Search NASA⌕ Search

SEARCH · Search NASA

Results for “Texturing”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Gas cluster ion beam for the characterization of organic materials in submarine basalts as Mars analogs

The solar system contains large quantities of organic compounds that can form complex molecular structures. The processing of organic compounds by biological systems leads to molecules with distinctive structural characteristics; thus, the detection and characterization of organic materials could lead to a high degree of confidence in the existence of extra-terrestrial life. Given the nature of the surface of most planetary bodies in the solar system, evidence of life is more likely to be found in the subsurface where conditions are more hospitable. Basalt is a common rock throughout the solar system and the primary rock type on Mars and Earth. Basalt is therefore a rock type that subsurface life might exploit and as such a suitable material for the study of methods required to detect and analyze organic material in rock. Telluric basalts from Earth represent an analog for extra-terrestrial rocks where the indigenous organic matter could be analyzed for molecular biosignatures. This study focuses on organic matter in the basalt with the use of surface analysis techniques utilizing Ar gas cluster ion beams (GCIB); time of flight secondary ion mass spectrometry (ToF-SIMS), and x-ray photoelectron spectroscopy (XPS), to characterize organic molecules. Tetramethylammonium hydroxide (TMAH) thermochemolysis was also used to support the data obtained using the surface analysis techniques. The authors demonstrate that organic molecules were found to be heterogeneously distributed within rock textures. A positive correlation was observed to exist between the presence of microtubule textures in the basalt and the organic compounds detected. From the results herein, the authors propose that ToF-SIMS with an Ar GCIB is effective at detecting organic materials in such geological samples, and ToF-SIMS combined with XPS and TMAH thermochemolysis may be a useful approach in the study of extra-terrestrial organic material and life.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

3D reconstruction and neural rendering for adversarial machine learning

While evasion attacks on computer vision systems have been widely studied, creating attacks that remain effective under significant changes in viewpoint continues to be challenging. Traditional approaches often rely on affine transformations of images, but these approaches degrade at larger perspective shifts and often produce unrealistic or ineffective perturbations. Recent methods use differentiable renderers to improve viewpoint robustness, but they typically depend on manually constructed 3D models. We introduce a semi-automated pipeline that generates physically printable and perspective-invariant adversarial patches using only a small set of 2D images. Our method integrates 3D reconstruction, neural rendering, adversarial patch optimization, and an object detection victim model into a unified workflow. We use 2D Gaussian Splatting for high fidelity mesh reconstruction and FlexPara for surface parameterization that produces texture maps suitable for patch editing. Together, these components form a fully differentiable pipeline in PyTorch3D that links texture modification to model outputs, enabling efficient optimization of patches that remain effective across many viewpoints. The complete process, from image capture to patch printing and physical evaluation, can be completed within a few hours. We demonstrate the effectiveness of the resulting patches through attacks on the YOLOv8 object detection model and discuss remaining challenges and opportunities for improving robustness and scalability.

Singhvi, Vivaan [ORNL] (ORCID:0009000586288221)↗
DOE OSTI2012

The relationship between anatomic noise and volumetric breast density for digital mammography

Purpose: The appearance of parenchymal/stromal patterns in mammography have been characterized as having a Wiener power spectrum with an inverse power-law shape described by the exponential parameter, {beta}. The amount of fibroglandular tissue, which can be quantified in terms of volumetric breast density (VBD), influences the texture and appearance of the patterns formed in a mammogram. Here, a large study is performed to investigate the variations in {beta} in a clinical population and to indicate the relationship between {beta} and breast density. Methods: From a set of 2686 cranio-caudal normal screening mammograms, the parameter {beta} was extracted from log-log fits to the Wiener spectrum over the range 0.15-1 mm{sup -1}. The Wiener spectrum was calculated from regions of interest in the compression paddle contact region of the breast. An in-house computer program, Cumulus V, was used to extract the volumetric breast density and identify the compression paddle contact regions of the breast. The Wiener spectra were calculated with and without modulation transfer function (MTF) correction to determine the impact of VBD on the intrinsic anatomic noise. Results: The mean volumetric breast density was 25.5% ({+-}12.6%) over all images. The mean {beta} following a MTF correction which decreased the {beta} slightly ( Almost-Equal-To -0.08) was found to be 2.87. Varying the maximum of the spatial frequency range of the fits from 0.7 to 1.0, 1.25 or 1.5 mm{sup -1} showing small decreases in the result, although the effect of the quantum noise power component on reducing {beta} was clearly observed at 1.5 mm{sup -1}. Conclusions: The texture parameter, {beta}, was found to increase with VBD at low volumetric breast densities with an apparent leveling off at higher densities. The relationship between {beta} and VBD measured here can be used to create probabilistic models for computer simulations of detectability. As breast density is a known risk predictor for breast cancer, the correlation between {beta} and VBD suggests that {beta} may provide predictive information and this will be investigated in the future.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Image features for misalignment correction in medical flat-detector CT

Purpose: Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment. Methods: Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry. Results: The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window). Conclusions: Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties

Purpose: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Methods: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum—midband fit, slope, and 0-MHz-intercept—were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Results: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004). Conclusions: Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Developing high-frequency ultrasound tomography for testicular tumor imaging in rats: An in vitro study

Purpose: This paper describes a feasibility study for developing a 35-MHz high-frequency ultrasound computed-tomography (HFUCT) system for imaging rat testicles. Methods: The performances of two kinds of HFUCT-attenuation and sound-speed UCT-based on transmission and pulse-echo modes were investigated in this study. Experiments were carried out using phantoms and actual rat testiclesin vitro. HFUCT images were reconstructed using a filtered backprojection algorithm. Results: The phantom experimental results indicated that all types of HFUCT can determine the dimensions of a plastic cylinder with a diameter of 500μm. Compared to sound-speed HFUCT, attenuation HFUCT exhibited a better performance in recognizing a tiny sclerosed region in a gelatin phantom. Therefore, the in vitro testicular experiments were performed using attenuation HFUCT based on transmission and pulse-echo modes. The experimentally measured attenuation coefficient and sound speed for healthy rat testicles were 2.92 ± 0.25 dB/mm and 1537 ± 25 m/s, respectively. Conclusions: A homogeneous texture was evident for healthy testicles using both modes. An artificial sclerosed tumor could also be clearly observed using two- and three-dimensional attenuation HFUCT in both modes. However, an object artifact was apparent in pulse-echo mode because of ultrasound beam refraction. All of the obtained experimental results indicate the potential of using HFUCT as a novel tool for monitoring the preclinical responses of testicular tumors in small animals.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-E-I-81: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Adult Anthropomorphic and ACR Phantoms

Purpose: To assess the impact of General Electrics (GE) automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of an adult anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, Auto mA (180 to 380 mA), noise index (NI) = 14, adaptive iterative statistical reconstruction (ASiR) of 20%, 0.8s rotation time. Image quality was evaluated by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: The CNR for the adult male was found to decrease from CNR = 0.912 ± 0.045 for the baseline protocol without kVa to a CNR = 0.756 ± 0.049 with kVa activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.903 ± 0.023. The difference in the central liver dose with and without kVa was found to be 0.07%. Conclusion: Dose reduction was insignificant in the adult phantom. As determined by NPS analysis, ASiR of 40% produced images with similar noise texture to the baseline protocol. However, the CNR at ASiR of 40% with kVa fails to meet the current ACR CNR passing requirement of 1.0.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-I-89: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Pediatric Anthropomorphic and ACR Phantoms

Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-QI-17: Dependence of 3D/4D PET Quantitative Image Features On Noise

Purpose: Quantitative imaging is a fast evolving discipline where a large number of features are extracted from images; i.e., radiomics. Some features have been shown to have diagnostic, prognostic and predictive value. However, they are sensitive to acquisition and processing factors; e.g., noise. In this study noise was added to positron emission tomography (PET) images to determine how features were affected by noise. Methods: Three levels of Gaussian noise were added to 8 lung cancer patients PET images acquired in 3D mode (static) and using respiratory tracking (4D); for the latter images from one of 10 phases were used. A total of 62 features: 14 shape, 19 intensity (1stO), 18 GLCM textures (2ndO; from grey level co-occurrence matrices) and 11 RLM textures (2ndO; from run-length matrices) features were extracted from segmented tumors. Dimensions of GLCM were 256×256, calculated using 3D images with a step size of 1 voxel in 13 directions. Grey levels were binned into 256 levels for RLM and features were calculated in all 13 directions. Results: Feature variation generally increased with noise. Shape features were the most stable while RLM were the most unstable. Intensity and GLCM features performed well; the latter being more robust. The most stable 1stO features were compactness, maximum and minimum length, standard deviation, root-mean-squared, I30, V10-V90, and entropy. The most stable 2ndO features were entropy, sum-average, sum-entropy, difference-average, difference-variance, difference-entropy, information-correlation-2, short-run-emphasis, long-run-emphasis, and run-percentage. In general, features computed from images from one of the phases of 4D scans were more stable than from 3D scans. Conclusion: This study shows the need to characterize image features carefully before they are used in research and medical applications. It also shows that the performance of features, and thereby feature selection, may be assessed in part by noise analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-QI-20: A Review of Advanced PET and CT Image Features for the Evaluation of Tumor Response

Purpose: To review the literature in using quantitative PET and CT image features for the evaluation of tumor response. Methods: We reviewed and summarized more than fifty papers that use advanced, quantitative PET/CT image features for the evaluation of tumor response. We also discussed future works on extracting disease-specific features, combining multiple and complementary features in response modeling, delineating tumor in multimodality images, and exploring biological explanations of these advanced features. Results: Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features (characterizing spatial distribution of FDG uptake) have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Conclusions: Advanced, quantitative FDG PET/CT image features have been shown promising for the evaluation of tumor response. With the emerging multi-modality imaging performed at multiple time points for each patient, it becomes more important to analyze the serial images quantitatively, select and combine both complementary and contradictory information from various sources, for accurate and personalized evaluation of tumor response to therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-F-18C-02: Evaluations of the Noise Power Spectrum of a CT Iterative Reconstruction Technique for Radiation Therapy

Purpose: To quantitatively assess the noise power spectrum (NPS) of the new, commercially released CT iterative reconstruction technique, iDose{sup 4} from Philips, to compare it with filtered back-projection techniques (FBP), and to provide clinical practice suggestions for radiation therapy. Methods: A uniform phantom was CT imaged with 120kVp tube potential over a range of mAs (250-3333). The image sets were reconstructed using two reconstruction algorithms (FBP and iDose{sup 4} with noise reduction levels 1, 3, and 6) and three reconstruction filters (standard B, smooth A, and sharp C), after which NPS variations were analyzed and compared on region of interest (ROI) sizes (16×16 to 128×128 pixels), ROI radii (0–65 mm), reconstruction algorithms, reconstruction filters, and mAs. Results: The NPS magnitude and shape depended considerably on ROI size and location for both reconstruction algorithms. Regional noise variance became more stationary as ROI size decreased, minimizing NPS artifacts. The optimal 32×32-pixel ROI size balanced the trade-off between stationary noise and adequate sampling. NPS artifacts were greatest at the center of reconstruction space and decreased with increasing ROI distance from the center. The optimal ROI position was located near the phantom's radial midpoint (∼40mm). For sharper filters, the NPS magnitude and the maximum magnitude frequency increased. Higher dose scans yielded lower NPS magnitudes for both reconstruction algorithms and all filters. Compared to FBP, the iDose{sup 4} algorithm reduced the NPS magnitude while preferentially reducing noise at mid-range spatial frequencies, altering noise texture. This reduction was more significant with increasing iDose{sup 4} noise reduction level. Conclusion: Compared to pixel standard deviation, NPS has greater clinical potential for task-based image quality assessment, describing both the magnitude and spatial frequency characteristics of image noise. While iDose{sup 4} reduces the NPS magnitude more dramatically than FBP, further studies are needed to assess the impacts of noise texture alteration on its clinical usage.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

WE-D-18A-05: Construction of Realistic Liver Phantoms From Patient Images and a Commercial 3D Printer

Purpose: To assess image quality and radiation dose reduction in abdominal CT imaging, physical phantoms having realistic background textures and lesions are highly desirable. The purpose of this work was to construct a liver phantom with realistic background and lesions using patient CT images and a 3D printer. Methods: Patient CT images containing liver lesions were segmented into liver tissue, contrast-enhanced vessels, and liver lesions using commercial software (Mimics, Materialise, Belgium). Stereolithography (STL) files of each segmented object were created and imported to a 3D printer (Object350 Connex, Stratasys, MN). After test scans were performed to map the eight available printing materials into CT numbers, printing materials were assigned to each object and a physical liver phantom printed. The printed phantom was scanned on a clinical CT scanner and resulting images were compared with the original patient CT images. Results: The eight available materials used to print the liver phantom had CT number ranging from 62 to 117 HU. In scans of the liver phantom, the liver lesions and veins represented in the STL files were all visible. Although the absolute value of the CT number in the background liver material (approx. 85 HU) was higher than in patients (approx. 40 HU), the difference in CT numbers between lesions and background were representative of the low contrast values needed for optimization tasks. Future work will investigate materials with contrast sufficient to emulate contrast-enhanced arteries. Conclusion: Realistic liver phantoms can be constructed from patient CT images using a commercial 3D printer. This technique may provide phantoms able to determine the effect of radiation dose reduction and noise reduction techniques on the ability to detect subtle liver lesions in the context of realistic background textures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-I-19: Optimization of Low Contrast Detectability Across Two CT Manufacturers

Purpose: Our goal was to evaluate low contrast detectability (LCD) for abdominal CT protocols across two CT scanner manufacturers, while producing a similar noise texture and CTDIvol for acquired images. Methods: A CIRS tissue equivalent LCD phantom containing three columns of 7 spherical targets, ranging from 10 mm to 2.4 mm, that are 5, 10, and 20 HU below the background matrix (HUBB) was scanned using two a GE HD750 64 slice scanner and a Siemens Somatom Definition AS 64 slice scanner. Protocols were designed to deliver a CTDIvol of 12.26 mGy and images were reconstructed with FBP, ASIR and Sapphire. Comparisons were made with those algorithms that had matching noise power spectrum peaks (NPS). NPS information was extracted from a previously published article that matched NPS peak frequencies across manufacturers by calculating the NPS from uniform phantom images reconstructed with several IR algorithms. Results: The minimum detectable lesion size in the 20 HUBB and 10 HUBB column was 6.3 mm, and 10 mm in the 5 HUBB column for the GE HD 750 scanner. The minimum detectable lesion size in the 20 HUBB column was 4.8 mm, in the 10 HUBB column, 9.5 mm, and the 5 HUBB column, 10 mm for the Siemens Somatom Definition AS. Conclusion: Reducing radiation dose while improving or maintaining LCD is possible with application of IR. However, there are several different IR algorithms, with each generating a different resolution and noise texture. In multi-manufacturer settings, matching only the CTDIvol between manufacturers may Result in a loss of clinically relevant information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-270: Repeated 18F-FDG PET/CTs Based Feature Analysis for the Predication of Anal Cancer Recurrence

Purpose: To identify PET/CT based imaging predictors of anal cancer recurrence and evaluate baseline vs. mid-treatment vs. post-treatment PET/CT scans in the tumor recurrence prediction. Methods: FDG-PET/CT scans were obtained at baseline, during chemoradiotherapy (CRT, midtreatment), and after CRT (post-treatment) in 17 patients of anal cancer. Four patients had tumor recurrence. For each patient, the mid-treatment and post-treatment scans were respectively aligned to the baseline scan by a rigid registration followed by a deformable registration. PET/CT image features were computed within the manually delineated tumor volume of each scan to characterize the intensity histogram, spatial patterns (texture), and shape of the tumors, as well as the changes of these features resulting from CRT. A total of 335 image features were extracted. An Exact Logistic Regression model was employed to analyze these PET/CT image features in order to identify potential predictors for tumor recurrence. Results: Eleven potential predictors of cancer recurrence were identified with p < 0.10, including five shape features, five statistical texture features, and one CT intensity histogram feature. Six features were indentified from posttreatment scans, 3 from mid-treatment scans, and 2 from baseline scans. These features indicated that there were differences in shape, intensity, and spatial pattern between tumors with and without recurrence. Recurrent tumors tended to have more compact shape (higher roundness and lower elongation) and larger intensity difference between baseline and follow-up scans, compared to non-recurrent tumors. Conclusion: PET/CT based anal cancer recurrence predictors were identified. The post-CRT PET/CT is the most important scan for the prediction of cancer recurrence. The baseline and mid-CRT PET/CT also showed value in the prediction and would be more useful for the predication of tumor recurrence in early stage of CRT. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-CD-207-08: Intrinsic Image Quality Comparison of Synthesized 2-D and FFDM Images

Purpose: With the combined interest of managing patient dose, maintaining or improving image quality, and maintaining or improving the diagnostic utility of mammographic data, this study aims to compare the intrinsic image quality of Hologic’s synthesized 2-D (C-View) and 2-D FFDM images in terms of resolution, contrast, and noise. Methods: This study utilized a novel 3-D printed anthropomorphic breast phantom in addition to the American College of Radiology (ACR) mammography accreditation phantom. Analysis of the 3-D anthropomorphic phantom included visual assessment of resolution and analysis of the normalized noise power spectrum. Analysis of the ACR phantom included both visual inspection and objective automated analysis using in-house software. The software incorporates image- and object-specific CNR visibility thresholds which account for image characteristics such as noise texture which affect object visualization. T- test statistical analysis was also performed on ACR phantom scores. Results: The spatial resolution of C-View images is markedly lower (at least 50% worse) than that of FFDM. And while this is generally associated with the benefit of reduced relative noise magnitude, the noise in C-View images tends to have a more mottled (predominantly low-frequency) texture. In general, for high contrast objects, C-View provides superior visualization over FFDM; however this benefit diminishes for low contrast objects and is applicable only to objects that are sufficiently larger than the spatial resolution threshold. Based on both observer and automated ACR phantom analysis, between 50–70% of C-View images failed to meet ACR minimum accreditation requirements – primarily due to insufficient (unbroken) fiber visibility. Conclusion: Compared to FFDM, C-View offers better depiction of objects of certain size and contrast, but provides poorer overall resolution and noise properties. Based on these findings, the utilization of C-View images in the clinical setting requires careful consideration, especially if considering the discontinuation of FFDM imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-07: Radiomics of CT Features and Associations and Correlation with Outcomes Following Lung SBRT

Objective: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. Methods: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures. Multivariate and univariate models containing CT features were generated to assess associations with clinicopathologic factors. Results: On univariate analysis, tumor size (HR 0.54, p=0.045) sumHU (HR 0.31, p=0.044) and short run grey level emphasis STD (HR 0.22, p=0.019) were associated with regional failure-free survival; meanHU (HR 0.30, p=0.035), long run emphasis (HR 0.21, p=0.011) and long run low grey level emphasis (HR 0.14, p=0.005) was associated with distant failure-free survival (DFFS). No features were significant on multivariate modeling however long run low grey level emphasis had a hazard ratio of 0.12 (p=0.061) for DFFS. Adenocarcinoma and squamous cell carcinoma differed with respect to long run emphasis STD (p=0.024), short run low grey level emphasis STD (p<0.001), and long run low grey level emphasis STD (p=0.024). Multivariate modeling of texture features associated with tumor histology was used to estimate histologies of 18 lesions treated without histologic confirmation. Of these, MVA suggested the same histology as a prior metachronous lung malignancy in 3/7 patients. Conclusion: Extracting radiomics features on clinical datasets was feasible with the ITK package with minimal effort to identify pre-treatment quantitative CT features with prognostic factors for distant control after lung SBRT.

60 APPLIED LIFE SCIENCES↗