Statistical normalization techniques for magnetic resonance imaging
Russell T. Shinohara,Elizabeth M. Sweeney,Jeff Goldsmith,Navid Shiee,Farrah J. Mateen,Peter A. Calabresi,Samson Jarso,Dzung L. Pham,Daniel S. Reich,Ciprian M. Crainiceanu +9 more
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TLDR
This work proposes simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria for the normalization of images.About:
This article is published in NeuroImage: Clinical.The article was published on 2014-01-01 and is currently open access. It has received 294 citations till now. The article focuses on the topics: Spatial normalization & Normalization (image processing).read more
Citations
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Journal ArticleDOI
Harmonization of cortical thickness measurements across scanners and sites.
Jean-Philippe Fortin,Nicholas C. Cullen,Yvette I. Sheline,Warren D. Taylor,Irem Aselcioglu,Philip A. Cook,Phil Adams,Crystal Cooper,Maurizio Fava,Patrick J. McGrath,Melvin G. McInnis,Mary L. Phillips,Madhukar H. Trivedi,Myrna M. Weissman,Russell T. Shinohara +14 more
TL;DR: It is shown that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses, and is useful for combining imaging data with the goal of studying life‐span trajectories in the brain.
Journal ArticleDOI
Harmonization of multi-site diffusion tensor imaging data.
Jean-Philippe Fortin,Drew Parker,Birkan Tunç,Takanori Watanabe,Mark A. Elliott,Kosha Ruparel,David R. Roalf,Theodore D. Satterthwaite,Ruben C. Gur,Raquel E. Gur,Robert T. Schultz,Ragini Verma,Russell T. Shinohara +12 more
TL;DR: It is shown that the DTI measurements are highly site‐specific, highlighting the need of correcting for site effects before performing downstream statistical analyses, and that ComBat, a popular batch‐effect correction tool used in genomics, performs best at modeling and removing the unwanted inter‐site variability in FA and MD maps.
Journal ArticleDOI
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.
Zhenyu Liu,Shuo Wang,Di Dong,Jingwei Wei,Cheng Fang,Xuezhi Zhou,Kai Sun,Longfei Li,Bo Li,Meiyun Wang,Jie Tian,Jie Tian +11 more
TL;DR: The recent methodological developments in radiomics are reviewed, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology.
Journal ArticleDOI
Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models
Philipp Kickingereder,Sina Burth,Antje Wick,Michael Götz,Oliver Eidel,Heinz Peter Schlemmer,Klaus H. Maier-Hein,Wolfgang Wick,Martin Bendszus,Alexander Radbruch,David Bonekamp +10 more
TL;DR: An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated.
Journal ArticleDOI
Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response
Philipp Kickingereder,Michael Götz,John Muschelli,Antje Wick,Ulf Neuberger,Russell T. Shinohara,Martin Sill,Martha Nowosielski,Heinz Peter Schlemmer,Alexander Radbruch,Wolfgang Wick,Martin Bendszus,Klaus H. Maier-Hein,David Bonekamp +13 more
TL;DR: The radiomic-based superpc signature emerges as a putative imaging biomarker for the identification of patients who may derive the most benefit from antiangiogenic therapy, advances the knowledge in the noninvasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision support in cancer treatment at low cost.
References
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Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Rafael A. Irizarry,Bridget G. Hobbs,Francois Collin,Yasmin Beazer-Barclay,Kristen J. Antonellis,Uwe Scherf,Terence P. Speed +6 more
TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
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A nonparametric method for automatic correction of intensity nonuniformity in MRI data
TL;DR: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present, and is applied at an early stage in an automated data analysis, before a tissue model is available.
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The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.
Clifford R. Jack,Matt A. Bernstein,Nick C. Fox,Paul M. Thompson,Gene E. Alexander,Danielle J Harvey,Bret J. Borowski,Paula J. Britson,Jennifer L. Whitwell,Chadwick P. Ward,Anders M. Dale,Joel P. Felmlee,Jeffrey L. Gunter,Derek L. G. Hill,Ronald J. Killiany,Norbert Schuff,Sabrina Fox-Bosetti,Chen Lin,Colin Studholme,Charles DeCarli,Gunnar Krueger,Heidi A. Ward,Gregory J. Metzger,Katherine T. Scott,Richard Philip Mallozzi,Daniel J. Blezek,Joshua Levy,Josef Phillip Debbins,Adam S. Fleisher,Marilyn S. Albert,Robert C. Green,George Bartzokis,Gary H. Glover,John P. Mugler,Michael W. Weiner +34 more
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A Class of Statistics with Asymptotically Normal Distribution
TL;DR: In this article, the authors considered the problem of estimating a U-statistic of the population characteristic of a regular functional function, where the sum ∑″ is extended over all permutations (α 1, α m ) of different integers, 1 α≤ (αi≤ n, n).
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