[1]
|
The National Lung Screening Trial Research Team (2011) Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. The New England Journal of Medicine, 365, 395-409. http://dx.doi.org/10.1056/NEJMoa1102873
|
[2]
|
Way, T., Chan, H.P., Hadjiiski, L., Sahiner, B., Chughtai, A., Song, T.K., Poopat, C., Stojanovska, J., Frank, L., Attili, A., Bogot, N., Cascade, P.N. and Kazerooni, E.A. (2010) Computer-Aided Diagnosis of Lung Nodules on CT Scans: ROC Study of Its Effect on Radiologists’ Performance. Academic Radiology, 17, 323-332. http://dx.doi.org/10.1016/j.acra.2009.10.016
|
[3]
|
Das, M., Mühlenbruch, G., Heinen, S., Mahnken, A.H., Salganicoff, M., Stanzel, S., Günther, R.W. and Wildberger, J.E. (2008) Performance Evaluation of a Computer-Aided Detection Algorithm for Solid Pulmonary Nodules in Low-Dose and Standard-Dose MDCT Chest Examinations and Its Influence on Radiologists. British Journal of Radiology, 81, 841-847. http://dx.doi.org/10.1259/bjr/50635688
|
[4]
|
Roos, J.E., Paik, D., Olsen, D., Liu, E.G., Chow, L.C., Leung, A.N., Mindelzun, R., Choudhury, K.R., Naidich, D.P., Napel, S. and Rubin, G.D. (2010) Computer-Aided Detection (CAD) of Lung Nodules in CT Scans: Radiologist Performance and Reading Time with Incremental CAD Assistance. European Radiology, 20, 549-557. http://dx.doi.org/10.1007/s00330-009-1596-y
|
[5]
|
Zhao, Y., de Bock, G.H., Vliegenthart, R., van Klaveren, R.J., Wang, Y., Bogoni, L., de Jong, P.A., Mali, W.P., van Ooijen, P.M. and Oudkerk, M. (2012) Performance of Computer-Aided Detection of Pulmonary Nodules in Low-Dose CT: Comparison with Double Reading by Nodule Volume. European Radiology, 22, 2076-2084. http://dx.doi.org/10.1007/s00330-012-2437-y
|
[6]
|
Cascio, D., Magro, R., Fauci, F., Iacomi, M. and Raso, G. (2012) Automatic Detection of Lung Nodules in CT Datasets Based on Stable 3D Mass-Spring Models. Computers in Biology and Medicine, 42, 1098-1109. http://dx.doi.org/10.1016/j.compbiomed.2012.09.002
|
[7]
|
Kusano, S., Nakagawa, T., Aoki, T., Nawa, T., Nakashima, K., Goto, Y. and Korogi, Y. (2010) Efficacy of Computer-Aided Diagnosis in Lung Cancer Screening with Low-Dose Spiral Computed Tomography: Receiver Operating Characteristic Analysis of Radiologists’ Performance. Japanese Journal of Radiology, 28, 649-655. http://dx.doi.org/10.1007/s11604-010-0486-1
|
[8]
|
Awai, K., Murao, K., Ozawa, A., Komi, M., Hayakawa, H., Hori, S. and Nishimura, Y. (2004) Pulmonary Nodules at Chest CT: Effect of Computer-Aided Diagnosis on Radiologists’ Detection Performance. Radiology, 230, 347-352. http://dx.doi.org/10.1148/radiol.2302030049
|
[9]
|
Li, Q., Li, F. and Doi, K. (2008) Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier. Academic Radiology, 15, 165-175. http://dx.doi.org/10.1016/j.acra.2007.09.018
|
[10]
|
Sahiner, B., Chan, H.P., Hadjiiski, L.M., Cascade, P.N., Kazerooni, E.A., Chughtai, A.R., Poopat, C., Song, T., Frank, L., Stojanovska, J. and Attili, A. (2009) Academic Radiology, 16, 1518-1530. http://dx.doi.org/10.1016/j.acra.2009.08.006
|
[11]
|
Messay, T., Hardie, R.C. and Rogers, S.K. (2010) A New Computationally Efficient CAD System for Pulmonary Nodule Detection in CT Imagery. Medical Image Analysis, 14, 390-406. http://dx.doi.org/10.1016/j.media.2010.02.004
|
[12]
|
Takizawa, H., Yamamoto, S. and Shiina, T. (2010) Recognition of Pulmonary Nodules in Thorasic CT Scans Using 3D Deformable Object Models of Different Classes. Algorithms, 10, 125-144. http://dx.doi.org/10.3390/a3020125
|
[13]
|
Retico, A., Delogu, P., Fantacci, M.E., Gori, I. and Preite Martinez, A. (2008) Lung Nodule Detection in Low-Dose and Thin-Slice Computed Tomography. Computers in Biology and Medicine, 38, 525-534. http://dx.doi.org/10.1016/j.compbiomed.2008.02.001
|
[14]
|
Naidich, D.P., Bankier, A.A., MacMahon, H., Schaefer-Prokop, C.M., Pistolesi, M., Goo, J.M., Macchiarini, P., Crapo, J.D., Herold, C.J., Austin, J.H. and Travis, W.D. (2013) Recommendations for the Management of Subsolid Pulmonary Nodules Detected at CT: A Statement from the Fleischner Society. Radiology, 266, 304-317. http://dx.doi.org/10.1148/radiol.12120628
|
[15]
|
Christe, A., Leidolt, L., Huber, A., Steiger, P., Szucs-Farkas, Z., Roos, J.E., Heverhagen, J.T. and Ebner, L. (2013) Lung Cancer Screening with CT: Evaluation of Radiologists and Different Computer Assisted Detection Software (CAD) as First and Second Readers for Lung Nodule Detection at Different Dose Levels. European Journal of Radiology, 82, e873-e878. http://dx.doi.org/10.1016/j.ejrad.2013.08.026
|
[16]
|
Ohkubo, M., Wada, S., Kunii, M., Matsumoto, T. and Nishizawa, K. (2008) Imaging of Small Spherical Structures in CT: Simulation Study Using Measured Point Spread Function. Medical & Biological Engineering & Computing, 46, 273-282. http://dx.doi.org/10.1007/s11517-007-0283-x
|
[17]
|
Ohno, K., Ohkubo, M., Marasinghe, J.C., Murao, K., Matsumoto, T. and Wada, S. (2012) Accuracy of Lung Nodule Density on HRCT: Analysis by PSF-Based Image Simulation. Journal of Applied Clinical Medical Physics, 13, 277-292.
|
[18]
|
Funaki, A., Ohkubo, M., Wada, S., Murao, K., Matsumoto, T. and Niizuma, S. (2012) Application of CT-PSF-Based Computer-Simulated Lung Nodules for Evaluating the Accuracy of Computer-Aided Volumetry. Radiological Physics and Technology, 5, 166-171. http://dx.doi.org/10.1007/s12194-012-0150-9
|
[19]
|
Kalender, W.A. (2005) Computed Tomography: Fundamentals, System Technology, Image Quality, Applications. 2nd Edition, Erlangen Publicis.
|
[20]
|
Polacin, A., Kalender, W.A., Brink, J. and Vannier, M.A. (1994) Measurement of Slice Sensitivity Profiles in Spiral CT. Medical Physics, 21, 133-140. http://dx.doi.org/10.1118/1.597251
|
[21]
|
Prevrhal, S., Fox, J.C., Shepherd, J.A. and Genant, H.K. (2003) Accuracy of CT-Based Thickness Measurement of Thin Structures: Modeling of Limited Spatial Resolution in All Three Dimensions. Medical Physics, 30, 1-8. http://dx.doi.org/10.1118/1.1521940
|
[22]
|
Ohkubo, M., Wada, S., Kayugawa, A., Matsumoto, T. and Murao, K. (2011) Image Filtering as an Alternative to the Application of a Different Reconstruction Kernel in CT Imaging: Feasibility Study in Lung Cancer Screening. Medical Physics, 38, 3915-3923. http://dx.doi.org/10.1118/1.3590363
|
[23]
|
Rollano-Hijarrubia, E., Stokking, R., van der Meer, F. and Niessen, W.J. (2006) Imaging of Small High-Density Structures in CT: A Phantom Study. Academic Radiology, 13, 893-908. http://dx.doi.org/10.1016/j.acra.2006.03.009
|
[24]
|
Ohkubo, M., Wada, S., Ida, S., Kunii, M., Kayugawa, A., Matsumoto, T., Nishizawa, K. and Murao, K. (2009) Determination of Point Spread Function in Computed Tomography Accompanied with Verification. Medical Physics, 36, 2089-2097. http://dx.doi.org/10.1118/1.3123762
|
[25]
|
Ohkubo, M., Wada, S., Matsumoto, T. and Nishizawa, K. (2006) An Effective Method to Verify Line and Point Spread Functions Measured in Computed Tomography. Medical Physics, 33, 2757-2764. http://dx.doi.org/10.1118/1.2214168
|
[26]
|
Awai, K., Murao, K., Ozawa, A., Nakayama, Y., Nakaura, T., Liu, D., Kawanaka, K., Funama, Y., Morishita, S. and Yamashita, Y. (2006) Pulmonary Nodules: Estimation of Malignancy at Thin-Section Helical CT—Effect of Computer-Aided Diagnosis on Performance of Radiologists. Radiology, 239, 276-284. http://dx.doi.org/10.1148/radiol.2383050167
|
[27]
|
Chakraborty, D.P. (2013) A Brief History of Free-Response Receiver Operating Characteristic Paradigm Data Analysis. Academic Radiology, 20, 915-919. http://dx.doi.org/10.1016/j.acra.2013.03.001
|
[28]
|
White, C.S., Pugatch, R., Koonce, T., Rust, S.W. and Dharaiya, E. (2008) Lung Nodule CAD Software as a Second Reader: A Multicenter Study. Academic Radiology, 15, 326-333. http://dx.doi.org/10.1016/j.acra.2007.09.027
|
[29]
|
Hwang, J., Chung, M.J., Bae, Y., Shin, K.M., Jeong, S.Y. and Lee, K.S. (2010) Computer-Aided Detection of Lung Nodules: Influence of the Image Reconstruction Kernel for Computer-Aided Detection Performance. Journal of Computer Assisted Tomography, 34, 31-34. http://dx.doi.org/10.1097/RCT.0b013e3181b5c630
|
[30]
|
Kim, J.S., Kim, J.H., Cho, G. and Bae, K.T. (2005) Automated Detection of Pulmonary Nodules on CT Images: Effect of Section Thickness and Reconstruction Interval-Initial Results. Radiology, 236, 295-299. http://dx.doi.org/10.1148/radiol.2361041288
|
[31]
|
Marten, K., Grillhösl, A., Seyfarth, T., Obenauer, S., Rummeny, E.J. and Engelke, C. (2005) Computer-Assisted Detection of Pulmonary Nodules: Evaluation of Diagnostic Performance Using an Expert Knowledge-Based Detection System with Variable Reconstruction Slice Thickness Settings. European Radiology, 15, 203-212. http://dx.doi.org/10.1007/s00330-004-2544-5
|
[32]
|
Lee, J.Y., Chung, M.J., Yi, C.A. and Lee, K.S. (2008) Ultra-Low-Dose MDCT of the Chest: Influence on Automated Lung Nodule Detection. Korean Journal of Radiology, 9, 95-101. http://dx.doi.org/10.3348/kjr.2008.9.2.95
|
[33]
|
Godoy, M.C., Kim, T.J., White, C.S., Bogoni, L., de Groot, P., Florin, C., Obuchowski, N., Babb, J.S., Salganicoff, M., Naidich, D.P., Anand, V., Park, S., Vlahos, I. and Ko, J.P. (2013) Benefit of Computer-Aided Detection Analysis for the Detection of Subsolid and Solid Lung Nodules on Thin- and Thick-Section CT. American Journal of Roentgenology, 200, 74-83. http://dx.doi.org/10.2214/AJR.11.7532
|
[34]
|
Pakdel, A., Mainprize, J.G., Robert, N., Fialkov, J. and Whyne, C.M. (2014) Model-Based PSF and MTF Estimation and Validation from Skeletal Clinical CT Images. Medical Physics, 41, 011906. http://dx.doi.org/10.1118/1.4835515
|
[35]
|
Sone, S., Hanaoka, T., Ogata, H., Takayama, F., Watanabe, T., Haniuda, M., Kaneko, K., Kondo, R., Yoshida, K. and Honda, T. (2012) Small Peripheral Lung Carcinomas with Five-Year Post-Surgical Follow-Up: Assessment by Semi-Automated Volumetric Measurement of Tumour Size, CT Value and Growth Rate on TSCT. European Radiology, 22, 104-119. http://dx.doi.org/10.1007/s00330-011-2241-0
|