No abstract

No abstract

The purpose of this study was to evaluate the feasibility of a centric-reordered modified rapid acquisition with relaxation enhancement (mRARE) sequence for single-shot diffusion-weighted magnetic resonance imaging (DWI) of soft-tissue tumors in the musculoskeletal system. In the evaluation of this sequence, DWI was performed in a liquid phantom, in excised human tumor samples embedded in bovine muscle, and in nine patients suffering from different types of soft-tissue tumors. The measurements were compared to DWI using a spin-echo sequence and a single-shot echo planar imaging (EPI) sequence. The phantom measurements in water and dimethyl sulfoxide showed a difference of less than 5% when comparing the apparent diffusion coefficients (ADCs) determined by the mRARE sequence and the two other techniques. Comparing mRARE and EPI, the differences in the ADCs were about 10% in the excised tumor tissue and, typically, about 15% in vivo. ADCs between 0.8 x 10(-3) mm2/s and 1.4 x 10(-3) mm2/s, depending on the tumor type, were found in solid tumor tissue; in cystic tumor areas, ADCs greater than 2.0 x 10(-3) mm2/s were determined with the mRARE and the EPI sequences. Diffusion-weighted images of the mRARE sequence were less distorted than those acquired with the single-shot EPI sequence, and provided more anatomic information, since the muscle and fat signals were considerably higher.

Summary In the quantitative analysis of dynamic contrast‐enhanced magnetic resonance imaging compartment models allow the uptake of contrast medium to be described with biologically meaningful kinetic parameters. As simple models often fail to describe adequately the observed uptake behaviour, more complex compartment models have been proposed. However, the non‐linear regression problem arising from more complex compartment models often suffers from parameter redundancy. We incorporate spatial smoothness on the kinetic parameters of a two‐tissue compartment model by imposing Gaussian Markov random‐field priors on them. We analyse to what extent this spatial regularization helps to avoid parameter redundancy and to obtain stable parameter point estimates per voxel. Choosing a full Bayesian approach, we obtain posteriors and point estimates by running Markov chain Monte Carlo simulations. The approach proposed is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study.

Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset.