Generation of treatment plans for Magnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU) in the liver

In this thesis, the self-scanning method is proposed to handle organ motion. It takes advantage of the perpetual respiratory motion to passively scan the tumor. In other words, we are placing the static focal point of the HIFU into the tumor. The motion caused by breathing shifts the tumor through this focal point. We anticipate at which time point tumor tissue is located under the focal spot and modulate the HIFU intensity based on this information. Once the tumor has been ablated along the self-scanned trajectory, the focal spot is relocated to a different but static position within the body. With this method, we combine the advantages of the gating and the tracking method: a HIFU device with a fixed focus can be used and a high duty cycle is achieved. Moreover, since with the self-scanning approach no lateral steering of the focal spot is required, fewer secondary lobes are generated and position-dependent decay of the focal spot intensity during lateral steering is avoided. However, this comes at the cost of an increased complexity at the planning stage

Model-guided respiratory organ motion prediction of the liver from 2D ultrasound

With the availability of new and more accurate tumour treatment modalities such as high-intensity focused ultrasound or proton therapy, accurate target location prediction has become a key issue. Various approaches for diverse application scenarios have been proposed over the last decade. Whereas external surrogate markers such as a breathing belt work to some extent, knowledge about the internal motion of the organs inherently provides more accurate results. In this paper, we combine a population-based statistical motion model and information from 2d ultrasound sequences in order to predict the respiratory motion of the right liver lobe. For this, the motion model is fitted to a 3d exhalation breath-hold scan of the liver acquired before prediction. Anatomical landmarks tracked in the ultrasound images together with the model are then used to reconstruct the complete organ position over time. The prediction is both spatial and temporal, can be computed in real-time and is evaluated on ground truth over long time scales (5.5 min). The method is quantitatively validated on eight volunteers where the ultrasound images are synchronously acquired with 4D-MRI, which provides ground-truth motion. With an average spatial prediction accuracy of 2.4 mm, we can predict tumour locations within clinically acceptable margins