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      Transcranial Magnetic Resonance-Guided Focused Ultrasound Thalamotomy in Essential Tremor: A Comprehensive Lesion Characterization

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          Abstract

          BACKGROUND

          Transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) thalamotomy is a novel and effective treatment for controlling tremor in essential tremor patients.

          OBJECTIVE

          To provide a comprehensive characterization of the radiological, topographical, and volumetric aspects of the tcMRgFUS thalamic lesion, and to quantify how they relate to the clinical outcomes.

          METHODS

          In this study, clinical and radiological data from forty patients with medically-refractory essential tremor treated with unilateral tcMRgFUS thalamotomy were retrospectively analyzed. Treatment efficacy was assessed with Clinical Rating Scale for Tremor (CRST). Lesions were manually segmented on T1, T2, and susceptibility-weighted images, and 3-dimensional topographical analysis was then carried out. Statistical comparisons were performed using nonparametric statistics.

          RESULTS

          The greatest clinical improvement was correlated with a more inferior and posterior lesion, a bigger lesion volume, and percentage of the ventral intermediate nucleus covered by the lesion; whereas, the largest lesions accounted for the occurrence of gait imbalance. Furthermore, the volume of the lesion was significantly predicted by the number of sonications surpassing 52°C.

          CONCLUSION

          Here we provide a comprehensive characterization of the thalamic tcMRgFUS lesion including radiological and topographical analysis. Our results indicate that the location and volume of the lesion were significantly associated with the clinical outcome and that mid-temperatures may be responsible for the lesion size. This could serve ultimately to improve targeting and judgment and to optimize clinical outcome of tcMRgFUS thalamotomy.

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          Most cited references41

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          Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

          One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
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            An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images.

            A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, et al., 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, et al., 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, et al., 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented .
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              A Randomized Trial of Focused Ultrasound Thalamotomy for Essential Tremor.

              Uncontrolled pilot studies have suggested the efficacy of focused ultrasound thalamotomy with magnetic resonance imaging (MRI) guidance for the treatment of essential tremor.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Neurosurgery
                Oxford University Press (OUP)
                0148-396X
                1524-4040
                August 2020
                August 01 2020
                October 01 2019
                August 2020
                August 01 2020
                October 01 2019
                : 87
                : 2
                : 256-265
                Affiliations
                [1 ]CINAC (Centro Integral de Neurociencias), University Hospital HM Puerta del Sur, CEU-San Pablo University, Móstoles, Madrid, Spain
                [2 ]Network Center for Biomedical Research on Neurodegenerative Diseases, Instituto Carlos III, Madrid, Spain
                [3 ]Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom
                [4 ]Movement Disorders, Neurology Department, FLENI, Buenos Aires, Argentina
                [5 ]Universidad Europea de Madrid, Faculty of Biomedical and Health Sciences, Department of Nursing
                [6 ]Radiology Department, University Hospital HM Puerta del Sur, Madrid, Spain
                Article
                10.1093/neuros/nyz395
                31574145
                bf03f054-37a3-46fa-8591-fe6451d78a3d
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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