Mario Ries

High Intensity Focused Ultrasound (HIFU) can be used to achieve a local temperature increase deep inside the human body in a non-invasive way. MRI guidance of the procedure allows in situ target definition. In addition, MRI can be used to provide continuous temperature mapping during HIFU for spatial and temporal control of the heating procedure and prediction of the final lesion based on the received thermal dose. Temperature mapping of mobile organs as kidney and liver is challenging, as well as real-time processing methods for feedback control of the HIFU procedure. In this paper, recent technological advances are reviewed in MR temperature mapping of these organs, in motion compensation of the HIFU beam, in intercostal HIFU sonication, and in volumetric ablation and feedback control strategies. Recent pre-clinical studies have demonstrated the feasibility of each of these novel methods. The perspectives to translate those advances into the clinic are addressed. It can be concluded that MR guided HIFU for ablation in liver and kidney appears feasible but requires further work on integration of technologically advanced methods.

Real-time magnetic resonance (MR) thermometry provides continuous temperature mapping inside the human body and is therefore a promising tool to monitor and control interventional therapies based on thermal ablation. Temperature information must be mapped to a reference position of observed organs in order to allow thermal dose computation, as the history of temperature is required for each pixel. Motion compensated MR-thermometry for thermotherapy has to cope with radio-frequency (RF) artifacts and relaxation-time changes of the monitored tissue. While purely optical-flow-based realignment may lead to temperature map computation errors for the case of local or global intensity changes, principal component analysis based realignment results in accurately registered temperature maps. The motion estimation process described in this paper consists of two steps : a parameterized flow models is initially computed using a principal component analysis during a preparative learning step; during the intervention, motion is characterized with a small set of parameters using a least square solver.

Magnetic resonance (MR) systems can be used to monitor temperature changes in and around a treated region during an hyperthermic ablation procedure. Dynamic temperature monitoring allows on-line prediction of cellular destruction during the intervention. MRI systems associate each volume unit with a complex number. Phase component is 2pi periodic (it is a function of the wrapped phase) and accounts for noise sources present in MR imaging. Robust spatial phase unwrapping is a necessary prerequisite for several applications. This study proposes a spatial phase unwrapping algorithm for MR images, allowing a real time implementation for on-line temperature monitoring.

Contrast-enhanced dynamic magnetic resonance imaging (MRI) acquisition is a common method to retrieve functional information from organs in the human body. Applied to the kidney, the observation of the signal evolution in the cortex of a MR-series gives access to the renal perfusion and filtration. The glomerular filtration rate (GFR) is in particular the most useful quantitative index of renal function. Since the rapid bolus passage hampers the use of gated sequences, fast sequences have to be employed to enable a data acquisition while free-breathing. As a result, the acquired data contains motion artifacts caused by the respiratory cycle, spontaneous movements and drifts which limit quantitative analysis of the data. Although these problems can in principle be addressed with motion correction algorithms applied in a post processing step, additional challenges arise from the fact that image amplitude changes not only due to motion but also due to the contrast change during bolus passage. This study proposes a 2D region tracking method for retrospective motion correction without sacrificing temporal resolution which addresses the latter point by a preparative learning phase.