Ek Tsoon Tan

Bio: Ek Tsoon Tan is an academic researcher from Hospital for Special Surgery. The author has contributed to research in topics: Medicine & Imaging phantom. The author has an hindex of 15, co-authored 72 publications receiving 807 citations. Previous affiliations of Ek Tsoon Tan include General Electric & National University of Singapore.

Accelerated diffusion spectrum imaging in the human brain using compressed sensing.

Abstract: We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4. Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc.

Peripheral nerve diffusion tensor imaging: Overview, pitfalls, and future directions

Abstract: Diffusion tensor imaging (DTI) is a noninvasive magnetic resonance imaging (MRI) technique that measures the extent of restricted water diffusion and anisotropy in biological tissue. Although DTI has been widely applied in the brain, more recently researchers have used it to characterize nerve pathology in the setting of entrapment neuropathy, traumatic injury, and tumor. DTI artifacts are exacerbated when imaging off isocenter in the body. Anecdotally, the most significant artifacts in peripheral nerve DTI include magnetic field inhomogeneity, motion, incomplete fat suppression, aliasing, and distortion. High spatial resolution is also required to reliably evaluate smaller peripheral nerves. This article provides an overview of such technical issues, particularly when trying to apply DTI in the clinical setting, and offers potential solutions. LEVEL OF EVIDENCE 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1171-1189.

Improved correction for gradient nonlinearity effects in diffusion-weighted imaging.

Abstract: Purpose To provide an improved correction for gradient nonlinearity (GN) effects in diffusion-weighted imaging (DWI). These effects produce spatially varying apparent diffusion coefficient (ADC), a result that will be significant in large field-of-view imaging, and may be confounded by distortion and concomitant fields related to the DWI acquisition. Materials and Methods The effect of more accurate gradient field maps on GN correction (GNC) of ADC was evaluated. A simulation compared GN effects in commonly imaged anatomy. A temperature-controlled phantom was imaged at positions 0 cm and 11 cm from isocenter and in two whole-body MRI systems at 1.5T with different patient bore diameters (55 cm and 60 cm). Varying correction methods were applied to determine the errors from spatial variance and interscanner reproducibility. Results As compared to conventional fifth-order spherical harmonics, a seventh-order GNC improved ADC accuracy by 1%. The combination of GNC with a dual-spin-echo pulse sequence and a retrospective concomitant field correction reduced ADC error due to spatial variance from 9.5% to 1.8% (55 cm bore) and from 4.2% to 1.8% (60 cm bore). The error in ADC attributed to interscanner reproducibility was reduced from 5.8% to 0.15% (at isocenter) and from 10% to 0.63% (11 cm from isocenter). Conclusion GNC in DWI improved spatial accuracy and interscanner reproducibility of ADC. J. Magn. Reson. Imaging 2013;38:448–453. © 2012 Wiley Periodicals, Inc.

Lightweight, compact, and high-performance 3T MR system for imaging the brain and extremities

Abstract: Purpose To build and evaluate a small-footprint, lightweight, high-performance 3T MRI scanner for advanced brain imaging with image quality that is equal to or better than conventional whole-body clinical 3T MRI scanners, while achieving substantial reductions in installation costs. Methods A conduction-cooled magnet was developed that uses less than 12 liters of liquid helium in a gas-charged sealed system, and standard NbTi wire, and weighs approximately 2000 kg. A 42-cm inner-diameter gradient coil with asymmetric transverse axes was developed to provide patient access for head and extremity exams, while minimizing magnet-gradient interactions that adversely affect image quality. The gradient coil was designed to achieve simultaneous operation of 80-mT/m peak gradient amplitude at a slew rate of 700 T/m/s on each gradient axis using readily available 1-MVA gradient drivers. Results In a comparison of anatomical imaging in 16 patients using T2 -weighted 3D fluid-attenuated inversion recovery (FLAIR) between the compact 3T and whole-body 3T, image quality was assessed as equivalent to or better across several metrics. The ability to fully use a high slew rate of 700 T/m/s simultaneously with 80-mT/m maximum gradient amplitude resulted in improvements in image quality across EPI, DWI, and anatomical imaging of the brain. Conclusions The compact 3T MRI system has been in continuous operation at the Mayo Clinic since March 2016. To date, over 200 patient studies have been completed, including 96 comparison studies with a clinical 3T whole-body MRI. The increased gradient performance has reliably resulted in consistently improved image quality.

Peripheral nerve stimulation characteristics of an asymmetric head-only gradient coil compatible with a high-channel-count receiver array.

Abstract: PURPOSE To characterize peripheral nerve stimulation (PNS) of an asymmetric head-only gradient coil that is compatible with a commercial high-channel-count receive-only array. METHODS Two prototypes of an asymmetric head-only gradient coil set with a 42-cm inner diameter were constructed for brain imaging at 3T with maximum performance specifications of up to 85 mT/m and 708 T/m/s. Tests were performed in 24 volunteers to measure PNS thresholds with the transverse (x = left-right; y = anterior-posterior [A/P]) gradient coils of both prototypes. Fourteen of these 24 volunteers were also tested for the z-gradient PNS in the second prototype and were scanned with high-slew-rate echo planar imaging (EPI) immediately after the PNS tests. RESULTS For both prototypes, the y-gradient PNS threshold was markedly higher than the x-gradient threshold. The z-gradient threshold was intermediate between those for the x- and y-coils. Of the 24 volunteers, only two experienced y-gradient PNS at 80 mT/m and 500 T/m/s. All volunteers underwent the EPI scan without PNS when the readout direction was set to A/P. CONCLUSION Measured PNS characteristics of asymmetric head-only gradient coil prototypes indicate that such coils, especially in the A/P direction, can be used for fast EPI readout in high-performance neuroimaging scans with substantially reduced PNS concerns compared with conventional whole body gradient coils. Magn Reson Med 76:1939-1950, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

Dipy, a library for the analysis of diffusion MRI data

Abstract: Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.