2-D magnetic resonance spectroscopic imaging of the pediatric brain using compressed sensing.
Background: Magnetic resonance spectroscopic imaging helps to determine abnormal brain tissue conditions by evaluating metabolite concentrations. Although a powerful technique, it is underutilized in routine clinical studies because of its long scan times.Objective: In this study, we evaluated the f...
| Publicado en: | Pediatric Radiology Vol. 49; no. 13; pp. 1798 - 1809 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2019
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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=139745167&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139745167 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03010449 O03 jtl: Pediatric Radiology issn: 03010449 maglogo: N pubinfo: dt: Dec2019 vid: 49 iid: 13 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139745167 139745167 NLM31463513 10.1007/s00247-019-04495-1 NLM31463513 139745167 ppf: 1798 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 2-D magnetic resonance spectroscopic imaging of the pediatric brain using compressed sensing. aug: au: Vidya Shankar, Rohini Hu, Houchun H. Bikkamane Jayadev, Nutandev Chang, John C. Kodibagkar, Vikram D. affil: School of Biological and Health Systems Engineering, Arizona State University, 501 East Tyler Mall, ECG346, 85287, Tempe, AZ, USA sug: subj: Seizures Magnetic Resonance Spectroscopy Methods Brain Neoplasms Image Interpretation, Computer Assisted Methods Aspartic Acid Metabolism Female Time Factors Brain Neoplasms Pathology Infant Sensitivity and Specificity Retrospective Design Sex Factors Male Brain Diseases Infant, Newborn Age Factors Brain Diseases Pathology Resource Databases Aspartic Acid Analogs and Derivatives Seizures Pathology Child Child, Preschool Prospective Studies Risk Assessment Adolescence Scales Infant: 1-23 months Infant, Newborn: birth-1 month Child: 6-12 years Child, Preschool: 2-5 years Adolescent: 13-18 years Female Male ab: Background: Magnetic resonance spectroscopic imaging helps to determine abnormal brain tissue conditions by evaluating metabolite concentrations. Although a powerful technique, it is underutilized in routine clinical studies because of its long scan times.Objective: In this study, we evaluated the feasibility of scan time reduction in metabolic imaging using compressed-sensing-based MR spectroscopic imaging in pediatric patients undergoing routine brain exams.Materials and Methods: We retrospectively evaluated compressed-sensing reconstructions in MR spectroscopic imaging datasets from 20 pediatric patients (11 males, 9 females; average age: 5.4±4.5 years; age range: 3 days to 16 years). We performed retrospective under-sampling of the MR spectroscopic imaging datasets to simulate accelerations of 2-, 3-, 4-, 5-, 7- and 10-fold, with subsequent reconstructions in MATLAB. Metabolite maps of N-acetylaspartate, creatine, choline and lactate (where applicable) were quantitatively evaluated in terms of the root-mean-square error (RMSE), peak amplitudes and total scan time. We used the two-tailed paired t-test along with linear regression analysis to statistically compare the compressed-sensing reconstructions at each acceleration with the fully sampled reference dataset.Results: High fidelity was maintained in the compressed-sensing MR spectroscopic imaging reconstructions from 50% to 80% under-sampling, with the RMSE not exceeding 3% in any dataset. Metabolite intensities and ratios evaluated on a voxel-by-voxel basis showed no statistically significant differences and mean metabolite intensities showed high correlation compared to the fully sampled reference dataset up to an acceleration factor of 5.Conclusion: Compressed-sensing MR spectroscopic imaging has the potential to reduce MR spectroscopic imaging scan times for pediatric patients, with negligible information loss. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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