The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy

Abstract

Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal. We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy. The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers. For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.

Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis

“…It can also better depict risk organs (OAR) to avoid doses in RTP ( 30 – 32 ). At present, texture analysis, automatic delineation, 4D-MRI, MRI accelerator, and other emerging technologies are gradually applied in clinical settings ( 33 – 36 ). This study may provide a new application direction for radiotherapy guided by MRI accelerator in the future.…”

Advances in MRI‐guided precision radiotherapy

“…[39][40][41] Retrospective binning is based on different types of breathing surrogates, which are either calculated from medical images (e.g., diaphragm and liver dome position, body area, thermal noise) or extracted from external devices (e.g., breath signal). 40,[42][43][44] Prospective 4D-MRI methods rely on fast 3D acquisition or respiratorygated two-dimensional (2D) acquisition. 45,46 3D acquisition mainly uses gradient-echo sequences and provides 3D T1-weighted images with a subsecond speed, while respiratory-gated 2D acquisition can monitor the data sufficiency of each phase bin in real time and overcome the data incompetency problem in retrospective sorting.…”

“…Retrospective 4D‐MRI continuously acquires images over the entire region of interest (ROI) and retrospectively sorts the images into respiratory phase bins 39–41 . Retrospective binning is based on different types of breathing surrogates, which are either calculated from medical images (e.g., diaphragm and liver dome position, body area, thermal noise) or extracted from external devices (e.g., breath signal) 40,42–44 . Prospective 4D‐MRI methods rely on fast 3D acquisition or respiratory‐gated two‐dimensional (2D) acquisition 45,46 .…”

Predicting 4D Liver MRI for MR-guided Interventions

“…Zhang et al [24] also performed interpolation of navigators by utilizing an intermediate motion field prediction using a CNN. Navest et al [13] used information always present in the raw MRI readout, eliminating the need for acquisition of an actual navigator frame.…”