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Environmental Sciences & Ecology↗
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C‐Cracking of Brittle‐Material Spheres From Eccentric Hertzian Contact
Contact of brittle-material spheres can occur while they are manufactured, handled, or used in operation as roller elements or in deformably processed composite material. Statistically, the directional contact between two spheres will nearly always be oblique or eccentric in an unconfined and mobile sphere population. Although analyses of oblique contact exist, the portrayal of its maximum (tensile) first principal stress ( S 1 ) field is lacking. This information is needed for improved judgment of the prospect of crack initiation. Given these, the S 1 due to eccentric contact and subsequent crack initiation was analyzed using finite element analysis (FEA) and corroborative demonstration of c-crack creation. The FEA shows that an asymmetric S 1 field is created about the contact patch and is caused by superimposed shear intrinsic to eccentric contact. A crack will initiate if the maximum S 1 exceeds the material's tensile strength, and its field will cause the crack to propagate and arrest to form a C-shaped crack, or c-crack. C-crack initiation trends with lower applied forces, with greater amount of contact eccentricity, higher coefficient of friction, and smaller spherical radius. Finally, these metrics deserve recognition, especially when c-cracked brittle-material spheres are used in a system whose functionality or reliability is potentially compromised by the c-cracks.
Hertzian contact↗
Micro-CT scouting for transmission electron microscopy of human tissue specimens
Transmission electron microscopy (TEM) provides sub-nanometre-scale details in volumetric samples. Samples such as pathology tissue specimens are often stained with a metal element to enhance contrast, which makes them opaque to optical microscopes. As a result, it can be a lengthy procedure to find the region of interest inside a sample through sectioning. Here, we describe micro-CT scouting for TEM that allows noninvasive identification of regions of interest within a block sample to guide the sectioning step. In a tissue pathology study, a bench-top micro-CT scanner with 10 m resolution was used to determine the location of patches of the mucous membrane in osmium-stained human nasal scraping samples. Furthermore, once the regions of interest were located, the sample block was sectioned to expose that location, followed by ultra-thin sectioning and TEM to inspect the internal structure of the cilia of the membrane epithelial cells with nanometre resolution. This method substantially reduced the time and labour of the search process from typically 20 sections for light microscopy to three sections with no added sample preparation. Lay description Electron microscopy provides very high levels of detail in a small area, and thus the question of where to look in an opaque sample, such as a stained tissue specimen, needs to be answered by sectioning the sample in small steps and examining the sections under a light microscope, until the region of interest is found. The search process can be lengthy and labor intensive, especially for a study involving a large number of samples. Small areas of interest can be missed in the process if not enough regions are examined. We also describe a method to directly locate the region of interest within a whole sample using micro-CT imaging, bypassing the need of blindly sectioning. Micro-CT enables locating the region within 3D space; this information provides a guide for sectioning the sample to expose that precise location for high resolution electron microscopy imaging. In a human tissue specimen study, this method considerably reduced the time and labor of the search process.
60 APPLIED LIFE SCIENCES↗
Activated KCNQ1 channel promotes fibrogenic response in hereditary gingival fibromatosis via clustering and activation of Ras
Abstract Background and Objective Activated potassium channels were found to be strongly correlated with gingival overgrowth (GO) phenotype as we reviewed syndromic hereditary gingival fibromatosis (HGF). Nevertheless, the functional roles of potassium channels in gingival fibrosis or gingival overgrowth remained uncovered. The aim of the present study was to explore the pathogenic role of aberrantly activated potassium channel in Hereditary Gingival Fibromatosis (HGF). Methods Gingival tissues were collected from 9 HGF patients and 15 normal controls. Expression of KCNQ1 was detected by immunohistochemistry. Gingival fibroblasts were isolated, and outward K + currents were detected by whole‐cell patch‐clamp analysis, transmembrane potential was determined by flow cytometry. Normal human gingival fibroblasts (NHGFs) were transfected with KCNQ1 adenovirus or treated with KCNQ1 selective agonist ML277 and antagonist chromanol 293B. Accumulation of Extracellular Matrix (ECM) was measured by Western blotting and Sircol Soluble Collagen Assay. Content of secreted TGF‐β1 was measured by ELISA. Active RAS pull‐down assay and cell immunofluorescence were utilized to verify RAS activation. Results KCNQ1 was upregulated in gingival tissues derived from HGF patients and HGF gingival fibroblasts presented increased outward K + currents than NHGFs. Overexpression of KCNQ1, or KCNQ1 agonist ML277, promoted fibrotic responses of NHGFs. TGF‐β1 and KCNQ1 channels formed a positive feed‐back loop. ML277 generated lateral clustering and activation of Ras on plasma membrane, followed by augmented MAPK/AP‐1 signaling pathway output. JNK or ERK1/2 inhibitors suppressed ML277‐induced AP‐1 and ECM upregulation. Conclusion Activation of KCNQ1 potassium channel promoted fibrogenic responses in NHGFs via Ras/MAPK/AP‐1 signaling.
Gao, Qian↗
Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries
We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.
42 ENGINEERING↗
Evaluation of normalized metal artifact reduction (NMAR) in kVCT using MVCT prior images for radiotherapy treatment planning
Purpose: To evaluate the metal artifacts in kilovoltage computed tomography (kVCT) images that are corrected using a normalized metal artifact reduction (NMAR) method with megavoltage CT (MVCT) prior images.Methods: Tissue characterization phantoms containing bilateral steel inserts are used in all experiments. Two MVCT images, one without any metal artifact corrections and the other corrected using a modified iterative maximum likelihood polychromatic algorithm for CT (IMPACT) are translated to pseudo-kVCT images. These are then used as prior images without tissue classification in an NMAR technique for correcting the experimental kVCT image. The IMPACT method in MVCT included an additional model for the pair/triplet production process and the energy dependent response of the MVCT detectors. An experimental kVCT image, without the metal inserts and reconstructed using the filtered back projection (FBP) method, is artificially patched with the known steel inserts to get a reference image. The regular NMAR image containing the steel inserts that uses tissue classified kVCT prior and the NMAR images reconstructed using MVCT priors are compared with the reference image for metal artifact reduction. The Eclipse treatment planning system is used to calculate radiotherapy dose distributions on the corrected images and on the reference image using the Anisotropic Analytical Algorithm with 6 MV parallel opposed 5 × 10 cm{sup 2} fields passing through the bilateral steel inserts, and the results are compared. Gafchromic film is used to measure the actual dose delivered in a plane perpendicular to the beams at the isocenter.Results: The streaking and shading in the NMAR image using tissue classifications are significantly reduced. However, the structures, including metal, are deformed. Some uniform regions appear to have eroded from one side. There is a large variation of attenuation values inside the metal inserts. Similar results are seen in commercially corrected image. Use of MVCT prior images without tissue classification in NMAR significantly reduces these problems. The radiation dose calculated on the reference image is close to the dose measured using the film. Compared to the reference image, the calculated dose difference in the conventional NMAR image, the corrected images using uncorrected MVCT image, and IMPACT corrected MVCT image as priors is ∼15.5%, ∼5%, and ∼2.7%, respectively, at the isocenter.Conclusions: The deformation and erosion of the structures present in regular NMAR corrected images can be largely reduced by using MVCT priors without tissue segmentation. The attenuation value of metal being incorrect, large dose differences relative to the true value can result when using the conventional NMAR image. This difference can be significantly reduced if MVCT images are used as priors. Reduced tissue deformation, better tissue visualization, and correct information about the electron density of the tissues and metals in the artifact corrected images could help delineate the structures better, as well as calculate radiation dose more correctly, thus enhancing the quality of the radiotherapy treatment planning.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning
Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.
60 APPLIED LIFE SCIENCES↗
SU-D-12A-02: DeTECT, a Method to Enhance Soft Tissue Contrast From Mega Voltage CT
Purpose: MVCT images have been used on TomoTherapy system to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation and adaptive radiation therapy is severely limited due to minimal photoelectric interaction and prominent presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. We aim to utilize a non-local means denoising method and texture analysis to recover the soft tissue information for MVCT. Methods: A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. BM3D is an imaging denoising algorithm developed from non-local means methods. BM3D additionally creates 3D groups by stacking 2D patches by the order of similarity. 3D denoising operation is then performed. The resultant 3D group is inversely transformed back to 2D images. In this study, BM3D was applied to MVCT images of a CT quality phantom, a head and neck and a prostate patient. Following denoising, imaging texture was enhanced to create the denoised and texture enhanced CT (DeTECT). Results: The original MVCT images show prevalent noise and poor soft tissue contrast. By applying BM3D denoising and texture enhancement, all MVCT images show remarkable improvements. For the phantom, the contrast to noise ratio for the low contrast plug was improved from 2.2 to 13.1 without compromising line pair conspicuity. For the head and neck patient, the lymph nodes and vein in the carotid space inconspicuous in the original MVCT image becomes highly visible in DeTECT. For the prostate patient, the boundary between the bladder and the prostate in the original MVCT is successfully recovered. Both results are visually validated by kVCT images of the corresponding patients. Conclusion: DeTECT showed the promise to drastically improve the soft tissue contrast of MVCT for image guided radiotherapy and adaptive radiotherapy.
60 APPLIED LIFE SCIENCES↗
WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging
Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
TH-A-12A-01: Medical Physicist's Role in Digital Information Security: Threats, Vulnerabilities and Best Practices
I. Information Security Background (Speaker = Kevin McDonald) Evolution of Medical Devices Living and Working in a Hostile Environment Attack Motivations Attack Vectors Simple Safety Strategies Medical Device Security in the News Medical Devices and Vendors Summary II. Keeping Radiation Oncology IT Systems Secure (Speaker = Bruce Curran) Hardware Security Double-lock Requirements “Foreign” computer systems Portable Device Encryption Patient Data Storage System Requirements Network Configuration Isolating Critical Devices Isolating Clinical Networks Remote Access Considerations Software Applications / Configuration Passwords / Screen Savers Restricted Services / access Software Configuration Restriction Use of DNS to restrict accesse. Patches / Upgrades Awareness Intrusion Prevention Intrusion Detection Threat Risk Analysis Conclusion Learning Objectives: Understanding how Hospital IT Requirements affect Radiation Oncology IT Systems. Illustrating sample practices for hardware, network, and software security. Discussing implementation of good IT security practices in radiation oncology. Understand overall risk and threats scenario in a networked environment.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection
Purpose: Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. Methods: Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors’ proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies. Results: The mean WHS Dice score of the proposed MAS method was 0.918 ± 0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901 ± 0.276, p < 0.01), locally weighted voting (0.905 ± 0.0247, p < 0.01), and probabilistic patch-based fusion (0.909 ± 0.0249, p < 0.01). In the atlas ranking study, the proposed criterion based on conditional entropy yielded a performance curve with higher WHS Dice scores compared to the conventional schemes (p < 0.03). In the atlas database study, the authors showed that the MAS using larger atlas databases generated better performance curves than the MAS using smaller ones, indicating larger atlas databases could produce more accurate segmentation. Conclusions: The authors have developed a new MAS framework for automatic WHS of CTA and investigated alternative implementations of MAS. With the proposed atlas ranking algorithm and joint label fusion, the MAS scheme is able to generate accurate segmentation within practically acceptable computation time. This method can be useful for the development of new clinical applications of cardiac CT.
60 APPLIED LIFE SCIENCES↗
SU-E-T-176: Clinical Experience of Brass Mesh Bolus: Patient-Specific Parameters as Predictors of Measured Dosimetric Effect
Purpose: Increasingly, brass mesh bolus is used to insure dosimetric coverage of the skin for patients treated post-mastectomy for breast cancer. Contribution of photoelectrons from interactions between the bolus and the primary beam increases dose superficially without affecting dose at greater depths. We present our experience using brass mesh bolus – including patients for whom the bolus was dosimetrically inadequate – along with analysis of relevant patient-specific parameters. Methods: Optically-stimulated luminescent dosimeters (OSLDs) were used to determine the effect of the bolus for 15 patients. They were positioned beneath the bolus within the tangent fields at three positions: 1.5–3cm inside the medial and lateral field edges, and midway between the two. All OSLDs were midfield in the cranial-caudal direction. The measurements were compared with patient-specific parameters including separation, chest wall/breast tissue thickness, beam angle incidence, and planned surface dose. Results: The average OSLD measurement at the medial field edge, midfield, and lateral field edge position was 86.8%, 101.8%, and 92.8% of the prescription dose, respectively. A measurement for one patient was low enough (77.0%) to warrant a switch to an alternative type of bolus. Anatomic parameters were analyzed to investigate the low dose in this case, not observed in the planning system. The patient was observed to have a thin chest wall and very oblique beam angles. A second patient was also switched to an alternative type of bolus due to her being high risk and treated with an electron patch that extended onto the breast. Conclusion: Brass mesh bolus increases dose superficially while leaving dose at greater depths unaffected. However, our results suggest that this effect may be insufficient in patients with a thin chest wall or very oblique beam angles. More data and analysis is necessary to proactively identify patients for whom brass mesh bolus is effective.
60 APPLIED LIFE SCIENCES↗
Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images
Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.
60 APPLIED LIFE SCIENCES↗
Enhancing atlas based segmentation with multiclass linear classifiers
Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.
60 APPLIED LIFE SCIENCES↗
Iterative reconstruction for CT perfusion with a prior-image induced hybrid nonlocal means regularization: Phantom studies
Purpose: In computed tomography perfusion (CTP) imaging, an initial phase CT acquired with a high-dose protocol can be used to improve the image quality of later phase CT acquired with a low-dose protocol. For dynamic regions, signals in the later low-dose CT may not be completely recovered if the initial CT heavily regularizes the iterative reconstruction process. The authors propose a hybrid nonlocal means (hNLM) regularization model for iterative reconstruction of low-dose CTP to overcome the limitation of the conventional prior-image induced penalty. Methods: The hybrid penalty was constructed by combining the NLM of the initial phase high-dose CT in the stationary region and later phase low-dose CT in the dynamic region. The stationary and dynamic regions were determined by the similarity between the initial high-dose scan and later low-dose scan. The similarity was defined as a Gaussian kernel-based distance between the patch-window of the same pixel in the two scans, and its measurement was then used to weigh the influence of the initial high-dose CT. For regions with high similarity (e.g., stationary region), initial high-dose CT played a dominant role for regularizing the solution. For regions with low similarity (e.g., dynamic region), the regularization relied on a low-dose scan itself. This new hNLM penalty was incorporated into the penalized weighted least-squares (PWLS) for CTP reconstruction. Digital and physical phantom studies were performed to evaluate the PWLS-hNLM algorithm. Results: Both phantom studies showed that the PWLS-hNLM algorithm is superior to the conventional prior-image induced penalty term without considering the signal changes within the dynamic region. In the dynamic region of the Catphan phantom, the reconstruction error measured by root mean square error was reduced by 42.9% in PWLS-hNLM reconstructed image. Conclusions: The PWLS-hNLM algorithm can effectively use the initial high-dose CT to reconstruct low-dose CTP in the stationary region while reducing its influence in the dynamic region.
60 APPLIED LIFE SCIENCES↗
SU-F-R-18: Updates to the Computational Environment for Radiological Research for Image Analysis
Purpose: To present new tools in CERR for Texture Analysis and Visualization. Method: (1) Quantitative Image Analysis: We added the ability to compute Haralick texture features based on local neighbourhood. The Texture features depend on many parameters used in their derivation. For example: (a) directionality, (b) quantization of image, (c) patch-size for the neighborhood, (d) handling of the edge voxels within the region of interest, (e) Averaging co-occurance matrix vs texture features for different directions etc. A graphical user interface was built to set these parameters and then visualize their impact on the resulting texture maps. The entire functionality was written in Matlab. Array indexing was used to speed up the texture calculation. The computation speed is very competitive with the ITK library. Moreover, our implementation works with multiple CPUs and the computation time can be further reduced by using multiple processor threads. In order to reduce the Haralick texture maps into scalar features, we propose the use of Texture Volume Histograms. This lets users make use of the entire distribution of texture values within the region of interest rather than using just the mean and the standard deviations. (2) Qualitative/Visualization tools: The derived texture maps are stored as a new scan (derived) within CERR’s planC data structure. A display that compares various scans was built to show the raw image and the derived texture maps side-by-side. These images are positionally linked and can be navigated together. CERR’s graphics handling was updated and sped-up to be compatible with the newer Matlab versions. As a result, the users can use (a) different window levels and colormaps for different viewports, (b) click-and-drag or use mouse scroll-wheel to navigate slices. Results: The new features and updates are available via https://www.github.com/adityaapte/cerr . Conclusion: Features added to CERR increase its utility in Radiomics and Outcomes modeling.
60 APPLIED LIFE SCIENCES↗
SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction
Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning based method.
60 APPLIED LIFE SCIENCES↗
SU-G-BRA-01: A Real-Time Tumor Localization and Guidance Platform for Radiotherapy Using US and MRI
Purpose: To develop and validate a real-time motion management platform for radiotherapy that directly tracks tumor motion using ultrasound and MRI. This will be a cost-effective and non-invasive real-time platform combining the excellent temporal resolution of ultrasound with the excellent soft-tissue contrast of MRI. Methods: A 4D planar ultrasound acquisition during the treatment that is coupled to a pre-treatment calibration training image set consisting of a simultaneous 4D ultrasound and 4D MRI acquisition. The image sets will be rapidly matched using advanced image and signal processing algorithms, allowing the display of virtual MR images of the tumor/organ motion in real-time from an ultrasound acquisition. Results: The completion of this work will result in several innovations including: a (2D) patch-like, MR and LINAC compatible 4D planar ultrasound transducer that is electronically steerable for hands-free operation to provide real-time virtual MR and ultrasound imaging for motion management during radiation therapy; a multi- modal tumor localization strategy that uses ultrasound and MRI; and fast and accurate image processing algorithms that provide real-time information about the motion and location of tumor or related soft-tissue structures within the patient. Conclusion: If successful, the proposed approach will provide real-time guidance for radiation therapy without degrading image or treatment plan quality. The approach would be equally suitable for image-guided proton beam or heavy ion-beam therapy. This work is partially funded by NIH grant R01CA190298.
60 APPLIED LIFE SCIENCES↗