S. Vinitski

Utililization of experimental animal model for correlative multispectral MRI and pathological analysis of brain tumors.

Magnetic resonance imaging is the method of choice for non-invasive detection and evaluation of tumors of the central nervous system. However, discrimination of tumor boundaries from normal tissue, and the evaluation of heterogeneous lesions have proven to be limitations in traditional magnetic resonance imaging. The use of post-image acquisition processing techniques, such as multispectral tissue segmentation analysis, may provide more accurate clinical information. In this report, we have employed an experimental animal model for brain tumors induced by glial cells transformed by the human neurotropic JC virus to examine the utility of multispectral tissue segmentation for tumor cell identification. Six individual tissue types were discriminated by segmentation analysis, including heterogeneous tumor tissue, a clear demarcation of the boundary between tumor and non-tumor tissue, deep and cortical gray matter, and cerebrospinal fluid. Furthermore, the segmentation analysis was confirmed by histopathological evaluation. The use of multispectral tissue segmentation analysis may optimize the non-invasive determination and volumetric analysis of CNS neoplasms, thus providing improved clinical evaluation of tumor growth and evaluation of the effectiveness of therapeutic treatments.

A simple method to improve image nonuniformity of brain MR images at the edges of a head coil.

One of the major sources of image nonuniformity in the high field MR scanners is the radiofrequency (RF) coil inhomogeneity. It degrades conspicuity of lesion(s) in the MR images of the brain and surrounding tissues and reduces accuracy of image postprocessing particularly at the edges of the coil. In this investigation, we have devised and tested a simple method to correct for nonuniformity of MR images of the brain at the edges of the RF head coil. Initially, a cylindrical oil phantom, which fit exactly in the head coil, was scanned on a 1.5 T imager. Then, a correction algorithm identified a reference pixel value in the phantom at the most homogeneous region of the RF coil. Next, every pixel inside the phantom was normalized relative to this reference value. The resulting set of coefficients or correction matrices was obtained for different types of MR contrast agent. Finally, brain MR images of normal subjects and multiple sclerosis patients were acquired and processed by the corresponding correction matrices obtained with different pulse sequences. Application of correction matrices to brain MR images showed a gain in pixel intensity particularly in the slices at the edge of the coil.

MR imaging of pulmonary parenchyma and emboli by paramagnetic and superparamagnetic contrast agents

Using experimentally induced pulmonary emboli in an animal model, three intravenously administered contrast agents, Gd-DTPA-albumin microspheres (8-15 microns, 0.2 M particles/mg protein, 39-106 micrograms Gd/mg, 50 mg/ml), Gd-DTPA-liposomes (15-30 microns, 130 micrograms/mg lipid, 6 mg Gd/ml) and superparamagnetic ferrosome, (60 nm, 100 mM iron and 20 mg lipid/ml) were examined for MR imaging. Gd-DTPA entrapped in lung capillaries did not enhance the signal intensity of lung parenchyma, but liposomes (5 ml) served as better Gd-DTPA carriers and increased the parenchymal signal intensity by up to a factor of 2.3. However, neither agent improved delineation of pulmonary emboli. Ferrosome decreased the intensity of lung parenchyma, improving detectability of pulmonary emboli by several factors.

Image nonuniformity correction in high field (1.5 T) MRI

The largest source of image nonuniformity in high field MRI systems arises from the specialized radiofrequency (RF) coils that are currently used. It degrades conspicuity of lesion(s), and reduces accuracy of image post-processing (e.g. MRA, tissue segmentation, etc.). In this investigation the authors devised a method to correct nonuniformity of MR images with correction matrices obtained from cylindrical uniform phantom. The phantom, filled with doped water and exactly fitted the volume of the head coil, was imaged using MRI/MRA clinical pulse sequences. The reference pixel intensity was defined using the most homogeneous region of the RF coil, and every voxel inside the phantom was normalized relative to the reference value. A correction matrix was obtained for each type of MRI contrast. MRI and MRA images of phantoms as well as normals and MS patients were obtained and processed by the correction matrices. Application of the correction matrices to phantom data resulted in up to 20 fold improvement in image uniformity. In humans, the corrected images improved sharpness and tissue contrast, leading to increased conspicuity of the lesions.

Fast tissue segmentation based on a 3D and 4D feature map: comparison study

The aim of this work was to develop a fast and accurate method for tissue segmentation in MRI based on 4D feature map and compare it with that derived from the 3D feature map. High resolution MR imaging was performed in 5 normals, 6 patients with brain MS, and 6 with malignant brain tumors. Three inputs: proton density, or weighted fast spin-echo, T1-weighted spin echo MR images were routinely utilized. As a fourth input, either magnetization transfer MRI was used in normals and some patients or Tl weighted post contract MRI in other patients. Modified k-Nearest Neighbor segmentation algorithm was optimized for maximum computation speed and high quality segmentation. In that regard: 1) the authors discarded the redundant seed points, 2) discarded the points within 0.5 standard deviation from the cluster center that were non overlapping with other tissue classes, 3) they removed outlying seed points outside 5 times the standard deviation from the cluster center of each tissue class. Their new technique utilizing all 4 MRI inputs provided better segmentation than that based on three inputs. (p<0.001) The tissues were smoother and the delineation of the tissues was increased. Details that were previously blurred or invisible now became apparent. In normals, detailed depiction of deep gray matter nuclei was obtained. In malignant tumors, up to 5 abnormal tissues were identified: 1) solid tumor core, 2) cyst, 3) edema in white matter 4) edema in gray matter and 5) necrosis. Delineation of MS plaque in different stages of demyelination, became much sharper. In conclusion, proposed methodology warrants further development and clinical evaluation.

Improvement in signal-to-noise ratio and reduction of chemical shift and motion-induced artifacts by summation of gradient and spin echo data acquisition

Narrow bandwidth magnetic resonance (MR) imaging allows an increase of signal-to-noise ratio (SNR) but causes increased chemical shift and motion-induced artifacts. To obtain MR images with SNR approximately equal to that obtained with narrow bandwidth but with less chemical shift and motion-induced artifact, we introduced triple readout gradient reversal centered around the spin echo. As a result, signals from two gradient echoes and a single spin echo can be collected and summed. Phantom, knee, shoulder, and abdominal MR images were obtained using a 1.5 T GE Signa System at sampling rates ranging from 10 to 60 kHz. Since the bandwidth per pixel was tripled, chemical shift misregistration was reduced by the same factor. The summation image of two gradient echoes and one spin echo had an SNR comparable with that of a single spin echo acquired within the same total sampling interval. Data acquisition at a high sampling ratio also minimizes the dispersion of T2* weighting among three echoes. In addition, summation of the three resulting images decreases motion artifact by effective averaging.

Validation of tissue segmentation based on 3D feature map in an animal model of a brain tumor

The purpose of this study was to validate our tissue segmentation technique by comparing its results with the composition of living biological tissues. A multispectral approach with three inputs was used. Volumetric MR images were obtained with steady state free procession, gradient echo, with RF spoiling and inversion recovery gradient echo techniques. The animal model used was brain tumors in hamsters. Immediately after imaging, animals were sacrificed and underwent thorough histological examination. Pre-segmentation image processing included our technique for correction of image non-uniformity, application of non-linear diffusion type filters, and, after collecting training points, cluster optimization. Finally, k-NN segmentation was used and a stack of color-coded segmented images was created. Results indicated that good quality of a small subject, such as a hamster brain MRI, can be obtained. Secondly, pre-processing steps vastly improved the results of segmentation-in particular, sharpness. We were able to identify up to eleven tissues. Most importantly, our findings were in full accord with histological exams.

Multiple flip angle MRA: data acquisition and post-processing

We have implemented a MRA (magnetic resonance angiography) data acquisition and post processing method to improve the assessment of the carotid artery bifurcation needed for correct diagnosis. Axial high resolution MRA images were acquired using 3D time-of-flight (TOF) MRA technique with a multiple flip angle. Composite MRA images of these data sets with at least two different flip angles were created using a MIP (maximum intensity projection) algorithm and formatted into sagittal angiographic projections. Next, color coded MRA images were created using a MIP segmentation algorithm. The resulting images demonstrated improved image quality of the carotid bifurcation. These post-processed MRA images also demonstrate significant increase in the SNR within the carotid bifurcation (p<0.05).