Yoshiharu Higashida

Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine

The purpose of this study was to develop a computerized method for detection of multiple sclerosis (MS) lesions in brain magnetic resonance (MR) images. We have proposed a new false positive reduction scheme, which consisted of a rule-based method, a level set method, and a support vector machine. We applied the proposed method to 49 slices selected from 6 studies of three MS cases including 168 MS lesions. As a result, the sensitivity for detection of MS lesions was 81.5% with 2.9 false positives per slice based on a leave-one-candidate-out test, and the similarity index between MS regions determined by the proposed method and neuroradiologists was 0.768 on average. These results indicate the proposed method would be useful for assisting neuroradiologists in assessing the MS in clinical practice.

Automated Method for Identification of Patients With Alzheimer's Disease Based on Three-dimensional MR Images

RATIONALE AND OBJECTIVES An automated method for identification of patients with cerebral atrophy due to Alzheimers disease (AD) was developed based on three-dimensional (3D) T1-weighted magnetic resonance (MR) images. MATERIALS AND METHODS Our proposed method consisted of determination of atrophic image features and identification of AD patients. The atrophic image features included white matter and gray matter volumes, cerebrospinal fluid (CSF) volume, and cerebral cortical thickness determined based on a level set method. The cortical thickness was measured with normal vectors on a voxel-by-voxel basis, which were determined by differentiating a level set function. The CSF spaces within cerebral sulci and lateral ventricles (LVs) were extracted by wrapping the brain tightly in a propagating surface determined with a level set method. Identification of AD cases was performed using a support vector machine (SVM) classifier, which was trained by the atrophic image features of AD and non-AD cases, and then an unknown case was classified into either AD or non-AD group based on an SVM model. We applied our proposed method to MR images of the whole brains obtained from 54 cases, including 29 clinically diagnosed AD cases (age range, 52-82 years; mean age, 70 years) and 25 non-AD cases (age range, 49-78 years; mean age, 62 years). RESULTS As a result, the area under a receiver operating characteristic (ROC) curve (Az value) obtained by our computerized method was 0.909 based on a leave-one-out test in identification of AD cases among 54 cases. CONCLUSION This preliminary result showed that our method may be promising for detecting AD patients.

Attempt at Visualizing Breast Cancer with X-ray Dark Field Imaging

X-ray dark-field imaging (DFI) can clearly visualize breast cancer phantoms and cancer cell nests, stroma, fat tissue, ductus lactiferi, muscle, collagen fibers at stroma and calcification in a 2.8-mm-thick breast cancer pathological specimen. The system comprises a Bragg asymmetric-cut monochro-collimator and a 2.124-mm-thick Si 440 Laue diffraction analyzer at 35 keV. Both optical elements are Floating Zone made silicon crystals. The view size of 33 mm (H) ×19.5 mm (V) and the spatial resolution of 10 µm or better are obtainable at the vertical wiggler beamline BL14B at the Photon Factory.

Partial volume estimation and segmentation of brain tissue based on diffusion tensor MRI

PURPOSE Brain tissue segmentation based on diffusion tensor magnetic resonance imaging (DT-MRI) data has been attempted by previous researchers. Due to inherent low spatial resolution of DT-MRI data, conventional methods suffered from partial volume averaging among the different types of tissues, which may result in inaccurate segmentation results. The purpose was to develop a new brain tissue segmentation method for DT-MRI data in which effect of the partial volume averaging is taken into account. METHODS The method estimates the partial volume fractions of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) within each voxel using a maximum a posteriori probability principle, based on five DT parameters (three eigenvalues, apparent diffusion coefficient, and fractional anisotropy). The authors evaluated the performance of the proposed method quantitatively by using digital phantom data. Moreover, the authors applied the method to real DT-MRI data of the human brain, and compared the results with those of a conventional segmentation method. RESULTS In the digital phantom experiments, the root mean square error in term of partial volume fraction with the method for WM, GM, and CSF were 0.137, 0.049, and 0.085, respectively. The volume overlap measures between the segmentation results and the ground truth of the digital phantom were more than 0.9 in all three tissue types, while those between the results by the conventional method and the ground truth ranged between 0.550 and 0.854. In visual comparisons for real DT-MRI, WM/GM/CSF regions estimated by the method were more similar to the corresponding regions depicted in the structural image than those estimated by the conventional method. CONCLUSIONS The results of the digital phantom experiment and real DT-MRI data demonstrated that the method improved accuracy in estimation and segmentation of brain tissue on DT-MRI data over the conventional method. This method may be useful in evaluating the cortical and subcortical diffusivity in neurological diseases.

Effect of test patterns on measurement of the luminance of LCD devices by use of a telescopic-type luminance meter

Quality assurance of electronic display devices is important for maintaining reliable soft-copy image interpretations. This paper presents effects of test patterns on measurement of the luminance of liquid-crystal display (LCD) devices by use of a telescopic-type luminance meter. The luminance for different types of test patterns having different backgrounds and measurement areas was measured and compared with the results obtained with AAPM task group-18 (TG-18) LN test patterns. The luminance measured for the test patterns with a black background was lower than that measured for TG-18 LN test patterns due to the light emitted from the outside the focused area of the telescopic-type luminance meter. Also, the luminance obtained with smaller measurement areas indicated lower luminance. These tendencies were particularly obvious at low luminance. The luminance of the LCD device by use of a telescopic-type luminance meter should be measured with test patterns that have a black background and a smaller measurement area than that for the TG-18 LN test patterns.

Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging

It has been reported that the severity of subcortical vascular dementia (VaD) correlated with an area ratio of white matter hyperintensity (WMH) regions to the brain parenchyma (WMH area ratio). The purpose of this study was to develop a computer-aided evaluation method of WMH regions for diagnosis of subcortical VaD based on magnetic resonance (MR) images. A brain parenchymal region was segmented based on the histogram analysis of a T1-weigthed image. The WMH regions were segmented on the subtraction image between a T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images using two segmentation methods, i.e., a region-growing technique and a level-set method, which were automatically and adaptively selected on each WMH region based on its image features by using a support vector machine. We applied the proposed method to 33 slices of the three types of MR images with 245 lesions, which were acquired from 10 patients (age range: 64-90 years, mean: 78) with a diagnosis of VaD on a 1.5-T MR imaging scanner. The average similarity index between regions determined by a manual method and the proposed method was 93.5+/-2.0% for brain parenchymal regions and 78.2+/-11.0% for WMH regions. The WMH area ratio obtained by the proposed method correlated with that determined by two neuroradiologists with a correlation coefficient of 0.992. The results presented in this study suggest that the proposed method could assist neuroradiologists in the evaluation of WMH regions related to the subcortical VaD.

Investigation of optimum anti-scatter grid selection for digital radiography: physical imaging properties and detectability of low-contrast signals

Our aim in this study was to evaluate the effect of the grid variations on the imaging performance for a computed radiographic system under identical exposure condition. Digital radiographies using a 20-cm Lucite phantom were performed without grid and with grid ratios of 5:1, 8:1, 10:1, 12:1, and 14:1. The scatter fraction, the incident dose to the image receptor, the Wiener spectrum (WS), and the noise-equivalent quanta (NEQ) were measured. Visibility of low-contrast signals was evaluated using a contrast-detail phantom. The scatter fractions decreased considerably with an increase in the grid ratio. On the other hand, the WSs were increased (the noise property deteriorated) as the grid ratio increased due to a decreased incident dose to the image receptor under the identical exposure condition. The NEQs were improved as the grid ratio increased. The high grid ratios provided higher low-contrast detectability compared to the low grid ratios. Our results indicated that the removal of scattered radiation was very effective in improvement of the NEQ in the digital system under the identical exposure condition.

Comparison of viewing angle and observer performances in different types of liquid-crystal display monitors

It is known that the performance of liquid-crystal display (LCD) monitors, such as the luminance and contrast ratio, is dependent on the viewing angle. Our purpose in this study was to compare the angular performance and the effect on observer performance of different types of LCD monitors. The luminance performance and contrast ratio as a function of viewing angle (−60° to 60°) in each direction for two types of LCD monitors, namely, a general-purpose LCD monitor and one especially designed for medical use, were measured in this study. Furthermore, the observer performance at various viewing angles in the horizontal direction for a medical-grade LCD monitor was investigated by eight observers based on a contrast-detail diagram. The two types of LCD monitors showed notable variations in luminance and contrast ratio as a function of the viewing angle. Acceptable viewing angles in terms of the contrast ratio were much smaller in each direction than those for nominal viewing angles in the specifications provided by the manufacturers, and those for the medical-grade LCD monitor in the horizontal and vertical directions were broader than those of the general-purpose LCD monitor. There was no significant difference in observer performance between 0° and 40°. On the other hand, our results showed a statistically significant difference in observer performance between 0° and 60°.

Estimation of white matter connectivity based on a three-dimensional directional diffusion function in diffusion tensor MRI

Diffusion tensor (DT) magnetic resonance imaging (MRI) provides the directional information of local neuronal fibers, and has been used to estimate the neuroanatomical connectivity in the cerebral white matter. Several methods for white matter tractography have been developed based on DT-MRI. However, it has been difficult to estimate the white matter tract pathways in the fiber crossing and branching region because of the ambiguity of the principal eigenvector and/or low anisotropy due to the partial volume effect. In this paper, we proposed a new method for white matter tractography, which permits fiber tract branching and passing through crossing regions. Our tractography method is based on a three-dimensional (3D) directional diffusion function (DDF), which was given by a 3D anisotropic Gaussian function defined by normalized three eigenvalues and their corresponding eigenvectors of DT. The DDF was used for generation of a 3D directional diffusion field and for determination of the connectivity between the voxels in fiber tracking. To extract the white matter tract region, DDF-based tractography (DDFT) method used the directional diffusion field instead of a threshold fractional anisotropy map, which has been used in the conventional methods, so that low anisotropy voxels in the branching and crossing regions may be included. We applied the DDFT method and two conventional tractography methods (a streamline technique and a tensorline algorithm) to DT-MRI data of five normal subjects for visualizing the pyramidal tract. Our method visualized the pathways connected to a large portion of the primary motor cortex, including foot, hand and face motor areas, passing through the crossing regions with other white matter tracts in all subjects, whereas the conventional methods showed only a small portion of the pyramidal tract. The pyramidal tract pathways estimated by our method were consistent with the neuroanatomical knowledge. In conclusion, the DDFT method may be useful in assisting neuroradiologists in estimating the white matter tracts.

Computerized method for automated measurement of thickness of cerebral cortex for 3-D MR images

Alzheimers disease (AD) is associated with the degeneration of cerebral cortex, which results in focal volume change or thinning in the cerebral cortex in magnetic resonance imaging (MRI). Therefore, the measurement of the cortical thickness is important for detection of the atrophy related to AD. Our purpose was to develop a computerized method for automated measurement of the cortical thickness for three-dimensional (3-D) MRI. The cortical thickness was measured with normal vectors from white matter surface to cortical gray matter surface on a voxel-by-voxel basis. First, a head region was segmented by use of an automatic thresholding technique, and then the head region was separated into the cranium region and brain region by means of a multiple gray level thresholding with monitoring the ratio of the first maximum volume to the second one. Next, a fine white matter region was determined based on a level set method as a seed region of the rough white matter region extracted from the brain region. Finally, the cortical thickness was measured by extending normal vectors from the white matter surface to gray matter surface (brain surface) on a voxel-by-voxel basis. We applied the computerized method to high-resolution 3-D T1-weighted images of the whole brains from 7 clinically diagnosed AD patients and 8 healthy subjects. The average cortical thicknesses in the upper slices for AD patients were thinner than those for non-AD subjects, whereas the average cortical thicknesses in the lower slices for most AD patients were slightly thinner. Our preliminary results suggest that the MRI-based computerized measurement of gray matter atrophy is promising for detecting AD.