Makoto Wakamiya

CT Diagnosis of Acute Mesenteric Ischemia from Various Causes

OBJECTIVE Acute mesenteric ischemia can be caused by various conditions such as arterial occlusion, venous occlusion, strangulating obstruction, and hypoperfusion associated with nonocclusive vascular disease, and the CT findings vary widely depending on the cause and underlying pathophysiology. The aim of this article is to review the CT appearances of acute mesenteric ischemia in various conditions. CONCLUSION Recognition of characteristic CT appearances and the variations associated with each cause may help in the accurate interpretation of CT in the diagnosis of mesenteric ischemia.

Assessment of small bowel motility function With cine-MRI using balanced steady-state free precession sequence

To evaluate the use of cine‐magnetic resonance imaging (MRI) with a steady‐state free precession sequence to monitor and assess small bowel motility.

Liver tumor detection in CT images by adaptive contrast enhancement and the EM/MPM algorithm

Automatic tumor detection and segmentation is essential for the computer-aided diagnosis of live tumors in CT images. However, it is a challenging task in low-contrast images as the low-level images are too weak to detect. In this paper, we propose a new method for the automatic detection of liver tumors. We first adaptively enhance the intensity contrast of CT images by probability density function estimation. Then, to detect tumorous regions, we use the expectation maximization/maximization of the posterior marginal (EM/MPM) algorithm, which utilizes both the intensity and label information of the adjacent regions. Finally, a shape constraint is applied to reduce noise and identify focal tumors. Quantitative evaluation experiments show that our method can accurately and effectively detect tumors even in poor-contrast CT images.

Preliminary study on statistical shape model applied to diagnosis of liver cirrhosis

In computational anatomy, statistical shape model (SSM) is used for the quantitative evaluation of variations in the shapes of different organs. This paper focuses on the construction of a SSM of the liver and its application to computer-assisted diagnosis of cirrhosis. We prove the potential application of SSMs in the classification of normal and cirrhotic livers. In constructing a SSM of the liver, we first normalize volume data followed by the construction of the model using principal component analysis. The coefficients of the model are used as indicators of liver pathology. The effectiveness of the constructed model is evaluated by the classification accuracy of both normal and abnormal data.

Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system

A computer-aided detection (CAD) system was evaluated for its ability to detect microcalcifications and masses on images obtained with a digital phase-contrast mammography (PCM) system, a system characterised by the sharp images provided by phase contrast and by the high resolution of 25-μm-pixel mammograms. Fifty abnormal and 50 normal mammograms were collected from about 3,500 mammograms and printed on film for reading on a light box. Seven qualified radiologists participated in an observer study based on receiver operating characteristic (ROC) analysis. The average of the areas under ROC curve (AUC) values for the ROC analysis with and without CAD were 0.927 and 0.897 respectively (P = 0.015). The AUC values improved from 0.840 to 0.888 for microcalcifications (P = 0.034) and from 0.947 to 0.962 for masses (P = 0.025) respectively. The application of CAD to the PCM system is a promising approach for the detection of breast cancer in its early stages.

Efficient shape representation and statistical shape modeling of the liver using spherical harmonic functions (SPHARM)

In the field of medical image analysis, the three-dimensional (3-D) shape representation and modeling of anatomic structures using only a few parameters is an important issue, and can be applied to computer assisted diagnosis, surgical simulations, visualization, and many other medical applications. In this paper, we show that the 3D anatomical structure such as the liver can be represented by a few coefficients of spherical harmonic functions (SPHARM). We also propose to use SPHARM based shape representation for statistical shape modeling. Since the dimension of SPHARM based shape representation vector is much lower than the conventional shape representation using coordinates of surface points, our proposed method can be used for small number of training samples and enhance the computation cost.