Development and evaluation of an automated phase contrast magnetic resonance imaging algorithm for pediatric and adult cerebral blood flow measurement.
Purpose: Accurate Cerebral Blood Flow (CBF) measurements are essential for studying pediatric cerebral hemodynamics. Phase Contrast (PC) imaging is a fast, non-invasive, and non-radiating technique for measuring flows. PC image processing traditionally includes manually segmenting, identifying, and...
| Publicado en: | Neuroradiology Vol. 68; no. 7; pp. 1835 - 1848 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2026
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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=195653164&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195653164 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2026 vid: 68 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 195653164 192535458 195653164 195653164 10.1007/s00234-026-03948-3 195653164 ppf: 1835 ppct: 13 formats: tig: atl: Development and evaluation of an automated phase contrast magnetic resonance imaging algorithm for pediatric and adult cerebral blood flow measurement. aug: au: Liu, Joseph Torres, Isabel Ranganathan, Sudarshan Sussman, Bethany L. Doyle, Eamon K. Karnwal, Abhishek Nimmo, Samantha T. Tamrazi, Benita De Souza, Bradley J. Chiarelli, Peter A. Braskie, Meredith N. Yassine, Hussein N. Wood, John C. Peterson, Bradley S. Borzage, Matthew T. affil: https://ror.org/00412ts95 Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Los Angeles, USA sug: subj: Cerebrovascular Circulation Evaluation Cerebrovascular Circulation Evaluation Cerebral Arteries Radiography Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Algorithms Human Male Female Infant, Newborn Infant Child, Preschool Child Middle Age United States Secondary Analysis Validation Studies Academic Medical Centers Hospitals, Pediatric Funding Source Intraclass Correlation Coefficient Two-Way Analysis of Variance Descriptive Statistics Data Analysis Software Automation Power Analysis Blood Flow Velocity Infant, Newborn: birth-1 month Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Middle Aged: 45-64 years Male Female ab: Purpose: Accurate Cerebral Blood Flow (CBF) measurements are essential for studying pediatric cerebral hemodynamics. Phase Contrast (PC) imaging is a fast, non-invasive, and non-radiating technique for measuring flows. PC image processing traditionally includes manually segmenting, identifying, and unaliasing vessels of interest, which are challenging in children and involve intra- and inter-observer variation. Methods: We acquired 3 T PC images from 59 children and 39 adults (mean and standard deviation 3.43 ± 2.60 and 57.28 ± 3.87 years). Our algorithm identified voxels that skew the PC image intensity, refined vessels with active contours (Chan-Vese), split adjacent vessels with watershedding, and used a heuristic to identify the correct arteries based on vessel characteristics. We developed an automated algorithm to process PC images, thereby ensuring high precision. Images were processed images manually (two analysts) and algorithmically to compare performance overall and for each component. Results: Total CBF measurements were correlated between ground truth and algorithm versus between two analysts (Intraclass Correlation Coefficient = 0.748—0.817 vs 0.810—0.919). A two-way analysis of variance indicated no difference between human and algorithm for total CBF (p = 0.1558). The performance of algorithm versus two human analysts were similar across components: segmentation (Matthew's Correlation Coefficient = 0.777—0.849 vs 0.830—0.890), unaliasing (Mean Absolute Error = 0.355—0.538 vs 0.410—0.555), and vessel identification in adults (Intraclass Correlation Coefficient = 1.000 vs 1.000). Analysts were similar versus algorithm at vessel identification in children (Intraclass Correlation Coefficient = 1.000 vs 0.983). Conclusion: Automated algorithm components performed similarly to gold standard manual analysis and ensured high precision. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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