Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning.
Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 we...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 289 - 306 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162233246&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233246 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233246 158407353 162233246 162233246 10.1007/s10278-022-00690-z 162233246 ppf: 289 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning. aug: au: Cluceru, Julia Lupo, Janine M. Interian, Yannet Bove, Riley Crane, Jason C. affil: Center for Intelligent Imaging, Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA sug: subj: Brain Physiology Magnetic Resonance Imaging Methods Machine Learning Magnetic Resonance Imaging Equipment and Supplies Scanners Human Prospective Studies Descriptive Statistics Glioma Diagnosis Research, Medical Neuroradiography ab: Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 weighted, T1 post-contrast, T2 weighted, T2-weighted FLAIR, proton-density weighted. Prior studies have investigated using imaging-based methods and DICOM metadata-based methods with success on cohorts of patients acquired as part of a clinical trial. This study compares the performance of these methods on heterogeneous clinical datasets acquired with many different scanners from many institutions. RF and CNN models were trained on metadata and pixel data, respectively. A combined RF model incorporated CNN logits from the pixel-based model together with metadata. Four cohorts were used for model development and evaluation: MS research (n = 11,106 series), MS clinical (n = 3244 series), glioma research (n = 612 series, test/validation only), and ADNI PTSD (n = 477 series, training only). Together, these cohorts represent a broad range of acquisition contexts (scanners, sequences, institutions) and subject pathologies. Pixel-based CNN and combined models achieved accuracies between 97 and 98% on the clinical MS cohort. Validation/test accuracies with the glioma cohort were 99.7% (metadata only) and 98.4 (CNN). Accurate and generalizable classification of MRI acquisition contrast types was demonstrated. Such methods are important for enabling automated data selection in high-throughput and big-data image analysis applications. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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