Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, Principal Component Analysis (PCA) is combined with a model-based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm - REconstruction of Principal COmponent coefficient Maps (REPCOM) using Compressed Sensing - is demonstrated in phantoms and in vivo and compared to two other algorithms previously developed for undersampled data.

An important goal in cancer chemotherapy is to sensitively and quantitatively monitor the response of individual patients' tumors to successful, or unsuccessful, therapy so that regimens can be altered iteratively. Currently, tumor response is monitored by frank changes in tumor morphology, yet these markers take long to manifest and are not quantitative. Recent studies suggest that the apparent diffusion coefficient of water (ADCw), measured noninvasively with magnetic resonance imaging, is sensitively and reliably increased in response to successful CTx. In the present study, we investigate the combination chemotherapy response of human breast cancer tumor xenografts sensitive or resistant to Paclitaxel by monitoring changes in the ADCw. Our results indicate that there is a clear, substantial, and early increase in the ADCw after successful therapy in drug sensitive tumors and that there is no change in the ADCw in p-glycoprotein-positive tumors, which are resistant to Paclitaxel. The mechanism underlying these changes is unknown yet is consistent with apoptotic cell shrinkage and a concomitant increase in the extracellular water fraction.

Purpose: To evaluate a multishot radial fast-spin echo (RAD-FSE) method developed to improve the quality of abdominal T2-weighted imaging as well as the characterization of focal liver lesions. Materials and Methods:The RAD-FSE sequence used in this work consisted of a preparatory period followed by a short echo train (ETL ϭ 16). A novel radial k-space trajectory was used to minimize streaking artifacts due to T2 variations and motion. Small diffusion gradients (b ϭ 1.2 mm/s 2 ) were used to improve flow suppression. The quality of images obtained with RAD-FSE was compared to multishot 2DFT fast spin-echo (2DFT-FSE) and half-Fourier acquisition single-shot turbo-spin-echo (HASTE) images using data from 16 patients. A postprocessing algorithm was used to generate multiple high-resolution images (at different effective TE values) as well as a T2 map from a single RAD-FSE data set. The T2 maps were used to differentiate malignant from benign lesions for a set of 33 lesions ranging from 0.8 -194 cm 3 .Results: RAD-FSE produces high-resolution images of the liver in a breath-hold without the motion artifacts of 2DFT-FSE methods, and without the blurriness and loss of small lesion detectability of HASTE. The inclusion of diffusion weighting in RAD-FSE decreases the signal from blood in hepatic vessels, which improves lesion visualization. The T2 values obtained by postprocessing a single RAD-FSE data set can differentiate malignant from benign lesions. The mean T2 values obtained for malignancies, hemangiomas, and cysts are 108 Ϯ 30 msec, 240 Ϯ 14 msec, and 572 Ϯ 334 msec, respectively. Conclusion:These results indicate that RAD-FSE produces abdominal images of higher quality than 2DFT-FSE and HASTE. In addition, lesions can be characterized using T2 maps generated from a single RAD-FSE data set.

Motion continues to be a significant problem in MRI, producing image artifacts that can severely degrade image quality. In diffusion-weighted imaging (DWI), the problem is amplified by the presence of large gradient fields used to produce the diffusion weighting. Three correction methods applicable for correction of specific classes of motion are described and compared. The first is based on a generalised projection onto convex sets (GPOCS) postprocessing algorithm. The second technique uses the collection of navigator echoes to track phase errors. The third technique is based on a radial-scan data acquisition combined with a modified projection-reconstruction algorithm. Although each technique corrects well for translations, the radial-scan method proves to be more robust when more complex motions are present. A detailed description of the causes of MR data errors caused by rigid body motion is included as an appendix.

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