Purpose: To establish a baseline of phase differences between tissues in a number of regions of the human brain as a means of detecting iron abnormalities using magnetic resonance imaging (MRI). Materials and Methods:A fully flow-compensated, threedimensional (3D), high-resolution, gradient-echo (GRE) susceptibility-weighted imaging (SWI) sequence was used to collect magnitude and phase data at 1.5T. The phase images were high-pass-filtered and processed region by region with hand-drawn areas. The regions evaluated included the motor cortex (MC), putamen (PUT), globus pallidus (GP), caudate nucleus (CN), substantia nigra (SN), and red nucleus (RN). A total of 75 subjects, ranging in age from 55 to 89 years, were analyzed. Results:The phase was found to have a Gaussian-like distribution with a standard deviation (SD) of 0.046 radians on a pixel-by-pixel basis. Most regions of interest (ROIs) contained at least 100 pixels, giving a standard error of the mean (SEM) of 0.0046 radians or less. In the MC, phase differences were found to be roughly 0.273 radians between CSF and gray matter (GM), and 0.083 radians between CSF and white matter (WM). The difference between CSF and the GP was 0.201 radians, and between CSF and the CN (head) it was 0.213 radians. For CSF and the PUT (the lower outer part) the difference was 0.449 radians, and between CSF and the RN (third slice vascularized region) it was 0.353 radians. Finally, the phase difference between CSF and SN was 0.345 radians. Conclusion:The Gaussian-like distributions in phase make it possible to predict deviations from normal phase behavior for tissues in the brain. Using phase as an iron marker may be useful for studying absorption of iron in diseases such as Parkinson's, Huntington's, neurodegeneration with brain iron accumulation (NBIA), Alzheimer's, and multiple sclerosis (MS), and other iron-related diseases. The phases quoted here will serve as a baseline for future studies that look for changes in iron content.
In this work, we present a new method for predicting changes in tumor vascularity using only one flip angle in dynamic contrast-enhanced (DCE) imaging. The usual DCE approach finds the tissue initial T 1 value T 1 (0) prior to injection of a contrast agent. We propose finding changes in the tissue contrast agent uptake characteristics pre-and postdrug treatment by fixing T 1 (0). Using both simulations and imaging pre-and postadministration of caffeine, we find that the relative change (NR50) in the median of the cumulative distribution (R50) is almost independent of T 1 (0). Fixing T 1 (0) leads to a concentration curve c(t) more robust to the presence of noise than calculating T 1 (0). Consequently, the NR50 for the tumor remains roughly the same as the ideal NR50 when T 1 (0) is exactly known. Further, variations in eating habits are shown to create significant changes in the R50 response for both liver and muscle. In conclusion, analyzing data with fixed T 1 (0) leads to a more stable measure of changes in NR50 and does not require knowl- Dynamic contrast-enhanced MRI (DCE-MRI) is a method for imaging the physiology of the microcirculation. A series of recent clinical studies have shown that DCE-MRIbased measures correlate well with tumor angiogenesis. DCE-MRI is performed after the administration of an intravenous contrast agent, gadolinium-DTPA, to noninvasively assess tumor vascular characteristics. Recently, DCE-MRI has been used to assess antiangiogenic cancer drug effectiveness in Phase I pharmaceutical trials (1-3) by acquiring data before and after drug treatment. The contrast enhancement patterns on DCE-MRI are influenced by tumor angiogenesis, as reflected by elevated vascular endothelial growth factor (VEGF) expression. Therefore, they become valuable indicators for assessing tumor angiogenic activity (4,5) and tumor neovascularization in vivo in hepatocellular carcinoma patients (6,7). The use of DCE has been so important that one would be hesitant to continue testing a drug in the absence of any volume or vascular changes appearing in DCE-MRI unless the patients' survival increased (8).Despite its promise, there are problems in the acquisition and processing of DCE data. Repeatability has been a major problem (9 -11). Given the wide clinical use of DCE-MRI, this is an important issue that must be directly addressed. One approach is to improve the methodology itself with more rapid high-resolution respiratory free scanning methods (12). And this will happen with the advent of parallel imaging (13,14). The other is to better process existing data.From our review of many DCE-MRI experiments and projects at the MRI Research facility in the Department of Radiology at Wayne State University, we have found that the causes of most of the DCE errors are related to noise in the T 1 estimates and to physiologic changes in the blood flow (BF) from one day to the next. Normally, an estimate for the baseline T 1 (referred to here as T 1 (0)) is obtained from multiple flip angle (FA) images (15). Any inconsist...
This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional.
The intensity-curvature functional (ICF) of a model polynomial function is defined on a pixel-by-pixel basis by the ratio between the intensity-curvature term before interpolation and the intensity-curvature term after interpolation. Through the comparison with the traditional high-pass filter (HPF), this work presents evidence that the ICFs of three model polynomial functions can be tuned as HPFs. The evidence consists of the mathematical characterization of the ICF-based HPFs, qualitative comparisons in magnetic resonance imaging (MRI) of the human brain, and the determination of the finite impulse response (FIR) of the filters. The ICF-based HPFs can remove periodic noise in the low-frequency band. K E Y W O R D S finite impulse response, high-pass filter, intensity-curvature functional, model polynomial function, magnetic resonance imaging wileyonlinelibrary.com/journal/ima Int J Imaging Syst Technol.
Purpose: To create a robust means to remove noise pixels using complex data. Materials and Methods:A receiver operating characteristic (ROC) curve was used to determine the appropriate choice of magnitude and phase thresholds as well as connectivity values to determine what pixels represent noise in the image. To fine-tune the results, a spike removal and hole replacement operator is applied to reduce Type I error and remove small islands of noise. Results:The use of phase information improves the magnitude-only thresholding approach by further recognizing pixels that contain only noise. The performance of the method is enhanced using local connectivity of magnitude and phase data. An ROC analysis on simulated data shows that the Type I and Type II errors are less than 10 Ϫ4 and 10 Ϫ3, respectively, without connectivity and 0 and 10 Ϫ3 , respectively, with connectivity for a signal-to-noise ratio (SNR) of 3:1 or higher. Conclusion:The joint use of both magnitude and phase images helps to improve the removal of noise points in magnetic resonance images. This can prove useful in automating the visualization of phase images without the highly distractive phase noise in noise regions. Also, it is useful in susceptibility weighted imaging when taking the minimum intensity projections of variably sized regions.
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