Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI

TLDR

In-room cine‑MRI guidance can provide non‑invasive target localization during radiotherapy, but finite imaging frequency and system latencies necessitate tumour motion prediction. This work proposes a motion‑prediction framework for cine‑MRI guidance to quantify geometric uncertainties in tumour tracking and beam gating. The framework estimates tumour position at 25 Hz using scale‑invariant features and three predictors—linear extrapolation, auto‑regressive, and support‑vector machine—compared to no‑prediction and surrogate‑based methods. Geometric uncertainties decrease to 0.2–1.2 mm RMS for acquisition periods of 250–750 ms and latencies of 50–300 ms, with prediction errors lower than surrogate‑based estimation, indicating cine‑MRI guidance can substantially reduce uncertainties.

Abstract

In-room cine-MRI guidance can provide non-invasive target localization during radiotherapy treatment. However, in order to cope with finite imaging frequency and system latencies between target localization and dose delivery, tumour motion prediction is required. This work proposes a framework for motion prediction dedicated to cine-MRI guidance, aiming at quantifying the geometric uncertainties introduced by this process for both tumour tracking and beam gating. The tumour position, identified through scale invariant features detected in cine-MRI slices, is estimated at high-frequency (25 Hz) using three independent predictors, one for each anatomical coordinate. Linear extrapolation, auto-regressive and support vector machine algorithms are compared against systems that use no prediction or surrogate-based motion estimation. Geometric uncertainties are reported as a function of image acquisition period and system latency. Average results show that the tracking error RMS can be decreased down to a [0.2; 1.2] mm range, for acquisition periods between 250 and 750 ms and system latencies between 50 and 300 ms. Except for the linear extrapolator, tracking and gating prediction errors were, on average, lower than those measured for surrogate-based motion estimation. This finding suggests that cine-MRI guidance, combined with appropriate prediction algorithms, could relevantly decrease geometric uncertainties in motion compensated treatments.