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

Journal: Magnetic resonance in medicine
Published Date: Nov 26, 2025

Abstract

PURPOSE: To 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. METHODS: A 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 (NRMSEdB) and mean absolute error (MAE). PROMPT was then implemented in late gadolinium enhancement (LGE) and compared with free-breathing retrospective gating in 14 patients. Residual in-plane motion was calculated to assess performance. RESULTS: The LSTM model achieved an NRMSEdB of -7.20 dB and an MAE of 1.97 mm between predicted and actual motion, demonstrating significantly higher accuracy than either regression models (p < 0.05). The residual in-plane motion in PROMPT-LGE was significantly lower than in FB-LGE (1.22 ± 0.38 mm vs. 1.35 ± 0.48 mm, p = 0.033). CONCLUSION: The 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.

Authors

  • Yue Pan
    Department of Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of TCM, Tianjin 300193, China.
  • Ning Jin
    National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China.
  • Kelvin Chow
    Siemens Medical Solutions USA, Inc, Chicago, IL, USA.
  • Mario Bacher
  • Peter Speier
    Siemens Healthineers AG, Erlangen, Germany.
  • Vedat O Yildiz
    Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, Ohio, USA.
  • Matthew Tong
    IBM Research, Almaden Research Center, San Jose, CA, 95120, USA.
  • Yuchi Han
    Cardiovascular Division, University of Pennsylvania, Philadelphia, USA.
  • Rizwan Ahmad
    Department of Biomedical Engineering, The Ohio State University.
  • Orlando P Simonetti
    Departments of Radiology and Medicine, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.

Keywords

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