Reducing residual-motion artifacts in iterative 3D CBCT reconstruction in image-guided radiation therapy.

PURPOSE: Recent evaluations of a 3D iterative cone-beam computed tomography (iCBCT) reconstruction method available on Varian radiation treatment devices demonstrated that iCBCT provides superior image quality when compared to analytical Feldkamp-Davis-Kress (FDK) method. However, iCBCT employs statistical penalized likelihood (PL) that is known to be highly sensitive to inconsistencies due to physiological motion occurring during the acquisition. We propose a computationally inexpensive extension of iCBCT addressing this deficiency. METHODS: During the iterative process, the gradients of PL are modified to avoid the generation of motion-related artifacts. To assess the impact of this modification, we propose a motion simulation generating CBCT projections of a moving anatomy together with artifact-free images used as ground truth. Contrast-to-noise ratio and power spectra of difference images are computed to quantify the impact of the motion on reconstructed CBCT volumes as well as the effect of the proposed modification. RESULTS: Using both simulated and clinical data, it is shown that the motion of patient's abdominal wall during the acquisition results in artifacts that can be quantified as low-frequency components in volumes reconstructed with iCBCT. Further, a quantitative evaluation demonstrates that the proposed modification of PL reduces these low-frequency components. While preserving the advantages of PL, it effectively suppresses the propagation of motion-related artifacts into clinically important regions, thus increasing the motion resiliency of iCBCT. CONCLUSIONS: The proposed modified iterative reconstruction method significantly improves the quality of CBCT images of anatomies suffering from residual motion.

Fast-switching dual energy cone beam computed tomography using the on-board imager of a commercial linear accelerator.

To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images. Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp-Davis-Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values. The results after VM-CBCT production showed good material decomposition and consistent HUVM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. RED values measured for our method resulted in a mean percentage error of 0.0% ± 1.8%. This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments.

Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT.

We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeamTM integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.

Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter - Part II: System modeling, scatter correction, and optimization.

PURPOSE: To correct for scatter in kV cone-beam CT (CBCT) projection data on a clinical system using a new tool, Acuros® CTS, that estimates scatter images rapidly and accurately by deterministically solving the linear Boltzmann transport equation. METHODS: Phantom and patient CBCT scans were acquired on TrueBeam® radiotherapy machines. A first-pass reconstruction was used to create water and bone density maps of the imaged object, which was updated to include a more accurate representation of the patient couch. The imaging system model accounted for the TrueBeam x-ray source (polychromatic spectrum, beam filtration, bowtie filter, and collimation hardware) and x-ray detection system (antiscatter grid, flat-panel imager). Acuros CTS then used the system and object models to estimate the scatter component of each projection image, which was subtracted from the measured projections. The corrected projections were then reconstructed to produce the final result. We examined the tradeoff between run time and accuracy using a Pareto optimization of key parameters, including the voxel size of the down-sampled object model, the number of pixels in the down-sampled detector, and the number of scatter images (angular down-sampling). All computations and reconstructions were performed on a research workstation containing two graphics processing units (GPUs). In addition, we established a method for selecting a subset of projections for which scatter images were calculated. The projections were selected to minimize interpolation errors in the remaining projections. Image quality improvement was assessed by measuring the accuracy of the reconstructed phantom and patient images. RESULTS: The Pareto optimization yielded a set of parameters with an average run time of 26 seconds for scatter correction while maintaining high accuracy of scatter estimation. This was achieved in part by means of optimizing the projection angles that were processed, thus favoring the use of more angles in the lateral (i.e., horizontal) direction and fewer angles in the AP direction. In a 40 cm solid water phantom reconstruction, nonuniformities were decreased from 217 HU without scatter correction to 51 HU with conventional (kernel-based) scatter correction to 17 HU with Acuros CTS-based scatter correction. In clinical pelvis scans, nonuniformities in the bladder were reduced from 85 HU with conventional scatter correction to 14 HU with Acuros CTS. CONCLUSIONS: Acuros CTS is a promising new tool for fast and accurate scatter correction for CBCT imaging. By carefully modeling the imaging chain and optimizing several parameters, we achieved high correction accuracies with computation times compatible with the clinical workflow. The improvement in image quality enables better soft-tissue visualization and potentially enables applications such as adaptive radiotherapy.

A modified McKinnon-Bates (MKB) algorithm for improved 4D cone-beam computed tomography (CBCT) of the lung.

PURPOSE: Four-dimensional (4D) cone-beam computed tomography (CBCT) of the lung is an effective tool for motion management in radiotherapy but presents a challenge because of slow gantry rotation times. Sorting the individual projections by breathing phase and using an established technique such as Feldkamp-Davis-Kress (FDK) to generate corresponding phase-correlated (PC) three-dimensional (3D) images results in reconstructions (FDK-PC) that often contain severe streaking artifacts due to the sparse angular sampling distributions. These can be reduced by further slowing down the gantry at the expense of incurring unwanted increases in scan times and dose. A computationally efficient alternative is the McKinnon-Bates (MKB) reconstruction algorithm that has shown promise in reducing view aliasing-induced streaking but can produce ghosting artifacts that reduce contrast and impede the determination of motion trajectories. The purpose of this work was to identify and correct shortcomings in the MKB algorithm. METHODS: In the general MKB approach, a time-averaged 3D prior image is first reconstructed. The prior is then forward-projected at the same angles as the original projection data creating time-averaged reprojections. These reprojections are subsequently subtracted from the original (unblurred) projections to create motion-encoded difference projections. The difference projections are reconstructed into PC difference images that are added to the well-sampled 3D prior to create the higher quality 4D image. The cause of the ghosting in the traditional 4D MKB images was studied and traced to motion-induced streaking in the prior that, when reprojected, has the undesirable effect of re-encoding for motion in what should be a purely time-averaged reprojection. A new method, designated as the modified McKinnon-Bates (mMKB) algorithm, was developed based on destreaking the prior. This was coupled with a postprocessing 4D bilateral filter for noise suppression and edge preservation (mMKBbf ). The algorithms were tested with the 4D XCAT phantom using four simulated scan times (57, 60, 120, 180 s) and with two in vivo thorax studies (acquisition time of 60 and 90 s). Contrast-to-noise ratios (CNRs) of the target lesions and overall visual quality of the images were assessed. RESULTS: Prior destreaking (mMKB algorithm) reduced ghosting artifacts and increased CNRs for all cases, with the biggest impacts seen in the end inhale (EI) and end exhale (EE) phases of the respiratory cycle. For the XCAT phantom, mMKB lesion CNR was 44% higher than the MKB lesion CNR and was 81% higher than the FDK-PC lesion CNR (EI and EE phases). The bilateral filter provided a further average CNR improvement of 87% with the highest increases associated with longer scan times. Across all phases and scan times, the maximum mMKBbf -to-FDK-PC CNR improvement was over 300%. In vivo results agreed with XCAT results. Significantly less ghosting was observed throughout the mMKB images including near the lesions-of-interest and the diaphragm allowing for, in one case, visualization of a small tumor with nearly 30 mm of motion. The maximum FDK-PC-to-MKBbf CNR improvement for Patient 1's lesion was 261% and for Patient 2's lesion was 318%. CONCLUSIONS: The 4D mMKB algorithm yields good quality coronal and sagittal images in the thorax that may provide sufficient information for patient verification.

Minimal shape and intensity cost path segmentation.

A new generic model-based segmentation algorithm is presented, which can be trained from examples akin to the active shape model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Whereas ASM alternates between shape and intensity information during search, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized noniteratively using dynamic programming, without the need for initialization. The algorithm was validated for segmentation of anatomical structures in chest and hand radiographs. In each experiment, the presented method had a significant higher performance when compared to the ASM schemes. As the method is highly effective, optimally suited for pathological cases and easy to implement, it is highly useful for many medical image segmentation tasks.

Moving metal artifact reduction in cone-beam CT scans with implanted cylindrical gold markers.

PURPOSE: Implanted gold markers for image-guided radiotherapy lead to streaking artifacts in cone-beam CT (CBCT) scans. Several methods for metal artifact reduction (MAR) have been published, but they all fail in scans with large motion. Here the authors propose and investigate a method for automatic moving metal artifact reduction (MMAR) in CBCT scans with cylindrical gold markers. METHODS: The MMAR CBCT reconstruction method has six steps. (1) Automatic segmentation of the cylindrical markers in the CBCT projections. (2) Removal of each marker in the projections by replacing the pixels within a masked area with interpolated values. (3) Reconstruction of a marker-free CBCT volume from the manipulated CBCT projections. (4) Reconstruction of a standard CBCT volume with metal artifacts from the original CBCT projections. (5) Estimation of the three-dimensional (3D) trajectory during CBCT acquisition for each marker based on the segmentation in Step 1, and identification of the smallest ellipsoidal volume that encompasses 95% of the visited 3D positions. (6) Generation of the final MMAR CBCT reconstruction from the marker-free CBCT volume of Step 3 by replacing the voxels in the 95% ellipsoid with the corresponding voxels of the standard CBCT volume of Step 4. The MMAR reconstruction was performed retrospectively using a half-fan CBCT scan for 29 consecutive stereotactic body radiation therapy patients with 2-3 gold markers implanted in the liver. The metal artifacts of the MMAR reconstructions were scored and compared with a standard MAR reconstruction by counting the streaks and by calculating the standard deviation of the Hounsfield units in a region around each marker. RESULTS: The markers were found with the same autosegmentation settings in 27 CBCT scans, while two scans needed slightly changed settings to find all markers automatically in Step 1 of the MMAR method. MMAR resulted in 15 scans with no streaking artifacts, 11 scans with 1-4 streaks, and 3 scans with severe streaking artifacts. The corresponding numbers for MAR were 8 (no streaks), 1 (1-4 streaks), and 20 (severe streaking artifacts). The MMAR method was superior to MAR in scans with more than 8 mm 3D marker motion and comparable to MAR for scans with less than 8 mm motion. In addition, the MMAR method was tested on a 4D CBCT reconstruction for which it worked equally well as for the 3D case. The markers in the 4D case had very low motion blur. CONCLUSIONS: An automatic method for MMAR in CBCT scans was proposed and shown to effectively remove almost all streaking artifacts in a large set of clinical CBCT scans with implanted gold markers in the liver. Residual streaking artifacts observed in three CBCT scans may be removed with better marker segmentation.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Construction and validation of mean shape atlas templates for atlas-based brain image segmentation.

In this paper, we evaluate different schemes for constructing a mean shape anatomical atlas for atlas-based segmentation of MR brain images. Each atlas is constructed and validated using a database of 20 images for which detailed manual delineations of 49 different subcortical structures are available. Atlas construction and atlas based segmentation are performed by non-rigid intensity-based registration using a viscous fluid deformation model with parameters that were optimally tuned for this particular task. The segmentation performance of each atlas scheme is evaluated on the same database using a leave-one-out approach and measured by the volume overlap of corresponding regions in the ground-truth manual segmentation and the warped atlas label image.