Evidence map›Paper›PMID 41296497›Full record

ArticleMagnetic resonance in medicine2026

Prospective Respiratory Motion Correction Using Machine Learning and Pilot Tone (PROMPT) in Cardiac MRI.

Yue Pan, Ning Jin, Kelvin Chow, Mario Bacher, Peter Speier, Vedat O Yildiz, Matthew Tong, Yuchi Han, Rizwan Ahmad, Orlando P Simonetti

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

10 authors.

Yue PanDepartment of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.ORCID https://orcid.org/0000-0001-5690-0137
Ning JinMR R&D, Siemens Medical Solutions USA, Inc., Malvern, Pennsylvania, USA.
Kelvin ChowMR R&D, Siemens Medical Solutions USA, Inc., Malvern, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-0698-1746
Mario BacherSiemens Healthineers AG, Forchheim, Germany.ORCID https://orcid.org/0000-0002-5529-3598
Peter SpeierSiemens Healthineers AG, Forchheim, Germany.
Vedat O YildizCenter for Biostatistics, Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, Ohio, USA.
Matthew TongDepartment of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
Yuchi HanDepartment of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
Rizwan AhmadDepartment of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.ORCID https://orcid.org/0000-0002-5917-3788
Orlando P SimonettiDepartment of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.ORCID https://orcid.org/0000-0002-8994-0095

Funding

Development and validation of cardiovascular MRI techniques on a low-field, ultra-wide bore system to assess patients with severe obesityR01HL161618 · NHLBI · OHIO STATE UNIVERSITY · PI Orlando Paul Simonetti · 2022 to 2026
$2.9M
NHLBI NIH HHS R01 HL161618NHLBI NIH HHS R01HL161618Robert F. Wolfe and Edgar T. Wolfe Foundation
6 · The paper itself

Abstract

purposeTo implement and evaluate the feasibility of a Pilot Tone (PT)-based prospective gating and tracking technique, which uses a long short-term memory (LSTM) neural network to predict respiratory motion from PT signals.

methodsA subject-specific calibration scan consisting of 100 ECG-triggered single-shot images was performed. Respiratory motion was estimated from the images and PT data were processed to extract the respiratory component. The LSTM model was trained to predict respiratory motion from PT signals. During respiratory-corrected scans, PROMPT-predicted slice-shifting parameters were used to update gating information and slice position for each heartbeat before image acquisition. The method was retrospectively evaluated in 12 healthy volunteers, comparing LSTM with linear and polynomial regression models using normalized root mean square error in decibels (NRMSE

resultsThe LSTM model achieved an NRMSE

conclusionThe proposed respiratory motion correction approach was successfully implemented. The LSTM-based predictive model outperformed linear and polynomial regression models. In LGE imaging, PROMPT significantly reduced in-plane motion and showed potential for limiting through-plane motion compared to the clinical protocol.

Indexed as

HeartImage Processing, Computer-AssistedMachine LearningMagnetic Resonance ImagingRespiratory-Gated Imaging TechniquesAdultAlgorithmsArtifactsCardiac-Gated Imaging TechniquesElectrocardiographyFeasibility StudiesFemaleHumansMaleMiddle AgedMotioncardiovascular MRILSTMPilot Toneprospective respiratory motion correction

Identifiers

PMID41296497
PMCPMC12746380

What OpenQuestion holds

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LicenceCC BY-NC-ND
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 OpenQuestion graph.