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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-I-65: The Joint Commission's Requirements for Annual Diagnostic Physics Testing of Nuclear Medicine Equipment, and a Clinically Relevant Methodology for Testing Low-Contrast Resolution

Purpose: To introduce the Joint Commission's requirements for annual diagnostic physics testing of all nuclear medicine equipment, effective 7/1/2014, and to highlight an acceptable methodology for testing lowcontrast resolution of the nuclear medicine imaging system. Methods: The Joint Commission's required diagnostic physics evaluations are to be conducted for all of the image types produced clinically by each scanner. Other accrediting bodies, such as the ACR and the IAC, have similar imaging metrics, but do not emphasize testing low-contrast resolution as it relates clinically. The proposed method for testing low contrast resolution introduces quantitative metrics that are clinically relevant. The acquisition protocol and calculation of contrast levels will utilize a modified version of the protocol defined in AAPM Report #52. Results: Using the Rose criterion for lesion detection with a SNRpixel = 4.335 and a CNRlesion = 4, the minimum contrast levels for 25.4 mm and 31.8 mm cold spheres were calculated to be 0.317 and 0.283, respectively. These contrast levels are the minimum threshold that must be attained to guard against false positive lesion detection. Conclusion: Low contrast resolution, or detectability, can be properly tested in a manner that is clinically relevant by measuring the contrast level of cold spheres within a Jaszczak phantom using pixel values within ROI's placed in the background and cold sphere regions. The measured contrast levels are then compared to a minimum threshold calculated using the Rose criterion and a CNRlesion = 4. The measured contrast levels must either meet or exceed this minimum threshold to prove acceptable lesion detectability. This research and development activity was performed by the authors while employed at West Physics Consulting, LLC. It is presented with the consent of West Physics, which has authorized the dissemination of the information and/or techniques described in the work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Magnetic Resonance Imaging-Based Target Volume Delineation in Radiation Therapy Treatment Planning for Brain Tumors Using Localized Region-Based Active Contour

Purpose: To evaluate the clinical application of a robust semiautomatic image segmentation method to determine the brain target volumes in radiation therapy treatment planning. Methods and Materials: A local robust region-based algorithm was used on MRI brain images to study the clinical target volume (CTV) of several patients. First, 3 oncologists delineated CTVs of 10 patients manually, and the process time for each patient was calculated. The averages of the oncologists’ contours were evaluated and considered as reference contours. Then, to determine the CTV through the semiautomatic method, a fourth oncologist who was blind to all manual contours selected 4-8 points around the edema and defined the initial contour. The time to obtain the final contour was calculated again for each patient. Manual and semiautomatic segmentation were compared using 3 different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. A comparison also was performed between volumes obtained from semiautomatic and manual methods. Results: Manual delineation processing time of tumors for each patient was dependent on its size and complexity and had a mean (±SD) of 12.33 ± 2.47 minutes, whereas it was 3.254 ± 1.7507 minutes for the semiautomatic method. Means of Dice coefficient, Hausdorff distance, and mean absolute distance between manual contours were 0.84 ± 0.02, 2.05 ± 0.66 cm, and 0.78 ± 0.15 cm, and they were 0.82 ± 0.03, 1.91 ± 0.65 cm, and 0.7 ± 0.22 cm between manual and semiautomatic contours, respectively. Moreover, the mean volume ratio (=semiautomatic/manual) calculated for all samples was 0.87. Conclusions: Given the deformability of this method, the results showed reasonable accuracy and similarity to the results of manual contouring by the oncologists. This study shows that the localized region-based algorithms can have great ability in determining the CTV and can be appropriate alternatives for manual approaches in brain cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0.078, SSIM 0.999±0.001, and HD 5.926±6.141mm. Conclusion: The developed automatic segmentation strategy, which yields accurate brain tumor delineation in evaluation cases, is promising for its application in SRS treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

Purpose: The use of positron emission tomography (PET) within radiotherapy treatment planning requires the availability of reliable and accurate segmentation tools. PET automatic segmentation (PET-AS) methods have been recommended for the delineation of tumors, but there is still a lack of thorough validation and cross-comparison of such methods using clinically relevant data. In particular, studies validating PET segmentation tools mainly use phantoms with thick plastic walls inserts of simple spherical geometry and have not specifically investigated the effect of the target object geometry on the delineation accuracy. Our work therefore aimed at generating clinically realistic data using nonspherical thin-wall plastic inserts, for the evaluation and comparison of a set of eight promising PET-AS approaches. Methods: Sixteen nonspherical inserts were manufactured with a plastic wall of 0.18 mm and scanned within a custom plastic phantom. These included ellipsoids and toroids derived with different volumes, as well as tubes, pear- and drop-shaped inserts with different aspect ratios. A set of six spheres of volumes ranging from 0.5 to 102 ml was used for a baseline study. A selection of eight PET-AS methods, written in house, was applied to the images obtained. The methods represented promising segmentation approaches such as adaptive iterative thresholding, region-growing, clustering and gradient-based schemes. The delineation accuracy was measured in terms of overlap with the computed tomography reference contour, using the dice similarity coefficient (DSC), and error in dimensions. Results: The delineation accuracy was lower for nonspherical inserts than for spheres of the same volume in 88% cases. Slice-by-slice gradient-based methods, showed particularly lower DSC for tori (DSC < 0.5), caused by a failure to recover the object geometry. The region-growing method reached high levels of accuracy for most inserts (DSC > 0.76 except for tori) but showed the largest errors in the recovery of pears and drops dimensions (higher than 10% and 30% of the true length, respectively). Large errors were visible for one of the gradient-based contouring methods when delineating drop-shaped inserts. Low DSC due to systematic underestimation of the volumes was observed for our fuzzy clustering method when using nonspherical inserts. The adaptive iterative thresholding method produced the highest DSC score on our nonspherical dataset (DSC > 0.83, except for tori) and showed robustness to the insert geometry. Conclusions: This study investigated the accuracy of eight PET-AS methods for the delineation of objects with a range of nonspherical geometries. The authors’ results confirmed the robustness of some segmentation approaches, but also showed the weaknesses of some of the other methods implemented, which were not observed with spherical inserts. This work therefore highlights the importance of using a variety of thin-wall inserts with complex geometries for the validation of PET-AS methods and provided useful information for further development of the methods tested.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2010

Lunar Terrain and Albedo Reconstruction from Apollo Imagery

Generating accurate three dimensional planetary models and albedo maps is becoming increasingly more important as NASA plans more robotics missions to the Moon in the coming years. This paper describes a novel approach for separation of topography and albedo maps from orbital Lunar images. Our method uses an optimal Bayesian correlator to refine the stereo disparity map and generate a set of accurate digital elevation models (DEM). The albedo maps are obtained using a multi-image formation model that relies on the derived DEMs and the Lunar- Lambert reflectance model. The method is demonstrated on a set of high resolution scanned images from the Apollo era missions.

Nefian, Ara V.↗
NASA NTRS2017

Intelligent multi-spectral IR image segmentation

We present a neural network based multi-­‐spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. The neural net based segmentation method is compared with Wavelet-­‐threshold and Grab-­‐Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-­‐spectral IR images.

Torres, Gilbert↗
DOE OSTI2015

The preliminary results: Seismic ambient noise Rayleigh wave tomography around Merapi volcano, central Java, Indonesia

Ambient noise tomography is relatively a new method for imaging the shallow structure of the Earth subsurface. We presents the application of this method to produce a Rayleigh wave group velocity maps around the Merapi Volcano, Central Java. Rayleigh waves group velocity maps were reconstructed from the cross-correlation of ambient noise recorded by the DOMERAPI array which consists 43 broadband seismometers. In the processing stage, we first filtered the observation data to separatethe noise from the signal that dominated by the strong volcanic activities. Next, we cross-correlate the filtered data and stack to obtain the Green’s function for all possible station pairs. Then we carefully picked the peak of each Green’s function to estimate the dispersion trend and appliedMultiple Filter Technique to obtain the dispersion curve. Inter-station group velocity curvesare inverted to produceRayleigh wave group velocity maps for periods 1 to 10 s. The resulted Rayleigh group velocity maps show the interesting features around the Merapi Volcano which generally agree with the previous studies. Merapi-Lawu Anomaly (MLA) is emerged as a relatively low anomaly in our group velocity maps.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning

Purpose: To develop an image fusion method using multiple contrast MRI scans for MR-based treatment planning. Methods: T1 weighted (T1-w), T2 weighted (T2-w) and diffusion weighted images (DWI) were acquired from liver cancer patient with breath-holding. Image fade correction and deformable image registration were performed using VelocityAI (Varian Medical Systems, CA). Registered images were normalized to mean voxel intensity for each image dataset. Contrast to noise ratio (CNR) between tumor and liver was quantified. Tumor area was defined as the GTV contoured by physicians. Normal liver area with equivalent dimension was used as background. Noise was defined by the standard deviation of voxel intensities in the same liver area. Linear weightings were applied to T1-w, T2-w and DWI images to generate composite image and CNR was calculated for each composite image. Optimization process were performed to achieve different clinical goals. Results: With a goal of maximizing tumor contrast, the composite image achieved a 7–12 fold increase in tumor CNR (142.8 vs. −2.3, 11.4 and 20.6 for T1-w, T2-w and DWI only, respectively), while anatomical details were largely invisible. With a weighting combination of 100%, −10% and −10%, respectively, tumor contrast was enhanced from −2.3 to −5.4, while the anatomical details were clear. With a weighting combination of 25%, 20% and 55%, balanced tumor contrast and anatomy was achieved. Conclusion: We have investigated the feasibility of performing image fusion optimization on multiple contrast MRI images. This mechanism could help utilize multiple contrast MRI scans to potentially facilitate future MR-based treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

An Automated Probabilistic Asteroid Prediscovery Pipeline

We present an automated and probabilistic method to make prediscovery detections of near-Earth asteroids (NEAs) in archival survey images, with the goal of reducing orbital uncertainty immediately after discovery. We refit the Minor Planet Center's astrometry and propagate the full six-parameter covariance to survey epochs to define search regions. We build low-threshold source catalogs for viable images and evaluate every detected source in a search region as a candidate prediscovery. We eliminate false positives by refitting a new orbit to each candidate and probabilistically linking detections across images using a likelihood ratio. Applied to the Zwicky Transient Facility's (ZTF) imaging, we identify approximately 3000 recently discovered NEAs with prediscovery potential, including a doubling of the observational arc for about 500. We use archival ZTF imaging to make prediscovery detections of the potentially hazardous asteroid 2021 DG1, extending its arc by 2.5 yr and reducing future apparition sky plane uncertainty from many degrees to arcseconds. We also recover 2025 FU24 nearly 7 yr before its first known observation, when its sky plane uncertainty covers hundreds of square degrees across thousands of ZTF images. The method is survey agnostic and scalable, enabling rapid orbit refinement for new discoveries from Rubin, NEO Surveyor, and NEOMIR.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Image processing in remote sensing data analysis - The state of the art

Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.

Rosenfeld, A.↗
NASA NTRS2005

Method and structure for minimizing error sources in image and position sensing detectors

A method and structure for minimizing one or more non-uniformities in image and position sensing detectors are provided. The structure is directed to a focal plane processor for removing non-uniformities which distort the computation of a desired property of an object of interest in an image field. The focal plane processor is capable of selectively disconnecting one or more rows and/or columns from further processing in the imaging array for those rows and/or columns which contribute to the presence of at least one non-uniformity in a video image generated by the focal plane processor. In one embodiment, the disconnection means is embodied as pre-processing circuitry which includes row and column shift registers which provide control signals to area-of-interest (AOI) switches. In another embodiment, the pixels which comprise the focal plane array are constructed in a manner which facilitates their individual isolation.

Strohbehn, Kim↗
DOE OSTI2015

SU-E-I-90: Characterizing Small Animal Lung Properties Using Speckle Observed with An In-Line X-Ray Phase Contrast Benchtop System

Purpose: We demonstrate a novel X-ray phase-contrast (XPC) method for lung imaging representing a paradigm shift in the way small animal functional imaging is performed. In our method, information regarding airway microstructure that is encoded within speckle texture of a single XPC radiograph is decoded to produce 2D parametric images that will spatially resolve changes in lung properties such as microstructure sizes and air volumes. Such information cannot be derived from conventional lung radiography or any other 2D imaging modality. By computing these images at different points within a breathing cycle, dynamic functional imaging will be readily achieved without the need for tomography. Methods: XPC mouse lung radiographs acquired in situ with an in-line X-ray phase contrast benchtop system. The lung air volume is varied and controlled with a small animal ventilator. XPC radiographs will be acquired for various lung air volume levels representing different phases of the respiratory cycle. Similar data will be acquired of microsphere-based lung phantoms containing hollow glass spheres with known distributions of diameters. Image texture analysis is applied to the data to investigate relationships between texture characteristics and airspace/microsphere physical properties. Results: Correlations between Fourier-based texture descriptors (FBTDs) and regional lung air volume indicate that the texture features in 2D radiographs reveal information on 3D properties of the lungs. For example, we find for a 350 × 350 πm2 lung ROI a linear relationship between injected air volume and FBTD value with slope and intercept of 8.9×10{sup 5} and 7.5, respectively. Conclusion: We demonstrate specific image texture measures related to lung speckle features are correlated with physical characteristics of refracting elements (i.e. lung air spaces). Furthermore, we present results indicating the feasibility of implementing the technique with a simple imaging system design, short exposures, and low dose which provides potential for widespread use in laboratory settings for in vivo studies. This research was supported in part by NSF Award CBET1263988.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-JeP3-06: Lower KV Image Dose Are Expected From a Limited-Angle Intra-Fractional Verification (LIVE) System for SBRT Treatments

Purpose: In order to track the tumor movement for patient positioning verification during arc treatment delivery or in between 3D/IMRT beams for stereotactic body radiation therapy (SBRT), the limited-angle kV projections acquisition simultaneously during arc treatment delivery or in-between static treatment beams as the gantry moves to the next beam angle was proposed. The purpose of this study is to estimate additional imaging dose resulting from multiple tomosynthesis acquisitions in-between static treatment beams and to compare with that of a conventional kV-CBCT acquisition. Methods: kV imaging system integrated into Varian TrueBeam accelerators was modeled using EGSnrc Monte Carlo user code, BEAMnrc and DOSXYZnrc code was used in dose calculations. The simulated realistic kV beams from the Varian TrueBeam OBI 1.5 system were used to calculate dose to patient based on CT images. Organ doses were analyzed using DVHs. The imaging dose to patient resulting from realistic multiple tomosynthesis acquisitions with each 25–30 degree kV source rotation between 6 treatment beam gantry angles was studied. Results: For a typical lung SBRT treatment delivery much lower (20–50%) kV imaging doses from the sum of realistic six tomosynthesis acquisitions with each 25–30 degree x-ray source rotation between six treatment beam gantry angles were observed compared to that from a single CBCT image acquisition. Conclusion: This work indicates that the kV imaging in this proposed Limited-angle Intra-fractional Verification (LIVE) System for SBRT Treatments has a negligible imaging dose increase. It is worth to note that the MV imaging dose caused by MV projection acquisition in-between static beams in LIVE can be minimized by restricting the imaging to the target region and reducing the number of projections acquired. For arc treatments, MV imaging acquisition in LIVE does not add additional imaging dose as the MV images are acquired from treatment beams directly during the treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-169: A Feasibility Study of Using MRI Alone in Abdominal Radiotherapy

Purpose: To demonstrate the feasibility of a MRI alone workflow to support treatment planning and image guidance for abdominal radiotherapy. Methods: Abdominal MR images (in-phase/out-phase/fat/water) were acquired for a patient with breath-hold using a Dixon pulse sequence. Air masks were created on in-phase images using intensity thresholding and morphological processing methods in order to separate air from bone. Pseudo-CT and DRRs were generated using a published method. To investigate the effect of heterogeneity corrections on dose calculations using pseudo-CT, three different plans (3-field 3D, 5-field IMRT and 2-arc VMAT) were performed to mimic pancreatic treatments (1.8Gy/fraction over 28 fractions). Results: The DRRs created from pseudo-CT were of comparable quality as those created from CT. Comparing dose calculations with and without heterogeneity corrections between the 3 different plans, the biggest dosimetric differences were seen in the VMAT plan where modulation must occur across air-tissue interfaces such as those of the stomach and bowel. The DVHs for the VMAT plan showed ∼84cc difference at V50Gy in the small bowel. In terms of pseudo-CT quality, some small volumes of air in the bowel and stomach were misclassified as bone. The VMAT plan was re-optimized on pseudo-CT with 0 HU in the misclassified areas. The V50Gy in the small bowel differed by ∼90cc between the new VMAT plan with and without heterogeneity corrections. Conclusion: We found that the use of MRI alone is feasible for abdominal treatment planning and image guidance. A difference between calculations with and without heterogeneity corrections was found that is most pronounced for VMAT where the traversal of air-tissue interfaces is unavoidable. Future work will be performed to minimize misclassification between bone and air.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Combining Poisson singular integral and total variation prior models in image restoration

In this study, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation(TV) image prior preserves edge structures while imposing smoothness on the solutions.However,it tends to oversmooth textured areas.To alleviate this problem we propose to combine the TV and the Poisson Singular Integral(PSI)models, which,as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle,and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach,in the experimental section we compare it with other restoration methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Reflective Solar Band Striping Mitigation Method for the GOES-R Series Advanced Baseline Imager Using Special Scans

The large focal plane arrays used in the new generation of the Geostationary Operational Environmental Satellites (GOES) advanced baseline imager (ABI) introduce new calibration challenges compared with the heritage GOES imagers. The increased number of detectors allows for increased spatial, spectral, and temporal performance, but as a trade-off, it has an increased risk of image striping. We detail the development of a new postlaunch relative calibration capability for ABI reflective solar bands that utilizes ABI special scans to generate a set of relative gains that can be applied to improve image quality and reduce image striping. Results demonstrate that the method reduces image striping in the ABI solar reflective bands over varying scene content and time, both diurnally and over an extended period. This methodology ensures a calibration strategy that is consistent with heritage approaches yet adapts to the new postlaunch validation challenges presented by the new class of operational imagers in the GOES-R series. The developed approach is ready for operational use, as needed, and can be easily implemented into operations to support the operational production of geostationary imagery of the Earth.

Monica Cook↗
NASA NTRS1991

The moderate resolution imaging spectrometer: An EOS facility instrument candidate for application of data compression methods

The Moderate Resolution Imaging Spectrometer (MODIS) observing facility will operate on the Earth Observing System (EOS) in the late 1990's. It is estimated that this observing facility will produce over 200 gigabytes of data per day requiring a storage capability of just over 300 gigabytes per day. Archiving, browsing, and distributing the data associated with MODIS represents a rich opportunity for testing and applying both lossless and lossy data compression methods.

Salomonson, Vincent V.↗
NASA NTRS1983

Restoration of multichannel microwave radiometric images

A constrained iterative image restoration method is applied to multichannel diffraction-limited imagery. This method is based on the Gerchberg-Papoulis algorithm utilizing incomplete information and partial constraints. The procedure is described using the orthogonal projection operators which project onto two prescribed subspaces iteratively. Some of its properties and limitations are also presented. The selection of appropriate constraints was emphasized in a practical application. Multichannel microwave images, each having different spatial resolution, were restored to a common highest resolution to demonstrate the effectiveness of the method. Both noise-free and noisy images were used in this investigation.

Chin, R. T.↗