Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations
Cerebral blood flow (CBF) supports brain function and health. Cerebral blood flow is affected by normal brain development, disease, medications use, and other interventions. One method to measure CBF is phase contrast magnetic resonance (PC MR) imaging, a particularly fast and reliable method to mea...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1527093/full |
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| author | Eamon K. Doyle Eamon K. Doyle Isabel Torres Isabel Torres Joseph Liu Abhishek Karnwal Sudarshan Ranganathan Sudarshan Ranganathan Bradley J. De Souza Payal Shah Bradley S. Peterson John C. Wood John C. Wood Matthew Thomas Borzage Matthew Thomas Borzage Matthew Thomas Borzage Matthew Thomas Borzage |
| author_facet | Eamon K. Doyle Eamon K. Doyle Isabel Torres Isabel Torres Joseph Liu Abhishek Karnwal Sudarshan Ranganathan Sudarshan Ranganathan Bradley J. De Souza Payal Shah Bradley S. Peterson John C. Wood John C. Wood Matthew Thomas Borzage Matthew Thomas Borzage Matthew Thomas Borzage Matthew Thomas Borzage |
| author_sort | Eamon K. Doyle |
| collection | DOAJ |
| description | Cerebral blood flow (CBF) supports brain function and health. Cerebral blood flow is affected by normal brain development, disease, medications use, and other interventions. One method to measure CBF is phase contrast magnetic resonance (PC MR) imaging, a particularly fast and reliable method to measure blood flow through major arteries such as the internal carotid (ICA) or vertebral arteries (VA). Unfortunately, sometimes PC MR can be compromised due to errors by the technologist during image acquisition, patient movement, or complex vessel structures. Our goal was to develop mathematical models to estimate CBF for a wide age range of patients whenever 1 or more vessels are not correctly measured. To investigate this, we studied a set of 258 PC MR acquisitions from a group of 196 patients with one or three acquisitions per subject (165 single images, 31 acquisitions of 3 images) ranging in age from 0.4 to 61.3 years (mean [μ] = 13.1, standard deviation [σ] = 12.3). We deliberately excluded measurements from one or more arteries in each volunteer to mimic situations with low image quality. Subsequently, we developed mathematical models to predict the missing data. Our predictive models performed well; across the human lifespan when at least one ICA measurement was available, our normalized root mean squared error values were low (<0.137), our R-squared values were high (>0.91), and we observed high intra-class correlation coefficients (>0.951). In summary, these imputation models are effective in estimating CBF in children and adults. |
| format | Article |
| id | doaj-art-8ce5f6235bdd45bdb8f022ae6e25b6b8 |
| institution | OA Journals |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-8ce5f6235bdd45bdb8f022ae6e25b6b82025-08-20T02:09:23ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-06-011610.3389/fphys.2025.15270931527093Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populationsEamon K. Doyle0Eamon K. Doyle1Isabel Torres2Isabel Torres3Joseph Liu4Abhishek Karnwal5Sudarshan Ranganathan6Sudarshan Ranganathan7Bradley J. De Souza8Payal Shah9Bradley S. Peterson10John C. Wood11John C. Wood12Matthew Thomas Borzage13Matthew Thomas Borzage14Matthew Thomas Borzage15Matthew Thomas Borzage16Department of Radiology, Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, United StatesDepartment of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United StatesDepartment of Radiology, Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, United StatesRudi Schulte Research Institute, Santa Barbara, CA, United StatesFetal and Neonatal Institute, Division of Neonatology, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesDepartment of Anesthesia Critical Care Medicine, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesFetal and Neonatal Institute, Division of Neonatology, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesDivision of Cardiology, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesDepartment of Anesthesia Critical Care Medicine, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesDivision of Cardiology, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesDepartment of Psychiatry, Keck School of Medicine at the University of Southern California, Los Angeles, CA, United StatesDepartment of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United StatesDivision of Cardiology, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesDepartment of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United StatesFetal and Neonatal Institute, Division of Neonatology, Children’s Hospital Los Angeles, Los Angeles, CA, United StatesAlfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United StatesDepartment of Regulatory and Quality Sciences, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United StatesCerebral blood flow (CBF) supports brain function and health. Cerebral blood flow is affected by normal brain development, disease, medications use, and other interventions. One method to measure CBF is phase contrast magnetic resonance (PC MR) imaging, a particularly fast and reliable method to measure blood flow through major arteries such as the internal carotid (ICA) or vertebral arteries (VA). Unfortunately, sometimes PC MR can be compromised due to errors by the technologist during image acquisition, patient movement, or complex vessel structures. Our goal was to develop mathematical models to estimate CBF for a wide age range of patients whenever 1 or more vessels are not correctly measured. To investigate this, we studied a set of 258 PC MR acquisitions from a group of 196 patients with one or three acquisitions per subject (165 single images, 31 acquisitions of 3 images) ranging in age from 0.4 to 61.3 years (mean [μ] = 13.1, standard deviation [σ] = 12.3). We deliberately excluded measurements from one or more arteries in each volunteer to mimic situations with low image quality. Subsequently, we developed mathematical models to predict the missing data. Our predictive models performed well; across the human lifespan when at least one ICA measurement was available, our normalized root mean squared error values were low (<0.137), our R-squared values were high (>0.91), and we observed high intra-class correlation coefficients (>0.951). In summary, these imputation models are effective in estimating CBF in children and adults.https://www.frontiersin.org/articles/10.3389/fphys.2025.1527093/fullinternal carotid artery (ICA)cerebral blood flow (CBF)magnetic resonance imaging (MRI)vertebral artery (VA)phase contrast (PC) |
| spellingShingle | Eamon K. Doyle Eamon K. Doyle Isabel Torres Isabel Torres Joseph Liu Abhishek Karnwal Sudarshan Ranganathan Sudarshan Ranganathan Bradley J. De Souza Payal Shah Bradley S. Peterson John C. Wood John C. Wood Matthew Thomas Borzage Matthew Thomas Borzage Matthew Thomas Borzage Matthew Thomas Borzage Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations Frontiers in Physiology internal carotid artery (ICA) cerebral blood flow (CBF) magnetic resonance imaging (MRI) vertebral artery (VA) phase contrast (PC) |
| title | Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations |
| title_full | Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations |
| title_fullStr | Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations |
| title_full_unstemmed | Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations |
| title_short | Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations |
| title_sort | imputation models and error analysis for phase contrast mr cerebral blood flow measurements in heterogeneous pediatric and adult populations |
| topic | internal carotid artery (ICA) cerebral blood flow (CBF) magnetic resonance imaging (MRI) vertebral artery (VA) phase contrast (PC) |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1527093/full |
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