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

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 LST

SUBMITTER: Pan Y 

PROVIDER: S-EPMC12746380 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature