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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Lesion insertion in the projection domain: Methods and initial results

Purpose: To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way of achieving this objective is to create hybrid images that combine patient images with inserted lesions. Because conventional hybrid images generated in the image domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Methods: Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion. Results: For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically acquired for the ACR phantom in terms of Hounsfield unit and high-contrast resolution. For the validation of the lesion realism, lesions of various types were successfully inserted, including well circumscribed and invasive lesions, homogeneous and heterogeneous lesions, high-contrast and low-contrast lesions, isolated and vessel-attached lesions, and small and large lesions. The two experienced radiologists who reviewed the original and inserted lesions could not identify the lesions that were inserted. The same lesion, when inserted into the projection domain and reconstructed with different parameters, demonstrated a parameter-dependent appearance. Conclusions: A framework has been developed for projection-domain insertion of lesions into commercial CT images, which can be potentially expanded to all geometries of CT scanners. Compared to conventional image-domain methods, the authors’ method reflected the impact of scan and reconstruction parameters on lesion appearance. Compared to prior projection-domain methods, the authors’ method has the potential to achieve higher anatomical complexity by employing clinical patient projections and real patient lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Purpose: A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data. Methods: The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts’ model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference. Results: The patients’ AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s. Conclusions: The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Quantitatively accurate activity measurements with a dedicated cardiac SPECT camera: Physical phantom experiments

Purpose: Recently, there has been increased interest in dedicated cardiac single photon emission computed tomography (SPECT) scanners with pinhole collimation and improved detector technology due to their improved count sensitivity and resolution over traditional parallel-hole cameras. With traditional cameras, energy-based approaches are often used in the clinic for scatter compensation because they are fast and easily implemented. Some of the cardiac cameras use cadmium-zinc-telluride (CZT) detectors which can complicate the use of energy-based scatter correction (SC) due to the low-energy tail—an increased number of unscattered photons detected with reduced energy. Modified energy-based scatter correction methods can be implemented, but their level of accuracy is unclear. In this study, the authors validated by physical phantom experiments the quantitative accuracy and reproducibility of easily implemented correction techniques applied to {sup 99m}Tc myocardial imaging with a CZT-detector-based gamma camera with multiple heads, each with a single-pinhole collimator. Methods: Activity in the cardiac compartment of an Anthropomorphic Torso phantom (Data Spectrum Corporation) was measured through 15 {sup 99m}Tc-SPECT acquisitions. The ratio of activity concentrations in organ compartments resembled a clinical {sup 99m}Tc-sestamibi scan and was kept consistent across all experiments (1.2:1 heart to liver and 1.5:1 heart to lung). Two background activity levels were considered: no activity (cold) and an activity concentration 1/10th of the heart (hot). A plastic “lesion” was placed inside of the septal wall of the myocardial insert to simulate the presence of a region without tracer uptake and contrast in this lesion was calculated for all images. The true net activity in each compartment was measured with a dose calibrator (CRC-25R, Capintec, Inc.). A 10 min SPECT image was acquired using a dedicated cardiac camera with CZT detectors (Discovery NM530c, GE Healthcare), followed by a CT scan for attenuation correction (AC). For each experiment, separate images were created including reconstruction with no corrections (NC), with AC, with attenuation and dual-energy window (DEW) scatter correction (ACSC), with attenuation and partial volume correction (PVC) applied (ACPVC), and with attenuation, scatter, and PVC applied (ACSCPVC). The DEW SC method used was modified to account for the presence of the low-energy tail. Results: T-tests showed that the mean error in absolute activity measurement was reduced significantly for AC and ACSC compared to NC for both (hot and cold) datasets (p < 0.001) and that ACSC, ACPVC, and ACSCPVC show significant reductions in mean differences compared to AC (p ≤ 0.001) without increasing the uncertainty (p > 0.4). The effect of SC and PVC was significant in reducing errors over AC in both datasets (p < 0.001 and p < 0.01, respectively), resulting in a mean error of 5% ± 4%. Conclusions: Quantitative measurements of cardiac {sup 99m}Tc activity are achievable using attenuation and scatter corrections, with the authors’ dedicated cardiac SPECT camera. Partial volume corrections offer improvements in measurement accuracy in AC images and ACSC images with elevated background activity; however, these improvements are not significant in ACSC images with low background activity.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penalty comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Limited-angle effect compensation for respiratory binned cardiac SPECT

Purpose: In cardiac single photon emission computed tomography (SPECT), respiratory-binned study is used to combat the motion blur associated with respiratory motion. However, owing to the variability in respiratory patterns during data acquisition, the acquired data counts can vary significantly both among respiratory bins and among projection angles within individual bins. If not properly accounted for, such variation could lead to artifacts similar to limited-angle effect in image reconstruction. In this work, the authors aim to investigate several reconstruction strategies for compensating the limited-angle effect in respiratory binned data for the purpose of reducing the image artifacts. Methods: The authors first consider a model based correction approach, in which the variation in acquisition time is directly incorporated into the imaging model, such that the data statistics are accurately described among both the projection angles and respiratory bins. Afterward, the authors consider an approximation approach, in which the acquired data are rescaled to accommodate the variation in acquisition time among different projection angles while the imaging model is kept unchanged. In addition, the authors also consider the use of a smoothing prior in reconstruction for suppressing the artifacts associated with limited-angle effect. In our evaluation study, the authors first used Monte Carlo simulated imaging with 4D NCAT phantom wherein the ground truth is known for quantitative comparison. The authors evaluated the accuracy of the reconstructed myocardium using a number of metrics, including regional and overall accuracy of the myocardium, uniformity and spatial resolution of the left ventricle (LV) wall, and detectability of perfusion defect using a channelized Hotelling observer. As a preliminary demonstration, the authors also tested the different approaches on five sets of clinical acquisitions. Results: The quantitative evaluation results show that the three compensation methods could all, but to different extents, reduce the reconstruction artifacts over no compensation. In particular, the model based approach reduced the mean-squared-error of the reconstructed myocardium by as much as 40%. Compared to the approach of data rescaling, the model based approach further improved both the overall and regional accuracy of the myocardium; it also further improved the lesion detectability and the uniformity of the LV wall. When ML reconstruction was used, the model based approach was notably more effective for improving the LV wall; when MAP reconstruction was used, the smoothing prior could reduce the noise level and artifacts with little or no increase in bias, but at the cost of a slight resolution loss of the LV wall. The improvements in image quality by the different compensation methods were also observed in the clinical acquisitions. Conclusions: Compensating for the uneven distribution of acquisition time among both projection angles and respiratory bins can effectively reduce the limited-angle artifacts in respiratory-binned cardiac SPECT reconstruction. Direct incorporation of the time variation into the imaging model together with a smoothing prior in reconstruction can lead to the most improvement in the accuracy of the reconstructed myocardium.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Estimating local noise power spectrum from a few FBP-reconstructed CT scans

Purpose: Traditional ways to estimate 2D CT noise power spectrum (NPS) involve an ensemble average of the power spectrums of many noisy scans. When only a few scans are available, regions of interest are often extracted from different locations to obtain sufficient samples to estimate the NPS. Using image samples from different locations ignores the nonstationarity of CT noise and thus cannot accurately characterize its local properties. The purpose of this work is to develop a method to estimate local NPS using only a few fan-beam CT scans. Methods: As a result of FBP reconstruction, the CT NPS has the same radial profile shape for all projection angles, with the magnitude varying with the noise level in the raw data measurement. This allows a 2D CT NPS to be factored into products of a 1D angular and a 1D radial function in polar coordinates. The polar separability of CT NPS greatly reduces the data requirement for estimating the NPS. The authors use this property and derive a radial NPS estimation method: in brief, the radial profile shape is estimated from a traditional NPS based on image samples extracted at multiple locations. The amplitudes are estimated by fitting the traditional local NPS to the estimated radial profile shape. The estimated radial profile shape and amplitudes are then combined to form a final estimate of the local NPS. We evaluate the accuracy of the radial NPS method and compared it to traditional NPS methods in terms of normalized mean squared error (NMSE) and signal detectability index. Results: For both simulated and real CT data sets, the local NPS estimated with no more than six scans using the radial NPS method was very close to the reference NPS, according to the metrics of NMSE and detectability index. Even with only two scans, the radial NPS method was able to achieve a fairly good accuracy. Compared to those estimated using traditional NPS methods, the accuracy improvement was substantial when a few scans were available. Conclusions: The radial NPS method was shown to be accurate and efficient in estimating the local NPS of FBP-reconstructed 2D CT images. It presents strong advantages over traditional NPS methods when the number of scans is limited and can be extended to estimate the in-plane NPS of cone-beam CT and multislice helical CT scans.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Comparison of proton therapy treatment planning for head tumors with a pencil beam algorithm on dual and single energy CT images

Purpose: Dual energy CT (DECT) has recently been proposed as an improvement over single energy CT (SECT) for stopping power ratio (SPR) estimation for proton therapy treatment planning (TP), thereby potentially reducing range uncertainties. Published literature investigated phantoms. This study aims at performing proton therapy TP on SECT and DECT head images of the same patients and at evaluating whether the reported improved DECT SPR accuracy translates into clinically relevant range shifts in clinical head treatment scenarios. Methods: Two phantoms were scanned at a last generation dual source DECT scanner at 90 and 150 kVp with Sn filtration. The first phantom (Gammex phantom) was used to calibrate the scanner in terms of SPR while the second served as evaluation (CIRS phantom). DECT images of five head trauma patients were used as surrogate cancer patient images for TP of proton therapy. Pencil beam algorithm based TP was performed on SECT and DECT images and the dose distributions corresponding to the optimized proton plans were calculated using a Monte Carlo (MC) simulation platform using the same patient geometry for both plans obtained from conversion of the 150 kVp images. Range shifts between the MC dose distributions from SECT and DECT plans were assessed using 2D range maps. Results: SPR root mean square errors (RMSEs) for the inserts of the Gammex phantom were 1.9%, 1.8%, and 1.2% for SECT phantom calibration (SECT{sub phantom}), SECT stoichiometric calibration (SECT{sub stoichiometric}), and DECT calibration, respectively. For the CIRS phantom, these were 3.6%, 1.6%, and 1.0%. When investigating patient anatomy, group median range differences of up to −1.4% were observed for head cases when comparing SECT{sub stoichiometric} with DECT. For this calibration the 25th and 75th percentiles varied from −2% to 0% across the five patients. The group median was found to be limited to 0.5% when using SECT{sub phantom} and the 25th and 75th percentiles varied from −1% to 2%. Conclusions: Proton therapy TP using a pencil beam algorithm and DECT images was performed for the first time. Given that the DECT accuracy as evaluated by two phantoms was 1.2% and 1.0% RMSE, it is questionable whether the range differences reported here are significant.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Initial clinical evaluation of PET-based ion beam therapy monitoring under consideration of organ motion

Purpose: Intrafractional organ motion imposes considerable challenges to scanned ion beam therapy and demands for a thorough verification of the applied treatment. At the Heidelberg Ion-Beam Therapy Center (HIT), the scanned ion beam delivery is verified by means of postirradiation positron-emission-tomography (PET) imaging. This work presents a first clinical evaluation of PET-based treatment monitoring in ion beam therapy under consideration of target motion. Methods: Three patients with mobile liver lesions underwent scanned carbon ion irradiation at HIT and postirradiation PET/CT (x-ray-computed-tomography) imaging with a commercial scanner. Respiratory motion was recorded during irradiation and subsequent image acquisition. This enabled a time-resolved (4D) calculation of the expected irradiation-induced activity pattern and, for one patient where an additional 4D CT was acquired at the PET/CT scanner after treatment, a motion-compensated PET image reconstruction. For the other patients, PET data were reconstructed statically. To verify the treatment, calculated prediction and reconstructed measurement were compared with a focus on the ion beam range. Results: Results in the current three patients suggest that for motion amplitudes in the order of 2 mm there is no benefit from incorporating respiratory motion information into PET-based treatment monitoring. For a target motion in the order of 10 mm, motion-related effects become more severe and a time-resolved modeling of the expected activity distribution can lead to an improved data interpretation if a sufficient number of true coincidences is detected. Benefits from motion-compensated PET image reconstruction could not be shown conclusively at the current stage. Conclusions: The feasibility of clinical PET-based treatment verification under consideration of organ motion has been shown for the first time. Improvements in noise-robust 4D PET image reconstruction are deemed necessary to enhance the clinical potential.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Effect of reconstruction methods and x-ray tube current–time product on nodule detection in an anthropomorphic thorax phantom: A crossed-modality JAFROC observer study

Purpose: To evaluate nodule detection in an anthropomorphic chest phantom in computed tomography (CT) images reconstructed with adaptive iterative dose reduction 3D (AIDR{sup 3D}) and filtered back projection (FBP) over a range of tube current–time product (mAs). Methods: Two phantoms were used in this study: (i) an anthropomorphic chest phantom was loaded with spherical simulated nodules of 5, 8, 10, and 12 mm in diameter and +100, −630, and −800 Hounsfield units electron density; this would generate CT images for the observer study; (ii) a whole-body dosimetry verification phantom was used to ultimately estimate effective dose and risk according to the model of the BEIR VII committee. Both phantoms were scanned over a mAs range (10, 20, 30, and 40), while all other acquisition parameters remained constant. Images were reconstructed with both AIDR{sup 3D} and FBP. For the observer study, 34 normal cases (no nodules) and 34 abnormal cases (containing 1–3 nodules, mean 1.35 ± 0.54) were chosen. Eleven observers evaluated images from all mAs and reconstruction methods under the free-response paradigm. A crossed-modality jackknife alternative free-response operating characteristic (JAFROC) analysis method was developed for data analysis, averaging data over the two factors influencing nodule detection in this study: mAs and image reconstruction (AIDR{sup 3D} or FBP). A Bonferroni correction was applied and the threshold for declaring significance was set at 0.025 to maintain the overall probability of Type I error at α = 0.05. Contrast-to-noise (CNR) was also measured for all nodules and evaluated by a linear least squares analysis. Results: For random-reader fixed-case crossed-modality JAFROC analysis, there was no significant difference in nodule detection between AIDR{sup 3D} and FBP when data were averaged over mAs [F(1, 10) = 0.08, p = 0.789]. However, when data were averaged over reconstruction methods, a significant difference was seen between multiple pairs of mAs settings [F(3, 30) = 15.96, p < 0.001]. Measurements of effective dose and effective risk showed the expected linear dependence on mAs. Nodule CNR was statistically higher for simulated nodules on images reconstructed with AIDR{sup 3D} (p < 0.001). Conclusions: No significant difference in nodule detection performance was demonstrated between images reconstructed with FBP and AIDR{sup 3D}. mAs was found to influence nodule detection, though further work is required for dose optimization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Patient motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging

Purpose: Patient motion is a common problem during dynamic positron emission tomography (PET) scans for quantification of myocardial blood flow (MBF). The purpose of this study was to quantify the prevalence of body motion in a clinical setting and evaluate with realistic phantoms the effects of motion on blood flow quantification, including CT attenuation correction (CTAC) artifacts that result from PET–CT misalignment. Methods: A cohort of 236 sequential patients was analyzed for patient motion under resting and peak stress conditions by two independent observers. The presence of motion, affected time-frames, and direction of motion was recorded; discrepancy between observers was resolved by consensus review. Based on these results, patient body motion effects on MBF quantification were characterized using the digital NURBS-based cardiac-torso phantom, with characteristic time activity curves (TACs) assigned to the heart wall (myocardium) and blood regions. Simulated projection data were corrected for attenuation and reconstructed using filtered back-projection. All simulations were performed without noise added, and a single CT image was used for attenuation correction and aligned to the early- or late-frame PET images. Results: In the patient cohort, mild motion of 0.5 ± 0.1 cm occurred in 24% and moderate motion of 1.0 ± 0.3 cm occurred in 38% of patients. Motion in the superior/inferior direction accounted for 45% of all detected motion, with 30% in the superior direction. Anterior/posterior motion was predominant (29%) in the posterior direction. Left/right motion occurred in 24% of cases, with similar proportions in the left and right directions. Computer simulation studies indicated that errors in MBF can approach 500% for scans with severe patient motion (up to 2 cm). The largest errors occurred when the heart wall was shifted left toward the adjacent lung region, resulting in a severe undercorrection for attenuation of the heart wall. Simulations also indicated that the magnitude of MBF errors resulting from motion in the superior/inferior and anterior/posterior directions was similar (up to 250%). Body motion effects were more detrimental for higher resolution PET imaging (2 vs 10 mm full-width at half-maximum), and for motion occurring during the mid-to-late time-frames. Motion correction of the reconstructed dynamic image series resulted in significant reduction in MBF errors, but did not account for the residual PET–CTAC misalignment artifacts. MBF bias was reduced further using global partial-volume correction, and using dynamic alignment of the PET projection data to the CT scan for accurate attenuation correction during image reconstruction. Conclusions: Patient body motion can produce MBF estimation errors up to 500%. To reduce these errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time-frames, since these conditions produce the largest errors in MBF, particularly for high resolution PET imaging. Ideally, motion correction should be done before or during image reconstruction to eliminate PET-CTAC misalignment artifacts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship

Purpose: Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed. Methods: Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context. Results: Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (−18 ± 92 mg/cm{sup 3}) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm{sup 3}), and 0.10 ± 0.24 mm (−10 ± 115 mg/cm{sup 3}) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm{sup 3}). A trend for the cortical thickness and density estimation errors to increase with voxel size was observed and was more pronounced for thin cortices. Using clinical CT data for 19 of the 23 samples, mean errors of 0.18 ± 0.24 mm for the cortical thickness and 15 ± 106 mg/cm{sup 3} for the density were found. The case-control study showed that osteoporotic patients had a thinner cortex and a lower cortical density, with average differences of −0.8 mm and −58.6 mg/cm{sup 3} at the proximal femur in comparison with age-matched controls (p-value < 0.001). Conclusions: This method might be a promising approach for the quantification of cortical bone thickness and density using clinical routine imaging techniques. Future work will concentrate on investigating how this approach can improve the estimation of mechanical strength of bony structures, the prevention of fracture, and the management of osteoporosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

3D microwave tomography of the breast using prior anatomical information

Purpose: The authors have developed a new 3D breast image reconstruction technique that utilizes the soft tissue spatial resolution of magnetic resonance imaging (MRI) and integrates the dielectric property differentiation from microwave imaging to produce a dual modality approach with the goal of augmenting the specificity of MR imaging, possibly without the need for nonspecific contrast agents. The integration is performed through the application of a soft prior regularization which imports segmented geometric meshes generated from MR exams and uses it to constrain the microwave tomography algorithm to recover nearly uniform property distributions within segmented regions with sharp delineation between these internal subzones. Methods: Previous investigations have demonstrated that this approach is effective in 2D simulation and phantom experiments and also in clinical exams. The current study extends the algorithm to 3D and provides a thorough analysis of the sensitivity and robustness to misalignment errors in size and location between the spatial prior information and the actual data. Results: Image results in 3D were not strongly dependent on reconstruction mesh density, and the changes of less than 30% in recovered property values arose from variations of more than 125% in target region size—an outcome which was more robust than in 2D. Similarly, changes of less than 13% occurred in the 3D image results from variations in target location of nearly 90% of the inclusion size. Permittivity and conductivity errors were about 5 times and 2 times smaller, respectively, with the 3D spatial prior algorithm in actual phantom experiments than those which occurred without priors. Conclusions: The presented study confirms that the incorporation of structural information in the form of a soft constraint can considerably improve the accuracy of the property estimates in predefined regions of interest. These findings are encouraging and establish a strong foundation for using the soft prior technique in clinical studies, where their microwave imaging system and MRI can simultaneously collect breast exam data in patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

The rotate-plus-shift C-arm trajectory. Part I. Complete data with less than 180° rotation

Purpose: In the last decade, C-arm-based cone-beam CT became a widely used modality for intraoperative imaging. Typically a C-arm CT scan is performed using a circular or elliptical 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. However, mobile C-arms designed with a focus on classical 2D applications like fluoroscopy may be limited to a mechanical rotation range of less than 180° to improve handling and usability. The method proposed in this paper allows for the acquisition of a fully sampled data set with a system limited to a mechanical rotation range of at least 180° minus fan angle using a new trajectory design. This enables CT like 3D imaging with a wide range of C-arm devices which are mainly designed for 2D imaging. Methods: The proposed trajectory extends the mechanical rotation range of the C-arm system with two additional linear shifts. Due to the divergent character of the fan-beam geometry, these two shifts lead to an additional angular range of half of the fan angle. Combining one shift at the beginning of the scan followed by a rotation and a second shift, the resulting rotate-plus-shift trajectory enables the acquisition of a completely sampled data set using only 180° minus fan angle of rotation. The shifts can be performed using, e.g., the two orthogonal positioning axes of a fully motorized C-arm system. The trajectory was evaluated in phantom and cadaver examinations using two prototype C-arm systems. Results: The proposed trajectory leads to reconstructions without limited angle artifacts. Compared to the limited angle reconstructions of 180° minus fan angle, image quality increased dramatically. Details in the rotate-plus-shift reconstructions were clearly depicted, whereas they are dominated by artifacts in the limited angle scan. Conclusions: The method proposed here employs 3D imaging using C-arms with less than 180° rotation range adding full 3D functionality to a C-arm device retaining both handling comfort and the usability of 2D imaging. This method has a clear potential for clinical use especially to meet the increasing demand for an intraoperative 3D imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

3D-SIFT-Flow for atlas-based CT liver image segmentation

Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Denoising of polychromatic CT images based on their own noise properties

Purpose: Because of high diagnostic accuracy and fast scan time, computed tomography (CT) has been widely used in various clinical applications. Since the CT scan introduces radiation exposure to patients, however, dose reduction has recently been recognized as an important issue in CT imaging. However, low-dose CT causes an increase of noise in the image and thereby deteriorates the accuracy of diagnosis. In this paper, the authors develop an efficient denoising algorithm for low-dose CT images obtained using a polychromatic x-ray source. The algorithm is based on two steps: (i) estimation of space variant noise statistics, which are uniquely determined according to the system geometry and scanned object, and (ii) subsequent novel conversion of the estimated noise to Gaussian noise so that an existing high performance Gaussian noise filtering algorithm can be directly applied to CT images with non-Gaussian noise. Methods: For efficient polychromatic CT image denoising, the authors first reconstruct an image with the iterative maximum-likelihood polychromatic algorithm for CT to alleviate the beam-hardening problem. We then estimate the space-variant noise variance distribution on the image domain. Since there are many high performance denoising algorithms available for the Gaussian noise, image denoising can become much more efficient if they can be used. Hence, the authors propose a novel conversion scheme to transform the estimated space-variant noise to near Gaussian noise. In the suggested scheme, the authors first convert the image so that its mean and variance can have a linear relationship, and then produce a Gaussian image via variance stabilizing transform. The authors then apply a block matching 4D algorithm that is optimized for noise reduction of the Gaussian image, and reconvert the result to obtain a final denoised image. To examine the performance of the proposed method, an XCAT phantom simulation and a physical phantom experiment were conducted. Results: Both simulation and experimental results show that, unlike the existing denoising algorithms, the proposed algorithm can effectively reduce the noise over the whole region of CT images while preventing degradation of image resolution. Conclusions: To effectively denoise polychromatic low-dose CT images, a novel denoising algorithm is proposed. Because this algorithm is based on the noise statistics of a reconstructed polychromatic CT image, the spatially varying noise on the image is effectively reduced so that the denoised image will have homogeneous quality over the image domain. Through a simulation and a real experiment, it is verified that the proposed algorithm can deliver considerably better performance compared to the existing denoising algorithms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Automatic coronary calcium scoring using noncontrast and contrast CT images

Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground truth. Conclusions: The calcified lesions in the noncontrast CT images can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrast CT images with a very high accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Image segmentation with a novel regularized composite shape prior based on surrogate study

Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Filtered-backprojection reconstruction for a cone-beam computed tomography scanner with independent source and detector rotations

Purpose: A new cone-beam CT scanner for image-guided radiotherapy (IGRT) can independently rotate the source and the detector along circular trajectories. Existing reconstruction algorithms are not suitable for this scanning geometry. The authors propose and evaluate a three-dimensional (3D) filtered-backprojection reconstruction for this situation. Methods: The source and the detector trajectories are tuned to image a field-of-view (FOV) that is offset with respect to the center-of-rotation. The new reconstruction formula is derived from the Feldkamp algorithm and results in a similar three-step algorithm: projection weighting, ramp filtering, and weighted backprojection. Simulations of a Shepp Logan digital phantom were used to evaluate the new algorithm with a 10 cm-offset FOV. A real cone-beam CT image with an 8.5 cm-offset FOV was also obtained from projections of an anthropomorphic head phantom. Results: The quality of the cone-beam CT images reconstructed using the new algorithm was similar to those using the Feldkamp algorithm which is used in conventional cone-beam CT. The real image of the head phantom exhibited comparable image quality to that of existing systems. Conclusions: The authors have proposed a 3D filtered-backprojection reconstruction for scanners with independent source and detector rotations that is practical and effective. This algorithm forms the basis for exploiting the scanner’s unique capabilities in IGRT protocols.

60 APPLIED LIFE SCIENCES↗