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In the direction where the phased array coil used in parallel magnetic resonance imaging (MRI) is perpendicular to the arrangement, sensitivity falls significantly. Moreover, in a 3.0 tesla (3T) abdominal MRI, the quality of the image is reduced by changes in the relaxation time, reinforcement of the magnetic susceptibility effect, etc. In a 3T MRI, which has a high resonant frequency, the signal of the depths (central part) is reduced in the trunk part. SCIC, which is sensitivity correction processing, has inadequate correction processing, such as that edges are emphasized and the central part is corrected. Therefore, we used 3T with a Gaussian distribution. The uneven compensation processing for sensitivity of an abdomen MR image was considered. The correction processing consisted of the following methods. 1) The center of gravity of the domain of the human body in an abdomen MR image was calculated. 2) The correction coefficient map was created from the center of gravity using the Gaussian distribution. 3) The sensitivity correction image was created from the correction coefficient map and the original picture image. Using the Gaussian correction to process the image, the uniformity calculated using the NEMA method was improved significantly compared to the original image of a phantom. In a visual evaluation by radiologists, the uniformity was improved significantly using the Gaussian correction processing. Because of the homogeneous improvement of the abdomen image taken using 3T MRI, the Gaussian correction processing is considered to be a very useful technique.

Rationale and Objectives: Acoustic noise both in terms of its magnitude and frequency during magnetic resonance imaging (MRI) scan is influenced by imaging parameters and pulse sequences. It varies because of many different factors such as structure, materials, and magnetic field strength. The purpose of our study is to evaluate the characteristics of acoustic noise independent of MRI scan protocol by measuring a gradient-pulse-to-acoustic-noise transfer function (GPAN-TF) at various MRI scanners., Materials and Methods: We measured sound pressure levels in the frequency domain in a 0.4-T, seven 1.5-T, and three 3.0-T clinical MRI systems when applying a simple narrower trapezoidal gradient pulse. We calculated a GPAN-TF [μPa/(mT/m)] in each gradient coil (ie, X, Y, and Z-axis) by the deconvolution process., Results: GPAN-TF at a high-frequency range (1000-10,000 Hz) was larger than that at low frequency for all MRI (P<0.01) scanners except for a low static field machine. For high frequency (>1000 Hz), the 3.0-T MRI scanner had a larger GPAN-TF than that of 0.4-T and 1.5-T (P < .01). MR scanner with a vacuum chamber reduced GPAN-TF at a lower frequency (P < .01), but this effect decreased at higher frequency., Conclusion: GPAN-TF analysis makes it possible to obtain more detailed information on acoustic noise properties among MRI scanners., (Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.)

Magnetic resonance imaging (MRI) is useful in the quantitative evaluation of brain atrophy, because the superior contrast resolution facilitates separation of the gray and white matter. Quantitative assessment of brain atrophy has mainly been performed by manual measurement, which requires considerable time and effort to determine the brain volume. Therefore, computer-aided quantitative measurement methods for the diagnosis of brain atrophy are required. We have developed a method of segmenting the cerebrum, cerebellum-brainstem, and temporal lobe simultaneously on MR images obtained in a single sequence. It is important to measure the volume of not only these regions but also the frontal lobe in clinical use. However, for segmenting the frontal lobe, it is necessary to identify the Sylvian fissure and the central sulcus, which represent boundaries. Here, we developed a method of identifying the central sulcus from MR images obtained with a 1.5 T MRI scanner. The brain and the cerebrospinal fluid (CSF) regions were segmented using semiautomated segmentation method on MR images. The central sulcus shows an oblique line from the inside to the outside on the convexity view. The almost straight appearance of the central sulcus was used for segmentation of the central sulcus from the segmented CSF images. The central sulcus was identified with this technique in 77% of the images obtained by all sequences. This technique for identifying the central sulcus is very important not only for volumetry, but also for clinical diagnosis.

Gd-EOB-DTPA is a new liver specific MRI contrast media. In the hepatobiliary phase, contrast media is trapped in normal liver tissue, a normal liver shows high intensity, tumor/liver contrast becomes high, and diagnostic ability improves. In order to indicate the degree of uptake of the contrast media, the enhancement ratio (ER) is calculated. The ER is obtained by calculating (signal intensity (SI) after injection-SI before injection) / SI before injection. However, because there is no linearity between contrast media concentration and SI, ER is not correctly estimated by this method. We discuss a method of measuring ER based on SI and T(1) values using the phantom. We used a column phantom, with an internal diameter of 3 cm, that was filled with Gd-EOB-DTPA diluted solution. Moreover, measurement of the T(1) value by the IR method was also performed. The ER measuring method of this technique consists of the following three components: 1) Measurement of ER based on differences in 1/T(1) values using the variable flip angle (FA) method, 2) Measurement of differences in SI, and 3) Measurement of differences in 1/T(1) values using the IR method. ER values calculated by these three methods were compared. In measurement made using the variable FA method and the IR method, linearity was found between contrast media concentration and ER. On the other hand, linearity was not found between contrast media concentration and SI. For calculation of ER using Gd-EOB-DTPA, a more correct ER is obtained by measuring the T(1) value using the variable FA method.

MR imaging (MRI) is an important method for the diagnosis of abnormalities of the brain. In the present report, a semi-automated method is presented to segment the brain and CSF region on brain MR images. MR images were obtained from 6 subjects by SE sequence and 8 subjects by GRE sequence. The semi-automated method consisted of the following three steps: (1) segmentation of the intracranial region using the region-growing technique, (2) segmentation of the brain region using histogram analysis and mathematical morphology, and (3) segmentation of the CSF region using the top-hat transformation technique. The average ratio of a correctly recognized region (CRR) between the semi-automated method and manual method was 87.9%, 85.1% for the intracranial region (IRR), and 94.8% and 86.8% for the brain region in the SE and GRE sequences.

Purpose: The aim of this study was to develop an automated method of segmenting the cerebrum, cerebellum-brain stem, and temporal lobe simultaneously on magnetic resonance (MR) images., Methods and Materials: We obtained T1-weighted MR images from 10 normal subjects and 19 patients with brain atrophy. To perform automated volumetry from MR images, we performed the following three steps: (1) segmentation of the brain region; (2) separation between the cerebrum and the cerebellum-brain stem; and (3) segmentation of the temporal lobe. Evaluation was based on the correctly recognized region (CRR) (i.e., the region recognized by both the automated and manual methods)., Results: The mean CRRs of the normal and atrophic brains were 98.2% and 97.9% for the cerebrum, 87.9% and 88.5% for the cerebellum-brain stem, and 76.9% and 85.8% for the temporal lobe, respectively., Conclusion: We introduce an automated volumetric method for the cerebrum, cerebellum-brain stem, and temporal lobe on brain MR images. Our method can be applied to not only the normal brain but also the atrophic brain.

Magnetic resonance (MR) imaging is useful for the diagnosis of brain atrophy and intracranial abnormalities. We have developed a method of automated volumetry to evaluate the degree of brain atrophy for the diagnosis of dementia. Whole-brain MR images with thin slices without gaps are required for segmentation and volumetry. However, obtaining such images requires that the patient remain at rest for a prolonged period, thereby reducing the throughput of MR imaging examinations. Therefore, a method is needed for the reconstruction of isotropic three-dimensional (3D) data using routine axial, sagittal, and coronal MR images with 30% gaps and measurement of brain volume. The method of reconstructing 3D data consists of four processes: 1) segmentation of the brain region on axial, sagittal, and coronal MR images using the region-growing technique; 2) setting data to a 3D domain; 3) registration by manual operation; and 4) interpolation between the data based on linear interpolation. In clinical MR images, the differences between this method and the conventional technique were less than 10%. These results demonstrate that this technique is able to construct 3D data from axial, sagittal, and coronal MR images.