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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
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DOE OSTI2015

WE-AB-BRA-12: Virtual Endoscope Tracking for Endoscopy-CT Image Registration

Purpose: The use of endoscopy in radiotherapy will remain limited until we can register endoscopic video to CT using standard clinical equipment. In this phantom study we tested a registration method using virtual endoscopy to measure CT-space positions from endoscopic video. Methods: Our phantom is a contorted clay cylinder with 2-mm-diameter markers in the luminal surface. These markers are visible on both CT and endoscopic video. Virtual endoscope images were rendered from a polygonal mesh created by segmenting the phantom’s luminal surface on CT. We tested registration accuracy by tracking the endoscope’s 6-degree-of-freedom coordinates frame-to-frame in a video recorded as it moved through the phantom, and using these coordinates to measure CT-space positions of markers visible in the final frame. To track the endoscope we used the Nelder-Mead method to search for coordinates that render the virtual frame most similar to the next recorded frame. We measured the endoscope’s initial-frame coordinates using a set of visible markers, and for image similarity we used a combination of mutual information and gradient alignment. CT-space marker positions were measured by projecting their final-frame pixel addresses through the virtual endoscope to intersect with the mesh. Registration error was quantified as the distance between this intersection and the marker’s manually-selected CT-space position. Results: Tracking succeeded for 6 of 8 videos, for which the mean registration error was 4.8±3.5mm (24 measurements total). The mean error in the axial direction (3.1±3.3mm) was larger than in the sagittal or coronal directions (2.0±2.3mm, 1.7±1.6mm). In the other 2 videos, the virtual endoscope got stuck in a false minimum. Conclusion: Our method can successfully track the position and orientation of an endoscope, and it provides accurate spatial mapping from endoscopic video to CT. This method will serve as a foundation for an endoscopy-CT registration framework that is clinically valuable and requires no specialized equipment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-AB-204-01: Performance Characterization of Regularized-Reconstruction Algorithm for 90Y PET/CT Images

Purpose: {sup 90}Y PET/CT imaging and quantification have recently been suggested as an approach of treatment verification. However, due to low positron yield (32ppm), the {sup 90}Y-PET/CT images are very noisy. Iterative reconstruction techniques that employ regularization, e.g. block sequential regularized expectation maximization (BSREM) algorithm (recently implemented on GE scanners – QClear™), has the potential to increase quantitative accuracy with lower noise penalty compared to OSEM. Our aim is to investigate the performance of RR algorithms in {sup 90}Y PET/CT studies. Methods: A NEMA IEC phantom filled with 3GBq {sup 90}YCl{sub 2} (to simulate patient treatment) was imaged on GE-D690 for 1800s/bed. The sphere-to-background ratio of 7. The data were reconstructed using OSEM and BSREM with PSF modeling and TOF correction while varying the iterations (IT) from 1–6 with fixed 24subsets. For BSREM, the edge-preservation parameter (γ ) was 2 and the penalty-parameters (β) was varied 350–950. In all cases a post-reconstruction filter of 5.2mm (2pixel) transaxial and standard z-axis were used. Sphere average activity concentration (AC) and background standard deviation (SD) were then calculated from VOIs drawn in the spheres and background. Results: Increasing IT from 1to6, the %SD in OSEM increased from 30% to 80%, whereas %SD in BSREM images increased by <5% for all βs. BSREM with β=350 didn’t offer any improvement over OSEM (convergence of mean achieved at 2 IT, in this study). Increasing β from 350 to 950 reduced the AC accuracy of small spheres (<20mm) by 10% and noise from 40% to 20%, which resulted in CNR increase from 11 to 17. Conclusion: In count-limited studies such as {sup 90}Y PET/CT, BSREM can be used to suppress image noise and increase CNR at the expense of a relatively small decrease of quantitative accuracy. The BSREM parameters need to be optimized for each study depending on the radionuclides and count densities. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA13898 and was also partly supported by General Electric.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-AB-204-04: Feature Selection and Clustering Optimization for Pseudo-CT Generation in MR-Based Attenuation Correction and Radiation Therapy Planning

Purpose: Accurate and robust photon attenuation derived from MR is essential for PET/MR and MR-based radiation treatment planning applications. Although the fuzzy C-means (FCM) algorithm has been applied for pseudo-CT generation, the input feature combination and the number of clusters have not been optimized. This study aims to optimize both for clinically practical pseudo-CT generation. Methods: Nine volunteers were recruited. A 190-second, single-acquisition UTE-mDixon with 25% (angular) sampling and 3D radial readout was performed to acquire three primitive MR features at TEs of 0.1, 1.5, and 2.8 ms: the free-induction-decay (FID), the first and the second echo images. Three derived images, Dixon-fat and Dixon-water generated by two-point Dixon water/fat separation, and R2* (1/T2*) map, were also created. To identify informative inputs for generating a pseudo-CT image volume, all 63 combinations, choosing one to six of the feature images, were used as inputs to FCM for pseudo-CT generation. Further, the number of clusters was varied from four to seven to find the optimal approach. Mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R) of different combinations were compared for feature selection. Results: Among the 63 feature combinations, the four that resulted in the best MAPD and R were further compared along with the set containing all six features. The results suggested that R2* and Dixon-water are the most informative features. Further, including FID also improved the performance of pseudo-CT generation. Consequently, the set containing FID, Dixon-water, and R2* resulted in the most accurate, robust pseudo-CT when the number of cluster equals to five (5C). The clusters were interpreted as air, fat, bone, brain, and fluid. The six-cluster Result additionally included bone marrow. Conclusion: The results suggested that FID, Dixon-water, R2* are the most important features. The findings can be used to facilitate pseudo-CT generation for unsupervised clustering. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-AB-204-09: Respiratory Motion Correction in 4D-PET by Simultaneous Motion Estimation and Image Reconstruction (SMEIR)

Purpose: In conventional 4D-PET, images from different frames are reconstructed individually and aligned by registration methods. Two issues with these approaches are: 1) Reconstruction algorithms do not make full use of all projections statistics; and 2) Image registration between noisy images can Result in poor alignment. In this study we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) method for cone beam CT for motion estimation/correction in 4D-PET. Methods: Modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM- TV) is used to obtain a primary motion-compensated PET (pmc-PET) from all projection data using Demons derived deformation vector fields (DVFs) as initial. Motion model update is done to obtain an optimal set of DVFs between the pmc-PET and other phases by matching the forward projection of the deformed pmc-PET and measured projections of other phases. Using updated DVFs, OSEM- TV image reconstruction is repeated and new DVFs are estimated based on updated images. 4D XCAT phantom with typical FDG biodistribution and a 10mm diameter tumor was used to evaluate the performance of the SMEIR algorithm. Results: Image quality of 4D-PET is greatly improved by the SMEIR algorithm. When all projections are used to reconstruct a 3D-PET, motion blurring artifacts are present, leading to a more than 5 times overestimation of the tumor size and 54% tumor to lung contrast ratio underestimation. This error reduced to 37% and 20% for post reconstruction registration methods and SMEIR respectively. Conclusion: SMEIR method can be used for motion estimation/correction in 4D-PET. The statistics is greatly improved since all projection data are combined together to update the image. The performance of the SMEIR algorithm for 4D-PET is sensitive to smoothness control parameters in the DVF estimation step.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-BRA-03: Four-Dimensional Dose Reconstruction Through Retrospective Phase Determination Using Cine Images of Electronic Portal Imaging Device

Purpose: To test a method to reconstruct a four-dimensional (4D) dose distribution using the correlation of pre-calculated 4D electronic portal imaging device (EPID) images and measured cine-EPID images. Methods: 1. A phantom designed to simulate a tumor in lung (a polystyrene block with 3.0 cm diameter embedded in cork) was placed on a sinusoidally moving platform with 2 cm amplitude and 4 sec/cycle. Ten-phase 4D CT images were acquired for treatment planning and dose reconstruction. A 6MV photon beam was irradiated on the phantom with static (field size=5×8.5 cm{sup 2}) and dynamic fields (sliding windows, 10×10 cm{sup 2}, X1 MLC closing in parallel with the tumor movement). 2. 4D and 3D doses were calculated forwardly on PTV (1 cm margin). 3. Dose images on EPID under the fields were calculated for 10 phases. 4. Cine EPID images were acquired during irradiation. 5. Their acquisition times were correlated to the phases of the phantom at which irradiation occurred by inter-comparing calculated “reference” EPID images with measured images (2D gamma comparison). For the dynamic beam, the tumor was hidden under MLCs during a portion of irradiation time; the correlation performed when the tumor was visible was extrapolated. 6. Dose for each phase was reconstructed on the 4D CT images and summed over all phases. The summation was compared with forwardly calculated 4D and 3D dose distributions. Monte Carlo methods were used for all calculations. Results: For the open and dynamic beams, the 4D reconstructed doses showed the pass rates of 92.7 % and 100 %, respectively, at the isocenter plane given 3% / 3 mm criteria. The better agreement of the dynamic beam was from its dose gradient which blurred the otherwise sharp difference between forward and reconstructed doses. This also contributed slightly better agreement in DVH of PTV. Conclusion: The feasibility of 4D reconstruction was demonstrated.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-204-06: An Open Source ImageJ CatPhan Analysis Tool

Purpose: The CatPhan is a popular QA device for assessing CT image quality. There are a number of software options which perform analysis of the CatPhan. However, there is often little ability for the user to adjust the analysis if it isn’t running properly, and these are all expensive options. An open source tool is an effective solution. Methods: To use the software, the user imports the CT as an image sequence in ImageJ. The user then scrolls to the slice with the lateral dots. The user then runs the plugin. If tolerance constraints are not already created, the user is prompted to enter them or to use generic tolerances. Upon completion of the analysis, the plugin calls pdfLaTex to compile the pdf report. There is a csv version of the report as well. A log of the results from all CatPhan scans is kept as a csv file. The user can use this to baseline the machine. Results: The tool is capable of detecting the orientation of the phantom. If the CatPhan was scanned backwards, one can simply flip the stack of images horizontally and proceed with the analysis. The analysis includes Sensitometry (estimating the effective beam energy), HU values and linearity, Low Contrast Visibility (using LDPE & Polystyrene), Contrast Scale, Geometric Accuracy, Slice Thickness Accuracy, Spatial resolution (giving the MTF using the line pairs as well as the point spread function), CNR, Low Contrast Detectability (including the raw data), Uniformity (including the Cupping Effect). Conclusion: This is a robust tool that analyzes more components of the CatPhan than other software options (with the exception of ImageOwl). It produces an elegant pdf and keeps a log of analyses for long-term tracking of the system. Because it is open source, users are able to customize any component of it.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-BRA-03: Catheter- Free Ablation with External Photon Radiation: Treatment Planning, Delivery Considerations, and Correlation of Effects with Delivered Dose

Purpose: To plan, target, and calculate delivered dose in atrioventricular node (AVN) ablation with volume-modulated arc therapy (VMAT) in an intact porcine model. Methods: Seven pigs underwent AVN irradiation, with prescription doses ranging between 25 and 55Gy in a single fraction. Cardiac CT scans were acquired at expiration. Two physicians contoured AVN targets on 10 phases, providing estimates of target motion and inter-physician variability. Treatment planning was conducted on a static phase-averaged CT. The volume designated to receive prescription dose covered the full extent of AVN cardiac motion, expanded by 4mm for setup uncertainty. Optimization limited doses to risk structures according to single-fraction tumor treatment protocols. Orthogonal kV images were used to align bony anatomy at time of treatment. Localization was further refined with respiratory-gated cone-beam CT, and range of cardiac motion was verified under fluoroscopy. Beam delivery was respiratory-gated for expiration with a mean efficiency of 60%. Deformable registration of the 10 cardiac CT phases was used to calculate actual delivered dose for comparison to electro-anatomical and visually evident lesions. Results: The mean [minimum,maximum] amplitude of AVN cardiac motion was LR 2.9 [1.7,3.9]mm, AP 6.6 [4.4,10.4]mm, and SI 5.6 [2.0,9.9]mm. Incorporating cardiac motion into the dose calculation showed the volume receiving full dose was 40–80% of the volume indicated on the static planning image, although the contoured AVN target received full dose in all animals. Initial results suggest the dimensions of the electro-anatomical lesion are correlated with the 40Gy isodose volume. Conclusion: Image-guidance techniques allow for accurate and precise delivery of VMAT for catheter-free arrhythmia ablation. An arsenal of advanced radiation planning, dose optimization, and image-guided delivery techniques was employed to assess and mitigate effects of cardiac and respiratory motion. Feasibility of delivery to the pulmonary veins and left ventricular myocardium will be investigated in future studies. D. Packer Disclosures: Abiomed, Biosense Webster, Inc., Boston Scientific Corp., CardioFocus, Inc., Johnson and Johnson, Excerpta Medica, Ortho-McNeil-Jannsen, Sanofi Aventis, CardioInsight Technologies, InfoBionic, SIEMENS, Medtronic, Inc., CardioDx, Inc., CardioInsight Technologies, FoxP2 Medica, Mediasphere Medical, Wiley-Blackwell, St. Jude Medical, Endosense, Thermedical, EP Advocate LLC, Hansen Medical, American Heart Association, EpiEP, NIH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-207-02: The Rotate-Plus-Shift C-Arm Trajectory: Theory and First Clinical Results

Purpose: The proposed method enables the acquisition of a complete dataset for 3D reconstruction of C-Arm data using less than 180° rotation. Methods: Typically a C–arm cone–beam CT scan is performed using a circle–like trajectory around a region of interest. Therefore an angular range of at least 180° plus fan–angle must be covered to ensure a completely sampled data set. This fact defines some constraints on the geometry and technical specifications of a C–arm system, for example a larger C radius or a smaller C opening respectively. This is even more important for mobile C-arm devices which are typically used in surgical applications.To overcome these limitations we propose a new trajectory which requires only 180° minusfan–angle of rotation for a complete data set. The trajectory consists of three parts: A rotation of the C around a defined iso–center and two translational movements parallel to the detector plane at the begin and at the end of the rotation (rotate plus shift trajectory). This enables the acquisition of a completely sampled dataset using only 180° minus fan–angle of rotation. Results: For the evaluation of the method we show simulated and measured data. The results show, that the rotate plus shift scan yields equivalent image quality compared to the short scan which is assumed to be the gold standard for C-arm CT today. Compared to the pure rotational scan over only 165°, the rotate plus shift scan shows strong improvements in image quality. Conclusion: The proposed method makes 3D imaging using C–arms with less than 180° rotation range possible. This enables integrating full 3D functionality into a C- arm device without any loss of handling and usability for 2D imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-207-04: An Inter-Projection Sensor Fusion (IPSF) Approach to Estimate Missing Projection Signal in Synchronized Moving Grid (SMOG) System

Purpose: A synchronized moving grid (SMOG) has been proposed to reduce scatter and lag artifacts in cone beam computed tomography (CBCT). However, information is missing in each projection because certain areas are blocked by the grid. A previous solution to this issue is acquiring 2 complimentary projections at each position, which increases scanning time. This study reports our first Result using an inter-projection sensor fusion (IPSF) method to estimate missing projection in our prototype SMOG-based CBCT system. Methods: An in-house SMOG assembling with a 1:1 grid of 3 mm gap has been installed in a CBCT benchtop. The grid moves back and forth in a 3-mm amplitude and up-to 20-Hz frequency. A control program in LabView synchronizes the grid motion with the platform rotation and x-ray firing so that the grid patterns for any two neighboring projections are complimentary. A Catphan was scanned with 360 projections. After scatter correction, the IPSF algorithm was applied to estimate missing signal for each projection using the information from the 2 neighboring projections. Feldkamp-Davis-Kress (FDK) algorithm was applied to reconstruct CBCT images. The CBCTs were compared to those reconstructed using normal projections without applying the SMOG system. Results: The SMOG-IPSF method may reduce image dose by half due to the blocked radiation by the grid. The method almost completely removed scatter related artifacts, such as the cupping artifacts. The evaluation of line pair patterns in the CatPhan suggested that the spatial resolution degradation was minimal. Conclusion: The SMOG-IPSF is promising in reducing scatter artifacts and improving image quality while reducing radiation dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-207-09: Single-Scan Dual-Energy CT Using Primary Modulation

Purpose: Compared with conventional CT, dual energy CT (DECT) provides better material differentiation but requires projection data with two different effective x-ray spectra. Current DECT scanners use either a two-scan setting or costly imaging components, which are not feasible or available on open-gantry cone-beam CT systems. We propose a hardware-based method which utilizes primary modulation to enable single-scan DECT on a conventional CT scanner. The CT imaging geometry of primary modulation is identical to that used in our previous method for scatter removal, making it possible for future combination with effective scatter correction on the same CT scanner. Methods: We insert an attenuation sheet with a spatially-varying pattern - primary modulator-between the x-ray source and the imaged object. During the CT scan, the modulator selectively hardens the x-ray beam at specific detector locations. Thus, the proposed method simultaneously acquires high and low energy data. High and low energy CT images are then reconstructed from projections with missing data via an iterative CT reconstruction algorithm with gradient weighting. Proof-of-concept studies are performed using a copper modulator on a cone-beam CT system. Results: Our preliminary results on the Catphan(c) 600 phantom indicate that the proposed method for single-scan DECT is able to successfully generate high-quality high and low energy CT images and distinguish different materials through basis material decomposition. By applying correction algorithms and using all of the acquired projection data, we can reconstruct a single CT image of comparable image quality to conventional CT images, i.e., without primary modulation. Conclusion: This work shows great promise in using a primary modulator to perform high-quality single-scan DECT imaging. Future studies will test method performance on anthropomorphic phantoms and perform quantitative analyses on image qualities and DECT decomposition accuracy. We will use simulations to optimize the modulator material and geometry parameters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-G-207-03: Mask Guided Image Reconstruction (MGIR): A Novel Method for Ultra-Low-Dose 3D and Enhanced 4D Cone-Beam Computer-Tomography

Purpose: Recently, compressed sensing (CS) based iterative reconstruction (IR) method is receiving attentions to reconstruct high quality cone beam computed tomography (CBCT) images using sparsely sampled or noisy projections. The aim of this study is to develop a novel baseline algorithm called Mask Guided Image Reconstruction (MGIR), which can provide superior image quality for both low-dose 3DCBCT and 4DCBCT under single mathematical framework. Methods: In MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions where anatomical structures are 1) within the priori-defined mask and 2) outside the mask. Then we update each part of images alternatively thorough solving minimization problems based on CS type IR. For low-dose 3DCBCT, the former region is defined as the anatomically complex region where it is focused to preserve edge information while latter region is defined as contrast uniform, and hence aggressively updated to remove noise/artifact. In 4DCBCT, the regions are separated as the common static part and moving part. Then, static volume and moving volumes were updated with global and phase sorted projection respectively, to optimize the image quality of both moving and static part simultaneously. Results: Examination of MGIR algorithm showed that high quality of both low-dose 3DCBCT and 4DCBCT images can be reconstructed without compromising the image resolution and imaging dose or scanning time respectively. For low-dose 3DCBCT, a clinical viable and high resolution head-and-neck image can be obtained while cutting the dose by 83%. In 4DCBCT, excellent quality 4DCBCT images could be reconstructed while requiring no more projection data and imaging dose than a typical clinical 3DCBCT scan. Conclusion: The results shown that the image quality of MGIR was superior compared to other published CS based IR algorithms for both 4DCBCT and low-dose 3DCBCT. This makes our MGIR algorithm potentially useful in various on-line clinical applications. Provisional Patent: UF#15476; WGS Ref. No. U1198.70067US00.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification performance. The best segmentation Result does not necessarily lead to the best classification Result. This work has been supported in part by grants from the NIH R21-EB015053. R Nishikawa is receives royalties form Hologic, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT

Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. Methods: In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial–temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial–temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial–temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. Results: In numerical simulations, the 240{sup ∘} short scan angular span was divided into four consecutive 60{sup ∘} angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200{sup ∘}, three 66{sup ∘} angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. Conclusions: In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60{sup ∘} angular subsectors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Generation of hybrid sinograms for the recovery of kV-CT images with metal artifacts for helical tomotherapy

Purpose: The overall goal of this study is to restore kilovoltage computed tomography (kV-CT) images which are disfigured by patients’ metal prostheses. By generating a hybrid sinogram that is a combination of kV and megavoltage (MV) projection data, the authors suggest a novel metal artifact-reduction (MAR) method that retains the image quality to match that of kV-CT and simultaneously restores the information of metal prostheses lost due to photon starvation. Methods: CT projection data contain information about attenuation coefficients and the total length of the attenuation. By normalizing raw kV projections with their own total lengths of attenuation, mean attenuation projections were obtained. In the same manner, mean density projections of MV-CT were obtained by the normalization of MV projections resulting from the forward projection of density-calibrated MV-CT images with the geometric parameters of the kV-CT device. To generate the hybrid sinogram, metal-affected signals of the kV sinogram were identified and replaced by the corresponding signals of the MV sinogram following a density calibration step with kV data. Filtered backprojection was implemented to reconstruct the hybrid CT image. To validate the authors’ approach, they simulated four different scenarios for three heads and one pelvis using metallic rod inserts within a cylindrical phantom. Five inserts describing human body elements were also included in the phantom. The authors compared the image qualities among the kV, MV, and hybrid CT images by measuring the contrast-to-noise ratio (CNR), the signal-to-noise ratio (SNR), the densities of all inserts, and the spatial resolution. In addition, the MAR performance was compared among three existing MAR methods and the authors’ hybrid method. Finally, for clinical trials, the authors produced hybrid images of three patients having dental metal prostheses to compare their MAR performances with those of the kV, MV, and three existing MAR methods. Results: The authors compared the image quality and MAR performance of the hybrid method with those of other imaging modalities and the three MAR methods, respectively. The total measured mean of the CNR (SNR) values for the nonmetal inserts was determined to be 14.3 (35.3), 15.3 (37.8), and 25.5 (64.3) for the kV, MV, and hybrid images, respectively, and the spatial resolutions of the hybrid images were similar to those of the kV images. The measured densities of the metal and nonmetal inserts in the hybrid images were in good agreement with their true densities, except in cases of extremely low densities, such as air and lung. Using the hybrid method, major streak artifacts were suitably removed and no secondary artifacts were introduced in the resultant image. In clinical trials, the authors verified that kV and MV projections were successfully combined and turned into the resultant hybrid image with high image contrast, accurate metal information, and few metal artifacts. The hybrid method also outperformed the three existing MAR methods with regard to metal information restoration and secondary artifact prevention. Conclusions: The authors have shown that the hybrid method can restore the overall image quality of kV-CT disfigured by severe metal artifacts and restore the information of metal prostheses lost due to photon starvation. The hybrid images may allow for the improved delineation of structures of interest and accurate dose calculations for radiation treatment planning for patients with metal prostheses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Dosimetric verification of lung cancer treatment using the CBCTs estimated from limited-angle on-board projections

Purpose: Lung cancer treatment is susceptible to treatment errors caused by interfractional anatomical and respirational variations of the patient. On-board treatment dose verification is especially critical for the lung stereotactic body radiation therapy due to its high fractional dose. This study investigates the feasibility of using cone-beam (CB)CT images estimated by a motion modeling and free-form deformation (MM-FD) technique for on-board dose verification. Methods: Both digital and physical phantom studies were performed. Various interfractional variations featuring patient motion pattern change, tumor size change, and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the planning CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses), and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the “gold-standard” on-board images (gold-standard doses). The absolute deviations of minimum dose (ΔD{sub min}), maximum dose (ΔD{sub max}), and mean dose (ΔD{sub mean}), and the absolute deviations of prescription dose coverage (ΔV{sub 100%}) were evaluated for the planning target volume (PTV). In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MM-FD in the physical phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. Results: Compared with the planned doses and the FDK doses, the MM-FD doses matched much better with the gold-standard doses. For the digital phantom study, the average (± standard deviation) ΔD{sub min}, ΔD{sub max}, ΔD{sub mean}, and ΔV{sub 100%} (values normalized by the prescription dose or the total PTV) between the planned and the gold-standard PTV doses were 32.9% (±28.6%), 3.0% (±2.9%), 3.8% (±4.0%), and 15.4% (±12.4%), respectively. The corresponding values of FDK PTV doses were 1.6% (±1.9%), 1.2% (±0.6%), 2.2% (±0.8%), and 17.4% (±15.3%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.3% (±0.2%), 0.9% (±0.6%), 0.6% (±0.4%), and 1.0% (±0.8%), respectively. Similarly, for the physical phantom study, the average ΔD{sub min}, ΔD{sub max}, ΔD{sub mean}, and ΔV{sub 100%} of planned PTV doses were 38.1% (±30.8%), 3.5% (±5.1%), 3.0% (±2.6%), and 8.8% (±8.0%), respectively. The corresponding values of FDK PTV doses were 5.8% (±4.5%), 1.6% (±1.6%), 2.0% (±0.9%), and 9.3% (±10.5%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (±0.8%), 0.8% (±1.0%), 0.5% (±0.4%), and 0.8% (±0.8%), respectively. For the 4D dose accumulation study, the average (± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.3% (±2.7%). The average gamma index (3%/3 mm) between the accumulated doses and the radiochromic film measured doses was 94.5% (±2.5%). Conclusions: MM-FD estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A general framework of noise suppression in material decomposition for dual-energy CT

Purpose: As a general problem of dual-energy CT (DECT), noise amplification in material decomposition severely reduces the signal-to-noise ratio on the decomposed images compared to that on the original CT images. In this work, the authors propose a general framework of noise suppression in material decomposition for DECT. The method is based on an iterative algorithm recently developed in their group for image-domain decomposition of DECT, with an extension to include nonlinear decomposition models. The generalized framework of iterative DECT decomposition enables beam-hardening correction with simultaneous noise suppression, which improves the clinical benefits of DECT. Methods: The authors propose to suppress noise on the decomposed images of DECT using convex optimization, which is formulated in the form of least-squares estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance–covariance matrix of the decomposed images as the penalty weight in the least-squares term. Analytical formulas are derived to compute the variance–covariance matrix for decomposed images with general-form numerical or analytical decomposition. As a demonstration, the authors implement the proposed algorithm on phantom data using an empirical polynomial function of decomposition measured on a calibration scan. The polynomial coefficients are determined from the projection data acquired on a wedge phantom, and the signal decomposition is performed in the projection domain. Results: On the Catphan{sup ®}600 phantom, the proposed noise suppression method reduces the average noise standard deviation of basis material images by one to two orders of magnitude, with a superior performance on spatial resolution as shown in comparisons of line-pair images and modulation transfer function measurements. On the synthesized monoenergetic CT images, the noise standard deviation is reduced by a factor of 2–3. By using nonlinear decomposition on projections, the authors’ method effectively suppresses the streaking artifacts of beam hardening and obtains more uniform images than their previous approach based on a linear model. Similar performance of noise suppression is observed in the results of an anthropomorphic head phantom and a pediatric chest phantom generated by the proposed method. With beam-hardening correction enabled by their approach, the image spatial nonuniformity on the head phantom is reduced from around 10% on the original CT images to 4.9% on the synthesized monoenergetic CT image. On the pediatric chest phantom, their method suppresses image noise standard deviation by a factor of around 7.5, and compared with linear decomposition, it reduces the estimation error of electron densities from 33.3% to 8.6%. Conclusions: The authors propose a general framework of noise suppression in material decomposition for DECT. Phantom studies have shown the proposed method improves the image uniformity and the accuracy of electron density measurements by effective beam-hardening correction and reduces noise level without noticeable resolution loss.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Toward 2D and 3D imaging of magnetic nanoparticles using EPR measurements

Purpose: Magnetic nanoparticles (MNPs) are an important asset in many biomedical applications. An effective working of these applications requires an accurate knowledge of the spatial MNP distribution. A promising, noninvasive, and sensitive technique to visualize MNP distributions in vivo is electron paramagnetic resonance (EPR). Currently only 1D MNP distributions can be reconstructed. In this paper, the authors propose extending 1D EPR toward 2D and 3D using computer simulations to allow accurate imaging of MNP distributions. Methods: To find the MNP distribution belonging to EPR measurements, an inverse problem needs to be solved. The solution of this inverse problem highly depends on the stability of the inverse problem. The authors adapt 1D EPR imaging to realize the imaging of multidimensional MNP distributions. Furthermore, the authors introduce partial volume excitation in which only parts of the volume are imaged to increase stability of the inverse solution and to speed up the measurements. The authors simulate EPR measurements of different 2D and 3D MNP distributions and solve the inverse problem. The stability is evaluated by calculating the condition measure and by comparing the actual MNP distribution to the reconstructed MNP distribution. Based on these simulations, the authors define requirements for the EPR system to cope with the added dimensions. Moreover, the authors investigate how EPR measurements should be conducted to improve the stability of the associated inverse problem and to increase reconstruction quality. Results: The approach used in 1D EPR can only be employed for the reconstruction of small volumes in 2D and 3D EPRs due to numerical instability of the inverse solution. The authors performed EPR measurements of increasing cylindrical volumes and evaluated the condition measure. This showed that a reduction of the inherent symmetry in the EPR methodology is necessary. By reducing the symmetry of the EPR setup, quantitative images of larger volumes can be obtained. The authors found that, by selectively exciting parts of the volume, the authors could increase the reconstruction quality even further while reducing the amount of measurements. Additionally, the inverse solution of this activation method degrades slower for increasing volumes. Finally, the methodology was applied to noisy EPR measurements: using the reduced EPR setup’s symmetry and the partial activation method, an increase in reconstruction quality of ≈80% can be seen with a speedup of the measurements with 10%. Conclusions: Applying the aforementioned requirements to the EPR setup and stabilizing the EPR measurements showed a tremendous increase in noise robustness, thereby making EPR a valuable method for quantitative imaging of multidimensional MNP distributions.

60 APPLIED LIFE SCIENCES↗