Biomedical subjects

Siamak Ardekani

Publications and source records attributed to Siamak Ardekani.

4 recordsLinked to original sources

PubMed2006

Statistical representation of mean diffusivity and fractional anisotropy brain maps of normal subjects.

PURPOSE: To create diffusion tensor atlases from echo planar imaging (EPI) images acquired at 3 T in 10 normal subjects. MATERIALS AND METHODS: Data from 10 right-handed healthy adult volunteers (mean age of 31 +/- 3 years; eight males) were acquired using a 3.0-T scanner. Geometric distortion artifacts correction was accomplished by combining parallel acquisition to reduce the distortion as well as postprocessing by registration to a geometrically accurate T2-weighted fast-spin-echo image. This reduced distortions to within a voxel for most of the internal structures of the brain. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) atlases were created by warping images using an iterative optical-flow-based local deformation algorithm that used two channels of data: ADC and FA. RESULTS: A three-dimensional distance measure was used to evaluate the accuracy of the registration algorithm with contours defined on two structures: the corpus callosum and cerebellum. The average three-dimensional distance value for the nine subjects (with the 10th as the reference) was 0.2 mm for the corpus callosum and 1.2 mm for the cerebellum. CONCLUSION: A high-resolution, diffusion MR atlas with full brain coverage was developed. Additionally, maps of the SD of the diffusion indices were also generated to provide an estimate of the variance within a normal population. Active shape and texture models were also generated for the corpus callosum as an alternate method of representing the variance in morphology and diffusion indices.

Adult↗

PubMed2006

Quantitative metrics for evaluating parallel acquisition techniques in diffusion tensor imaging at 3 Tesla.

OBJECTIVES: Single-shot echo-planar based diffusion tensor imaging is prone to geometric and intensity distortions. Parallel imaging is a means of reducing these distortions while preserving spatial resolution. A quantitative comparison at 3 T of parallel imaging for diffusion tensor images (DTI) using k-space (generalized auto-calibrating partially parallel acquisitions; GRAPPA) and image domain (sensitivity encoding; SENSE) reconstructions at different acceleration factors, R, is reported here. MATERIALS AND METHODS: Images were evaluated using 8 human subjects with repeated scans for 2 subjects to estimate reproducibility. Mutual information (MI) was used to assess the global changes in geometric distortions. The effects of parallel imaging techniques on random noise and reconstruction artifacts were evaluated by placing 26 regions of interest and computing the standard deviation of apparent diffusion coefficient and fractional anisotropy along with the error of fitting the data to the diffusion model (residual error). RESULTS: The larger positive values in mutual information index with increasing R values confirmed the anticipated decrease in distortions. Further, the MI index of GRAPPA sequences for a given R factor was larger than the corresponding mSENSE images. The residual error was lowest in the images acquired without parallel imaging and among the parallel reconstruction methods, the R = 2 acquisitions had the least error. The standard deviation, accuracy, and reproducibility of the apparent diffusion coefficient and fractional anisotropy in homogenous tissue regions showed that GRAPPA acquired with R = 2 had the least amount of systematic and random noise and of these, significant differences with mSENSE, R = 2 were found only for the fractional anisotropy index. CONCLUSION: Evaluation of the current implementation of parallel reconstruction algorithms identified GRAPPA acquired with R = 2 as optimal for diffusion tensor imaging.

Adult↗

PubMed2005

Geometric distortion correction of high-resolution 3 T diffusion tensor brain images.

Diffusion-weighted images based on echo planar sequences suffer from distortions due to field inhomogeneities from susceptibility differences as well as from eddy currents arising from diffusion gradients. In this paper, a novel approach using nonlinear warping based on optic flow to correct distortions of baseline and diffusion weighted echo planar images (EPI) acquired at 3 T is presented. The distortion correction was estimated by warping the echo planar images to the anatomically correct T2-weighted fast spin echo images (T2-FSE). A global histogram intensity matching of the T2-FSE precedes the base line EPI image distortion correction. A local intensity-matching algorithm was used to transform labeled T2-FSE regions to match intensities of diffusion-weighted EPI images prior to distortion correction of these images. Evaluation was performed using three methods: (i) visual comparison of overlaid contours, (ii) a global mutual information index, and (iii) a local distance measure between homologous points. Visual assessment and the global index demonstrated a decrease in geometrical distortion and the distance measure showed that distortions are reduced to a subvoxel level. In conclusion, the warping algorithm is effective in reducing geometric distortions, enabling generation of anatomically correct diffusion tensor images at 3 T.

Adult↗

PubMed2004

Parametric brain MR atlases: standardization for imaging informatics.

This paper is focused on the development of normal MR brain atlases of intrinsic MR parameters. These parameters permit quantitative comparisons across imaging studies (as opposed to raw image intensity values) and are important markers of neurological diseases. The development includes fast sequences to generate three parameters (T1: spin-lattice relaxation, T2: spin-spin relaxation, and Diffusion Tensor) covering the whole brain with isotropic and high-resolution images. The analysis of raw data to generate the parametric images is followed by registration algorithms to bring the image studies acquired on normal subjects aligned to a common frame of reference. The registration method includes both linear and non-linear algorithms. Two atlas schemes are discussed: an average atlas and a probabilistic atlas. Initial results on sequence development and registration are presented. The atlases are envisaged as an integral part of an imaging informatics infrastructure that enables image analysis across imaging studies to perform automated image data mining.

Algorithms↗