Evidence map›Paper›PMID 40297418›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Technical Optimization of SyntheticMR for the Head and Neck on a 3T MR-Simulator and 1.5T MR-Linac: A Prospective R-IDEAL Stage 2a Technology Innovation Report.

Lucas McCullum, Samuel L Mulder, Natalie A West, Hayden Scott, Diana Carrasco Rojas, Zayne Belal, Cem Dede, Warren Floyd, Alaa Mohamed Shawky Ali, Abdallah Sherif Radwan Mohamed and 6 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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

16 authors.

Lucas McCullumUT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, USA.ORCID 0000-0001-9788-7987
Samuel L MulderUT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, USA.ORCID 0000-0001-5185-4805
Natalie A WestUT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, USA.ORCID 0009-0007-0211-8845
Hayden ScottUT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, USA.ORCID 0000-0003-1066-1999
Diana Carrasco RojasUT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, USA.ORCID 0009-0000-3318-6487
Zayne BelalDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0009-0006-2668-9717
Cem DedeUT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, USA.ORCID 0000-0002-0543-9325
Warren FloydDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0003-4371-8453
Alaa Mohamed Shawky AliDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0001-7472-3701
Abdallah Sherif Radwan MohamedDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0003-2064-7613
Alex DresnerPhilips Healthcare, Cleveland, Ohio, USA.ORCID 0000-0002-0469-6012
Ergys SubashiDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0001-5168-6928
Dan MaDepartment of Neurosurgery and Biomedical Engineering, Duke University, Durham, NC, USA.ORCID 0000-0003-1664-9579
R Jason StaffordDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0003-4091-8417
Ken-Pin HwangDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0002-8196-3794
Clifton D FullerDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0002-5264-3994

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac deviceR01DE028290 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHRISTODOULEAS, JOHN PAUL, FULLER, CLIFTON DAVID · 2019 to 2023
$4.3M
NRSA Training CoreTL1TR003169 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHANDRA, JOYA, FROST, JEFFREY A. · 2019 to 2023
$3.7M
Development of fast diffusion magnetic resonance fingerprinting of the prostate to avoid unnecessary biopsiesR01CA292091 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI Yong Chen, Leonardo Kayat Bittencourt · 2024 to 2026
$3.2M
Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular OsteoradionecrosisR01DE025248 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAI, STEPHEN Y · 2016 to 2020
$2.9M
Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer TherapyR01CA258827 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2021 to 2025
$2.9M
Quantitative Imaging Biomarker Prospective Validation of Dynamic Contrast-Enhanced MRI as a Metric of Orodental Injury After Radiotherapy (QI-ProVE-MRI)U01DE032168 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Clifton David Fuller, STEPHEN Y LAI · 2023 to 2026
$2.8M
Development of Magnetic Resonance Fingerprinting (MRF) to Assess Response to Neoadjuvant Chemotherapy in Breast CancerR01CA282516 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI Yong Chen, Dan Ma · 2023 to 2026
$2.4M
SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck CancerR01CA257814 · NCI · RICE UNIVERSITY · PI FULLER, CLIFTON DAVID, SCHAEFER, ANDREW J · 2021 to 2024
$1.8M
MR Fingerprinting based Quantitative Imaging and Analysis Platform (MRF-QIA) for brain tumors.R01CA269604 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI BADVE, CHAITRA, DAVATZIKOS, CHRISTOS · 2023 to 2025
$1.7M
CTSA Predoctoral T32 at the University of Texas Health Science Center at HoustonT32TR004905 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Joya Chandra, Jeffrey A. Frost · 2024 to 2026
$811k
NCATS NIH HHS T32 TR004905NCATS NIH HHS TL1 TR003169NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA257814NCI NIH HHS R01 CA258827NCI NIH HHS R01 CA269604NCI NIH HHS R01 CA282516NCI NIH HHS R01 CA292091NIDCR NIH HHS R01 DE025248NIDCR NIH HHS R01 DE028290NIDCR NIH HHS U01 DE032168
6 · The paper itself

Abstract

Objective: The purpose of this study was to optimize the technical tradeoffs associated with integrating the quantitative maps available from SyntheticMR into the head and neck adaptive radiation oncology workflow. Recent work has begun to investigate SyntheticMR in the adaptive radiation oncology workflow, however no studies have investigated the variation in acquisition parameters and their relationship to the resulting quantitative maps. Filling this gap will facilitate SyntheticMR's translation to the adaptive radiation therapy setting in the head and neck due to the reduced bias and increase repeatability and reproducibility of its generated quantitative relaxometric biomarkers. Approach: The 2D multi-echo multi-delay (MDME) sequence offered from SyntheticMR was acquired on a MR-Simulation and MR-Linac device in both a phantom and healthy volunteers. Scans were optimized across acceleration factors, slice gaps, acquired voxel sizes, repetition times, echo times, refocusing flip angles, noise-limited gradients, number of slices, and echo train lengths. Quantitative relaxometric T1, T2, and PD maps were tested for accuracy using both correlation and mean absolute bias analysis. Noise profiles were evaluated using the coefficient of variation (CoV) in uniform regions of interest. Main Results: The following main findings were reported: (1) noise-limited gradients did not affect the bias or CoV, (2) increasing acceleration factor did not affect bias, but it significantly increased CoV, (3) increasing the number of slices resulted in different significant changes in the quantitative parameters between the MR-Simulator and MR-Linac, (4) reducing the echo train length led to generally reduced bias and CoV, and (5) the repeatability CoV was within previously reported literature values on similar scanners. Significance: The behavior of the 2D-MDME sequence from SyntheticMR was characterized across a wide range of acquisition parameters designed for ideal head and neck imaging on both the MR-Simulation and MR-Linac devices in phantom and healthy volunteers. Significant differences existed across several acquisition parameters which should be accounted for when making adjustments for application-specific field-of-views, scan time, and desired ranges of T1, T2, and PD. Application of these findings to the development of efficient head and neck adaptive radiation therapy MRI protocols can enrich the current quantitative biomarker landscape allowing for more informed treatment adaptations.

Indexed as

Adaptive Radiation TherapyHead and Neck CancerMRIMR-LinacQuantitative ImagingRadiation TherapyRadiotherapySyMRISyntheticMR

Identifiers

PMID40297418
PMCPMC12036405

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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 OpenQuestion graph.