorithm is best suited for clinical usage of MAR. Furthermore the algorithm is fairly simple and can be computational very efficient making it well suited for clinical use. Nevertheless, the overall improvement is highly dependent on the individual characteristics of the original image. For dental implants no correction is recommended.