Ek Tsoon Tan

Accelerated diffusion spectrum imaging in the human brain using compressed sensing

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.

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

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.

Improved correction for gradient nonlinearity effects in diffusion-weighted imaging

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.

Dynamic slice‐dependent shim and center frequency update in 3 T breast diffusion weighted imaging

To demonstrate dynamic slice‐dependent shim update as a simple method to reduce susceptibility‐induced B0 inhomogeneity and associated pixel shift artifacts in diffusion‐weighted echo planar imaging (DW‐EPI) in 3 T breast imaging.

High slew-rate head-only gradient for improving distortion in echo planar imaging: Preliminary experience.

To investigate the effects on echo planar imaging (EPI) distortion of using high gradient slew rates (SR) of up to 700 T/m/s for in vivo human brain imaging, with a dedicated, head‐only gradient coil.

Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests

Accurate automated segmentation of brain tumors in MR images is challenging due to overlapping tissue intensity distributions and amorphous tumor shape. However, a clinically viable solution providing precise quantification of tumor and edema volume would enable better pre-operative planning, treatment monitoring and drug development. Our contributions are threefold. First, we design efficient gradient and LBPTOP based texture features which improve classification accuracy over standard intensity features. Second, we extend our texture and intensity features to symmetric texture and symmetric intensity which further improve the accuracy for all tissue classes. Third, we demonstrate further accuracy enhancement by extending our long range features from 100 mm to a full 200 mm. We assess our brain segmentation technique on 20 patients in the BraTS 2012 dataset. Impact from each contribution is measured and the combination of all the features is shown to yield state-of-the-art accuracy and speed.

Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial

To evaluate a gradient nonlinearity correction (GNC) program for quantitative apparent diffusion coefficient (ADC) measurements on phantom and human subject diffusion‐weighted (DW) magnetic resonance imaging (MRI) scans in a multicenter breast cancer treatment response study

Multi‐directional anisotropy from diffusion orientation distribution functions

To evaluate a model‐independent, multi‐directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single‐direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi‐directional diffusivity.

iSTAPLE: improved label fusion for segmentation by combining STAPLE with image intensity

Multi-atlas based methods have been a trend for robust and automated image segmentation. In general these methods first transfer prior manual segmentations, i.e., label maps, on a set of atlases to a given target image through image registration. These multiple label maps are then fused together to produce segmentations of the target image through voting strategy or statistical fusing, e.g., STAPLE. STAPLE simultaneously estimates the true segmentation and the label map performance level, but has been shown inaccurate for multi-atlas segmentation because it is determined completely on the propagated label maps without considering the target image intensity. We develop a new method, called iSTAPLE, that combines target image intensity into a similar maximum likelihood estimate (MLE) framework as in STAPLE to take advantage of both intensity-based segmentation and statistical label fusion based on atlas consensus and performance level. The MLE framework is then solved using a modified EM algorithm to simultaneously estimate the intensity profiles of structures of interest as well as the true segmentation and atlas performance level. Unlike other methods, iSTAPLE does not require the target image to have same image contrast and intensity range as the atlas images, which greatly extends the use of atlases. Experiments on whole brain segmentation showed that iSTAPLE performed consistently better than STAPLE.

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

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.