TISSUE CHARACTERIZATION WITH SLAB SCAN DIFFUSION MRI

点击翻译按钮获取中文摘要

In medical imaging, tissue characterization is fundamental for the assessment of brain tumor pathology and morphology. On T1 or T2- weighted MR images obtained without contrast agents., distinction of pathologic tissue from normal tissue often is limited. Excellent contrast, however, results from diffusion weighted MR imaging in the case of acute stroke. Moreover, tensor diffusion weighting over a considerable wider range than typically used, it has been demonstrated that diffusion in brain tissue is described better with two diffusion components. This finding, referred to as multi--component diffusion, may be due to separate water compartments, such as the extra- and intra-cellular water pools, or due to micro-structural features Preliminary data obtained with line scan diffusion imaging (LSDI) in brain tumors indicates that the two apparent diffusion co-efficients (ADC) and their respective amplitudes permit a clear differentiation among tumor, surrounding edema, and normal tissue. Images based on these parameters, together with the detailed mapping of the fiber tracts through diffusion tensor imaging, provide a tissue characterization that is very suitable for the assessment of brain tumor pathology and morphology. A principal goal of the research proposed here is to implement a fast diffusion imaging technique, referred to as slab scan diffusion imaging (SSDI), for tensor and multi-component ADC tissue characterization of the entire brain volume in a scan time of a few minutes. The proposed diffusion imaging technique does not require cardiac gating or post processing for the suppression of motion artifacts. With SSDI, contrary to single-shot echo planar diffusion-weighted imaging, distortion artifacts in areas of large susceptibility variations are minimal. Diffusion tensor and multi-component ADC data will be collected in normal volunteers and in patients with different brain tumors. Normal tissue values and their respective direction variability (anisotropy) will be established with the ADC data obtained in normal subjects. Tissue separations with diffusion tensor data and multi- component ADC parameters can be compared with tumor margins established by the use of contrast agents and conventional T1-weighted MR imaging. The plan is to correlate the diffusion tensor data and multi- component ADC parameters with the specific tumor pathologies, malignancy guide, and different components of a tumor, i.e., necrotic part, margin, and infiltrated surrounding tissue. The study, therefore, proposes direct applications of basic research to clinical situations demanding the highest possible sensitivity and specific for invasive treatment of other therapeutic decisions.