Evidence map›Paper›PMID 41015261›Full record

ArticleMagnetic resonance imaging2025

PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI.

Zhengyi Lu, Hao Liang, Ming Lu, Dann Martin, Benjamin M Hardy, Benoit M Dawant, Xiao Wang, Xinqiang Yan, Yuankai Huo

Abstract read
In one paragraph

Article in Magnetic resonance imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhengyi LuDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Hao LiangVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Ming LuVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Dann MartinDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA.
Benjamin M HardyRemcom Inc., State College, PA, USA.
Benoit M DawantDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Xiao WangComputational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Xinqiang YanDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Yuankai HuoDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA. Electronic address: yuankai.huo@vanderbilt.edu.

Funding

Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRIR01EB017230 · NIBIB · VANDERBILT UNIVERSITY · PI LANDMAN, BENNETT A. · 2015 to 2024
$5.1M
RESOLUTION OF GLOMERULOSCLEROSISR01DK056942 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI FOGO, AGNES B. · 2001 to 2021
$4.9M
Complementary ex vivo multimodal optical imaging and in vivo Raman spectroscopy to understand tissue dynamicsR01EB033385 · NIBIB · VANDERBILT UNIVERSITY · PI MAHADEVAN-JANSEN, ANITA, REESE, JOHN JEFFREY · 2022 to 2025
$2.6M
AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney HistopathologyR01DK135597 · NIDDK · VANDERBILT UNIVERSITY · PI Yuankai Huo · 2023 to 2026
$2.1M
Upgrade and Refurbishment of a 7T MRI Scanner for ResearchS10OD030389 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GORE, JOHN C · 2021 to 2021
$2.0M
Integrated Next-generation RF Transmit, Receive and B0 shimming coil system for brain and spinal cord MRI at 7 TeslaR01EB031078 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI YAN, XINQIANG · 2022 to 2025
$1.7M
Dual-wavelength endoscopic Raman probe for eosinophilic esophagitisR01DK132338 · NIDDK · VANDERBILT UNIVERSITY · PI MAHADEVAN-JANSEN, ANITA · 2022 to 2025
$1.7M
fMRI physiological signatures of aging and Alzheimer's DiseaseRF1MH125931 · NIMH · VANDERBILT UNIVERSITY · PI CHANG, CATHERINE ELIZABETH · 2021 to 2021
$1.1M
Passive antennas for improved image quality in transcranial MR-guided focused ultrasoundR21EB029639 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI YAN, XINQIANG · 2020 to 2022
$675k
Miniature and integrable balun for light-weight and flexible MRI RF coilsR03EB034366 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI YAN, XINQIANG · 2023 to 2024
$175k
NIBIB NIH HHS R01 EB017230NIBIB NIH HHS R01 EB031078NIBIB NIH HHS R01 EB033385NIBIB NIH HHS R03 EB034366NIBIB NIH HHS R21 EB029639NIDDK NIH HHS R01 DK056942NIDDK NIH HHS R01 DK132338NIDDK NIH HHS R01 DK135597NIH HHS S10 OD030389NIMH NIH HHS RF1 MH125931
6 · The paper itself

Abstract

Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. This paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: https://github.com/hrlblab/PHASE.

Indexed as

HeadImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultAlgorithmsBrainComputer SimulationElectromagnetic FieldsFemaleHumansMalePhantoms, ImagingTomography, X-Ray ComputedDeep learningEM simulationHuman head modelSAR

Identifiers

PMID41015261
PMCPMC12515100

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.