Feature analysis of hyperpolarized helium-3 pulmonary MRI: A study of asthmatics versus nonasthmatics
A computational framework is described that was developed for quantitative analysis of hyperpolarized helium‐3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respi...
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| Published in | Magnetic resonance in medicine Vol. 63; no. 6; pp. 1448 - 1455 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.06.2010
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| Online Access | Get full text |
| ISSN | 0740-3194 1522-2594 1522-2594 |
| DOI | 10.1002/mrm.22390 |
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| Abstract | A computational framework is described that was developed for quantitative analysis of hyperpolarized helium‐3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information‐based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold‐standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc. |
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| AbstractList | A computational framework is described that was developed for quantitative analysis of hyperpolarized helium‐3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information‐based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold‐standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc. A computational framework is described that was developed for quantitative analysis of hyperpolarized helium-3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information-based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold-standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content. A computational framework is described that was developed for quantitative analysis of hyperpolarized helium-3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information-based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold-standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content. Magn Reson Med, 2010. [copy 2010 Wiley-Liss, Inc. A computational framework is described that was developed for quantitative analysis of hyperpolarized helium-3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information-based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold-standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content.A computational framework is described that was developed for quantitative analysis of hyperpolarized helium-3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information-based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold-standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content. |
| Author | Mugler III, John P. de Lange, Eduard E. Altes, Talissa A. Song, Gang Gee, James C. Tustison, Nicholas J. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20512846$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Xu Y, van Beek EJR, Hwanjo Y, Guo J, McLennan G, Hoffman EA. Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). Acad Radiol 2008; 13: 969-978. – reference: Barrett WA, Mortensen EN. Interactive live-wire boundary extraction. Med Image Anal 1997; 1: 331-341. – reference: Nakano Y, Coxson HO, Bosan S, Rogers RM, Sciurba FC, Keenan RJ, et al. Core to rind distribution of severe emphysema predicts outcome of lung volume reduction surgery. Am J Respir Crit Care Med 2001; 164: 2195-2199. – reference: Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005; 27: 1226-1238. – reference: Tustison NJ, Gee JC. Stochastic fractal dimension image. Insight J 2009; 2009: 1-5. – reference: Tustison NJ, Gee JC. Run length matrices for texture analysis. Insight J 2008; 2008: 1-6. – reference: Parraga G, Ouriadov A, Evans A, McKay S, Lam WW, Fenster A, Etemad-Rezai R, McCormack D, Giles S. Hyperpolarized 3He ventilation defects and apparent diffusion coefficients in chronic obstructive pulmonary disease: preliminary results at 3.0 tesla. Invest Radiol 2007; 42: 384-391. – reference: Dasarathy BR, Holder EB. Image characterizations based on joint gray-level run-length distributions. Pattern Recog Lett 1991; 12: 497-502. – reference: Proceedings of the ATS workshop on refractory asthma: current understanding, recommendations, and unanswered questions. American Thoracic Society; Am J Respir crit Care Med 2000; 162: 2341-51. – reference: Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610-621. – reference: Lutey BA, Lefrak SS, Woods JC, Tanoli T, Quirk JD, Bashir A, Yablonskiy DA, Conradi MS, Bartel ST, Pilgram TK, Cooper JD, Gierada DS. 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| SubjectTerms | asthma Asthma - physiopathology Computer Simulation feature analysis Helium Humans hyperpolarized helium-3 MRI Lung - diagnostic imaging Magnetic Resonance Imaging - methods mRMR algorithm Radiography Reference Standards Respiratory Function Tests Spirometry |
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| Title | Feature analysis of hyperpolarized helium-3 pulmonary MRI: A study of asthmatics versus nonasthmatics |
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