Hiromichi Ando

Radiological response and histological changes in malignant astrocytic tumors after stereotactic radiosurgery

Stereotactic radiosurgery is an encouraging approach to deliver higher doses of radiation boost for malignant gliomas safely and precisely. The purpose of this study was to investigate the radiation response and histological changes of malignant astrocytic tumors after stereotactic linac radiosurgery (SLRS). We studied an autopsy case of recurrent glioblastoma multiforme (GBM) and two surgical cases with gross total removal of recurrent GBM and anaplastic astrocytoma transformed from fibrillary astrocytoma treated with SLRS. Destructive changes, such as the disappearance of viable cells, coagulation necrosis, and fibrinoid degeneration of vascular walls, were observed in the center of the target of SLRS, which showed histologically similar radiobiological reactions to well-known delayed central nervous system radiation necrosis caused by conventional radiotherapy. The region showing such radiation necrosis was within the area irradiated with approximately 15–20Gy or more by SLRS; however, dense viable tumor cells remained in the periphery that was irradiated with less than 15 Gy. In a comparative immunohistochemical study of the tumors before and after SLRS, neither MIB-1 and p53 labeling indices nor immunoreactivity for GFAP represented any persistent tendencies. There were very few TUNEL-positive cells in either tumor before and after SLRS. These results showed that radiosurgery for malignant gliomas leads to earlier radiation necrosis than conventional radiation and that it is useful in eradicating tumor cells in the center of the target. However, some viable tumor cells may remain in the periphery irradiated with an insufficient dose for cell death and may be partly transformed in character by DNA damage due to radiation. Proton magnetic resonance spectroscopy (MRS) was suggested to characterize the radiation response in radiosurgery tumor targets for correlation with histological findings.

Automated Classification of Cerebral Arteries in MRA Images and Its Application to Maximum Intensity Projection

Detection of unruptured aneurysms is a major task in magnetic resonance angiography (MRA). However, it is difficult for radiologists to detect small aneurysms on the maximum intensity projection (MIP) images because adjacent vessels may overlap with the aneurysms. Therefore, we proposed a method for making a new MIP image, the SelMIP image, with the interested vessels only, as opposed to all vessels, by manually selecting a cerebral artery from a list of cerebral arteries recognized automatically. By using our new SelMIP viewing technique, the selected vessel regions can also be observed from various directions and would further facilitate the radiologists in detecting small aneurysms. For automated classification of cerebral arteries, two 3D images, a target image and a reference image, are compared. Image registration is performed using the global matching and feature correspondence techniques. Segmentation of vessels in the target image is performed using the thresholding and region growing techniques. The segmented vessel regions were classified into eight cerebral arteries by calculating the Euclidean distance between a voxel in the target image and each of the voxels in the labeled eight vessel regions in the reference image. In applying the automated cerebral arteries recognization algorithm to thirteen MRA studies, results of 10 MRA studies were evaluated as clinically acceptable. Our new viewing technique would be useful in assisting radiologists for detection of aneurysms and for reducing the interpretation time

Expression of hepatocyte growth factor and matrix metalloproteinase-2 in human glioma

Hepatocyte growth factor (HGF) has a stimulatory effect on the synthesis of matrix metalloproteinase-2 (MMP-2), which is involved in glioma invasion. In this study, to clarify the correlation between the expression of HGF and MMP-2 in glioma tissues, immunohistochemical analysis of HGF and MMP-2 was performed in 11 cases of astrocytoma, 10 cases of anaplastic astrocytoma, and 9 cases of glioblastoma. As a result, expression of HGF and MMP-2 was correlated with the grade of malignancy (P=0.0181 and 0.0001, respectively), and a significant correlation between the immunoreactivity of HGF and that of MMP-2 was observed (P<0.05). Immunofluorescence study revealed the concomitant expression of HGF and MMP-2 in glioma tissue. In cultured glioma cell lines (SNB-19, U87MG, and U373MG), exogenous recombinant HGF effectively acted on the production of the active and latent forms of MMP-2 protein in a dose-dependent manner. Active MMP-2 increased more effectively than the latent form. Taken together, these results suggest that HGF may promote glioma invasion in vivo by production of MMP-2.

Improvement of Automated Detection Method of Lacunar Infarcts in Brain MR Images

The detection of asymptomatic lacunar infarcts on magnetic resonance (MR) images are important tasks for radiologists to ensure the prevention of severe cerebral infarction. However, their accurate identification is often difficult task. Therefore, the purpose of this study is to develop a computer- aided diagnosis scheme for the detection of lacunar infarcts. Our database consisted of 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. We first segmented the cerebral region in the Tl- weighted image by using a region growing technique. For identifying the initial lacunar infarcts candidates, white top-hat transform and multiple-phase binarization were then applied to the T2- weighted image. For eliminating false positives (FPs), we determined 12 features, i.e., the locations x and y, density differences in the Tl- and T2-weighted images, nodular components (NC), and nodular & linear components (NLC) from a scale 1 to 4. The NCs and NLCs were obtained using filter bank technique. The rule-based scheme and a neural network with 12 features were employed as the first step for eliminating FPs. The modular classifier was then used for eliminating three typical sources of FPs. As a result, the sensitivity of the detection of lacunar infarcts was 96.8% with 0.30 FP per image. Our computerized scheme would assist radiologists in identifying lacunar infarcts on MR images.

Computerized scheme for detection of arterial occlusion in brain MRA images

Magnetic resonance angiography (MRA) is routinely employed in the diagnosis of cerebrovascular disease. Unruptured aneurysms and arterial occlusions can be detected in examinations using MRA. This paper describes a computerized detection method of arterial occlusion in MRA studies. Our database consists of 100 MRA studies, including 85 normal cases and 15 abnormal cases with arterial occlusion. Detection of abnormality is based on comparison with a reference (normal) MRA study with all the vessel known. Vessel regions in a 3D target MRA study is first segmented by using thresholding and region growing techniques. Image registration is then performed so as to maximize the overlapping of the vessel regions in the target image and the reference image. The segmented vessel regions are then classified into eight arteries based on comparison of the target image and the reference image. Relative lengths of the eight arteries are used as eight features in classifying the normal and arterial occlusion cases. Classifier based on the distance of a case from the center of distribution of normal cases is employed for distinguishing between normal cases and abnormal cases. The sensitivity and specificity for the detection of abnormal cases with arterial occlusion is 80.0% (12/15) and 95.3% (81/85), respectively. The potential of our proposed method in detecting arterial occlusion is demonstrated.

Automatic segmentation of different-sized leukoaraiosis regions in brain MR images

Cerebrovascular diseases are the third leading cause of death in Japan. Therefore, a screening system for the early detection of asymptomatic brain diseases is widely used. In this screening system, leukoaraiosis is often detected in magnetic resonance (MR) images. The quantitative analysis of leukoaraiosis is important because its presence and extension is associated with an increased risk of severe stroke. However, thus far, the diagnosis of leukoaraiosis has generally been limited to subjective judgments by radiologists. Therefore, the purpose of this study was to develop a computerized method for the segmentation of leukoaraiosis, and provide an objective measurement of the lesion volume. Our database comprised of T1- and T2-weighted images obtained from 73 patients. The locations of leukoaraiosis regions were determined by an experienced neuroradiologist. We first segment cerebral parenchymal regions in T1-weighted images by using a region growing technique. For determining the initial candidate regions for leukoaraiosis, the k-means clustering of pixel values in the T1- and T2-weighted images was applied to the segmented cerebral region. For the elimination of false positives (FPs), we determined features such as the location, size, and circularity from each of the initial candidates. Finally, rule-based schemes and a quadratic discriminant analysis with these features were employed for distinguishing between the leukoaraiosis regions and the FPs. The results indicated that the sensitivity for the detection of leukoaraiosis was 100% with 5.84 FPs per image. Our computerized scheme can be useful in assisting radiologists for the quantitative analysis of leukoaraiosis in T1- and T2-weighted images.

CAD scheme for detection of aneurysms in MRA images

CAD system for detecting clustered microcalcifications in digital mammograms using independent component analysis and neural network Jun Zheng Æ Emma Regentova Department of Computer Science, Queens College – CUNY, Flushing, NY, USA Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA

Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features

The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.