While holding vast potential, diffusion tensor imaging (DTI) with single-excitation protocols still faces serious challenges. Limited spatial resolution, susceptibility to magnetic field inhomogeneity, and low signal-to-noise ratio (SNR) may be considered the most prominent limitations. It is demonstrated that all of these shortcomings can be effectively mitigated by the transition to parallel imaging technology and high magnetic field strength. Using the sensitivity encoding (SENSE) technique at 3 T, brain DTI was performed in nine healthy volunteers. Despite enhanced field inhomogeneity, parallel acquisition permitted both controlling geometric distortions and enhancing spatial resolution up to 0.8 mm in-plane. Heightened SNR requirements were met in part by high base sensitivity at 3 T. Diffusion tensor imaging (1,2) is a promising noninvasive method for studying white matter structure of the human brain in vivo. Based on the concept of anisotropic water diffusion across tissue, the measurement of 3D diffusion properties, as described by a local diffusion tensor, allows the characterization of the axonal architecture of white matter networks. For that purpose, a 3D tracking of axonal projections, known as fiber tracking (3-7), is required. However, the low SNR and the limited spatial resolution (8 -10) of the method severely impair its application. A serious resolution limit stems from the strong link between voxel size and SNR, the latter being inherently low due to diffusion weighting. Only improving the SNR of the initial diffusion-weighted (DW) images will enable better spatial resolution. Therefore, the use of high magnetic fields and the related SNR gain could considerably enhance the performance of DTI and fiber tracking.The calculation of the local diffusion tensor requires a set of DW images, acquired with diffusion gradients applied in at least six noncollinear directions, plus a reference image without diffusion weighting. The sequence most commonly used for DTI is spin-echo single-shot EPI (SE-sshEPI). It allows for whole brain coverage in an acceptable scan time and is insensitive to bulk motion due to its speed. Critical shortcomings of sshEPI are image blurring due to T* 2 decay during the EPI readout interval and off-resonance effects, caused by the long EPI echo train (11,12). Both effects scale with the main magnetic field B 0 , making the transition to higher field strength challenging. At 3 T, signal alteration and geometric distortion due to static resonance offset effects, e.g., in the vicinity of airtissue interfaces, are a serious problem when using sshEPIbased protocols.Recently, the potential of parallel imaging techniques, such as simultaneous acquisition of spatial harmonics (SMASH) (13) and sensitivity encoding (SENSE) (14), has been demonstrated for sshEPI in general (15), as well as for diffusion-weighted MRI (DWI) (16 -18) and DTI (19) at 1.5 T. Parallel imaging techniques were shown to significantly reduce EPI-related artifacts as a result of shortening the echo train by facto...

A statistical method for the evaluation of image registration for a series of images based on the assessment of consistency properties of the registration results is proposed. Consistency is defined as the residual error of the composition of cyclic registrations. By combining the transformations of different algorithms the consistency error allows a quantitative comparison without the use of ground truth, specifically, it allows a determination as to whether the algorithms are compatible and hence provide comparable registrations. Consistency testing is applied to evaluate retrospective correction of eddy current-induced image distortion in diffusion tensor imaging of the brain. In the literature several image transformations and similarity measures have been proposed, generally showing a significant reduction of distortion in side-by-side comparison of parametric maps before and after registration. Transformations derived from imaging physics and a three-dimensional affine transformation as well as mutual information (MI) and local correlation (LC) similarity are compared to each other by means of consistency testing. The dedicated transformations could not demonstrate a significant difference for more than half of the series considered. LC similarity is well-suited for distortion correction providing more consistent registrations which are comparable to MI.

A method is described for the automated detection of microcalcifications in digitized mammograms. The method is based on the Laplacian scale-space representation of the mammogram only. First, possible locations of microcalcifications are identified as local maxima in the filtered image on a range of scales. For each finding, the size and local contrast is estimated, based on the Laplacian response denoted as the scale-space signature. A finding is marked as a microcalcification if the estimated contrast is larger than a predefined threshold which depends on the size of the finding. It is shown that the signature has a characteristic peak, revealing the corresponding image features. This peak can be robustly determined. The basic method is significantly improved by consideration of the statistical variation of the estimated contrast, which is the result of the complex noise characteristic of the mammograms. The method is evaluated with the Nijmegen database and compared to other methods using these mammograms. Results are presented as the free-response receiver operating characteristic (FROC) performance. At a rate of one false positive cluster per image the method reaches a sensitivity of 0.84, which is comparable to the best results achieved so far.

Purpose: To propose and to evaluate a novel method for the automatic segmentation of the heart's two ventricles from dynamic ("cine") short-axis "steady state free precession" (SSFP) MR images. This segmentation task is of significant clinical importance. Previously published automated methods have various disadvantages for routine clinical use. Materials and Methods:The proposed method is primarily image-driven: it exploits the spatiotemporal information provided by modern 3Dϩtime SSFP cardiac MRI, and makes only few and plausible assumptions about the image acquisition and about the imaged heart. Specifically, the method does not require previously trained statistical shape models or gray-level appearance models, as often used by other methods. Conclusion:The proposed method is feasible, fast, and robust against anatomical variability and image contrast variations.

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