Thermotherapies can now be guided in real-time using magnetic resonance imaging (MRI). This technique is rapidly gaining importance in interventional therapies for abdominal organs such as liver and kidney. An accurate online estimation and characterization of organ displacement is mandatory to prevent misregistration and correct for motion related thermometry artifacts. In addition, when the ablation is performed with an extracorporal heating device such as high intensity focused ultrasound (HIFU), the continuous estimation of the organ displacement is the basis for the dynamic adjustment of the focal point position to track the targeted pathological tissue. In this paper, we describe the use of an optimized principal component analysis (PCA)-based motion descriptor to characterize in real-time the complex organ deformation during the therapy. The PCA was used to detect, in a preparative learning step, spatio-temporal coherences in the motion of the targeted organ. During hyperthermia, incoherent motion patterns could be discarded, which enabled improvements in motion estimation robustness, the compensation of motion related errors in thermal maps, and the adjustment of the beam position. The suggested method was evaluated for a moving phantom, and tested in vivo in the kidney and the liver of 12 healthy volunteers under free breathing conditions. The ability to perform a MR-guided thermotherapy in vivo during HIFU intervention was finally demonstrated on a porcine kidney.
Abstract-Real time magnetic resonance (MR) imaging is a promising tool for image-guided interventions. For applications such as thermotherapy on moving organs, a fine image-based compensation of motion is required in real time to allow quantitative analysis, retro-control of the interventional device, or determination of the therapy endpoint. Since interventional procedures are usually restricted to a part of the organ/tissue under study, reduced FOV imaging represents a promising way to improve spatial and / or temporal resolution. However, it introduces new challenges for the target motion estimation since structures near the target may appear transiently due to the respiratory motion and the limited spatial coverage.In this paper, a new image based motion estimation method is proposed combining a global motion estimation with a novel optical flow approach extending the initial Horn & Schunck (H&S) method by an additional regularization term. This term integrates the displacement of physiological landmarks, which are obtained in a preparation step by pattern matching into the variational formulation of the optical flow problem. A smooth regulation of the constraint point influences is achieved using a spatial weighting function. The method was compared to the same registration pipeline employing the H&S approach. A first evaluation was performed on synthetic dataset where the accuracy of the motion estimated with the H&S method was improved by a factor of 2 using the proposed approach. An in vivo study was then realized on both the heart and the kidney of twelve volunteers. Compared to the H&S approach, a significant improvement (p<0.05) of the DICE similarity criterion computed between the reference and the registered organ positions was achieved.
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. However, organ displacements due to physiological activity (respiratory cycle) may induce important artifacts in computed temperature maps.In addition, focused ultrasound (FUS) is an extra-corporal heating device which makes possible to perform local hyperthermia noninvasively. For intervention on mobile organs, the position of the focal point must be adjusted dynamically to track the targeted pathologic tissue. Without such corrections, treatment is inefficient or may induce unwanted destruction of healthy tissue.In this paper, image processing methods are developed to propose an efficient solution to correct on-line motion artifacts on temperature maps, and to adjust the focal point position of the FUS device in order to track the targeted organ moving.
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