Visualization of thermal washout due to spatiotemporally heterogenous perfusion in the application of a model-based control algorithm for MR-HIFU mediated hyperthermia.

PURPOSE: This article will report results from the in-vivo application of a previously published model-predictive control algorithm for MR-HIFU hyperthermia. The purpose of the investigation was to test the controller's in-vivo performance and behavior in the presence of heterogeneous perfusion. MATERIALS AND METHODS: Hyperthermia at 42°C was induced and maintained for up to 30 min in a circular section of a thermometry slice in the biceps femoris of German landrace pigs (n=5) using a commercial MR-HIFU system and a recently developed MPC algorithm. The heating power allocation was correlated with heat sink maps and contrast-enhanced MRI images. The temporal change in perfusion was estimated based on the power required to maintain hyperthermia. RESULTS: The controller performed well throughout the treatments with an absolute average tracking error of 0.27 ± 0.15 °C and an average difference of 1.25 ± 0.22 °C between T10 and T90. The MPC algorithm allocates additional heating power to sub-volumes with elevated heat sink effects, which are colocalized with blood vessels visible on contrast-enhanced MRI. The perfusion appeared to have increased by at least a factor of ∼1.86 on average. CONCLUSIONS: The MPC controller generates temperature distributions with a narrow spectrum of voxel temperatures inside the target ROI despite the presence of spatiotemporally heterogeneous perfusion due to the rapid thermometry feedback available with MR-HIFU and the flexible allocation of heating power. The visualization of spatiotemporally heterogeneous perfusion presents new research opportunities for the investigation of stimulated perfusion in hypoxic tumor regions.

Model-based geometric calibration for medical x-ray systems.

PURPOSE: Currently applied calibration approaches lead to satisfying 3D roadmapping overlay and 3D reconstruction accuracies; however, the required calibration times are extensive. The aim of this paper is the introduction of a novel model-based approach for geometric system calibrations, leading to a significant reduction of calibration times. METHODS: By using physical insight into the system, a physical model is derived which can be exploited to predict geometric calibrations parameters. Model-parameters are estimated using a limited set of phantom-based measurement data. Effectively, the calibration procedure is recast to a parameter identification experiment. RESULTS: The potential of the proposed approach is illustrated by virtue of a benchmark object, successful reconstruction of a clinical phantom, and comparison to phantom-based accuracies. CONCLUSIONS: Accurate models are required to achieve the desired accuracies. Based on the results in this work, the approach seems to be feasible for practical applications; however, to achieve all the desired specifications, future research should focus on enhanced modeling techniques.