In vivo validation of spatio-temporal liver motion prediction from motion tracked on MR thermometry images
Magnetic resonance-guided focused ultrasound (MRgFUS) of the liver during free-breathing requires spatio-temporal prediction of the liver motion from partial motion observations. The study purpose is to evaluate the prediction accuracy for a realistic MRgFUS therapy scenario, namely for human in vivo data, tracking based on MR images routinely acquired during MRgFUS and in vivo deformations caused by the FUS probe.
In vivo validation of the motion model was based on a 3D breath-hold image and an interleaved acquisition of two MR slices. Prediction accuracy was determined with respect to manually annotated landmarks. A statistical population liver motion model was used for predicting the liver motion for not tracked regions. This model was individualized by mapping it to end-exhale 3D breath-hold images. Spatial correspondence between tracking and model positions was established by affine 3D-to-2D image registration. For spatio-temporal prediction, MR tracking results were temporally extrapolated.
Performance was evaluated for 10 volunteers, of which 5 had a dummy FUS probe put on their abdomen. MR tracking had a mean (95 %) accuracy of 1.1 (2.4) mm. The motion of the liver on the evaluation MR slice was spatio-temporally predicted with an accuracy of 1.9 (4.4) mm for a latency of 216 ms. A simple translation model performed similarly (2.1 (4.8) mm) as the two MR slices were relatively close (mean 38 mm). Temporal prediction was important (10 % error reduction), while registration effects could only partially be assessed and showed no benefits. On average, motion magnitude, motion amplitude and breathing frequency increased by 24, 16 and 8 %, respectively, for the cases with FUS probe placement. This motion increase could be reduced by the spatio-temporal prediction.
The study shows that tracking liver vessels on MR images, which are also used for MR thermometry, is a viable approach.
KeywordsFocused ultrasound Respiration Motion prediction Tracking
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