In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.

The objective of this study was to develop anatomically correct whole body human models of an adult male (34 years old), an adult female (26 years old) and two children (an 11-year-old girl and a six-year-old boy) for the optimized evaluation of electromagnetic exposure. These four models are referred to as the Virtual Family. They are based on high resolution magnetic resonance (MR) images of healthy volunteers. More than 80 different tissue types were distinguished during the segmentation. To improve the accuracy and the effectiveness of the segmentation, a novel semi-automated tool was used to analyze and segment the data. All tissues and organs were reconstructed as three-dimensional (3D) unstructured triangulated surface objects, yielding high precision images of individual features of the body. This greatly enhances the meshing flexibility and the accuracy with respect to thin tissue layers and small organs in comparison with the traditional voxel-based representation of anatomical models. Conformal computational techniques were also applied. The techniques and tools developed in this study can be used to more effectively develop future models and further improve the accuracy of the models for various applications. For research purposes, the four models are provided for free to the scientific community.

The aim of this study was to determine apparent diffusion coefficients (ADCs) of focal liver lesions on the basis of a respiratory triggered diffusion-weighted single-shot echo-planar MR imaging sequence (DW-SS-EPI) and to evaluate whether ADC measurements can be used to characterize lesions. One hundred and two patients with focal liver lesions [11 hepatocellular carcinomas (HCC), 82 metastases, 4 focal nodular hyperplasias (FNH), 56 hemangiomas and 51 cysts; mean size, 16.6 mm; range 5-92 mm] were examined on a 1.5-T system using respiratory triggered DW-SS-EPI (b-values: 50, 300, 600 s/mm2). Results were correlated with histopathologic data and follow-up imaging. The ADCs of different lesion types were compared, and lesion discrimination using optimal thresholds for ADCs was evaluated. Mean ADCs (x10(-3)mm2/s) were 1.24 and 1.04 for normal and cirrhotic liver parenchyma and 1.05, 1.22, 1.40, 1.92 and 3.02 for HCCs, metastases, FNHs, hemangiomas and cysts, respectively. Mean ADCs differed significantly for all lesion types except for comparison of metastases with HCCs and FNHs. Overall, 88% of lesions were correctly classified as benign or malignant using a threshold value of 1.63 x 10(-3)mm2/s. Measurements of the ADCs of focal liver lesions on the basis of a respiratory triggered DW-SS-EPI sequence may constitute a useful supplementary method for lesion characterization.

DESSwe permits accurate and precise analysis of cartilage morphology in the femorotibial joint at 3 T. Further studies are needed to examine the accuracy of DESSwe in the femoropatellar joint and its ability to characterise sensitivity to longitudinal changes in cartilage morphology.

This multi-step adaptive fitting approach performed well in both simulated and initial clinical evaluation, and shows potential in the quantification of hepatic steatosis.