Kumiko Ando

Clinicoradiological factors influencing the reversibility of posterior reversible encephalopathy syndrome : a multicenter study

PurposeThe aim of this retrospective study was to clarify the relation between the reversibility of posterior reversible encephalopathy syndrome (PRES) with three factors: the anatomical region of the brain involved, the background clinical cause, and the diffusion weighted image (DWI) intensity of PRES lesions.Material and methodsThis multicenter study, conducted by the PRES Study Group of the Neuroradiology Workshop, involved 52 cases from 28 institutions. Initial and follow-up magnetic resonance imaging were compared regarding the reversibility of PRES lesions according to anatomical location and clinical background. Initial DWI and apparent diffusion coefficient (ADC) maps were reviewed in 20 cases.ResultsReversibility was significantly lower (P < 0.01) in the brain stem (44%) and deep white matter (47%) compared to the other cortical and subcortical areas (76%–91%). The reversibility was greater in the eclampsia subgroup followed by the hypertension and chemotherapy subgroups. DWI, even with ADC maps, had limitations in predicting the outcome of PRES lesions.ConclusionThe typical cortical and subcortical PRES lesions showed reversibility, whereas the brain stem and deep white matter lesions showed less reversibility. PRES due to eclampsia showed maximum reversibility compared to hypertension- and drug-related PRES. DWI, even with ADC maps, had limitations in predicting the course of PRES.

Evaluation of vascular supply with cone-beam computed tomography during intraarterial chemotherapy for a skull base tumor.

A cone-beam lowers the X-ray exposure level and the contrast material dose used compared to those for the conventional angiography-computed tomography (angio-CT) technique. Herein we present a patient with a metastatic skull base bone tumor in which the subtraction image of cone-beam CT with a flat panel detector was useful for evaluating the vascular supply during superselective intraarterial chemotherapy. Although the image quality of cone-beam CT is poorer than that of conventional angio-CT, the cone-beam CT system is sufficient for clinical use.

A case of Walker-Warburg syndrome

Walker-Warburg syndrome (WWS) is an autosomal recessive disorder characterized by type II lissencephaly, cerebellar and retinal anomalies, and congenital muscular dystrophy. We report a female diagnosed with WWS based on clinical criteria. This patient was found to have fetal hydrocephalus on ultrasonography at 29 weeks of gestation, and exhibited severe hypotonia, ocular malformations, and hydrocephalus at birth. MRI revealed type II lissencephaly, hydrocephalus, and other severe brain malformations. Genetic analysis was performed to distinguish WWS from severe Fukuyama-type congenital muscular dystrophy (FCMD), which has numerous findings in common. This revealed no expression of the founder haplotype or single-stranded conformation polymorphism (SSCP) abnormalities. Since the life expectancy of patients with FCMD is longer, differential diagnosis should be performed precisely.

Potential of superparamagnetic iron oxide in the differential diagnosis of metastasis and inflammation in bone marrow: experimental study.

Rationale and Objectives:The utility of ferucarbotran for the diagnosis of bone metastases was investigated using tumor-implanted rabbits. The potential of ferucarbotran in the differential diagnosis of metastasis and inflammation was also investigated. Methods:Twelve rabbits were divided into 2 groups (tumor and inflammation groups). Six rabbits of tumor group were inoculated with VX2 tumor cell suspension, and the 6 rabbits of the inflammation group were inoculated with 10% croton oil in the bone marrow of the right femur. All rabbits were imaged using a clinical MRI system. Signal intensity in the bone marrow of the right femur was measured in each rabbit before and after the intravenous injection of 8 &mgr;mol Fe/kg of ferucarbotran. As a control, the signal intensity in the bone marrow of the left femur (the normal, intact femur) was measured in each rabbit. The change in signal intensity of each group was compared statistically. After MRI imaging, the femora were removed, and sections were prepared for microscopic examination. Results:Signal intensity in the right femur of the tumor group did not change after injection, although that of the inflammation group and the control group decreased. In the histologic findings, tumors were widely spread in the right femur of the tumor group. The infiltration of pseudoeosinocytes was induced in the right femur of the inflammation group. Conclusions:This animal study showed that ferucarbotran was useful to detect bone marrow tumors. In addition, ferucarbotran may have potential in the differential diagnosis of bone metastasis and some kinds of inflammation.

Fuzzy-ASM Based Automated Skull Stripping Method from Infantile Brain MR Images

Automated stripping of skulls from infantile brain MR images is the fundamental work to visualize cerebral surface and to measure cerebral volumes. They are important to evaluate cerebral diseases because most cerebral diseases cause morphometric changes in cerebrum. This study proposes a novel image segmentation method based on fuzzy rule-based active surface model. The proposed method was validated by applying it to two neonatal (3W and 4W) and six infantile (5W to 4Y2M) subjects. The mean sensitivity was 98.84 %, and false-positive rate was 1.21 %, and the cerebral surface was visualized well.

Serial fetal magnetic resonance imaging of cloacal exstrophy

Cloacal exstrophy (CE) is a rare congenital malformation involving the urinary, intestinal, and genital systems. We present a case of CE in which characteristic findings were detected at two serial fetal magnetic resonance imaging (MRI) sessions. At 18 weeks’ gestation, the initial fetal MRI revealed a cystic mass protruding from the infra-umbilical abdominal wall. During fetal development, the cystic mass disappeared, and an omphalocele and heterogeneous soft tissue mass were recognized at 28 weeks’ gestation. The bladder was not visualized on either examination. CE can be diagnosed by prenatal MRI, thereby permitting prenatal counseling and appropriate postnatal management.

Neonatal Brain Age Estimation Using Manifold Learning Regression Analysis

The neonatal cerebral disorders severly languish the quality of life (QOL) of patients and also their families. It is required to detect and cure in their early stage for the sake of decreasing the degree of symptoms. However, it is difficult to evaluate neonatal brain disorders based on morphological analysis because the neonatal brain grows quickly and the brain development progress is different from person to person. Previously, we proposed a method of calculating growth index using Manifold learning. The growth index is effective to evaluate the brain morphological development progress, although, it does not directly correspond to the brain development delay. To evaluate brain development delay, this paper proposes an estimation method of neonatal brain age using Manifold learning, principal component analysis, and multiple regression model. The regression model is trained using a 4-D standard brain, which is constructed using training subjects with growth index. To evaluate the proposed method, we constructed a multiple regression model using 11 normal subjects (revised age: 0-4 month old), and estimated brain age of 4 normal subjects. And, we estimated brain age of 4 abnormal subjects to evaluate the detection accuracy of brain development abnormality. The results showed that the method found the differences of brain development for abnormal subjects.

Fuzzy object growth model for newborn brain using Manifold learning

To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based methods for segmenting parenchymal region in MR images. Because neonatal cerebrum deforms quickly by natural growth, we desire an atlas growth model to improve the accuracy of segmenting parenchymal region. This paper proposes a method for generating fuzzy object growth model (FOGM), which is an extension of fuzzy object model (FOM). FOGM is composed of some growth index weighted FOMs. To define the growth index, this paper introduces two methods. The first method calculates the growth index from revised age. Because the growth index will be different from person to person even through the same age, the second method estimates the growth index from cerebral shape using Manifold learning. To evaluate the proposed methods, we segment the parenchymal region of 16 subjects (revised age; 0-2 years old) using the synthesized FOGM. The results showed that FOGM was superior to FOM, and the Manifold learning based method gave the best accuracy. And, the growth index estimated with Manifold learning was significantly correlated with both of revised age and cerebral volume (p<;0.001).

Cortex Classification of the Infantile Brain in MRI Images Using Fuzzy Logic

Hypoxic ischemic encephalopathy (HIE) caused by asphyxia in the womb causes the decrement of the white matter (WM). Therefore, calculating the volume of the cerebral tissues for the infant with such symptom helps us for the purpose of quantifying the acuteness of symptom. Many methods for classifying the adult brain tissues with magnetic resonance (MR) images have been studied. However, these methods cannot be applied to classify the infantile brain tissues because the WM undergoes a myelination process in infantile brain, and the infantile brain image features are very different from adult one. This paper aims to propose a method for classifying the brain tissues in the myelination process. The proposed method addresses the intensity nonuniformity (INU) artifact by locally adapting a fuzzy spatial model of MR signals. The fuzzy model represents transition of MR signals on a line from the cerebral contour to inside the cerebrum. By using the fuzzy spatial model, the proposed method assigns fuzzy degree belonging to the cerebral cortex into voxels dependent of their locations.

Neonatal brain MRI normalization with 3-D cerebral sulci registration

MR image registration (IR) has been used in brain function analysis, voxel-based-morphometry, etc. The conventional IR methods mainly use MR signal based likelihood. However, they cannot prevent miss registration of different gyri because they do not evaluate correspondence of sulci. Also, we cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal and sulcal width. This paper focuses on neonatal brain MR images, and introduces a new feature called sulcal-distribution index (SDI), which is calculated from MR signal around the cerebral surface. Next, this paper proposes a non-rigid 3-D IR method based on a flattening with SDI. The likelihood used is mutual information of SDI. The new method evaluates the correspondence of cerebral sulci. And, the method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the MR signal around the cerebral surface. Results in 3 neonates (modified age; 3-5 weeks) showed that the method registered one brain with the other brain successfully.