Proceedings of SPIE--the International Society for Optical Engineering最新文献

{"title":"Learning-based Free-Water Correction using Single-shell Diffusion MRI.","authors":"Tianyuan Yao, Derek B Archer, Praitayini Kanakaraj, Nancy Newlin, Shunxing Bao, Daniel Moyer, Kurt Schilling, Bennett A Landman, Yuankai Huo","doi":"10.1117/12.3006901","DOIUrl":"10.1117/12.3006901","url":null,"abstract":"<p><p>Diffusion magnetic resonance imaging (dMRI) offers the ability to assess subvoxel brain microstructure through the extraction of biomarkers like fractional anisotropy, as well as to unveil brain connectivity by reconstructing white matter fiber trajectories. However, accurate analysis becomes challenging at the interface between cerebrospinal fluid and white matter, where the MRI signal originates from both the cerebrospinal fluid and the white matter partial volume. The presence of free water partial volume effects introduces a substantial bias in estimating diffusion properties, thereby limiting the clinical utility of DWI. Moreover, current mathematical models often lack applicability to single-shell acquisitions commonly encountered in clinical settings. Without appropriate regularization, direct model fitting becomes impractical. We propose a novel voxel-based deep learning method for mapping and correcting free-water partial volume contamination in DWI to address these limitations. This approach leverages data-driven techniques to reliably infer plausible free-water volumes across different diffusion MRI acquisition schemes, including single-shell acquisitions. Our evaluation demonstrates that the introduced methodology consistently produces more consistent and plausible results than previous approaches. By effectively mitigating the impact of free water partial volume effects, our approach enhances the accuracy and reliability of DWI analysis for single-shell dMRI, thereby expanding its applications in assessing brain microstructure and connectivity.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11394251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesizing High-Resolution Dual-Energy Radiographs from Coronary Artery Calcium CT Images.","authors":"Kian Shaker, Linxi Shi, Scott Hsieh, Akyl Swaby, Shiva Abbaszadeh, Adam S Wang","doi":"10.1117/12.3006250","DOIUrl":"10.1117/12.3006250","url":null,"abstract":"<p><p>Generating realistic radiographs from CT is mainly limited by the native spatial resolution of the latter. Here we present a general approach for synthesizing high-resolution digitally reconstructed radiographs (DRRs) from an arbitrary resolution CT volume. Our approach is based on an upsampling framework where tissues of interest are first segmented from the original CT volume and then upsampled individually to the desired voxelization (here ~1 mm → 0.2 mm). Next, we create high-resolution 2D tissue maps by cone-beam projection of individual tissues in the desired radiography direction. We demonstrate this approach on a coronary artery calcium (CAC) patient CT scan and show that our approach preserves individual tissue volumes, yet enhances the tissue interfaces, creating a sharper DRR without introducing artificial features. Lastly, we model a dual-layer detector to synthesize high-resolution dual-energy (DE) anteroposterior and lateral radiographs from the patient CT to visualize the CAC in 2D through material decomposition. On a general level, we envision that this approach is valuable for creating libraries of synthetic yet realistic radiographs from corresponding large CT datasets.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PixelPrint: Generating Patient-Specific Phantoms for Spectral CT using Dual Filament 3D Printing.","authors":"Pouyan Pasyar, Jessica Im, Kai Mei, Leening Liu, Olivia Sandvold, Michael Geagan, Peter B Noël","doi":"10.1117/12.3006512","DOIUrl":"10.1117/12.3006512","url":null,"abstract":"<p><p>In recent years, the importance of spectral CT scanners in clinical settings has significantly increased, necessitating the development of phantoms with spectral capabilities. This study introduces a dual-filament 3D printing technique for the fabrication of multi-material phantoms suitable for spectral CT, focusing particularly on creating realistic phantoms with orthopedic implants to mimic metal artifacts. Previously, we developed PixelPrint for creating patient-specific lung phantoms that accurately replicate lung properties through precise attenuation profiles and textures. This research extends PixelPrint's utility by incorporating a dual-filament printing approach, which merges materials such as calcium-doped Polylactic Acid (PLA) and metal-doped PLA, to emulate both soft tissue and bone, as well as orthopedic implants. The PixelPrint dual-filament technique utilizes an interleaved approach for material usage, whereby alternating lines of calcium-doped and metal-doped PLA are laid down. The development of specialized filament extruders and deposition mechanisms in this study allows for controlled layering of materials. The effectiveness of this technique was evaluated using various phantom types, including one with a dual filament orthopedic implant and another based on a human knee CT scan with a medical implant. Spectral CT scanner results demonstrated a high degree of similarity between the phantoms and the original patient scans in terms of texture, density, and the creation of realistic metal artifacts. The PixelPrint technology's ability to produce multi-material, lifelike phantoms present new opportunities for evaluating and developing metal artifact reduction (MAR) algorithms and strategies.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing Low-cost Registration for Photographic Images to Computed Tomography.","authors":"Michael E Kim, Ho Hin Lee, Karthik Ramadass, Chenyu Gao, Katherine Van Schaik, Eric Tkaczyk, Jeffrey Spraggins, Daniel C Moyer, Bennett A Landman","doi":"10.1117/12.3005578","DOIUrl":"10.1117/12.3005578","url":null,"abstract":"<p><p>Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary. Here, we compare the integration of a commercial 3-D camera and cell phone imaging with a surface registration pipeline. Using surgical implants and chuck-eye steak as phantom tests, we obtain 3-D CT reconstruction and sets of photographic images from two sources: Canfield Imaging's H1 camera and an iPhone 14 Pro. We perform surface reconstruction from the photographic images using commercial tools and open-source code for Neural Radiance Fields (NeRF) respectively. We complete surface registration of the reconstructed surfaces with the iterative closest point (ICP) method. Manually placed landmarks were identified at three locations on each of the surfaces. Registration of the Canfield surfaces for three objects yields landmark distance errors of 1.747, 3.932, and 1.692 mm, while registration of the respective iPhone camera surfaces yields errors of 1.222, 2.061, and 5.155 mm. Photographic imaging of an organ sample prior to tissue sectioning provides a low-cost alternative to establish correspondence between histological samples and 3-D anatomical samples.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning site-invariant features of connectomes to harmonize complex network measures.","authors":"Nancy R Newlin, Praitayini Kanakaraj, Thomas Li, Kimberly Pechman, Derek Archer, Angela Jefferson, Bennett Landman, Daniel Moyer","doi":"10.1117/12.3009645","DOIUrl":"10.1117/12.3009645","url":null,"abstract":"<p><p>Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FNPC-SAM: Uncertainty-Guided False Negative/Positive Control for SAM on Noisy Medical Images.","authors":"Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C Byram, Ipek Oguz","doi":"10.1117/12.3006867","DOIUrl":"10.1117/12.3006867","url":null,"abstract":"<p><p>The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code has been released at: https://github.com/MedICL-VU/FNPC-SAM.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11182739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Age from White Matter Diffusivity with Residual Learning.","authors":"Chenyu Gao, Michael E Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy R Newlin, Derek B Archer, Angela L Jefferson, Warren D Taylor, Brian D Boyd, Lori L Beason-Held, Susan M Resnick, Yuankai Huo, Katherine D Van Schaik, Kurt G Schilling, Daniel Moyer, Ivana Išgum, Bennett A Landman","doi":"10.1117/12.3006525","DOIUrl":"10.1117/12.3006525","url":null,"abstract":"<p><p>Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11415267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of aneurysm flow divertor (stent) treatment using multi-angled 1000 fps High-Speed Angiography (HSA) and Optical Flow (OF).","authors":"E A Vanderbilt, R White, S V Setlur Nagesh, V K Chivukula, D R Bednarek, C N Ionita, S Rudin","doi":"10.1117/12.3006922","DOIUrl":"10.1117/12.3006922","url":null,"abstract":"<p><p>Understanding detailed hemodynamics is critical in the treatment of aneurysms and other vascular diseases; however, traditional digital subtraction angiography (DSA) does not provide detailed quantitative flow information. Instead, 1000 fps High-Speed Angiography (HSA) can be used for high-temporal visualization and evaluation of detailed blood flow patterns and velocity distributions. In the treatment of aneurysms, flow diverter expansion and positioning play a critical role in affecting the hemodynamics and optimal patient outcomes. Patient-specific aneurysm phantom imaging was done with a CdTe photon-counting detector (Aries, Varex). Treatment was done with a Pipeline Flex Embolization Device on a 3D-printed fusiform aneurysm phantom. The untreated aneurysm and two treatment stent expansions and positions were imaged, and velocity calculations were generated using Optical Flow (OF). Pre- and post-treatment images were then compared between different HSA image sequences and evaluated using OF with different stent positions. Differences in flow patterns due to changes in stent placement characteristics were identified and quantified with OF velocimetry. The velocity results within the aneurysm post-treatment showed significant flow reduction. Differences in stent placement result in substantial changes in velocities. The peak velocities found in the aneurysm dome show a reduction with the widened stent placement compared to the narrowed placement and both are reduced compared to the untreated aneurysm. The stent placements were compared quantitatively with the adjusted widened stent clearly better diverting the flow away from the aneurysm with decreased velocity in the aneurysm dome compared to both the narrowed stent placement and the untreated aneurysm. Providing this information in-clinic can help improve treatment and patient outcomes.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11533909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate.","authors":"Teja Pathour, Ling Ma, Douglas W Strand, Jeffrey Gahan, Brett A Johnson, Shashank R Sirsi, Baowei Fei","doi":"10.1117/12.3008643","DOIUrl":"https://doi.org/10.1117/12.3008643","url":null,"abstract":"<p><p>Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using <i>ex-vivo</i> whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12932 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11069342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lifelike <i>PixelPrint</i> phantoms for assessing clinical image quality and dose reduction capabilities of a deep learning CT reconstruction algorithm.","authors":"Jessica Y Im, Sandra S Halliburton, Kai Mei, Amy E Perkins, Eddy Wong, Leonid Roshkovan, Grace J Gang, Peter B Noël","doi":"10.1117/12.3006547","DOIUrl":"10.1117/12.3006547","url":null,"abstract":"<p><p>Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDI_vol: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}