دورية أكاديمية
أعرض في EDS

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network.

التفاصيل البيبلوغرافية
العنوان: Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network.
المؤلفون: Weaver JM; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States.; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States., DiPiero M; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States.; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States., Rodrigues PG; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States., Cordash H; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States., Davidson RJ; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States.; Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States.; Center for Healthy Minds, University of Wisconsin-Madison, Madison WI, United States.; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States., Planalp EM; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States.; Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States., Dean DC 3rd; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States.; Waisman Center, University of Wisconsin-Madison, Madison, WI, United States.; Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, United States.
المصدر: Imaging neuroscience (Cambridge, Mass.) [Imaging Neurosci (Camb)] 2023; Vol. 1. Date of Electronic Publication: 2023 Oct 17.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: The MIT Press Country of Publication: United States NLM ID: 9918663686606676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2837-6056 (Electronic) Linking ISSN: 28376056 NLM ISO Abbreviation: Imaging Neurosci (Camb) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Cambridge, Massachusetts : The MIT Press, [2023]-
مستخلص: Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.
Competing Interests: DECLARATION OF COMPETING INTEREST R.J.D. is the founder, president, and serves on the board of directors for the non-profit organization, Healthy Minds Innovations, Inc. In addition, R.J.D. served on the board of directors for the Mind & Life Institute from 1992 to 2017. All other authors declare that they have no competing interests.
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معلومات مُعتمدة: K01 MH113710 United States MH NIMH NIH HHS; P50 HD105353 United States HD NICHD NIH HHS; P50 MH100031 United States MH NIMH NIH HHS; U54 HD090256 United States HD NICHD NIH HHS; U01 DA055370 United States DA NIDA NIH HHS; R00 MH110596 United States MH NIMH NIH HHS
فهرسة مساهمة: Keywords: convolutional neural network; diffusion tensor imaging; diffusion weighted imaging; motion artifacts; pediatric neuroimaging; quality control
تواريخ الأحداث: Date Created: 20240212 Latest Revision: 20240214
رمز التحديث: 20240214
مُعرف محوري في PubMed: PMC10854394
DOI: 10.1162/imag_a_00023
PMID: 38344118
قاعدة البيانات: MEDLINE
الوصف
تدمد:2837-6056
DOI:10.1162/imag_a_00023