Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations

Eamon K Doyle; Isabel Torres; Joseph Liu; Abhishek Karnwal; Sudarshan Ranganathan; Bradley J De Souza; Payal Shah; Bradley S Peterson; John C Wood; Matthew Thomas Borzage

doi:10.3389/fphys.2025.1527093

Imputation models and error analysis for phase contrast MR cerebral blood flow measurements in heterogeneous pediatric and adult populations

Front Physiol. 2025 Jun 11:16:1527093. doi: 10.3389/fphys.2025.1527093. eCollection 2025.

Authors

Eamon K Doyle^{1

2}, Isabel Torres^{1

3}, Joseph Liu⁴, Abhishek Karnwal⁵, Sudarshan Ranganathan^{4

6}, Bradley J De Souza⁵, Payal Shah⁶, Bradley S Peterson⁷, John C Wood^{2

6}, Matthew Thomas Borzage^{2

4

8

9}

Affiliations

¹ Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, United States.
² Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
³ Rudi Schulte Research Institute, Santa Barbara, CA, United States.
⁴ Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Los Angeles, CA, United States.
⁵ Department of Anesthesia Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA, United States.
⁶ Division of Cardiology, Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, United States.
⁷ Department of Psychiatry, Keck School of Medicine at the University of Southern California, Los Angeles, CA, United States.
⁸ Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
⁹ Department of Regulatory and Quality Sciences, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States.

Abstract

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.

Keywords: cerebral blood flow (CBF); internal carotid artery (ICA); magnetic resonance imaging (MRI); phase contrast (PC); vertebral artery (VA).

Abstract

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