A method was developed for separation of water and fat MR images in a single scan with correction of static field inhomogeneity. The imaging sequence uses a single radiofrequency (RF) echo that is "sandwiched" between two gradient echoes. The gradient echoes are used to determine the B(0) distribution and to produce out-of-phase images after phase correction using the field map. An algorithm was developed to unwrap the phase images for quantitating the B(0) inhomogeneity. To account for differences in geometric distortion between the RF echo image and the gradient echo images due to the reversal of the read gradients, methods were developed to correct the images before the calculation of the final water and fat images. The proposed technique was implemented at .35 T. Both phantom and human images were acquired using the method. It is shown that water- and fat-separated images can be obtained in a single scan using the "sandwich" echoes in the presence of a relatively large B(0) inhomogeneity.

Acquisition of relaxation rate dispersion curves from magnetic resonance images was demonstrated on a clinical, whole-body imaging system. Study of the behavior of relaxation rates over a range of field strengths probes the structural environment of imaged hydrogen protons and reveals information about the composition of tissue. The authors determined relaxation rates in extremities and heads of healthy volunteers. The sensitivity of the measurement is sufficient to obtain a distinctive relaxation rate dispersion behavior for different tissues.

Neglecting coil attenuation for this anterior array coil results in a small but significant reduction in liver SUV(mean) but was not found to change the clinical interpretation of the PET images.

We present a tabletop and coil holder for an anterior array coil to be used with a GE SIGNA PET/MR scanner, for scanning patients in the radiotherapy work flow. Implementing attenuation correction of the added hardware from the radiotherapy setup leads to acceptable PET image quantitation. The drop in SNR in MR images may require adjustment of the imaging protocols.

Magnetic Resonance Imaging (MRI) is emerging as a powerful tool for the diagnosis of breast abnormalities. Dynamic analysis of the temporal pattern of contrast uptake has been applied in differential diagnosis of benign and malignant lesions to improve specificity. Selecting a region of interest (ROI) is an almost universal step in the process of examining the contrast uptake characteristics of a breast lesion. We propose an ROI selection method that combines modelbased clustering of the pixels with Bayesian morphology, a new statistical image segmentation method. We then investigate tools for subsequent analysis of signal intensity time course data in the selected region. Results on a data base of 19 patients are promising. The method provides informative segmentations and good detection rates are obtained.