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(1995) ArticleTitleMaking filmless radiology work J Digit Imaging 8 151–155 Occurrence Handle8573623 Occurrence Handle1:STN:280:DyaK287lvVCjtw%3D%3D Occurrence Handle10.1007\u002FBF03168713\nLF Rogers (2001) ArticleTitlePACS: Radiology in the digital world Am J Roentgenol 177 499 Occurrence Handle1:STN:280:DC%2BD3MvntFKktA%3D%3D\nMD Cohen (2001) ArticleTitleDetermining the costs of imaging services Radiology 220 563–565 Occurrence Handle11526248 Occurrence Handle1:STN:280:DC%2BD3Mvotlaisg%3D%3D\nJH Sunshine MR Mabry S Bansal (1991) ArticleTitleThe volume and cost of radiologic services in the United States in 1990 Am J Roentgenol 157 609–613 Occurrence Handle1:STN:280:DyaK3Mzjs1Wqtg%3D%3D",{"EN":136},"In this study, the costs and cost savings associated with departmentwide implementation of a picture archiving and communication system (PACS) as compared to the projected budget at the time of inception were evaluated. An average of $214,460 was saved each year with a total savings of $1,072,300 from 1999 to 2003, which is significantly less than the $2,943,750 projected savings. This discrepancy can be attributed to four different factors: (1) overexpenditures, (2) insufficient cost savings, (3) unanticipated costs, and (4) project management issues. Although the implementation of PACS leads to cost savings, actual savings will be much lower than expected unless extraordinary care is taken when devising the budget.",{"EN":138},"Budget Variance Analysis of a Departmentwide Implementation of a PACS at a Major Academic Medical Center",{"VOID":140},"10.1007\u002Fs10278-006-0852-9","PUBLICATION","VERIFIED","2025-01-22T23:59:42.999+00:00","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-006-0852-9",[147,163,175],{"id":148,"sortIndex":21,"researcher":20,"roles":149,"affiliations":151,"properties":160},"e6d08497-f186-4d05-ac05-8557fd333b15",[150],"AUTHOR",[152],{"id":20,"sortIndex":21,"affiliation":153,"properties":20},{"id":154,"createTime":155,"updateTime":155,"relativeEntities":156,"slug":20,"properties":157,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e1a4a98c-e1c1-4146-8310-859a08a80bd8","2023-12-29T14:57:36.905+00:00",[],{"title":158},{"VI":159},"Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA",{"title":161},{"VI":162},"Arra Suresh Reddy",{"id":164,"sortIndex":115,"researcher":20,"roles":165,"affiliations":166,"properties":172},"f313ae24-b522-4311-ace3-794d4ca342cd",[150],[167],{"id":20,"sortIndex":21,"affiliation":168,"properties":20},{"id":154,"createTime":155,"updateTime":155,"relativeEntities":169,"slug":20,"properties":170,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":171},{"VI":159},{"title":173},{"VI":174},"Shaun Loh",{"id":176,"sortIndex":118,"researcher":20,"roles":177,"affiliations":178,"properties":184},"b7122c1d-ec70-4b37-b1fc-dd71dede0a37",[150],[179],{"id":20,"sortIndex":21,"affiliation":180,"properties":20},{"id":154,"createTime":155,"updateTime":155,"relativeEntities":181,"slug":20,"properties":182,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":183},{"VI":159},{"title":185},{"VI":186},"Robert A. Kane","ARTICLE",{"url":145,"publisher":189,"properties":217},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":190,"slug":10,"properties":191,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":195,"manageAffiliations":196,"indexDatabases":197,"url":110,"thumbnailPath":20,"statistic":212,"gsStatistic":20,"type":121,"analyzePriority":20},[],{"issn":192,"eissn":193,"title":194},{"VOID":13},{"VOID":15},{"EN":17},[],[],[198,205],{"id":93,"indexDatabase":199,"url":20,"indexYears":20,"academicFieldIds":204,"indexDatabaseRanking":20},{"id":95,"createTime":96,"updateTime":97,"relativeEntities":200,"label":201,"description":202,"key":104,"publicationTags":203,"standard":20},[],{"EN":100,"VI":100},{"VI":102,"EN":103},[106,107],[109],{"id":72,"indexDatabase":206,"url":85,"indexYears":86,"academicFieldIds":211,"indexDatabaseRanking":91},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":207,"label":208,"description":209,"key":82,"publicationTags":210,"standard":20},[],{"EN":79,"VI":79},{"EN":79,"VI":81},[84],[88,89,90],{"impactFactor":21,"impactFactorByYear":213,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":113,"totalPublicationByYear":214,"totalCitation":21,"totalCitationByYear":215,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":216,"hindexLast5Year":21,"hindex":21},{},{"1989":115,"1993":115,"1997":115,"1998":116,"1999":115,"2000":115,"2001":115,"2008":117,"2015":115,"2016":115,"2019":115,"2020":118,"2022":115},{},{},{"volume":218,"pages":220},{"VOID":219},"19",{"VOID":221},"66-71","2006-09-05",2006,false,{"id":226,"createTime":227,"updateTime":228,"relativeEntities":229,"slug":230,"properties":231,"entityType":141,"verifyStatus":142,"verifyTime":228,"verifyNote":144,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":240,"fullTextUrl":20,"authors":241,"publicationType":187,"publisherRelationship":279,"citationCount":20,"citationInfo":20,"publishDate":313,"publishYear":314,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":224},"6415bcd5-fac3-4b59-86c7-53b6cb22135d","2023-12-28T11:09:48.393+00:00","2024-12-10T23:59:00.482+00:00",[],"Digital-Radiographic-Image-Denoising-Via-Wavelet-Based-Hidden-Markov-Model-Estimation",{"references":232,"abstract":234,"title":236,"doi":238},{"VOID":233},"M Crouse R Nowak R Baraniuk (1998) ArticleTitleWavelet-based statistical signal processing using hidden Markov models IEEE Trans Signal Process 46 886–902 Occurrence Handle10.1109\u002F78.668544 Occurrence HandleMR1665651\nD Donoho (1995) ArticleTitleDe-noising by soft-thresholding IEEE Trans Inf Theory 41 613–627 Occurrence Handle10.1109\u002F18.382009\nJ Romberg H Choi R Baraniuk (2001) ArticleTitleBayesian tree-structured image modeling using wavelet-domain hidden Markov models IEEE Trans Image Process 10 1056–1068 Occurrence Handle10.1109\u002F83.931100\nS Dippel M Stahl R Wiemker T Blaffert (2002) ArticleTitleMultiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform IEEE Trans Med Imag 21 343–353 Occurrence Handle10.1109\u002FTMI.2002.1000258\nDurand S, Froment J: Artifact free signal denoising with wavelets. In: International Conference in Acoustics, Speech and Signal Processing. Salt Lake City, Utah, USA, 2001, pp. 3685–3688\nBradley A: Shift-invariance in discrete wavelet transform. In: Sun C, Talbot H, Ourselin S, Adriaansen T (Eds). Proceedings of the Seventh Digital Image Computing: Techniques and Applications. CSIRO Publishing, Macquarie University, Sydney, Australia, 2003, pp 29–38\nJ Starck F Murtagh A Bijaoui (1998) Image processing and data analysis: the multiscale approach Cambridge University Press Cambridge\nN Kingsbury (1999) ArticleTitleImage processing with complex wavelets Philos Trans R Soc Lond 357 2543–2560\nV Lee (2000) Denoising of multidimensional data using complex wavelets and hidden Markov trees Signal Processing Laboratory University of Cambridge Cambridge 64\nWinsor R: Filmless x-ray apparatus and method of using the same. Imaging Dynamics Company Ltd, USA, 1992, p 7\nGonzalez R, Woods R: Digital image processing. Addison-Wesley, 1992\nA Jain (1989) Fundamentals of digital image processing Prentice Hall Englewood Cliffs, NJ, USA\nD Donoho I Johnstone (1995) ArticleTitleAdapting to unknown smoothness via wavelet shrinkage J Am Stat Assoc 90 1200–1224\nA Laine S Schuler J Fan W Huda (1994) ArticleTitleMammographic feature enhancement by multiscale analysis IEEE Trans Med Imag 13 725–740 Occurrence Handle10.1109\u002F42.363095",{"EN":235},"This paper presents a technique for denoising digital radiographic images based upon the wavelet-domain Hidden Markov tree (HMT) model. The method uses the Anscombe’s transformation to adjust the original image, corrupted by Poisson noise, to a Gaussian noise model. The image is then decomposed in different subbands of frequency and orientation responses using the dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different correction functions were used to shrink the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image. Fifteen radiographic images of extremities along with images of a hand, a line-pair, and contrast–detail phantoms were analyzed. Quantitative and qualitative assessment showed that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction, quality of details, and bone sharpness. In some images, the proposed algorithm introduced some undesirable artifacts near the edges.",{"EN":237},"Digital Radiographic Image Denoising Via Wavelet-Based Hidden Markov Model Estimation",{"VOID":239},"10.1007\u002Fs10278-004-1908-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-004-1908-3",[242,267],{"id":243,"sortIndex":21,"researcher":20,"roles":244,"affiliations":245,"properties":264},"7707d00d-d74a-48ff-ac41-dd019b951ad7",[150],[246,254],{"id":20,"sortIndex":21,"affiliation":247,"properties":20},{"id":248,"createTime":249,"updateTime":249,"relativeEntities":250,"slug":20,"properties":251,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1644c792-fbfb-4006-8071-c5ecba7832a5","2023-12-17T16:36:20.067+00:00",[],{"title":252},{"VI":253},"Department of Computing Science, University of Alberta, Edmonton, Canada",{"id":255,"sortIndex":115,"affiliation":256,"properties":263},"69971868-9eef-4113-8f45-004a4f2e26ef",{"id":257,"createTime":258,"updateTime":258,"relativeEntities":259,"slug":20,"properties":260,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"43c40d59-a265-4940-95b0-e76600405d3f","2023-12-28T11:09:48.457+00:00",[],{"title":261},{"VI":262},"Imaging Dynamics Company Ltd., Calgary, Canada",{},{"title":265},{"VI":266},"Ricardo J. Ferrari",{"id":268,"sortIndex":115,"researcher":20,"roles":269,"affiliations":270,"properties":276},"7597fc76-f3ec-4002-a37f-a1c5606129f2",[150],[271],{"id":20,"sortIndex":21,"affiliation":272,"properties":20},{"id":257,"createTime":258,"updateTime":258,"relativeEntities":273,"slug":20,"properties":274,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":275},{"VI":262},{"title":277},{"VI":278},"Robin Winsor",{"url":240,"publisher":280,"properties":308},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":281,"slug":10,"properties":282,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":286,"manageAffiliations":287,"indexDatabases":288,"url":110,"thumbnailPath":20,"statistic":303,"gsStatistic":20,"type":121,"analyzePriority":20},[],{"issn":283,"eissn":284,"title":285},{"VOID":13},{"VOID":15},{"EN":17},[],[],[289,296],{"id":93,"indexDatabase":290,"url":20,"indexYears":20,"academicFieldIds":295,"indexDatabaseRanking":20},{"id":95,"createTime":96,"updateTime":97,"relativeEntities":291,"label":292,"description":293,"key":104,"publicationTags":294,"standard":20},[],{"EN":100,"VI":100},{"VI":102,"EN":103},[106,107],[109],{"id":72,"indexDatabase":297,"url":85,"indexYears":86,"academicFieldIds":302,"indexDatabaseRanking":91},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":298,"label":299,"description":300,"key":82,"publicationTags":301,"standard":20},[],{"EN":79,"VI":79},{"EN":79,"VI":81},[84],[88,89,90],{"impactFactor":21,"impactFactorByYear":304,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":113,"totalPublicationByYear":305,"totalCitation":21,"totalCitationByYear":306,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":307,"hindexLast5Year":21,"hindex":21},{},{"1989":115,"1993":115,"1997":115,"1998":116,"1999":115,"2000":115,"2001":115,"2008":117,"2015":115,"2016":115,"2019":115,"2020":118,"2022":115},{},{},{"volume":309,"pages":311},{"VOID":310},"18",{"VOID":312},"154-167","2005-04-19",2005,{"id":316,"createTime":317,"updateTime":318,"relativeEntities":319,"slug":320,"properties":321,"entityType":141,"verifyStatus":142,"verifyTime":318,"verifyNote":144,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":330,"fullTextUrl":20,"authors":331,"publicationType":187,"publisherRelationship":400,"citationCount":20,"citationInfo":20,"publishDate":434,"publishYear":435,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":224},"4bd2160b-7d4e-48ba-9e97-90ff8a7cce83","2023-12-08T12:24:22.616+00:00","2025-01-19T23:58:10.570+00:00",[],"An-Algorithm-for-Intelligent-Sorting-of-CT-Related-Dose-Parameters",{"references":322,"abstract":324,"title":326,"doi":328},{"VOID":323},"Maitino AJ, Levin DC, Parker L, Rao VM, Sunshine JH: Nationwide trends in rates of utilization of noninvasive diagnostic imaging among the medicare population between 1993 and 1999. Radiology 227:113–117, 2003\nLevin DC, Rao VM, Parker L, Frangos AJ, Sunshine JH: Recent trends in utilization rates of abdominal imaging: the relative roles of radiologists and nonradiologist physicians. JACR 5:744–747, 2008\nSinclair WK, Adelstein SJ, Carter MW, Harley JH, Moeller DW: Ionizing radiation exposure of the population of the United States. Tech. Rep. 93, National Council of Radiation Protection, 1987\nKase KR et al: Ionizing radiation exposure of the population of the United States. Tech. Rep. 160, National Council of Radiation Protection, 2009\nBrenner DJ, Hall EJ: Computed tomography: an increasing source of radiation exposure. NEJM 357(22):2277–2284, 2007\nBrody A, Frush D, Huda W, Brent R: Radiation risk to children from computed tomography. Pediatrics 120(3):677, 2007\nde Gonzalez AB, Mahesh M, Kim K, Bhargavan M, Lewis R, Mettler F, Land C: Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med 169(22):2071–2077, 2009\nMartin D, Semelka R: Health effects of ionising radiation from diagnostic CT. Lancet 367(9524):1712–1714, 2006\nAmis JES, Butler PF, Applegate KE, Birnbaum SB, Brateman LF, Hevezi JM, Mettler FA, Morin RL, Pentecost MJ, Smith GG, Strauss KJ, Zeman RK: American College of Radiology white paper on radiation dose in medicine. JACR 4:272–284, 2007\nDICOM Standards Committee: DICOM standard supplement 127: CT radiation dose reporting, 2007\nDICOM Standards Committee: DICOM standard part 16: Content mapping resource, 2008\nRadiation Exposure Monitoring: http:\u002F\u002Fwiki.ihe.net\u002Findex.php?title=Radiation_Exposure_Management, accessed March 15, 2010\nGoske M, Applegate K, Boylan J, Butler P, Callahan M, Coley B, Farley S, Frush D, Hernanz-Schulman M, Jaramillo D, et al: The Image Gently campaign: increasing CT radiation dose awareness through a national education and awareness program. Pediatr Radiol 38(3):265–269, 2008\nNational Radiology Data Registry: http:\u002F\u002Fwww.acr.org\u002FSecondaryMainMenuCategories\u002Fquality_safety\u002FNRDR.aspx, accessed March 15, 2010\nWhite Paper: Initiative to Reduce Unnecessary Radiation Exposure from Medical Imaging. http:\u002F\u002Fwww.fda.gov\u002FRadiation-EmittingProducts\u002FRadiationSafety\u002FRadiationDoseReduction\u002Fucm199994.htm#_Toc253092884, accessed March 15, 2010\nNeumann RD, Bluemke DA: Tracking radiation exposure from diagnostic imaging devices at the NIH. JACR 7(2):87–89, 2010\nCook T, Zimmerman SL, Maidment AD, Kim W, Boonn WW: Automated extraction of radiation dose information for CT examinations. J Am Coll Rad 7(11):871–877, 2010. doi:10.1016\u002Fj.jacr.2010.06.026\nChristner JA, Kofler JM, McCollough CH: Estimating effective dose for CT using dose-length product compared with using organ doses: consequences of adopting International Commission on Radiological Protection Publication 103 or dual-energy scanning. Am J Roentgenol 194(4):881–889, 2010. doi:10.2214\u002FAJR.09.3462\nRadLex Playbook: http:\u002F\u002Fwww.rsna.org\u002FInformatics\u002Fradlex_playbook.cfm, accessed January 11, 2011\nMcCollough CH, Leng S, Yu L, Cody DD, Boone JM, McNitt-Gray MF: CT dose index and patient dose: they are not the same thing. Radiology 259(2):311–316, 2011. doi:10.1148\u002Fradiol.11101800",{"EN":325},"Imaging centers nationwide are seeking innovative means to record and monitor computed tomography (CT)-related radiation dose in light of multiple instances of patient overexposure to medical radiation. As a solution, we have developed RADIANCE, an automated pipeline for extraction, archival, and reporting of CT-related dose parameters. Estimation of whole-body effective dose from CT dose length product (DLP)—an indirect estimate of radiation dose—requires anatomy-specific conversion factors that cannot be applied to total DLP, but instead necessitate individual anatomy-based DLPs. A challenge exists because the total DLP reported on a dose sheet often includes multiple separate examinations (e.g., chest CT followed by abdominopelvic CT). Furthermore, the individual reported series DLPs may not be clearly or consistently labeled. For example, “arterial” could refer to the arterial phase of the triple liver CT or the arterial phase of a CT angiogram. To address this problem, we have designed an intelligent algorithm to parse dose sheets for multi-series CT examinations and correctly separate the total DLP into its anatomic components. The algorithm uses information from the departmental PACS to determine how many distinct CT examinations were concurrently performed. Then, it matches the number of distinct accession numbers to the series that were acquired and anatomically matches individual series DLPs to their appropriate CT examinations. This algorithm allows for more accurate dose analytics, but there remain instances where automatic sorting is not feasible. To ultimately improve radiology patient care, we must standardize series names and exam names to unequivocally sort exams by anatomy and correctly estimate whole-body effective dose.",{"EN":327},"An Algorithm for Intelligent Sorting of CT-Related Dose Parameters",{"VOID":329},"10.1007\u002Fs10278-011-9410-1","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10278-011-9410-1",[332,347,359,376,388],{"id":333,"sortIndex":21,"researcher":20,"roles":334,"affiliations":335,"properties":344},"f816c031-513e-407e-8c60-e972a0c2bc11",[150],[336],{"id":20,"sortIndex":21,"affiliation":337,"properties":20},{"id":338,"createTime":339,"updateTime":339,"relativeEntities":340,"slug":20,"properties":341,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"2359d3d1-ba49-4b8d-a399-42980ac8943c","2024-01-22T15:13:34.208+00:00",[],{"title":342},{"VI":343},"Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, USA",{"title":345},{"VI":346},"Tessa S. 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Med Phys 22:171–181, 1995\nKyprianou IS, Rudin S, Bednarek DR, Hoffmann KR: Generalizing the MTF and DQE to include X-ray scatter and focal spot unsharpness: Application to a new microangiographic system. Med Phys 32:613–626, 2005\nShannon CE: A mathematical theory of communication. Bell Syst Tech J 27:379–423\u002F623–656, 1948\nShannon CE, Weaver W: The mathematical Theory of Communication. University of Illinois Press, Urbana, 1949\nUchida S, Tsai DY: Evaluation of radiographic images by entropy: Application to development process. Jpn J Appl Phys 17:2029–2034, 1978\nUchida S, Tsai DY: Reliability of the modulation transfer function of radiographic screen-film system measured by the slit method. Jpn J Appl Phys 18:1571–1574, 1979\nUchida S, Fujita H: Assessment of radiographic granularity by a single number. Jpn J Appl Phys 19:1403–1410, 1980\nKim B, Boes JL, Frey KA, et al: Mutual information for automated unwarping of rate brain autoradiographs. Neuroimage 5:31–40, 1997\nStudholme C, Hill DLG, Hawkes DJ: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recog 32:71–86, 1999\nHayton PM, Brady M, Smith SM, et al: A non-rigid registration algorithm for dynamic breast MR images. Artif Intell 114:125–156, 1999\nBruckner T, Lucht R, Brix G: Comparison of rigid and elastic matching of dynamic magnetic resonance mammographic images by mutual information. Med Phys 27:2456–2461, 2000\nThurfjll L, Lau YH, Andersson JLR, et al: Improved efficiency for MRI-SPET registration based on mutual information. Eur J Nucl Med 27:847–856, 2000\nSkouson MB, Guo Q, Liang ZP: A bound on mutual information for image registration. IEEE Trans Med Imag 20:843–846, 2001\nShekhar Rand, Zagrodsky V: Mutual information-based rigid and nonrigid registration of ultrasound volumes. IEEE Trans Med Imag 21:9–22, 2002\nQu G, Zhang D, Yan P: Information measure for performance of image fusion. Electron Lett 38:313–315, 2002\nPluim JPW, Maintz JBA, Viergever MZ: Mutual-information-based registration of medical images: A survey. IEEE Trans Med Imag 22:986–1004, 2003\nFilev P, Hadjiiski L, Shiner B, et al: Comparison of similarity measures for the task of template matching of masses on serial mammograms. Med Phys 32:515–529, 2005\nAttneave F: Applications of Information Theory to Psychology. New York: Holt, Rinehart and Winston, 1959\nSprawls P: Physical Principles of Medical Imaging. Madison, WI: Medical Physics Publishing, 1995, pp 273–274\nRuss JC: The Image Processing Handbook, 2nd edition. London: CRC, 1995",{"EN":446},"This paper presents a simple and straightforward method for synthetically evaluating digital radiographic images by a single parameter in terms of transmitted information (TI). The features of our proposed method are (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image noise and resolution (blur). Two acrylic step wedges with 0–1–2–3–4–5 and 0–2–4–6–8–10 mm in thickness were used as phantoms for experiments. In the present study, three experiments were conducted. First, to investigate the relation between the value of TI and image noise, various radiation doses by changing exposure time were employed. Second, we examined the relation between the value of TI and image blurring by shifting the phantoms away from the center of the X-ray beam area toward the cathode end when imaging was performed. Third, we analyzed the combined effect of deteriorated blur and noise on the images by employing three smoothing filters. Experimental results show that the amount of TI is closely related to both image noise and image blurring. The results demonstrate the usefulness of our method for evaluation of physical image quality in medical imaging.",{"EN":448},"Information Entropy Measure for Evaluation of Image Quality",{"VOID":450},"10.1007\u002Fs10278-007-9044-5","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-007-9044-5",[453,468,480],{"id":454,"sortIndex":21,"researcher":20,"roles":455,"affiliations":456,"properties":465},"47d87811-9251-4d65-a4e1-7e98083c8287",[150],[457],{"id":20,"sortIndex":21,"affiliation":458,"properties":20},{"id":459,"createTime":460,"updateTime":460,"relativeEntities":461,"slug":20,"properties":462,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"7388da5b-8b80-4aec-b1f3-177865e353f2","2024-02-06T06:45:22.647+00:00",[],{"title":463},{"VI":464},"Department of Radiological Technology, School of Health Sciences, Niigata University, Niigata, Japan",{"title":466},{"VI":467},"Du-Yih 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Karimi, Q. Zeng, P. Mathur, A. Avinash, S. Mahdavi, I. Spadinger, P. Abolmaesumi, S.E. Salcudean, Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images, Med. Image Anal. 57 (2019) 186–196.",{"doi":701},"10.1016\u002Fj.media.2019.07.005",{"id":20,"text":703,"url":20,"identifiers":704},"M.A. Kollmeier, Combined brachytherapy and ultra-hypofractionated radiotherapy for intermediate-risk prostate cancer: Comparison of toxicity outcomes using a high-dose-rate (HDR) versus low-dose-rate (LDR) brachytherapy boost, Brachytherapy. 21 (2022) 599–604.",{"doi":705},"10.1016\u002Fj.brachy.2022.04.006",{"id":20,"text":707,"url":20,"identifiers":708},"S. Nouranian, M. Ramezani, I. Spadinger, W.J. Morris, S.E. Salcudean, P. Abolmaesumi, Learning-based multi-label segmentation of transrectal ultrasound images for prostate brachytherapy, IEEE Trans. Med. Imaging. 35 (2016) 921–932.",{"doi":709},"10.1109\u002FTMI.2015.2502540",{"id":20,"text":711,"url":20,"identifiers":712},"X. Xu, T. Sanford, B. Turkbey, S. Xu, B.J. Wood, P. Yan, Shadow-consistent Semi-supervised learning for prostate ultrasound segmentation, IEEE Trans. Med. Imaging. 41 (2022) 1331–1345.",{"doi":713},"10.1109\u002FTMI.2021.3139999",{"id":20,"text":715,"url":20,"identifiers":716},"L. Rundo, C. Han, Y. Nagano, J. Zhang, R. Hataya, C. Militello, A. Tangherloni, M.S. Nobile, C. Ferretti, D. Besozzi, M.C. Gilardi, S. Vitabile, G. Mauri, H. Nakayama, P. Cazzaniga, USE-Net: Incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets, Neurocomputing. 365 (2019) 31–43.",{"doi":717},"10.1016\u002Fj.neucom.2019.07.006",{"id":20,"text":719,"url":20,"identifiers":720},"X. Yang, S. Zhan, D. Xie, H. Zhao, T. 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Jani, H. Mao, W.J. Curran, T. Liu, X. Yang, Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net, Med. Phys. 46 (2019) 3194–3206.",{"doi":875},"10.1002\u002Fmp.13577",{"id":20,"text":877,"url":20,"identifiers":878},"Y. Wang, H. Dou, X. Hu, L. Zhu, X. Yang, M. Xu, J. Qin, P.-A. Heng, T. Wang, D. Ni, Deep attentive features for prostate segmentation in 3d transrectal ultrasound, IEEE Trans. Med. Imaging. 38 (2019) 2768–2778.",{"doi":879},"10.1109\u002FTMI.2019.2913184",{"id":20,"text":881,"url":20,"identifiers":882},"K.B. Girum, A. Lalande, R. Hussain, G. Créhange, A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy, Int. J. Comput. Assist. Radiol. Surg. 15 (2020) 1467–1476.",{"doi":883},"10.1007\u002Fs11548-020-02231-x",{"id":885,"createTime":886,"updateTime":887,"relativeEntities":888,"slug":889,"properties":890,"entityType":141,"verifyStatus":142,"verifyTime":887,"verifyNote":144,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":899,"fullTextUrl":20,"authors":900,"publicationType":187,"publisherRelationship":976,"citationCount":20,"citationInfo":20,"publishDate":1009,"publishYear":530,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":224},"21fab1ba-4657-4a64-8f73-cd86fd5bda9d","2024-02-02T04:25:24.744+00:00","2024-12-25T23:54:46.306+00:00",[],"Validity-and-Reliability-of-Active-Shape-Models-for-the-Estimation-of-Cobb-Angle-in-Patients-with-Adolescent-Idiopathic-Scoliosis",{"references":891,"abstract":893,"title":895,"doi":897},{"VOID":892},"Lonstein JG: Adolescent idiopathic scoliosis. Lancet 344:1407–1412, 1994\nScoliosis Research Society. In Depth Review of Scoliosis. Available at: http:\u002F\u002Fwww.srs.org\u002Fpatients\u002F\nCobb JR: Outline for the study of scoliosis. Am Acad Orthop Surg Inst Course Lect 5:261–275, 1948\nChockalingam N, Dangerfield PH, Giakas G, Cochrane T, Dorgan JC: Computer-assisted Cobb measurement of scoliosis. Eur Spine J 11:353–357, 2002\nLoder RT, Spiegel D, Gutknecht S, Kleist K, Ly T, Mehbod A: The assessment of intraobserver and interobserver error in the measurement of noncongenital scoliosis in children ≤10 years of age. Spine 29:2548–2553, 2004\nMior SA, Kopansky-Giles DR, Crowther ER, Wright JG: A comparison of radiographic and electrogoniometric angles in adolescent idiopathic scoliosis. Spine 21:1549–1555, 1996\nCobb JR: Outlines for the study of scoliosis measurements from spinal roentgenograms. Phys Ther 59:764–765, 1948\nRosenfeldt MP, Harding IJ, Hauptfleisch JT, Fairbank JT: A comparison of traditional protractor versus Oxford Cobbometer radiographic measurement—intraobserver measurement variability for Cobb angles. Spine 30:440–443, 2005\nShea KG, Stevens PM, Nelson M, Smith JT, Masters KS, Yandow S: A comparison of manual versus computer-assisted radiographic measurement: Intraobserver measurement variability for Cobb angles. Spine 23:551–555, 1998\nCootes TF, Hill A, Taylor CJ, Haslam J: The use of active shape models for locating structures in medical images. Image Vis Comput 12:355–366, 1994\nCootes TF, Taylor CJ, Lanitis A: Active shape models: Evaluation of a multi-resolution method for improving image search. Proceedings of the British Machine Vision Conference, pp 327–336, 1994\nCootes TF, Taylor CJ, Cooper DH, Graham J: Active shape models—their training and application. Comput Vis Image Underst 61:38–59, 1995\nLindley K: Model based interpretation of lumbar spine radiographs. MSc Thesis, University of Manchester, 1992\nKrebs DE: Declare your ICC type [letter]. Phys Ther 66:1431, 1986\nScientific Advisory Committee of the Medical Outcomes Trust: Assessing health status and quality-of-life instruments: Attributes and review criteria. Qual Life Res 11:193–205, 2002\nStreiner DL, Norman GR: Health Measurement Scales: A Practical Guide to their Development and Use, 2nd edition. New York, New York: Oxford Medical Publications:106–119, 1995",{"EN":894},"Choosing the most suitable treatment for scoliosis relies heavily on accurate and reproducible Cobb angle measurement from successive radiographs. The objective is to reduce variability of Cobb angle measurement by reducing user intervention and bias. Custom software to increase automation of the Cobb angle measurement from posteroanterior radiographs was developed using active shape models. Validity and reliability of the automated system against a manual and semiautomated measurement method was conducted by two examiners each performing measurements on three occasions from a test set (N = 22). A training set (N = 47) of radiographs representative of curves seen in a scoliosis clinic was used to train the software to recognize vertebrae from T4 to L4. Images with a maximum Cobb angle between 20° and 50°, excluding surgical cases, were selected for training and test sets. Automated Cobb angles were calculated using best-fit slopes of the detected vertebrae endplates. Intraclass correlation coefficient (ICC) and standard error of measurement (SEM) showed high intraexaminer (ICC > 0.90, SEM 2°–3°) and interexaminer (ICC > 0.82, SEM 2°–4°), but poor intermethod reliability (ICC = 0.30, SEM 8°–9°). The automated method underestimated large curves. The reliability improved (ICC = 0.70, SEM 4°–5°) with exclusion of the four largest curves (>40°) in the test set. The automated method was reliable for moderate-sized curves, and did detect vertebrae in larger curves with a modified training set of larger curves.",{"EN":896},"Validity and Reliability of Active Shape Models for the Estimation of Cobb Angle in Patients with Adolescent Idiopathic Scoliosis",{"VOID":898},"10.1007\u002Fs10278-007-9026-7","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-007-9026-7",[901,916,928,940,952,964],{"id":902,"sortIndex":115,"researcher":20,"roles":903,"affiliations":904,"properties":913},"2a5270de-9fc9-460a-a33b-6091e6a4f011",[150],[905],{"id":20,"sortIndex":21,"affiliation":906,"properties":20},{"id":907,"createTime":908,"updateTime":908,"relativeEntities":909,"slug":20,"properties":910,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"ba164a9b-4644-4ac3-a86e-a4436eb674bf","2024-02-02T04:25:24.831+00:00",[],{"title":911},{"VI":912},"Department of Rehabilitation Technology, Capital Health, Glenrose Rehabilitation Hospital Site, Edmonton, Canada",{"title":914},{"VI":915},"Eric Parent",{"id":917,"sortIndex":21,"researcher":20,"roles":918,"affiliations":919,"properties":925},"d48b43bf-9a6a-4d4a-9334-b1d744d62e68",[150],[920],{"id":20,"sortIndex":21,"affiliation":921,"properties":20},{"id":907,"createTime":908,"updateTime":908,"relativeEntities":922,"slug":20,"properties":923,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":924},{"VI":912},{"title":926},{"VI":927},"Shannon Allen",{"id":929,"sortIndex":606,"researcher":20,"roles":930,"affiliations":931,"properties":937},"900f320c-ad8f-45a1-9a46-4ce78b3c1407",[150],[932],{"id":20,"sortIndex":21,"affiliation":933,"properties":20},{"id":907,"createTime":908,"updateTime":908,"relativeEntities":934,"slug":20,"properties":935,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":936},{"VI":912},{"title":938},{"VI":939},"James V. 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Hill",{"url":899,"publisher":977,"properties":1005},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":978,"slug":10,"properties":979,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":983,"manageAffiliations":984,"indexDatabases":985,"url":110,"thumbnailPath":20,"statistic":1000,"gsStatistic":20,"type":121,"analyzePriority":20},[],{"issn":980,"eissn":981,"title":982},{"VOID":13},{"VOID":15},{"EN":17},[],[],[986,993],{"id":93,"indexDatabase":987,"url":20,"indexYears":20,"academicFieldIds":992,"indexDatabaseRanking":20},{"id":95,"createTime":96,"updateTime":97,"relativeEntities":988,"label":989,"description":990,"key":104,"publicationTags":991,"standard":20},[],{"EN":100,"VI":100},{"VI":102,"EN":103},[106,107],[109],{"id":72,"indexDatabase":994,"url":85,"indexYears":86,"academicFieldIds":999,"indexDatabaseRanking":91},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":995,"label":996,"description":997,"key":82,"publicationTags":998,"standard":20},[],{"EN":79,"VI":79},{"EN":79,"VI":81},[84],[88,89,90],{"impactFactor":21,"impactFactorByYear":1001,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":113,"totalPublicationByYear":1002,"totalCitation":21,"totalCitationByYear":1003,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1004,"hindexLast5Year":21,"hindex":21},{},{"1989":115,"1993":115,"1997":115,"1998":116,"1999":115,"2000":115,"2001":115,"2008":117,"2015":115,"2016":115,"2019":115,"2020":118,"2022":115},{},{},{"volume":1006,"pages":1007},{"VOID":526},{"VOID":1008},"208-218","2007-03-06",{"id":1011,"createTime":1012,"updateTime":1013,"relativeEntities":1014,"slug":1015,"properties":1016,"entityType":141,"verifyStatus":142,"verifyTime":1013,"verifyNote":144,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1025,"fullTextUrl":20,"authors":1026,"publicationType":187,"publisherRelationship":1078,"citationCount":20,"citationInfo":20,"publishDate":1112,"publishYear":1113,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":224},"0289fbc4-ce0a-45d5-a653-740f0cc74f18","2024-01-13T01:13:55.036+00:00","2025-02-01T23:53:43.927+00:00",[],"Binary-Classification-of-Alzheimer-s-Disease-Using-sMRI-Imaging-Modality-and-Deep-Learning",{"references":1017,"abstract":1019,"title":1021,"doi":1023},{"VOID":1018},"Kelley BJ, Petersen RC: Alzheimer’s disease and mild cognitiveimpairment. Neurol Clin 25(3):577–609, 2007\nSperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack, Jr CR, Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH: Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3):280–292, 2011\nAisen PS, Cummings J, Jack, Jr CR, Morris JC, Sperling R, Frölich L, Jones RW, Dowsett SA, Matthews BR, Raskin J, Scheltens P, Dubois B: On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimers Res Ther 9(1):60, 2017\nCuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2):766–781, 2011\nArevalo-Rodriguez I, Smailagic N, Roque IFM, Ciapponi A, Sanchez-Perez E, Giannakou A, Pedraza OL, Bonfill Cosp X, Cullum S: Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev, 2015\nHarrell LE, Marson D, Chatterjee A, Parrish JA: The Severe Mini-Mental State Examination: a new neuropsychologic instrument for the bedside assessment of severely impaired patients with Alzheimer disease. Alzheimer Dis Assoc Disord 14(3):168–175, 2000\nPangman VC, Sloan J, Guse L: An examination of psychometric properties of the mini-mental state examination and the standardized minimental state examination: implications for clinical practice. Appl Nurs Res 13(4):209–213, 2000\nAkkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ: Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30(4):449–459, 2017\nLiu M, Cheng D, Yan W, Alzheimer’s Disease Neuroimaging Initiative: Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform, 2018. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffninf.2018.00035\nSarraf S, Tofighi G: DeepAD: Alzheimer′s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv, 2016. https:\u002F\u002Fdoi.org\u002F10.1101\u002F070441\nFischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM: Automatically parcellating the human cerebral cortex. Cereb Cortex 14(1):11–22, 2004\nBechara A, Damasio H, Damasio AR: Emotion, decision making and the orbitofrontal cortex. Cereb Cortex 10(3):295–307, 2000\nJones BF, Barnes J, Uylings HB, Fox NC, Frost C, Witter MP, Scheltens P: Differential regional atrophy of the cingulate gyrus in Alzheimer disease: a volumetric MRI study. Cereb Cortex 16(12):1701–1708, 2006\nCoulthard EJ, Love S: A broader view of dementia: multiple co-pathologies are the norm. Brain 141(7):1894–1897, 2018\nGao S, Casey AE, Sargeant TJ, Mäkinen V-P: Genetic variation within endolysosomal system is associated with late-onset Alzheimer’s disease. Brain 141(9):2711–2720, 2018\nLorenzetti V, Allen NB, Fornito A, Yucel M: Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. J Affect Disord 117(1–2):1–17, 2009\nWen J, Thibeau-Sutre E, Samper-Gonzalez J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. arXiv preprint arXiv 1904:07773v1, 2019\nKarim Aderghal, Manuel Boissenin, Jenny Benois-Pineau, Gwenaelle Catheline, Karim Afdel: Classification of sMRI for AD diagnosis with convolutional neuronal networks: a pilot 2-D+E Study on ADNI. MultiMedia modeling, lecture notes in computer science. Springer International Publishing, DOI: https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-51811-4_56, 690–701, 2017\nKarim Aderghal, Alexander Khvostikov, Andrei Krylov, Jenny Benois-Pineau, Karim Afdel, Gwenaelle Catheline: Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS): 345–350, 2018\nBäckström K, Nazari M, Gu IY-H, Jakola AS: An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images. IEEE 15th International Symposium on Biomedical Imaging (ISBI):149–153, 2018\nDanni Cheng, Manhua Liu, Jianliang Fu, Yaping Wang: Classification of MR Brain images by combination of multi-CNNs for AD diagnosis. SPIE ninth international conference on digital image processing (ICDIP): 1042042–1-5, 2017. DOI: https:\u002F\u002Fdoi.org\u002F10.1117\u002F12.2281808\nDanni Cheng, Manhua Liu: CNNs based multi-modality classification for AD diagnosis. IEEE 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017\nSergey Korolev, Amir Safiullin, Mikhail Belyaev, Yulia Dodonova: Residual and plain convolutional neural networks for 3D brain MRI classification. IEEE 14th International Symposium on Biomedical Imaging (ISBI): 835–838, 2017\nFan Li, Danni Cheng, Manhua Liu: Alzheimer’s disease classification based on combination of multi-model convolutional networks. IEEE International Conference on Imaging Systems and Techniques (IST): 1–5, 2017\nLi F, Liu M: Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Computerized Medical Imaging and Graphics 70:101–110, 2018\nChunfeng Lian, Mingxia Liu, Jun Zhang, Dinggang Shen: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), DOI: https:\u002F\u002Fdoi.org\u002F10.1109\u002FTPAMI.2018.2889096, 21 December 2018, 2018\nNie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, Wang Q, Shen D: Medical image synthesis with deep convolutional adversarial networks. IEEE Transactions on Biomedical Engineering 65(12):2720–2730, 2018\nLiu M, Zhang J, Adeli E, Shen D: Landmark-based deep multi-instance learning for brain disease diagnosis. Medical Image Analysis 43:157–168, 2018\nLiu M, Zhang J, Nie D, Yap P-T, Shen D: Anatomical landmark based deep feature representation for MR images in brain disease diagnosis. IEEE Journal of Biomedical and Health Informatics 22(5):1476–1485, 2018\nQiu S, Chang GH, Panagia M, Gopal DM, Aue R, Kolachalama VB: Fusion of deep learning models of MRI scans, mini–mental state examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 10:737–749, 2018\nUpul Senanayake, Arcot Sowmya, Laughlin Dawes: Deep fusion pipeline for mild cognitive impairment diagnosis. IEEE 15th International Symposium on Biomedical Imaging (ISBI): 1394–1397, 2018\nShmulev Y, Belyaev M: Predicting conversion of mild cognitive impairments to Alzheimer’s disease and exploring impact of neuroimaging. In: Stoyanov D et al. Eds. Graphs in Biomedical image analysis and integrating medical imaging and non-imaging modalities. GRAIL 2018, beyond MIC 2018. Lecture notes in computer science, vol 11044. Cham: Springer, 2018, pp. 83–91\nValliani A, Soni A: Deep residual nets for improved Alzheimer’s diagnosis. ACM 8th International Conference on Bioinformatics. Computational Biology,and Health Informatics (ACM-BCB):615–615, 2017\nChollet F: Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv 1610:02357v3, 2016\nSzegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z: Rethinking the inception architecture for computer vision. arXiv preprint arXiv 1512:00567v3, 2015\nMarcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL: Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9):1498–1507, 2007\nJohn C: Morris: The clinical dementia rating (CDR). Neurology 43(11):2412–2412-a, 1993\nHon M, Khan N: Towards Alzheimer’s disease classification through transfer learning. arXiv preprint arXiv 1711:11117v1, 2017\nYosinski J, Clune J, Bengio Y, Lipson H: How transferable are features in deep neural networks? arXiv preprint arXiv 1411:1792v1, 2014",{"EN":1020},"Alzheimer’s disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) play an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception, as well as a custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning-based approaches demonstrating the effectiveness of these approaches for the binary AD classification task.",{"EN":1022},"Binary Classification of Alzheimer’s Disease Using sMRI Imaging Modality and Deep Learning",{"VOID":1024},"10.1007\u002Fs10278-019-00265-5","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-019-00265-5",[1027,1044,1056],{"id":1028,"sortIndex":118,"researcher":20,"roles":1029,"affiliations":1030,"properties":1041},"cfc81e87-6a45-43d9-9b46-c6eb662471f8",[150],[1031],{"id":20,"sortIndex":21,"affiliation":1032,"properties":20},{"id":1033,"createTime":1034,"updateTime":1035,"relativeEntities":1036,"slug":1037,"properties":1038,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"16c5bec7-4f65-46d5-b346-1e1fa7dafa67","2023-11-26T17:00:47.503+00:00","2025-01-03T11:40:48.632+00:00",[],"Harbin-Institute-of-Technology-Harbin-China",{"title":1039},{"VI":1040},"Harbin Institute of Technology, Harbin, China",{"title":1042},{"VI":1043},"Qiu-Na Zhang",{"id":1045,"sortIndex":115,"researcher":20,"roles":1046,"affiliations":1047,"properties":1053},"9e3ad938-82d4-4e22-8c04-9ad3f4955898",[150],[1048],{"id":20,"sortIndex":21,"affiliation":1049,"properties":20},{"id":1033,"createTime":1034,"updateTime":1035,"relativeEntities":1050,"slug":1037,"properties":1051,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1052},{"VI":1040},{"title":1054},{"VI":1055},"Yong-Kui Ma",{"id":1057,"sortIndex":21,"researcher":20,"roles":1058,"affiliations":1059,"properties":1075},"6f369ee5-4d00-45ee-b31a-a6bfd1bed189",[150],[1060,1070],{"id":1061,"sortIndex":115,"affiliation":1062,"properties":1069},"338fc734-9e3e-4b7e-a759-f9b032f830b3",{"id":1063,"createTime":1064,"updateTime":1064,"relativeEntities":1065,"slug":20,"properties":1066,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"5a1d693d-f060-4425-8f05-87fc79882121","2023-12-11T19:07:11.571+00:00",[],{"title":1067},{"VI":1068},"COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan",{},{"id":20,"sortIndex":21,"affiliation":1071,"properties":20},{"id":1033,"createTime":1034,"updateTime":1035,"relativeEntities":1072,"slug":1037,"properties":1073,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1074},{"VI":1040},{"title":1076},{"VI":1077},"Ahsan Bin Tufail",{"url":1025,"publisher":1079,"properties":1107},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1080,"slug":10,"properties":1081,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1085,"manageAffiliations":1086,"indexDatabases":1087,"url":110,"thumbnailPath":20,"statistic":1102,"gsStatistic":20,"type":121,"analyzePriority":20},[],{"issn":1082,"eissn":1083,"title":1084},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1088,1095],{"id":93,"indexDatabase":1089,"url":20,"indexYears":20,"academicFieldIds":1094,"indexDatabaseRanking":20},{"id":95,"createTime":96,"updateTime":97,"relativeEntities":1090,"label":1091,"description":1092,"key":104,"publicationTags":1093,"standard":20},[],{"EN":100,"VI":100},{"VI":102,"EN":103},[106,107],[109],{"id":72,"indexDatabase":1096,"url":85,"indexYears":86,"academicFieldIds":1101,"indexDatabaseRanking":91},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":1097,"label":1098,"description":1099,"key":82,"publicationTags":1100,"standard":20},[],{"EN":79,"VI":79},{"EN":79,"VI":81},[84],[88,89,90],{"impactFactor":21,"impactFactorByYear":1103,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":113,"totalPublicationByYear":1104,"totalCitation":21,"totalCitationByYear":1105,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1106,"hindexLast5Year":21,"hindex":21},{},{"1989":115,"1993":115,"1997":115,"1998":116,"1999":115,"2000":115,"2001":115,"2008":117,"2015":115,"2016":115,"2019":115,"2020":118,"2022":115},{},{},{"volume":1108,"pages":1110},{"VOID":1109},"33",{"VOID":1111},"1073-1090","2020-07-29",2020,{"id":1115,"createTime":1116,"updateTime":1117,"relativeEntities":1118,"slug":1119,"properties":1120,"entityType":141,"verifyStatus":142,"verifyTime":1117,"verifyNote":144,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1129,"fullTextUrl":20,"authors":1130,"publicationType":187,"publisherRelationship":1218,"citationCount":20,"citationInfo":20,"publishDate":1252,"publishYear":1253,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":224},"9fac123d-7505-430c-82c5-69321f844675","2023-12-11T01:51:42.894+00:00","2024-10-23T23:53:40.268+00:00",[],"Characterization-of-monochrome-CRT-display-systems-in-the-field",{"references":1121,"abstract":1123,"title":1125,"doi":1127},{"VOID":1122},"Honeyman JC, Forst MM, Staab EV, et al: Prototype picture archiving and communication system in clinical neuroradiology. Program of the Annual Meeting of the RSNA, Chicago, IL, November 1990\nHoneyman JC, Frost MM, Staab EV: Initial experiences with PACS in a clinical and research environment. SPE Medical Imaging V, San Jose, CA, February 1991\nCapp MP, Roehrig H, Seeley GW: The Digital radiology department of the future. Radiol Clin North Am, 1985\nSeeley GW, Ovitt TW, Capp MP: The total digital radiology department: An alternative view. AJR 144:421, 1985\nHuang HK (moderator): Advances in medical imaging. Ann Intern Med 112:203–220, 1990\nGoodman LR, Wilson CR, Foley WD: Digital radiography of the chest: Promises and problems. AJR 150:1241–1252, 1988\nChang J, Channin S, Przybylowicz J, et al: The Lightbox Part 1. RSNA Special Course in Computers in Radiology 1997, pp 61–70\nScott W, Bluemke D, Mysko W, et al: Interpretation of emergency department radiographs by radiologists and emergency medicine physicians: Teleradiology workstation versus radiographs readings. Radiology 195:223–229, 1995\nScott W, Rosenbaum J, Ackerman S, et al: Subtle Orthopedic Fractures: Teleradiology Workstation versus film interpretation. Radiology 187: 811–815, 1993\nMcLelland R, Hendrick RE, Zinninger MD, et al: The American College of Radiology Mammography Accreditation Program. AJR 157:473–479, 1991\nAmerican College of Radiology-Committee on Quality Assurance in Mammography, Mammography Quality Control-Medical Physicist's Manual, Appendix 2 (Measurement of viewbox luminance, illuminance and colortemperature). American College of Radiology. Reston, VA, 1992\nHendrick RE: Standardization of image quality and radiation dose in mammograph. Radiology 174:648–654, 1990\nBlume H: The ACR-NEMA Proposal for a Gray-Scale Display Function Standard. Proc SPIE 2707:344–360, 1996\nAAPM Task Force 18: Acceptance Testing and Quality Control of Electronic Display Devices for Soft-Copy Display of Medical Images, University of South Carolina, November 1998\nSymer O, Orwin Associates: Personal communication, May 1994\nGray JE, Stears J, Wondrow M: Quality Control of Video Components and Display Devices. Proc SPIE 486:64–71, 1984\nGray JE, Lisk KG, Haddick DH, et al: Test pattern for video displays and hard-copy cameras. Radiology 154:519–527, 1985\nLisk KG: SMPTE test pattern for certification of medical diagnostic display devices. Proc SPIE 486:79–82, 1984\nParsons DM, Kim Y: Quality control assessment for the medical diagnostic imaging support (MDIS) system's display monitors. SPIE Medical Imaging 2164:186–197, 1994\nSamei E, Flynn MJ: Acceptance testing of image display monitors. Henry Ford Health System, Detroit, MI; Personal communication, October 1998\nHemminger BM, Johnston RE, Rolland JP, et al: Perceptual linearization of video display monitors for medical image presentation. Proc SPIE 2164:222–241, 1994\nBlume H, Roehrig H, Ji T-L: Very high resolution CRT display systems: Update on the state of the art of very high resolution monochrome CRT displays. SID 92 Digest 1992, pp 699–702\nBlume H, Roehrig H, Ji T-L, et al: Very-high resolution monochrome CRT displays: How good are they really?. SID 91 Digest 1991, pp 355–358\nRoehrig H, Blume H, Ji T-L, et al: Performance test and quality control of cathode ray tube displays. J Digit Imaging 3:134–145, 1990\nRoehrig H, Blume H, Ji T-L, et al: Noise of CRT display systems. Proc SPIE 1897:232–245, 1993\nRoehrig H, Dallas WJ, Ji T-L, et al: Physical evaluation of CRTs for use in digital radiography. Proc SPIE 1091:262–278, 1989\nRoehrig H, Ji T-L, Browne M, et al: Signal-to-noise ratio and maximum information content of images displayed by a CRT. Proc SPIE. 1232:115–133, 1990\nPrzybylowicz J: Dome Imaging Systems; Personal communication, November 1997\nMatthijs P: BARCO Display Systems; Personal communication, June 1999\nCompton K: Clinton Electronics; Personal communication, May 1999\nVolbrecht M: Image Systems Inc; Personal communication, May 1999\nEckhardt W, Siemens AG: Personal communication, May 1999\nwww.image-smiths.com\nVan Metter R, Zhao BS, Kohm K: The sensitivity of visual targets for display quality assessment. Proc SPIE 3658: 254–268, 1999\nBriggs SJ: Digital display test target development. Boeing Aerospace Co, Report No.D190-15960-1, 1977\nBriggs SJ, Heagy D, Holmes R: Visual test target for display evaluation. SID 93 Digest 1993, pp 396–399\nBriggs SJ: Manual: Digital test target BTP #4. Boeing Aerospace Co, Report No. DI 80 25066-1, 1979\nSofTrack Version 3.0: A Quality Control System for Display Performance; National Information Display Laboratory, a Division of David Sarnoff Research Center Inc, Princeton, NJ\nHangiandreou NJ, Fetterly KA, Bernatz SN, et al: Quantitative evaluation of overall electronic display quality. J Digit Imaging 11:180–186, 1998",{"EN":1124},"This article presents a review of image quality assessment methods for monochrome CRTs in the field as opposed to the laboratory. The review includes image quality programs at the University of Washington, the University of Texas at Houston, the University of Michigan, and the University of Arizona. CRT manufacturers and display-board suppliers also are concerned with image quality, particularly with respect to the life time of the CRT. The programs show that the need for image quality assessment for CRTs in the clinic is recognized. Although several experimental programs are in place, there is no universally accepted program. In fact, the clinical consequences of degraded monitor performance are not even well known and must be established. The existing programs mainly are based on the most comprehensive test pattern, the SMPTE pattern. The programs permit assessment of maximum luminance, display function, dynamic range, and contrast. They do not permit assessment of spatial resolution. There is no easy method to determine the spatial resolution in the field as precisely as desired simply because there are no visual aids (test patterns) to reliably determine loss of spatial resolution and signal-to-noise ratio using human observers. This report also presents initial and encouraging data obtained at the University of Arizona with a CCD camera. This CCD camera has the potential to be developed into an important tool for practical CRT evaluation for the clinic.",{"EN":1126},"Characterization of monochrome CRT display systems in the field",{"VOID":1128},"10.1007\u002FBF03168851","http:\u002F\u002Flink.springer.com\u002F10.1007\u002FBF03168851",[1131,1166,1192],{"id":1132,"sortIndex":115,"researcher":20,"roles":1133,"affiliations":1134,"properties":1163},"d18b0b82-7051-4114-a46a-0de17dba814d",[150],[1135,1145,1153],{"id":1136,"sortIndex":115,"affiliation":1137,"properties":1144},"a026a97c-83d0-45a4-b627-db63e32dc259",{"id":1138,"createTime":1139,"updateTime":1139,"relativeEntities":1140,"slug":20,"properties":1141,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"4e639ba4-da40-4462-842f-cac7ce12c0ce","2023-12-11T01:51:42.914+00:00",[],{"title":1142},{"VI":1143},"Diagnostic Imaging Services, Texas Children's Hospital, Tucson",{},{"id":20,"sortIndex":21,"affiliation":1146,"properties":20},{"id":1147,"createTime":1148,"updateTime":1148,"relativeEntities":1149,"slug":20,"properties":1150,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"635909c2-559a-4b40-9bf1-ce842b13123d","2023-12-11T01:51:42.909+00:00",[],{"title":1151},{"VI":1152},"Department of Radiology, University of Arizona, Radiology Research Lab, Tucson",{"id":1154,"sortIndex":118,"affiliation":1155,"properties":1162},"3d1d2c40-b5a4-4508-90b3-a97917ef7983",{"id":1156,"createTime":1157,"updateTime":1157,"relativeEntities":1158,"slug":20,"properties":1159,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"83fb3b2d-bef4-4850-a307-06795505738c","2023-12-11T01:51:42.939+00:00",[],{"title":1160},{"VI":1161},"Roper Scientific Corp, Tucson",{},{"title":1164},{"VI":1165},"Charles E. 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Devasena, C., Hemalatha, M.: Noise Removal in Magnetic Resonance Images using Hybrid KSL Filtering Technique: International Journal of Computer Applications. 2011.\nPáez Aguilar, S.E., Mújica-Vargas, D., Vianney Kinani, J.M.: Supresión de ruido Riciano en imágenes de resonancia magnética del cerebro utilizando un algoritmo de promedio local y global: Research in Computing Science. 2018.\nV.R., S., Edla, D.R., Joseph, J., Kuppili, V.: Analysis of controversies in the formulation and evaluation of restoration algorithms for MR Images: Expert Systems with Applications. 2019.\nWiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C.: Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI: Presented at the 2008.\nAnand, C.S., Sahambi, J.S.: MRI denoising using bilateral filter in redundant wavelet domain: In: IEEE Region 10 Annual International Conference, Proceedings\u002FTENCON. 2008.\nLambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., Van Stiphout, R.G.P.M., Granton, P., Zegers, C.M.L., Gillies, R., Boellard, R., Dekker, A., Aerts, H.J.W.L.: Radiomics: Extracting more information from medical images using advanced feature analysis: European Journal of Cancer. 2012.\nExhibit, S., Company, F., Palomo, R.: Analysis of weekly MR image quality assurance controls in spectroscopy quantification. 1–7 , 2013.\nMartí-Bonmatí, L., Alberich-Bayarri, Á., Ladenstein, R., Blanquer, I., Segrelles, J.D., Cerdá-Alberich, L., Gkontra, P., Hero, B., García-Aznar, J.M., Keim, D., Jentner, W., Seymour, K., Jiménez-Pastor, A., González-Valverde, I., Martínez de las Heras, B., Essiaf, S., Walker, D., Rochette, M., Bubak, M., Mestres, J., Viceconti, M., Martí-Besa, G., Cañete, A., Richmond, P., Wertheim, K.Y., Gubala, T., Kasztelnik, M., Meizner, J., Nowakowski, P., Gilpérez, S., Suárez, A., Aznar, M., Restante, G., Neri, E.: PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers: European Radiology Experimental. 2020.\nIsa, I.S., Sulaiman, S.N., Mustapha, M., Darus, S.: Evaluating denoising performances of fundamental filters for T2-weighted MRI images: In: Procedia Computer Science 2015.\nAlvarez, L., Lions, P.L., Morel, J.M.: Image selective smoothing and edge detection by nonlinear diffusion. II: SIAM Journal on Numerical Analysis. 1992.\nSethian, J. a.: Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. 1999.\nCappabianco, F.A.M., Dos Santos, S.R.B., Ide, J.S., Da Silva, P.P.C.E.: Non-Local Operational Anisotropic Diffusion Filter: In: Proceedings - International Conference on Image Processing, ICIP. 2019.\nBuades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. II: 60–65 , 2005.\nManjón, J. V., Carbonell-Caballero, J., Lull, J.J., García-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using Non-Local Means: Medical Image Analysis. 2008.\nUdomhunsakul, S., Wongsita, P.: Feature extraction in medical MRI images: In: 2004 IEEE Conference on Cybernetics and Intelligent Systems. pp. 340–344 2004.\nBalafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain MRI image segmentation methods, https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10462-010-9155-0. 2010.\nXiao, K., Ho, S.H., Salih, Q.: A study: Segmentation of lateral ventricles in brain MRI using fuzzy C-means clustering with gaussian smoothing: In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 161–170. Springer Verlag 2007.\nDas, P., Pal, C., Chakrabarti, A., Acharyya, A., Basu, S.: Adaptive denoising of 3D volumetric MR images using local variance based estimator: Biomedical Signal Processing and Control. 2020.\nNair, R.R., David, E., Rajagopal, S.: A robust anisotropic diffusion filter with low arithmetic complexity for images: Eurasip Journal on Image and Video Processing. 2019.\nKaimal, A.B., Priestly Shan, B.: Removing the traces of median filtering via unsharp masking as an anti-forensic approach in medical imaging: Biomedical and Pharmacology Journal. 2019.\nBiswas, S., Aggarwal, H.K., Jacob, M.: Dynamic MRI using model‐based deep learning and SToRM priors: MoDL‐SToRM: Magnetic Resonance in Medicine. 82: 485–494 , 2019.\nKidoh, M., Shinoda, K., Kitajima, M., Isogawa, K., Nambu, M., Uetani, H., Morita, K., Nakaura, T., Tateishi, M., Yamashita, Y., Yamashita, Y.: Deep learning based noise reduction for brain MR imaging: Tests on phantoms and healthy volunteers: Magnetic Resonance in Medical Sciences. 19: 195–206 , 2020.\nZhang, X., Feng, X., Wang, W., Xue, W.: Edge strength similarity for image quality assessment: IEEE Signal Processing Letters. 2013.\nIsaksson, L.J., Raimondi, S., Botta, F., Pepa, M., Gugliandolo, S.G., De Angelis, S.P., Marvaso, G., Petralia, G., De Cobelli, O., Gandini, S., Cremonesi, M., Cattani, F., Summers, P., Jereczek-Fossa, B.A.: Effects of MRI image normalization techniques in prostate cancer radiomics: Physica Medica. 71: 7–13 , 2020.\nAetesam, H., Maji, S.K.: ℓ2-ℓ1 Fidelity based Elastic Net Regularisation for Magnetic Resonance Image Denoising: 2020 International Conference on Contemporary Computing and Applications, IC3A. 2020: 137–142 , 2020.\nRoy, S., Whitehead, T.D., Quirk, J.D., Salter, A., Ademuyiwa, F.O., Li, S., An, H., Shoghi, K.I.: Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging: EBioMedicine. 59: 102963 , 2020.\nBologna, M., Corino, V., Mainardi, L.: Technical Note : Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. 1–8 , 2019.\nMoradmand, H., Aghamiri, S.M.R., Ghaderi, R.: Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma: Journal of Applied Clinical Medical Physics. 21: 179–190 , 2020.",{"EN":1264},"Several noise sources, such as the Johnson–Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity–based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.",{"EN":1266},"MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging",{"VOID":1268},"10.1007\u002Fs10278-021-00512-8","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10278-021-00512-8",[1271,1286,1301,1316,1328,1348,1360],{"id":1272,"sortIndex":118,"researcher":20,"roles":1273,"affiliations":1274,"properties":1283},"a1fdb12b-a73f-4c47-af60-e862ec8acb6f",[150],[1275],{"id":20,"sortIndex":21,"affiliation":1276,"properties":20},{"id":1277,"createTime":1278,"updateTime":1278,"relativeEntities":1279,"slug":20,"properties":1280,"entityType":57,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"49b6d447-a360-4e31-bbf1-bdb61c629f59","2024-01-17T05:52:24.701+00:00",[],{"title":1281},{"VI":1282},"Área Clínica de Imagen Médica, Hospital 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YS: Keratoconus[J]. Surv Ophthalmol, 297–319, 1998\nReeves SW, Stinnett S, Adelman RA, Afshari NA: Risk factors for progression to penetrating keratoplasty in patients with keratoconus. Am J Ophthalmol, 140:607-611, 2005\nPerez-Straziota C, Gaster RN, Rabinowitz YS: Corneal cross-linking for pediatric keratcoconus review. Cornea, 37(6):802–809, 2018\nDaxer A, Misof K, Grabner B, Ettl A, Fratzl P: Collagen fibrils in the human corneal stroma: structure and aging. Invest Ophthalmol Vis Sci, 39(3):644–648, 1998\nLéoni-Mesplié S, Mortemousque B, Mesplié N, Touboul D, Praud D, Malet F, Colin J: Epidemiological aspects of keratoconus in children[J]. Journal francais d'ophtalmologie, 35(10): 776-785, 2012\nBarr JT, Wilson BS, Gordon MO, Rah MJ, Riley C, Kollbaum PS, Zadnik K; CLEK Study Group: Estimation of the incidence and factors predictive of corneal scarring in the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) Study. Cornea, 25(1):16–25, 2006\nLéoni-Mesplié S, Mortemousque B, Touboul D, Malet F, Praud D, Mesplié N, Colin J: Scalability and severity of keratoconus in children. Am J Ophthalmol, 154(1):56–62.e1, 2012\nShalchi Z, Wang X, Nanavaty MA: Safety and efficacy of epithelium removal and transepithelial corneal collagen crosslinking for keratoconus. Eye (Lond), 29(1):15–29, 2015\nKankariya VP, Kymionis GD, Diakonis VF, Yoo SH: Management of pediatric keratoconus - evolving role of corneal collagen cross-linking: an update. Indian J Ophthalmol, 61(8):435-440, 2013\nAmbrosio R, Jr., Alonso RS, Luz A, Coca Velarde LG: Corneal-thickness spatial profile and corneal-volume distribution: tomographic indices to detect keratoconus. J Cataract Refract Surg, 32:1851-1859, 2006\nWang MFZ, Fernandez-Gonzalez R: (Machine-)learning to analyze in vivo microscopy: support vector machines. Biochim Biophys Acta Proteins Proteom, 1865:1719-1727, 2017\nHersh PS, Greenstein SA, Fry KL: Corneal collagen crosslinking for keratoconus and corneal ectasia: one-year results. J Cataract Refract Surg, 37:149-160, 2011\nKoller T, Pajic B, Vinciguerra P, Seiler T: Flattening of the cornea after collagen crosslinking for keratoconus. J Cataract Refract Surg, 37(8):1488–1492, 2011\nMukhtar S, Ambati BK: Pediatric keratoconus: a review of the literature. Int Ophthalmol, 38(5):2257-2266, 2018\nDuncan JK, Belin MW, Borgstrom M: Assessing progression of keratoconus: novel tomographic determinants. Eye Vis (Lond), 3:6, 2016\nGomes JA, Tan D, Rapuano CJ, Belin MW, Ambrósio R Jr, Guell JL, Malecaze F, Nishida K, Sangwan VS: Global consensus on keratoconus and ectatic diseases. Cornea, 34(4):359-369, 2015\nMahmoud AM, Nuñez MX, Blanco C, Koch DD, Wang L, Weikert MP, Frueh BE, Tappeiner C, Twa MD, Roberts CJ: Expanding the cone location and magnitude index to include corneal thickness and posterior surface information for the detection of keratoconus. Am J Ophthalmol, 156(6):1102–1111, 2013\nKanellopoulos AJ, Moustou V, Asimellis G: Evaluation of visual acuity, pachymetry and anterior-surface irregularity in keratoconus and crosslinking intervention follow-up in 737 cases. J Kerat Ect Cor Dis, 2(3):95–103, 2013\nSuzuki M, Amano S, Honda N, Usui T, Yamagami S, Oshika T: Longitudinal changes in corneal irregular astigmatism and visual acuity in eyes with keratoconus. Jpn J Ophthalmol, 51(4):265–269, 2007\nSefic Kasumovic S, Racic-Sakovic A, Kasumovic A, Pavljasevic S, Duric-Colic B, Cabric E, Mavija M, Lepara O, Jankov M: Assessment of the tomographic values in keratoconic eyes after collagen crosslinking procedure. Med Arch, 69(2):91–94, 2015\nKanellopoulos AJ, Asimellis G: Revisiting keratoconus diagnosis and progression classification based on evaluation of corneal asymmetry indices, derived from Scheimpflug imaging in keratoconic and suspect cases. Clin Ophthalmol, 7:1539–1548, 2013\nMaeda N, Klyce SD, Smolek MK, Thompson HW: automated keratoconus screening with corneal topography analysis. Invest Ophthalmol Vis Sci, 35:2749–2757, 1994\nSaad A, Gatinel D: Topographic and tomographic properties of forme fruste keratoconus corneas. Invest Ophthalmol Vis Sci, 51:5546–5554, 2010\nUçakhan ÖÖ, Cetinkor V, Özkan M, Kanpolat A: Evaluation of Scheimpflug imaging parameters in subclinical keratoconus, keratoconus, and normal eyes. J Cataract Refract Surg, 37:1116–1124, 2011\nArbelaez MC, Versaci F, Vestri G, Barboni P, Savini G: Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology, 119(11):2231-2238, 2012\nKohlhaas M, Spoerl E, Schilde T, Unger G, Wittig C, Pillunat LE: Biomechanical evidence of the distribution of cross-links in corneas treated with riboflavin and ultraviolet A light. J Cataract Refract Surg, 32(2):279–283, 2006\nMazzotta C, Balestrazzi A, Traversi C, Baiocchi S, Caporossi T, Tommasi C, Caporossi A: Treatment of progressive keratoconus by riboflavin-UVA-induced cross-linking of corneal collagen: ultrastructural analysis by Heidelberg Retinal Tomograph II in vivo confocal microscopy in humans. Cornea, 26(4):390–397, 2007\nCaporossi A, Mazzotta C, Baiocchi S, Caporossi T, Denaro R, Balestrazzi A: Riboflavin-UVA-induced corneal collagen cross-linking in pediatric patients. Cornea, 31(3):227–231, 2012\nVinciguerra P, Albé E, Frueh BE, Trazza S, Epstein D: Two-year corneal cross-linking results in patients younger than 18 years with documented progressive keratoconus. Am J Ophthalmol, 154(3):520–526, 2012\nMazzotta C, Caporossi T, Denaro R, Bovone C, Sparano C, Paradiso A, Baiocchi S, Caporossi A: Morphological and Functional Correlations in Riboflavin Uv a Corneal Collagen Cross-Linking for Keratoconus. Acta Ophthalmol, 90:259-265, 2012\nMagli A, Forte R, Tortori A, Capasso L, Marsico G, Piozzi E: Epithelium-off corneal collagen cross-linking versus transepithelial cross-linking for pediatric keratoconus. Cornea, 32(5):597–601, 2013\nKoller T, Iseli HP, Donitzky C, Ing D, Papadopoulos N, Seiler T: Topography-guided surface ablation for forme fruste keratoconus. Ophthalmology, 113(12):2198–2202, 2006\nKanellopoulos AJ, Binder PS: Collagen cross-linking (CCL) with sequential topography-guided PRK: a temporizing alternative for keratoconus to penetrating keratoplasty. Cornea, 26(7):891–895, 2007\nAmbrósio R Jr, Klyce SD, Wilson SE: Corneal topographic and pachymetric screening of keratorefractive patients. J Refract Surg, 19(1):24-29, 2003\nPiñero DP, Alió JL, Alesón A, Escaf Vergara M, Miranda M: Corneal volume, pachymetry, and correlation of anterior and posterior corneal shape in subclinical and different stages of clinical keratoconus. J Cataract Refract Surg, 36(5):814–825, 2010\nKobashi H, Rong SS: Corneal collagen cross-linking for keratoconus: systematic review. Biomed Res Int, 2017:8145651, 2017\nCavas-Martínez F, De la Cruz Sánchez E, Nieto Martínez J, Fernández Cañavate FJ, Fernández-Pacheco DG: Corneal topography in keratoconus: state of the art. Eye Vis (Lond), 3:5, 2016",{"EN":1418},"The study aimed to evaluate the keratectasia volume (KEV) before and after corneal cross-linking (CXL) in pediatric patients. This study included 40 eyes of 25 pediatric patients (10–19 years) undergoing standard CXL. The support vector machine (SVM) algorithm was applied to transform mass pixels in corneal topography into a three-dimensioned model to calculate the KEV. The KEV, Kmax, K1, K2, Kave, keratectasia area (KEA), and thinnest corneal thickness (TCT) were determined before CXL and at 3, 6, and 12 months after surgery. The correlation between KEV and other parameters (Kmax, TCT, max decentration, eccentricity, and so on) was calculated. The KEV was 4.75 ± 0.74 preoperatively and 4.43 ± 1.22 postoperatively at last follow-up (p \u003C 0.002). There was strong positive correlation between the KEV and Kmax (r = 0.806, p \u003C 0.0005). The preoperat ive KEV was 4.32 ± 0.69 in mild to moderate keratoconus (Kmax \u003C 58D) and 5.27 ± 0.37 in advanced keratoconus (Kmax > 58D) (p \u003C 0.0005, t-test). Postoperative KEV and K readings remained stable at the early stage, and the KEV showed a more drastic decreasing trend than Kmax at sixth month. Statistical significance was found in the KEV between preoperative and 6 months after surgery (p \u003C 0.0005), but not in Kmax and other parameters. In 83.3% (15 eyes out of 18 eyes) of the eyes, the preoperative KEV was greater than 4.6 in patients with significant flattening after CXL. Compared with K readings, the KEV can be regarded as a more sensitive index to evaluate the postoperative morphological changes after CXL in pediatric patients.",{"EN":1420},"The Keratectasia Volume (KEV) in Corneal Topography to Evaluate the Effect of Corneal Collagen Cross-linking in Pediatric 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