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DOE OSTI2015

MO-FG-204-04: How Iterative Reconstruction Algorithms Affect the NPS of CT Images

Purpose: To evaluate how the third generation model based iterative reconstruction (MBIR) compares with filtered back-projection (FBP), adaptive statistical iterative reconstruction (ASiR), and the second generation MBIR based on noise power spectrum (NPS) analysis over a wide range of clinically applicable dose levels. Methods: The Catphan 600 CTP515 module, surrounded by an oval, fat-equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 19mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 0.984 pitch and reconstructed thickness 2.5mm (VEO3.0: Abd/Pelvis with Texture and NR05). At each CTDIvol level, 10 repeated scans were acquired for achieving sufficient data sampling. The images were reconstructed using Standard kernel with FBP; 20%, 40% and 70% ASiR; and two versions of MBIR (VEO2.0 and 3.0). For evaluating the effect of the ROI spatial location to the Result of NPS, 4 ROI groups were categorized based on their distances from the center of the phantom. Results: VEO3.0 performed inferiorly comparing to VEO2.0 over all dose levels. On the other hand, at low dose levels (less than 3 mGy), it clearly outperformed ASiR and FBP, in NPS values. Therefore, the lower the dose level, the relative performance of MBIR improves. However, the shapes of the NPS show substantial differences in horizontal and vertical sampling dimensions. These differences may determine the characteristics of the noise/texture features in images, and hence, play an important role in image interpretation. Conclusion: The third generation MBIR did not improve over the second generation MBIR in term of NPS analysis. The overall performance of both versions of MBIR improved as compared to other reconstruction algorithms when dose was reduced. The shapes of the NPS curves provided additional value for future characterization of the image noise/texture features.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-FG-204-06: A New Algorithm for Gold Nano-Particle Concentration Identification in Dual Energy CT

Purpose: Gold nano-particle (GNP) has recently attracted a lot of attentions due to its potential as an imaging contrast agent and radiotherapy sensitiser. Imaging the GNP at its low contraction is a challenging problem. We propose a new algorithm to improve the identification of GNP based on dual energy CT (DECT). Methods: We consider three base materials: water, bone, and gold. Determining three density images from two images in DECT is an under-determined problem. We propose to solve this problem by exploring image domain sparsity via an optimization approach. The objective function contains four terms. A data-fidelity term ensures the fidelity between the identified material densities and the DECT images, while the other three terms enforces the sparsity in the gradient domain of the three images corresponding to the density of the base materials by using total variation (TV) regularization. A primal-dual algorithm is applied to solve the proposed optimization problem. We have performed simulation studies to test this model. Results: Our digital phantom in the tests contains water, bone regions and gold inserts of different sizes and densities. The gold inserts contain mixed material consisting of water with 1g/cm3 and gold at a certain density. At a low gold density of 0.0008 g/cm3, the insert is hardly visible in DECT images, especially for those with small sizes. Our algorithm is able to decompose the DECT into three density images. Those gold inserts at a low density can be clearly visualized in the density image. Conclusion: We have developed a new algorithm to decompose DECT images into three different material density images, in particular, to retrieve density of gold. Numerical studies showed promising results.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-FG-204-09: High Spatial Resolution and Artifact-Free CT Bone Imaging at Off-Centered Positions: An Application of Model-Based Iterative Reconstruction

Purpose: Although the anatomy of interest should be positioned as close as possible to the isocenter of CT scanners, off-centering may be inevitable during certain exams in clinical practice such as lumbar spine and elbow imaging. Off-centering degrades image sharpness, generates streak artifacts, and sometimes creates blooming artifacts due to truncation. The purpose of this work was to investigate whether the use of model-based image reconstruction (MBIR) can alleviate the negative impacts of off-centering to achieve high quality CT bone imaging. Methods: Both an anthropomorphic phantom and an ex vivo swine elbow sample were scanned at centered and off-centered positions using clinical CT bone scan protocols. The magnitude of off-centering was determined from localizer radiographs. Both FBP and MBIR reconstructions were performed. For FBP, both standard and Bone Plus kernels commonly used in bone imaging were used. Objective assessment of image sharpness, noise standard deviation, and noise nonuniformity were performed. Additionally, we retrospectively analyzed human subject data acquired under off-centered conditions as a validation study. Results: In FBP images of the phantom, off-centering by 10 cm led to a 14% increase in noise (p<1e-3) and a 68% increase in noise nonuniformity (p<0.02). A visible drop in bone sharpness was observed. In contrast, no significant difference in the noise magnitude or the noise nonuniformity between the centered and off-centered MBIR images was found. The image sharpness of off-centered MBIR images outperformed that of FBP images reconstructed with the Bone Plus kernel. In images of the swine elbow off-centered by 20 cm, not only was the noise and spatial resolution performance improved by MBIR, truncation artifacts were also elliminated. The human subject study generated similar results, in which the visibility of the off-centered lumbar spine was significantly improved. Conclusion: High quality CT bone imaging at off-centered positions can be achieved using MBIR. This work was partially supported by an NIH grant R01CA169331 and GE Healthcare. K. Li, D. Gomez-Cardona: Nothing to disclose. G.-H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX. A. Budde, J. Hsieh: Employee, GE Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-F-CAMPUS-J-04: Tissue Segmentation-Based MR Electron Density Mapping Method for MR-Only Radiation Treatment Planning of Brain

Purpose: Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours. Methods: 20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution. Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated. Results: The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively. Conclusion: The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR-only treatment planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-F-CAMPUS-J-04: One-Year Analysis of Elekta CBCT Image Quality Using NPS and MTF

Purpose: To compare quantitative image quality (IQ) evaluation methods using Noise Power Spectrum (NPS) and Modulation Transfer Function (MTF) with standard IQ analyses for minimizing the observer subjectivity of the standard methods and maximizing the information content. Methods: For our routine IQ tests of Elekta XVI Cone-Beam CT, image noise was quantified by the standard deviation of CT number (CT#) (Sigma) over a small area in an IQ test phantom (CatPhan), and the high spatial resolution (HSR) was evaluated by the number of line-pairs (LP#) visually recognizable on the image. We also measured the image uniformity, the low contrast resolution ratio, and the distances of two points for geometrical accuracy. For this study, we did additional evaluation of the XVI data for 12 monthly IQ tests by using NPS for noise, MTF for HSR, and the CT#-to-density relationship. NPS was obtained by applying Fourier analysis in a small area on the uniformity test section of CatPhan. The MTF analysis was performed by applying the Droege-Morin (D-M) method to the line pairs on the phantom. The CT#-to-density was obtained for inserts in the low-contrast test section of the phantom. Results: All the quantities showed a noticeable change over the one-year period. Especially the noise level changed significantly after a repair of the imager. NPS was more sensitive to the IQ change than Sigma. MTF could provide more quantitative and objective evaluation of the HSR. The CT# was very different from the expected CT#; but, the CT#-to-density curves were constant within 5% except two months. Conclusion: Since the D-M method is easy to implement, we recommend using MTF instead of the LP# even for routine periodic QA. The month-to-month variation of IQ was not negligible; hence a routine IQ test must be performed, particularly after any modification of hardware including detector calibration.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.

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DOE OSTI2015

TU-AB-303-09: Investigation of Simple Method to Guide Adaptive Radiotherapy of Head-And-Neck Treatment Using Portal Imager

Purpose: Planned dose distribution for IMRT or VMAT could be altered by tissue changes during treatment course as in head and neck patients. Thus the aim of our study is to investigate a simple method that guides decision for re-planning. We will correlate changes in exit fluence tracked through entire treatment course with the anatomical changes. Methods: Fifteen patients were planned for IMRT; weekly CT scan was registered to original CT. Volume changes of different structures were assessed for each week. Frequently integrated images were acquired for all fields twice weekly via portal imager. The delivered fluences were compared with reference images. Gamma analysis (3% and 3 mm) including maximum γ, and percentage of points with γ≥ 1, were calculated for all fields. Results: Continuous reduction in GTV, CTV1 and parotids volumes during treatment was found [median value of 5.3 (1.3–20.8), 23.8 (3.0–128.7) and 1.7 (0.5–6.3) cm3 respectively] with significant change on week 2 (p= < 0.005). Also the value of gamma area >1 parameter continued to increase, significantly (P < 0.005), starting from second week (1.8 ± 1.9%) reaching 4.1 ± 1.6% by the end of treatment. Most of these changes in the exit fluences are significantly correlated to patient anatomy variations. Pearson correlation coefficient was −0.984 (P = 0.002) and −0.984 (P = 0.014) for the maximum gamma value and the value of gamma area >1, respectively. Conclusion: The changes in exit fluences are reflecting the corresponding anatomical changes. We proposed a method to track the changes in the averaged maximum gamma value and the gamma area > 1 value for the exit fluences during treatment. Maximum gamma value of 2.6% and Maximum gamma area > 1 value of 1.8% would be useful boarder lines that if exceeded a re-planning would be recommended.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. A total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.

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DOE OSTI2015

TU-CD-BRA-08: Single-Energy Computed Tomography-Based Pulmonary Perfusion Imaging: Proof-Of-Principle in a Canine Model

Purpose: Pulmonary perfusion imaging has provided significant insights into pulmonary diseases, and can be useful in radiotherapy. The purpose of this study was to prospectively establish proof-of-principle in a canine model for single-energy CT-based perfusion imaging, which has the potential for widespread clinical implementation. Methods: Single-energy CT perfusion imaging is based on: (1) acquisition of inspiratory breath-hold CT scans before and after intravenous injection of iodinated contrast medium, (2) deformable image registration (DIR) of the two CT image data sets, and (3) subtraction of the pre-contrast image from post-contrast image, yielding a map of Hounsfield unit (HU) enhancement. These subtraction image data sets hypothetically represent perfused blood volume, a surrogate for perfusion. In an IACUC-approved clinical trial, we acquired pre- and post-contrast CT scans in the prone posture for six anesthetized, mechanically-ventilated dogs. The elastix algorithm was used for DIR. The registration accuracy was quantified using the target registration errors (TREs) for 50 pulmonary landmarks in each dog. The gradient of HU enhancement between gravity-dependent (ventral) and non-dependent (dorsal) regions was evaluated to quantify the known effect of gravity, i.e., greater perfusion in ventral regions. Results: The lung volume difference between the two scans was 4.3±3.5% on average (range 0.3%–10.1%). DIR demonstrated an average TRE of 0.7±1.0 mm. HU enhancement in lung parenchyma was 34±10 HU on average and varied considerably between individual dogs, indicating the need for improvement of the contrast injection protocol. HU enhancement in ventral (gravity-dependent) regions was found to be greater than in dorsal regions. A population average ventral-to-dorsal gradient of HU enhancement was strong (R{sup 2}=0.94) and statistically significant (p<0.01). Conclusion: This canine study demonstrated relatively accurate DIR and a strong ventral-to-dorsal gradient of HU enhancement, providing proof-of-principle for single-energy CT pulmonary perfusion imaging. This ongoing study will enroll more dogs and investigate the physiological significance. This study was supported by a Philips Healthcare/Radiological Society of North America (RSNA) Research Seed Grant (RSD1458)

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DOE OSTI2015

TU-EF-204-04: Feasibility Study of a Novel Stationary Spectral CT Scanner

Purpose: To evaluate a cadmium zinc telluride (CZT) detector module and to develop a proof-of-concept bench-top system to examine the concept and feasibility of a stationary spectral scanner that would consist of a CZT detector ring and a distributed source for rotationless imaging. Methods: The bench-top system employs a double-rotation set-up with two concentric but independent rotating stages, one for turning the object at sparse angles to mimic the imaging from a sequentially activated source distributed on a ring, while the other for translating the detector module to simulate a projection arc along a virtual detector ring. A source of 30-degree fan is used in the set-up, and the ring is designed to be 85cm in diameter in order to provide a 22cm field-of-view that is large enough for an average human head. The CZT detector module has 16×16 0.5mm pixels and eight global energy thresholds, and is attached to an experimental data acquisition board from the same vendor (Nova R&D). Its pixel gain is first calibrated using radioisotope Tc-99m to align the photo-peaks. Count rate performance is assessed using the X-ray source. Air and offset corrections are done prior to each CT scan. A 10cm acrylic phantom with 5 different inserts is imaged at 100kVp, 2mA, with an effective exposure window of 150ms per projection. Reconstruction is done using a total variation-type iterative method. Results: The detector module exhibited large gain variations among pixels. Average count rate was over 1Mcps per pixel. Parallax effect was observed along the arcs but was corrected. Reconstructed images from two energy bins demonstrated good contrast, spatial resolution and image uniformity, with little artifacts. Conclusion: The CZT detector tested is suitable for imaging relatively small objects. A stationary spectral CT appears feasible should we have enough buttable modules and a distributed source. This work was supported by Ministry of Science and Technology of China through grants 11YQ040082 and 2012BAI13B04.

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DOE OSTI2015

TU-EF-204-07: Add Tube Current Modulation to a Low Dose Simulation Tool for CT Systems

Purpose: We extended the capabilities of a low dose simulation tool to model Tube-Current Modulation (TCM). TCM is widely used in clinical practice to reduce radiation dose in CT scans. We expect the tool to be valuable for various clinical applications (e.g., optimize protocols, compare reconstruction techniques and evaluate TCM methods). Methods: The tube current is input as a function of z location, instead of a fixed value. Starting from the line integrals of a scan, a new Poisson noise realization at a lower dose is generated for each view. To validate the new functionality, we compared simulated scans with real scans in image space. Results: First we assessed noise in the difference between the low-dose simulations and the original high-dose scan. When the simulated tube current is a step function of z location, the noise at each segment matches the noise of 3 separate constant-tube-current-simulations. Secondly, with a phantom that forces TCM, we compared a low-dose simulation with an equivalent real low-dose scan. The mean CT number of the simulated scan and the real low-dose scan were 137.7±0.6 and 137.8±0.5 respectively. Furthermore, with 240 ROIs, the noise of the simulated scan and the real low-dose scan were 24.03±0.45 and 23.99±0.43 respectively, and they were not statistically different (2-sample t-test, p-value=0.28). The facts that the noise reflected the trend of the TCM curve, and that the absolute noise measurements were not statistically different validated the TCM function. Conclusion: We successfully added tube-current modulation functionality in an existing low dose simulation tool. We demonstrated that the noise reflected an input tube-current modulation curve. In addition, we verified that the noise and mean CT number of our simulation agreed with a real low dose scan. The authors are all employees of Philips. Yijun Ding is also supported by NIBIB P41EB002035 and NIBIB R01EB000803.

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DOE OSTI2015

TU-EF-204-09: A Preliminary Method of Risk-Informed Optimization of Tube Current Modulation for Dose Reduction in CT

Purpose: X-rays from CT scans can increase cancer risk to patients. Lifetime Attributable Risk of Cancer Incidence for adult patients has been investigated and shown to decrease as patient age. However, a new risk model shows an increasing risk trend for several radiosensitive organs for middle age patients. This study investigates the feasibility of a general method for optimizing tube current modulation (TCM) functions to minimize risk by reducing radiation dose to radiosensitive organs of patients. Methods: Organ-based TCM has been investigated in literature for eye lens dose and breast dose. Adopting the concept in organ-based TCM, this study seeks to find an optimized tube current for minimal total risk to breasts and lungs by reducing dose to these organs. The contributions of each CT view to organ dose are determined through simulations of CT scan view-by-view using a GPU-based fast Monte Carlo code, ARCHER. A Linear Programming problem is established for tube current optimization, with Monte Carlo results as weighting factors at each view. A pre-determined dose is used as upper dose boundary, and tube current of each view is optimized to minimize the total risk. Results: An optimized tube current is found to minimize the total risk of lungs and breasts: compared to fixed current, the risk is reduced by 13%, with breast dose reduced by 38% and lung dose reduced by 7%. The average tube current is maintained during optimization to maintain image quality. In addition, dose to other organs in chest region is slightly affected, with relative change in dose smaller than 10%. Conclusion: Optimized tube current plans can be generated to minimize cancer risk to lungs and breasts while maintaining image quality. In the future, various risk models and greater number of projections per rotation will be simulated on phantoms of different gender and age. National Institutes of Health R01EB015478.

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DOE OSTI2015

TU-EF-204-10: A Preliminary Study Evaluating Calculation Variations of Local Noise Power Spectra for CT Simulation in Radiation Therapy

Purpose: Local noise power spectrum (NPS) properties are significantly affected by calculation variables and CT acquisition and reconstruction parameters, but a thoughtful analysis of these effects is absent. In this study, we performed a complete analysis of the effects of calculation and imaging parameters on the NPS. Methods: The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64-slice CT simulator using various scanning protocols. Images were reconstructed using both FBP and iDose4 reconstruction algorithms. From these images, local NPS were calculated for regions of interest (ROI) of varying locations and sizes, using four image background removal methods. Additionally, using a predetermined ground truth, NPS calculation accuracy for various calculation parameters was compared for computer simulated ROIs. A complete analysis of the effects of calculation, acquisition, and reconstruction parameters on the NPS was conducted. Results: The local NPS varied with ROI size and image background removal method, particularly at low spatial frequencies. The image subtraction method was the most accurate according to the computer simulation study, and was also the most effective at removing low frequency background components in the acquired data. However, first-order polynomial fitting using residual sum of squares and principle component analysis provided comparable accuracy under certain situations. Similar general trends were observed when comparing the NPS for FBP to that of iDose4 while varying other calculation and scanning parameters. However, while iDose4 reduces the noise magnitude compared to FBP, this reduction is spatial-frequency dependent, further affecting NPS variations at low spatial frequencies. Conclusion: The local NPS varies significantly depending on calculation parameters, image acquisition parameters, and reconstruction techniques. Appropriate local NPS calculation should be performed to capture spatial variations of noise; calculation methodology should be selected with consideration of image reconstruction effects and the desired purpose of CT simulation for radiotherapy tasks.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-EF-204-11: Impact of Using Multi-Slice Training Sets On the Performance of a Channelized Hotelling Observer in a Low-Contrast Detection Task in CT

Purpose: To investigate using multiple CT image slices from a single acquisition as independent training images for a channelized Hotelling observer (CHO) model to reduce the number of repeated scans for CHO-based CT image quality assessment. Methods: We applied a previously validated CHO model to detect low contrast disk objects formed from cross-sectional images of three epoxy-resin-based rods (diameters: 3, 5, and 9 mm; length: ∼5cm). The rods were submerged in a 35x 25 cm2 iodine-doped water filled phantom, yielding-15 HU object contrast. The phantom was scanned 100 times with and without the rods present. Scan and reconstruction parameters include: 5 mm slice thickness at 0.5 mm intervals, 120 kV, 480 Quality Reference mAs, and a 128-slice scanner. The CHO’s detectability index was evaluated as a function of factors related to incorporating multi-slice image data: object misalignment along the z-axis, inter-slice pixel correlation, and number of unique slice locations. In each case, the CHO training set was fixed to 100 images. Results: Artificially shifting the object’s center position by as much as 3 pixels in any direction relative to the Gabor channel filters had insignificant impact on object detectability. An inter-slice pixel correlation of >∼0.2 yielded positive bias in the model’s performance. Incorporating multi-slice image data yielded slight negative bias in detectability with increasing number of slices, likely due to physical variations in the objects. However, inclusion of image data from up to 5 slice locations yielded detectability indices within measurement error of the single slice value. Conclusion: For the investigated model and task, incorporating image data from 5 different slice locations of at least 5 mm intervals into the CHO model yielded detectability indices within measurement error of the single slice value. Consequently, this methodology would Result in a 5-fold reduction in number of image acquisitions. This project was supported by National Institutes of Health grants R01 EB017095 and U01 EB017185 from the National Institute of Biomedical Imaging and Bioengineering.

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DOE OSTI2015

TU-EF-204-12: Quantitative Evaluation of Spectral Detector CT Using Virtual Monochromatic Images: Initial Results

Purpose To evaluate the image quality and spectral information of a spectral detector CT (SDCT) scanner using virtual monochromatic (VM) energy images. Methods The SDCT scanner (Philips Healthcare) was equipped with a dual-layer detector and spectral iterative reconstruction (IR), which generates conventional 80–140 kV polychromatic energy (PE) CT images using both detector layers, PE images from the low-energy (upper) and high-energy (lower) detector layers and VM images. A solid water phantom with iodine (2.0–20.0 mg I/ml) and calcium (50.0–600.0 mg Ca/ml) rod inserts was used to evaluate effective energy estimate (EEE) and iodine contrast to noise ratio (CNR). The EEE corresponding to an insert CT number in a PE image was calculated from a CT number fit to the VM image set. Since PE image is prone to beam-hardening artifact EEE may underestimate the actual energy separation from two layers of the detector. A 30-cm-diameter water phantom was used to evaluate noise power spectrum (NPS). The phantoms were scanned at 120 and 140 kV with the same CTDIvol. Results The CT number difference for contrast inserts in VM images (50–150 keV) was 1.3±6% between 120 and 140 kV scans. The difference of EEE calculated from low- and high-energy detector images was 11.5 and 16.7 keV for 120 and 140 kV scans, respectively. The differences calculated from 140 and 100 kV conventional PE images were 12.8, and 20.1 keV from 140 and 80 kV conventional PE images. The iodine CNR increased monotonically with decreased keV. Compared to conventional PE images, the peak of NPS curves from VM images were shifted to lower frequency. Conclusion The EEE results indicates that SDCT at 120 and 140 kV may have energy separation comparable to 100/140 kV and 80/140 kV dual-kV imaging. The effects of IR on CNR and NPS require further investigation for SDCT. Author YY and AD are Philips Healthcare employees.

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DOE OSTI2015

TU-EF-207-01: Introductory Remarks on Recent Advances in Breast Imaging

Breast imaging technology is advancing on several fronts. In digital mammography, the major technological trend has been on optimization of approaches for performing combined mammography and tomosynthesis using the same system. In parallel, photon-counting slot-scan mammography is now in clinical use and more efforts are directed towards further development of this approach for spectral imaging. Spectral imaging refers to simultaneous acquisition of two or more energy-windowed images. Depending on the detector and associated electronics, there are a number of ways this can be accomplished. Spectral mammography using photon-counting detectors can suppress electronic noise and importantly, it enables decomposition of the image into various material compositions of interest facilitating quantitative imaging. Spectral imaging can be particularly important in intravenously injected contrast mammography and eventually tomosynthesis. The various approaches and applications of spectral mammography are discussed. Digital breast tomosynthesis relies on the mechanical movement of the x-ray tube to acquire a number of projections in a predefined arc, typically from 9 to 25 projections over a scan angle of +/−7.5 to 25 degrees depending on the particular system. The mechanical x-ray tube motion requires relatively long acquisition time, typically between 3.7 to 25 seconds depending on the system. Moreover, mechanical scanning may have an effect on the spatial resolution due to internal x-ray filament or external mechanical vibrations. New x-ray source arrays have been developed and they are aimed at replacing the scanned x-ray tube for improved acquisition time and potentially for higher spatial resolution. The potential advantages and challenges of this approach are described. Combination of digital mammography and tomosynthesis in a single system places increased demands on certain functional aspects of the detector and overall performance, particularly in the tomosynthesis mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-EF-207-02: Spectral Mammography Based on Photon Counting Detectors

60 APPLIED LIFE SCIENCES↗