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Li Y, Zhao Y, Yang P, Li C, Liu L, Zhao X, Tang H, Mao Y. Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01158-y. [PMID: 38955963 DOI: 10.1007/s10278-024-01158-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/15/2024] [Accepted: 05/28/2024] [Indexed: 07/04/2024]
Abstract
Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.
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Affiliation(s)
- Yi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | | | - Ping Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Caihong Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Liu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiaofang Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Huali Tang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yun Mao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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Li J, Li H, Zhang Y, Wang Z, Zhu S, Li X, Hu K, Gao X. MCNet: A multi-level context-aware network for the segmentation of adrenal gland in CT images. Neural Netw 2024; 170:136-148. [PMID: 37979222 DOI: 10.1016/j.neunet.2023.11.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/14/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Accurate segmentation of the adrenal gland from abdominal computed tomography (CT) scans is a crucial step towards facilitating the computer-aided diagnosis of adrenal-related diseases such as essential hypertension and adrenal tumors. However, the small size of the adrenal gland, which occupies less than 1% of the abdominal CT slice, poses a significant challenge to accurate segmentation. To address this problem, we propose a novel multi-level context-aware network (MCNet) to segment adrenal glands in CT images. Our MCNet mainly consists of two components, i.e., the multi-level context aggregation (MCA) module and multi-level context guidance (MCG) module. Specifically, the MCA module employs multi-branch dilated convolutional layers to capture geometric information, which enables handling of changes in complex scenarios such as variations in the size and shape of objects. The MCG module, on the other hand, gathers valuable features from the shallow layer and leverages the complete utilization of feature information at different resolutions in various codec stages. Finally, we evaluate the performance of the MCNet on two CT datasets, including our clinical dataset (Ad-Seg) and a publicly available dataset known as Distorted Golden Standards (DGS), from different perspectives. Compared to ten other state-of-the-art segmentation methods, our MCNet achieves 71.34% and 75.29% of the best Dice similarity coefficient on the two datasets, respectively, which is at least 2.46% and 1.19% higher than other segmentation methods.
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Affiliation(s)
- Jinhao Li
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - Huying Li
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - Yuan Zhang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - Zhiqiang Wang
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou 423000, China; College of Medical Imaging Laboratory and Rehabilitation, Xiangnan University, Chenzhou 423000, China.
| | - Sheng Zhu
- Department of Nuclear Medicine, Affiliated Hospital of Xiangnan University, Chenzhou 423000, China
| | | | - Kai Hu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China; Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou 423000, China.
| | - Xieping Gao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China
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Robinson-Weiss C, Patel J, Bizzo BC, Glazer DI, Bridge CP, Andriole KP, Dabiri B, Chin JK, Dreyer K, Kalpathy-Cramer J, Mayo-Smith WW. Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology 2023; 306:e220101. [PMID: 36125375 DOI: 10.1148/radiol.220101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contrast-enhanced CT images and classify glands as normal or mass-containing and to assess algorithm performance. Materials and Methods This retrospective study included two groups of contrast-enhanced abdominal CT examinations (development data set and secondary test set). Adrenal glands in the development data set were manually segmented by radiologists. Images in both the development data set and the secondary test set were manually classified as normal or mass-containing. Deep learning segmentation and classification models were trained on the development data set and evaluated on both data sets. Segmentation performance was evaluated with use of the Dice similarity coefficient (DSC), and classification performance with use of sensitivity and specificity. Results The development data set contained 274 CT examinations (251 patients; median age, 61 years; 133 women), and the secondary test set contained 991 CT examinations (991 patients; median age, 62 years; 578 women). The median model DSC on the development test set was 0.80 (IQR, 0.78-0.89) for normal glands and 0.84 (IQR, 0.79-0.90) for adrenal masses. On the development reader set, the median interreader DSC was 0.89 (IQR, 0.78-0.93) for normal glands and 0.89 (IQR, 0.85-0.97) for adrenal masses. Interreader DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = .35). On the development test set, the model had a classification sensitivity of 83% (95% CI: 55, 95) and specificity of 89% (95% CI: 75, 96). On the secondary test set, the model had a classification sensitivity of 69% (95% CI: 58, 79) and specificity of 91% (95% CI: 90, 92). Conclusion A two-stage machine learning pipeline was able to segment the adrenal glands and differentiate normal adrenal glands from those containing masses. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Cory Robinson-Weiss
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Jay Patel
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Bernardo C Bizzo
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Daniel I Glazer
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Christopher P Bridge
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Katherine P Andriole
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Borna Dabiri
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - John K Chin
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Keith Dreyer
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Jayashree Kalpathy-Cramer
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - William W Mayo-Smith
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
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Barat M, Gaillard M, Cottereau AS, Fishman EK, Assié G, Jouinot A, Hoeffel C, Soyer P, Dohan A. Artificial intelligence in adrenal imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:37-42. [PMID: 36163169 DOI: 10.1016/j.diii.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 09/14/2022] [Indexed: 01/10/2023]
Abstract
In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France.
| | - Martin Gaillard
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Digestive, Hepatobiliary and Pancreatic Surgery, Hôpital Cochin, AP-HP, Paris 75014, France
| | - Anne-Ségolène Cottereau
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Nuclear Medicine, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Guillaume Assié
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Endocrinology, Center for Rare Adrenal Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | - Anne Jouinot
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Endocrinology, Center for Rare Adrenal Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | | | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France
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Sebek J, Cappiello G, Rahmani G, Zeinali N, Keating M, Fayemiwo M, Harkin J, McDaid L, Gardiner B, Sheppard D, Senanayake R, Gurnell M, O’Halloran M, Dennedy MC, Prakash P. Image-based computer modeling assessment of microwave ablation for treatment of adrenal tumors. Int J Hyperthermia 2022; 39:1264-1275. [PMID: 36137605 PMCID: PMC9820798 DOI: 10.1080/02656736.2022.2125590] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
PURPOSE To assess the feasibility of delivering microwave ablation for targeted treatment of aldosterone producing adenomas using image-based computational models. METHODS We curated an anonymized dataset of diagnostic 11C-metomidate PET/CT images of 14 patients with aldosterone producing adenomas (APA). A semi-automated approach was developed to segment the APA, adrenal gland, and adjacent organs within 2 cm of the APA boundary. The segmented volumes were used to implement patient-specific 3D electromagnetic-bioheat transfer models of microwave ablation with a 2.45 GHz directional microwave ablation applicator. Ablation profiles were quantitatively assessed based on the extent of the APA target encompassed by an ablative thermal dose, while limiting thermal damage to the adjacent normal adrenal tissue and sensitive critical structures. RESULTS Across the 14 patients, adrenal tumor volumes ranged between 393 mm3 and 2,395 mm3. On average, 70% of the adrenal tumor volumes received an ablative thermal dose of 240CEM43, while limiting thermal damage to non-target structures, and thermally sparing 83.5-96.4% of normal adrenal gland. Average ablation duration was 293 s (range: 60-600 s). Simulations indicated coverage of the APA with an ablative dose was limited when the axis of the ablation applicator was not well aligned with the major axis of the targeted APA. CONCLUSIONS Image-based computational models demonstrate the potential for delivering microwave ablation to APA targets within the adrenal gland, while limiting thermal damage to surrounding non-target structures.
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Affiliation(s)
- Jan Sebek
- Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
| | - Grazia Cappiello
- Translational Medical Devices Lab, National University of Ireland, Galway, Republic of Ireland
| | - George Rahmani
- Department of Radiology, Galway University Hospitals, Galway, Republic Ireland
| | - Nooshin Zeinali
- Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
| | - Muireann Keating
- School of Medicine, National University of Ireland, Galway, Republic Ireland
| | - Michael Fayemiwo
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Londonderry, Northern Ireland
| | - Jim Harkin
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Londonderry, Northern Ireland
| | - Liam McDaid
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Londonderry, Northern Ireland
| | - Bryan Gardiner
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Londonderry, Northern Ireland
| | - Declan Sheppard
- Department of Radiology, Galway University Hospitals, Galway, Republic Ireland
| | | | - Mark Gurnell
- Institute of Metabolic Science, University of Cambridge, United Kingdom
| | - Martin O’Halloran
- Translational Medical Devices Lab, National University of Ireland, Galway, Republic of Ireland
| | - M. Conall Dennedy
- School of Medicine, National University of Ireland, Galway, Republic Ireland
| | - Punit Prakash
- Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA.,Author to whom correspondence should be addressed: Punit Prakash, 3078 Engineering Hall, 1701D Platt St, Kansas State University, Manhattan, KS 66506, USA.
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