Abstract: Methods and apparatus for processing magnetic resonance imaging (MRI) data to perform bone segmentation. MRI data comprising a set of gradient-echo images acquired throughout a spin echo is processed to generate a bone segmentation image. The bone segmentation image is generated based, at least in part, on at least two images in the set of gradient-echo images, wherein the at least two images include a first image corresponding to a beginning portion of the spin echo and a second image corresponding to a central portion of the spin echo.

Abstract: Methods and apparatus for processing magnetic resonance imaging (MRI) data to perform bone segmentation. MRI data comprising a set of gradient-echo images acquired throughout a spin echo is processed to generate a bone segmentation image. The bone segmentation image is generated based, at least in part, on at least two images in the set of gradient-echo images, wherein the at least two images include a first image corresponding to a beginning portion of the spin echo and a second image corresponding to a central portion of the spin echo.

Abstract: The present invention provides a method for producing density-compensated MR images having improved signal-to-noise ratio and with reduced computational burden. The method includes sampling MR data using a rotationally symmetric acquisition trajectory, generating a cross-correlation matrix, and applying a discreet Fourier transform (DFT) to the acquired MR data and the cross correlation matrix. The method further includes employing the transformed MR data and transformed cross-correlation matrix in a linear system to produce a set of density-compensated transformed MR data, which may transformed into density compensated MR data by application of an inverse DFT. The density compensated MR data may be reconstructed by a variety of techniques to produce density compensated images.

Abstract: The present invention provides a method for producing density-compensated MR images having improved signal-to-noise ratio and with reduced computational burden. The method includes sampling MR data using a rotationally symmetric acquisition trajectory, generating a cross-correlation matrix, and applying a discreet Fourier transform (DFT) to the acquired MR data and the cross correlation matrix. The method further includes employing the transformed MR data and transformed cross-correlation matrix in a linear system to produce a set of density-compensated transformed MR data, which may transformed into density compensated MR data by application of an inverse DFT. The density compensated MR data may be reconstructed by a variety of techniques to produce density compensated images.

Abstract: Quantified differences, such as chi2 error parameters, between a mono-exponential, logarithmic best fit of a series of line scan diffusion-weighted magnetic resonance signals taken over a range of b-factors between about 100 and about 5000 sec/mm2 are obtained. The quantified differences so generated are displayed as an image wherein the brightness of each pixel depends upon the size of its associated quantified difference. The resulting image is characterized by high signal to noise ratio and distinctness between varying tissue types.