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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.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction

Purpose: Penalized-weighted least-square reconstruction has become an important research topic in CT, to reduce dose without affecting image quality. Two components impact image quality in this reconstruction: the statistical weights and the use of an edge-preserving penalty term. We are interested in assessing the influence of statistical weights on their own, without the edge-preserving feature. Methods: The influence of statistical weights on image quality was assessed in terms of low-contrast detail detection using LROC analysis. The task amounted to detect and localize a 6-mm lesion with random contrast inside the FORBILD head phantom. A two-alternative forced-choice experiment was used with two human observers performing the task. Reconstructions without and with statistical weights were compared, both using the same quadratic penalty term. The beam energy was set to 30keV to amplify spatial differences in attenuation and thereby the role of statistical weights. A fan-beam data acquisition geometry was used. Results: Visual inspection of images clearly showed a difference in noise between the two reconstructions methods. As expected, the reconstruction without statistical weights exhibited noise streaks. The other reconstruction appeared better in this aspect, but presented other disturbing noise patterns and artifacts induced by the weights. The LROC analysis yield the following 95-percent confidence interval for the difference in reader-averaged AUC (reconstruction without weights minus reconstruction with weights): [0.0026,0.0599]. The mean AUC value was 0.9094. Conclusion: We have investigated the impact of statistical weights without the use of edge-preserving penalty in penalized weighted least-square reconstruction. A decrease rather than increase in image quality was observed when using statistical weights. Thus, the observers were better able to cope with the noise streaks than the noise patterns and artifacts induced by the statistical weights. It may be that different results would be obtained if the penalty term was used with a pixel-dependent weight. F Noo receives research support from Siemens Healthcare GmbH.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2014

Characterization of a commercial hybrid iterative and model-based reconstruction algorithm in radiation oncology

Purpose: Iterative reconstruction (IR) reduces noise, thereby allowing dose reduction in computed tomography (CT) while maintaining comparable image quality to filtered back-projection (FBP). This study sought to characterize image quality metrics, delineation, dosimetric assessment, and other aspects necessary to integrate IR into treatment planning. Methods: CT images (Brilliance Big Bore v3.6, Philips Healthcare) were acquired of several phantoms using 120 kVp and 25–800 mAs. IR was applied at levels corresponding to noise reduction of 0.89–0.55 with respect to FBP. Noise power spectrum (NPS) analysis was used to characterize noise magnitude and texture. CT to electron density (CT-ED) curves were generated over all IR levels. Uniformity as well as spatial and low contrast resolution were quantified using a CATPHAN phantom. Task specific modulation transfer functions (MTF{sub task}) were developed to characterize spatial frequency across objects of varied contrast. A prospective dose reduction study was conducted for 14 patients undergoing interfraction CT scans for high-dose rate brachytherapy. Three physicians performed image quality assessment using a six-point grading scale between the normal-dose FBP (reference), low-dose FBP, and low-dose IR scans for the following metrics: image noise, detectability of the vaginal cuff/bladder interface, spatial resolution, texture, segmentation confidence, and overall image quality. Contouring differences between FBP and IR were quantified for the bladder and rectum via overlap indices (OI) and Dice similarity coefficients (DSC). Line profile and region of interest analyses quantified noise and boundary changes. For two subjects, the impact of IR on external beam dose calculation was assessed via gamma analysis and changes in digitally reconstructed radiographs (DRRs) were quantified. Results: NPS showed large reduction in noise magnitude (50%), and a slight spatial frequency shift (∼0.1 mm{sup −1}) with application of IR at L6. No appreciable changes were observed for CT-ED curves between FBP and IR levels [maximum difference ∼13 HU for bone (∼1% difference)]. For uniformity, differences were ∼1 HU between FBP and IR. Spatial resolution was well conserved; the largest MTF{sub task} decrease between FBP and IR levels was 0.08 A.U. No notable changes in low-contrast detectability were observed and CNR increased substantially with IR. For the patient study, qualitative image grading showed low-dose IR was equivalent to or slightly worse than normal dose FBP, and is superior to low-dose FBP (p < 0.001 for noise), although these did not translate to differences in CT number, contouring ability, or dose calculation. The largest CT number discrepancy from FBP occurred at a bone/tissue interface using the most aggressive IR level [−1.2 ± 4.9 HU (range: −17.6–12.5 HU)]. No clinically significant contour differences were found between IR and FBP, with OIs and DSCs ranging from 0.85 to 0.95. Negligible changes in dose calculation were observed. DRRs preserved anatomical detail with <2% difference in intensity from FBP combined with aggressive IRL6. Conclusions: These results support integrating IR into treatment planning. While slight degradation in edges and shift in texture were observed in phantom, patient results show qualitative image grading, contouring ability, and dosimetric parameters were not adversely affected.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-FG-207B-11: Objective Image Characterization of Spectral CT with a Dual-Layer Detector

Purpose: To obtain objective reference data for the spectral performance on a dual-layer detector CT platform (IQon, Philips) and compare virtual monoenergetic to conventional CT images. Methods: Scanning was performed using the hospital’s clinical adult body protocol: helical acquisition at 120kVp, with CTDIvol=15mGy. Multiple modules (591, 515, 528) of a CATPHAN 600 phantom and a 20 cm diameter cylindrical water phantom were scanned. No modifications to the standard protocol were necessary to enable spectral imaging. Both conventional and virtual monoenergetic images were generated from acquired data. Noise characteristics were assessed through Noise Power Spectra (NPS) and pixel standard deviation from water phantom images. Spatial resolution was evaluated using Modulation Transfer Functions (MTF) of a tungsten wire as well as resolution bars. Low-contrast detectability was studied using contrast-to-noise ratio (CNR) of a low contrast object. Results: MTF curves of monoenergetic and conventional images were almost identical. MTF 50%, 10%, and 5% levels for monoenergetic images agreed with conventional images within 0.05lp/cm. These observations were verified by the resolution bars, which were clearly resolved at 7lp/cm but started blurring at 8lp/cm for this protocol in both conventional and 70 keV images. NPS curves indicated that, compared to conventional images, the noise power distribution of 70 keV monoenergetic images is similar (i.e. noise texture is similar) but exhibit a low frequency peak at keVs higher and lower than 70 keV. Standard deviation measurements show monoenergetic images have lower noise except at 40 keV where it is slightly higher. CNR of monoenergetic images is mostly flat across keV values and is superior to that of conventional images. Conclusion: Values for standard image quality metrics are the same or better for monoenergetic images compared to conventional images. Results indicate virtual monoenergetic images can be used without any loss in image quality or noise penalties relative to conventional images. This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-207B-12: Quantification of Clinical Feedback On Image Quality Differences Between Two CT Scanner Models

Purpose: This work sought to quantify a radiology team’s assessment of image quality differences between two CT scanner models currently in clinical use, with emphasis on noise and low-contrast detectability (LCD). Methods: A water phantom and a Kagaku anthropomorphic body phantom were scanned on GE Discovery CT750 HD and LightSpeed VCT scanners (4 each) with identical scan parameters and reconstructed to 2.5mm/5.0mm thicknesses. Images of water phantom were analyzed at the scanner console with a built-in LCD tool that uses statistical methods to compute requisite CT-number contrast for 95% confidence in detection of a user-defined object size. LCD value was computed for 5mm, 3mm, and 1mm objects. Analysis of standard deviation and LCD values were performed on Kagaku phantom images within liver, stomach, and spleen. LCD value was computed for 4mm, 3mm, and 1mm objects using a benchmarked MATLAB implementation of the GE scanner-console tool. Results: Water LCD values were larger (poorer performance) for all HD scanners compared to VCT scanners. Mean scanner model difference in requisite CT-number contrast for 5mm, 3mm, and 1mm objects for 5.0mm/2.5mm images was 3.0%/3.4% (p=0.02/p=0.10), 5.3%/5.7% (0.00002/0.02), and 8.5%/8.2% (0.0004/0.002), respectively. Mean standard deviations within Kagaku phantom ROIs were greater in HD compared to VCT images, with mean differences for the liver, stomach, and spleen for 5.0mm/2.5mm of 16%/12% (p=0.04/0.10), 8%/12% (0.15/0.11), and 16%/15% (0.05/0.11), respectively. Mean LCD value difference between HD and VCT scanners over all ROIs for 4mm, 3m, and 1mm objects and 5.0mm/2.5mm was 34%/9%, 16%/8%, and 18%/10%, respectively. HD scanners outperformed VCT scanners only for the 4mm stomach object. Conclusion: Using both water and anthropomorphic phantoms, it was shown that HD scanners are outperformed by VCT scanners with respect to noise and LCD in a consistent and in most cases statistically significant manner. The relationship between statistical and clinical significance demands further work.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-AB-207A-09: Tailoring TCM Schemes to a Task: Evaluating the Impact of Customized TCM Profiles On Detection of Lung Nodules in Simulated CT Lung Cancer Screening

Purpose: Recent work has shown that current TCM profile designs boost detection of low-contrast lung lesions in the lung apices, but yield reduced detection performance in the mid and lower lung regions relative to fixed tube current cases. This observed imbalance suggests that the TCM scheme might be tailored in new ways to maximize nodule detection throughout the entire lung. In this work, we begin a preliminary investigation into custom TCM profiles in an attempt to achieve uniform lesion detection throughout the extent of the lung. Methods: Low-contrast (25HU), 6mm nodules representing ground glass opacities were simulated at 1mm intervals over the length the lungs in a voxelized model of the XCAT phantom, one nodule per lung, per simulated scan. Voxel values represented attenuation values at 80keV. CT projection data was created by simulating a finite focal spot and using Joseph’s method for forward projection; scanner geometry was that of the Siemens Sensation 64 and the X-ray source was simulated as an 80keV monochromatic beam. Noise realizations were created using Poisson statistics, a realistic bowtie filter and varying tube current. 500 noise realizations were created for the custom TCM designs. All reconstruction was done with FreeCT-wFBP. An SKE/BKE task was used in conjunction with a 2D Hotelling Observer to calculate area-under-the-curve (AUC) as a proxy for “detectability.” AUC was plotted as a function of nodule Z-location to create a “detectability map.” The detectability map for the custom TCM curve was qualitatively assessed relative to previous results for the fixed TC and clinical TCM cases for uniformity. Results: Detection uniformity was improved throughout the mid and lower lungs, however detection remained disproportionately high in the upper lung region. Conclusion: Detection uniformity was improved with a custom TC profile. Future work will incorporate an analytic, task-specific approach to optimize the TC scheme for nodule detection. J. Hoffman: Part-time intern, Toshiba Medical Research Institute; M. McNitt-Gray: Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; F. Noo: Institutional research agreement, Siemens Healthcare; Receives research funding from Siemens Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-143: Initial Assessment of Image Quality of An Integrated MR-Linac System with ACR Phantom

Purpose/Objective(s): To assess the image quality of an integrated MR-Linac system and compare with other MRI systems that are primarily used for diagnostic purposes. Materials/Methods: An ACR MRI quality control (QC) phantom was used to evaluate the image quality of a fully integrated 1.5T MRI-Linac system recently installed at our institution. This system has a new split magnet design which gives the magnetic field strength of 1.5T. All images were acquired with a set of phased-array surface coils which are designed to have minimal attention of radiation beam. The anterior coil rests on a coil holder which keeps the anterior coil’s position consistent for QA purposes. The posterior coil is imbedded in the patient couch. Multiple sets of T1, T2/PD images were acquired using the protocols as prescribed by the ACR on three different dates, ranging 3 months apart. Results: The geometric distortion are within 0.5 mm in the axial scans and within 1mm in the saggital (z-direction) scans. Slice thickness accuracy, image uniformity, ghosting ratio, high contrast detectability are comparable to other 1.5T diagnostic MRI scanners. The low-contrast object detectability are lower comparatively, which is a result of using the body array coil. Additionally, the beam’s-eye-view images (oblique coronal and saggital images) have minimal geometric distortion at all linac gantry angles tested. No observable changes or drift in image quality is found from images acquired 3 month apart. Conclusion: Despite the use of a body array surface coil, the image quality is comparable to that of an 1.5T MRI scanner and is of sufficient quality to pass the ACR MRI accreditation program. The geometric distortion of the MRI system of the integrated MR-Linac is within 1mm for an object size similar to the ACR phantom, sufficient for radiation therapy treatment purpose. The authors received corporate sponsored research grants from Elekta which is the vendor for the MR-Linac evaluated in this study.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-I-60: Quality Assurance Testing Methods and Customized Phantom for Magnetic Resonance Imaging and Spectroscopy

Purpose: The objectives of this study are to develop an magnetic resonance imaging and spectroscopy (MRI-MRS) fused phantom along with the inserts for metabolite quantification and to conduct quantitative analysis and evaluation of the layered vials of brain-mimicking solution for quality assurance (QA) performance, according to the localization sequence. Methods: The outer cylindrical phantom body is made of acrylic materials. The section other than where the inner vials are located was filled with copper sulfate and diluted with water so as to reduce the T1 relaxation time. Sodium chloride was included to provide conductivity similar to the human body. All measurements of MRI and MRS were made using a 3.0 T scanner (Achiva Tx 3.0 T; Philips Medical Systems, Netherlands). The MRI scan parameters were as follows: (1) spin echo (SE) T1-weighted image: repetition time (TR), 500ms; echo time (TE), 20ms; matrix, 256×256; field of view (FOV), 250mm; gap, 1mm; number of signal averages (NSA), 1; (2) SE T2-weighted image: TR, 2,500 ms; TE, 80 ms; matrix, 256×256; FOV, 250mm; gap, 1mm; NSA, 1; 23 slice images were obtained with slice thickness of 5mm. The water signal of each volume of interest was suppressed by variable pulse power and optimized relaxation delays (VAPOR) applied before the scan. By applying a point-resolved spectroscopy sequence, the MRS scan parameters were as follows: voxel size, 0.8×0.8×0.8 cm{sup 3}; TR, 2,000ms; TE, 35ms; NSA, 128. Results: Using the fused phantom, the results of measuring MRI factors were: geometric distortion, <2% and ±2 mm; image intensity uniformity, 83.09±1.33%; percent-signal ghosting, 0.025±0.004; low-contrast object detectability, 27.85±0.80. In addition, the signal-to-noise ratio of N-acetyl-aspartate was consistently high (42.00±5.66). Conclusion: The MRI-MRS QA factors obtained simultaneously using the phantom can facilitate evaluation of both images and spectra, and provide guidelines for obtaining MRI and MRS QA factors simultaneously. This study was supported by grant (2012-007883 and 2014R1A2A1A10050270) from the Mid-career Researcher Program through the NRF funded by Ministry of Science. In addition, this study was supported by the Industrial R&D of MOTIE/KEIT (10048997, Development of the core technology for integrated therapy devices based on real-time MRI-guided tumor tracking)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-U-02: The Development of a Practical Ultrasonic System for Cross-Sectional Imaging of Small Animals

Purpose: To test the feasibility of developing a practical medium frequency ultrasound tomography method for small animal imaging. The ability to produce cross-sectional or full body images of a live small animal using a low-cost tabletop ultrasound scanner without any special license would be very beneficial to long term biological studies, where repeated scanning is often required over an extended period of time. Methods: The cross sectional images were produced by compounding multiple B-scans of a laboratory phantom or an animal acquired at different projection angles. Two imaging systems were used to test the concept. The first system included a programmable 64-channel phased array controller driving a 128-channel, 5–10 MHz linear probe to produce 143 B-Mode projections of the spinning object. The second system designed and manufactured in house, produced 64 or 128 B-Mode projections with a single unfocused 8 MHz transducer scanning with a 0.116 mm step size. Results: The phased array system provided good penetration through the phantoms/mice (with the exception of the lungs) and allowed to acquire data in a very short time. The cross-sectional images have enough resolution and dynamic range to detect both high- and low-contrast organs. The single transducer system takes longer to scan, and the data require more sophisticated processing. To date, our images allow seeing details as small as 1–2 mm in the phantoms and in small animals, with the contrast mostly due to highly reflecting bones and air inclusions. Conclusion: The work indicates that very detailed and anatomically correct images can be created by relatively simple and inexpensive means. With more advanced algorithms and improved system design, scan time can be reduced considerably, enabling high-resolution full 3D imaging. This will allow for quick and easy scans that can help monitor tumor growth and/or regression without contributing any dose to the animal. The authors would like to acknowledge the financial and engineering support from Tessonics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TH-A-18C-09: Ultra-Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma

Purpose: Application of cone-beam CT (CBCT) to low-contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high-quality CBCT imaging of acute brain injury. Methods: The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC-GPU), with baseline simulation speed of ~1e7 photons/sec. MC-GPU is accelerated by a novel, GPU-optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced for additional speed-up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source-detector distance 700 mm, source-axis distance 480 mm). Results: For a fixed run-time of ~1 sec/projection, GPU-optimized VR reduces the noise in MC-GPU scatter estimates by a factor of 4. For scatter correction, MC-GPU with VR is executed with 4-fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run-time per scan, and de-noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast-tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction. Conclusion: Acceleration of MC-GPU achieved through GPU-optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications. Research grant from Carestream Health. JY is an employee of Carestream Health.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

When will low-contrast features be visible in a STEM X-ray spectrum image?

When will a small or low-contrast feature, such as an embedded second-phase particle, be visible in a scanning transmission electron microscopy (STEM) X-ray map? This work illustrates a computationally inexpensive method to simulate X-ray maps and spectrum images (SIs), based upon the equations of X-ray generation and detection. To particularize the general procedure, an example of nanostructured ferritic alloy (NFA) containing nm-sized Y 2 Ti 2 O 7 embedded precipitates in ferritic stainless steel matrix is chosen. The proposed model produces physically appearing simulated SI data sets, which can either be reduced to X-ray dot maps or analyzed via multivariate statistical analysis. Comparison to NFA X-ray maps acquired using three different STEM instruments match the generated simulations quite well, despite the large number of simplifying assumptions used. A figure of merit of electron dose multiplied by X-ray collection solid angle is proposed to compare feature detectability from one data set (simulated or experimental) to another. The proposed method can scope experiments that are feasible under specific analysis conditions on a given microscope. As a result, future applications, such as spallation proton–neutron irradiations, core-shell nanoparticles, or dopants in polycrystalline photovoltaic solar cells, are proposed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

ACCESS: A Visual to Near-infrared Spectrum of the Hot Jupiter WASP-43b with Evidence of H{sub 2}O, but No Evidence of Na or K

We present a new ground-based visual transmission spectrum of the hot Jupiter WASP-43b, obtained as part of the ACCESS Survey. The spectrum was derived from four transits observed between 2015 and 2018, with combined wavelength coverage between 5300 and 9000 Å and an average photometric precision of 708 ppm in 230 Å bins. We perform an atmospheric retrieval of our transmission spectrum combined with literature Hubble Space Telescope/WFC3 observations to search for the presence of clouds/hazes as well as Na, K, Hα, and H{sub 2}O planetary absorption and stellar spot contamination over a combined spectral range of 5318–16420 Å. We do not detect a statistically significant presence of Na i or K i alkali lines, or Hα in the atmosphere of WASP-43b. We find that the observed transmission spectrum can be best explained by a combination of heterogeneities on the photosphere of the host star and a clear planetary atmosphere with H{sub 2}O. This model yields a log evidence of 8.26 ± 0.42 higher than a flat (featureless) spectrum. In particular, the observations marginally favor the presence of large, low-contrast spots over the four ACCESS transit epochs with an average covering fraction f{sub het}=0.27{sub −0.16}{sup +0.42} and temperature contrast ΔT = 132 K ± 132 K. Within the planet’s atmosphere, we recover a log H{sub 2}O volume mixing ratio of −2.78{sub −1.47}{sup +1.38}, which is consistent with previous H{sub 2}O abundance determinations for this planet.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

WE-G-18A-01: JUNIOR INVESTIGATOR WINNER - Low-Dose C-Arm Cone-Beam CT with Model-Based Image Reconstruction for High-Quality Guidance of Neurosurgical Intervention

Purpose: To address the challenges of image quality, radiation dose, and reconstruction speed in intraoperative cone-beam CT (CBCT) for neurosurgery by combining model-based image reconstruction (MBIR) with accelerated algorithmic and computational methods. Methods: Preclinical studies involved a mobile C-arm for CBCT imaging of two anthropomorphic head phantoms that included simulated imaging targets (ventricles, soft-tissue structures/bleeds) and neurosurgical procedures (deep brain stimulation (DBS) electrode insertion) for assessment of image quality. The penalized likelihood (PL) framework was used for MBIR, incorporating a statistical model with image regularization via an edgepreserving penalty. To accelerate PL reconstruction, the ordered-subset, separable quadratic surrogates (OS-SQS) algorithm was modified to incorporate Nesterov's method and implemented on a multi-GPU system. A fair comparison of image quality between PL and conventional filtered backprojection (FBP) was performed by selecting reconstruction parameters that provided matched low-contrast spatial resolution. Results: CBCT images of the head phantoms demonstrated that PL reconstruction improved image quality (∼28% higher CNR) even at half the radiation dose (3.3 mGy) compared to FBP. A combination of Nesterov's method and fast projectors yielded a PL reconstruction run-time of 251 sec (cf., 5729 sec for OS-SQS, 13 sec for FBP). Insertion of a DBS electrode resulted in severe metal artifact streaks in FBP reconstructions, whereas PL was intrinsically robust against metal artifact. The combination of noise and artifact was reduced from 32.2 HU in FBP to 9.5 HU in PL, thereby providing better assessment of device placement and potential complications. Conclusion: The methods can be applied to intraoperative CBCT for guidance and verification of neurosurgical procedures (DBS electrode insertion, biopsy, tumor resection) and detection of complications (intracranial hemorrhage). Significant improvement in image quality, dose reduction, and reconstruction time of ∼4 min will enable practical deployment of low-dose C-arm CBCT within the operating room. AAPM Research Seed Funding (2013-2014); NIH Fellowship F32EB017571; Siemens Healthcare (XP Division)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis

X-ray free electron laser experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the “droplet-type” models, which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent x-ray speckle patterns, our algorithm achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now has not been accessible. Our artificial intelligence-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

Characteristic image quality of a third generation dual-source MDCT scanner: Noise, resolution, and detectability

Purpose: The purpose of this work was to assess the inherent image quality characteristics of a new multidetector computed tomography system in terms of noise, resolution, and detectability index as a function of image acquisition and reconstruction for a range of clinically relevant settings. Methods: A multisized image quality phantom (37, 30, 23, 18.5, and 12 cm physical diameter) was imaged on a SOMATOM Force scanner (Siemens Medical Solutions) under variable dose, kVp, and tube current modulation settings. Images were reconstructed with filtered back projection (FBP) and with advanced modeled iterative reconstruction (ADMIRE) with iterative strengths of 3, 4, and 5. Image quality was assessed in terms of the noise power spectrum (NPS), task transfer function (TTF), and detectability index for a range of detection tasks (contrasts of approximately 45, 90, 300, −900, and 1000 HU, and 2–20 mm diameter) based on a non-prewhitening matched filter model observer with eye filter. Results: Image noise magnitude decreased with decreasing phantom size, increasing dose, and increasing ADMIRE strength, offering up to 64% noise reduction relative to FBP. Noise texture in terms of the NPS was similar between FBP and ADMIRE (<5% shift in peak frequency). The resolution, based on the TTF, improved with increased ADMIRE strength by an average of 15% in the TTF 50% frequency for ADMIRE-5. The detectability index increased with increasing dose and ADMIRE strength by an average of 55%, 90%, and 163% for ADMIRE 3, 4, and 5, respectively. Assessing the impact of mA modulation for a fixed average dose over the length of the phantom, detectability was up to 49% lower in smaller phantom sections and up to 26% higher in larger phantom sections for the modulated scan compared to a fixed tube current scan. Overall, the detectability exhibited less variability with phantom size for modulated scans compared to fixed tube current scans. Conclusions: Image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose. The use of tube current modulation resulted in more consistent image quality with changing phantom size.

60 APPLIED LIFE SCIENCES↗