Evidence map›Paper›PMID 41962172›Full record

ArticleNeuroImage. Clinical2026

Enhancing 7T MRI for deep brain stimulation with deep-learning based image reconstruction and dynamic parallel transmission.

Justyna O Ekert, Vishal Patel, Xiangzhi Zhou, Shengzhen Tao, Patrick Liebig, Jürgen Herrler, Thomas Yu, Dominik Nickel, Gian Franco Piredda, Erin M Westerhold and 3 more

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

13 authors.

Justyna O EkertDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Vishal PatelDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Xiangzhi ZhouDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Shengzhen TaoDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Patrick LiebigMR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany.
Jürgen HerrlerSiemens Healthcare, Erlangen, Bavaria, Germany.
Thomas YuSwiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Dominik NickelMR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany.
Gian Franco PireddaSwiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland.
Erin M WesterholdDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Vivek GuptaDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Sanjeet S GrewalDepartment of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
Erik H MiddlebrooksDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA; Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA. Electronic address: Middlebrooks.Erik@mayo.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePrecise targeting of subcortical structures is crucial for deep brain stimulation (DBS). Although 7T MRI provides superior resolution and contrast, its clinical adoption remains limited by B1+ transmit inhomogeneity, prolonged scan times, and motion sensitivity. This study applied deep learning (DL)-based image reconstruction and dynamic parallel transmission (pTx) to optimize DBS protocols and improve image quality.

methodsThirteen patients scanned using a conventional 7T DBS protocol were compared to 13 imaged after implementing DL reconstruction and dynamic pTx. Two readers scored image quality, motion artifact, and target conspicuity on 5-point Likert scales. Ordinal logistic regression was used to calculate odds ratios (OR) for improvements with the enhanced protocol, adjusted for multiple comparisons.

resultsEnhanced MP2RAGE reduced voxel volume by 65.8% and scan time by 32.9%, with improved image quality (OR = 4.4;p = 0.003), target conspicuity (OR = 3.4;p = 0.011), and reduced motion artifacts (OR = 3.8;p = 0.006). Fast gray matter acquisition T1 inversion recovery (FGATIR) scan time decreased by 45.2% with improved target delineation of both globus pallidus interna (OR = 22.9;p < 0.001) and dentato-rubro-thalamic tract (OR = 8.8;p < 0.001). T2-weighted sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) improved subthalamic nucleus (STN) delineation (OR = 25.3;p < 0.001). Susceptibility-weighted imaging (SWI) improved image quality (OR = 17.4;p < 0.001), STN delineation (OR = 16.9;p < 0.001), and reduced scan time by 42.6%. Enhanced 3D spoiled gradient recall echo improved image quality (OR = 17.4;p < 0.001) and vessel visualization (OR = 26.1;p < 0.001) with reduced motion artifact (OR = 8.8;p < 0.001). Scan time decreased from 4:33 to 1:35, reducing protocol duration from 42:16 to 26:40 (36.9%).

conclusionsDL reconstruction and dynamic pTx improved image quality, target definition, and motion robustness while shortening 7T DBS protocol time.

Indexed as

BrainDeep Brain StimulationDeep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingParkinson DiseaseAdultAgedFemaleHumansMaleMiddle AgedSubthalamic NucleusEpilepsyNeuromodulationParallel transmissionParkinson’s diseaseTremorUltra-high field MRI

Identifiers

PMID41962172
PMCPMC13091332

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.