Correlation of diffusion tensor tractography and intraoperative macrostimulation during deep brain stimulation for Parkinson disease

Abstract

Introduction Diffusion-MRI is a rapidly evolving research field that has produced a wealth of algorithms for the analysis of white matter fibre architecture and disorders in the brain. Camino is a free, open-source toolkit designed to make a selection of this technology available and convenient to use for the diffusion MRI research community. Camino implements a data processing pipeline, which allows for easy scripting and flexible integration with other software. This abstract summarises the features of Camino at each stage of the pipeline from the raw data to the statistics used by clinicians and researchers.Design Camino is written in Java, and designed for a Unix-style interface. The user documentation is in the form of Unix manual pages, and each program has a shell wrapper, so users do not require any knowledge of Java. The data pipeline provides flexibility, by allowing data to be imported and exported to other software, and transparency, because the output of each Camino program can be analyzed in detail. Fig. 1 illustrates the pipeline. Camino processes all data in voxel order, where the measurements for each voxel are stored together. This ordering facilitates the data pipeline model and allows each voxel to be processed independently, which simplifies parallel processing. All of Camino's output is in a documented raw binary format. The tractography module optionally outputs Analyze images for easy integration with visualisation software. DataThe data source for Camino can be raw data from a scanner or from Camino's data synthesiser. The data synthesiser emulates scanner sequences and provides synthetic data from a range of customisable test functions. Data from scanners is not typically in voxel order, so Camino contains tools for rearranging data into the correct format. Associated with each data file is the scheme file, which is a text file that describes the acquisition parameters for each measurement.Reconstruction The reconstruction stage takes as input raw data and reconstructs information about the diffusion in each voxel. Camino supports a range of standard and advanced reconstruction algorithms. The simplest and fastest inversion is the linear diffusion tensor fit, in which the elements of the diffusion tensor are calculated from a standard linear least-squares fit to the log of the measurements. With the dtfit program, a linear diffusion tensor fit takes about two minutes for a 128 × 128 × 60 voxel data set, on a standard Pentium IV 2.4 GHz workstation. Nonlinear fitting is slower but potentially more accurate. Camino supports nonlinear least-squares diffusion tensor fitting and the RESTORE method [1]. Camino also supports non-tensor based diffusion reconstruction, including mixture modelling [2], PAS-MRI [3], Q-ball [4] and spherical deconvolution [5]. Spherical harmonic voxel classification [6] detects non-Gaussian diffusion. The thresholds used in classification can be set interactively (Fig. 2) and the classification can be used to drive reconstruction, fitting multi-fibre models only wh...

Postoperative neuroimaging analysis of DRT deep brain stimulation revision surgery for complicated essential tremor

“…We were recently able to investigate the accuracy of the DTI-FT approach for DBS and despite the results of other groups [20], concluded that the accuracy of the approach allows for direct targeting in DBS surgery [5, 6]. Other groups are now starting to use probabilistic tracking algorithms to resolve problems of inferior resolution, kissing, branching, and crossing fibers and the problem of deformed fibers [15].…”

Electrophysiologic Validation of Diffusion Tensor Imaging Tractography during Deep Brain Stimulation Surgery

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Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery

“…Biophysical models like the volume theoretically activated by the DBS are some well-described techniques to prevent this effect [27,28]. With contradictory results [29][30][31], these models have been combined with the use of tractography to compute the overlap between the volume of tissue theoretically activated and the pyramidal tract. Those works have opened the path for the hotly anticipated directional leads that might widen the therapeutic window and be beneficial for the patients [32][33][34].…”