Kitakyūshū, Japan
Kitakyūshū, Japan

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Amako M.,Kurume University | Yamamoto Y.,Kyoaikai Tobata Kyoritsu Hospital | Nakamura K.,Kyoaikai Tobata Kyoritsu Hospital | Tobinaga S.,Kurume University | And 6 more authors.
Kurume Medical Journal | Year: 2011

To improve our ability to visualize the Adamkiewicz artery (AKA), we developed a modified intravenous CT angiography technique, which we refer to as right atrial CT (RA-CT) angiography. In this study, AKA detection rate and visualization of the arterial continuity from the aorta to the anterior spinal cord artery (ASA) was evaluated using RA-CT angiography. We performed RA-CT angiography in 110 patients with abdominal, thoracic descending, or thoracoabdominal aortic aneurysms. In RA-CT angiography, contrast medium with a high iodine concentration (370 mg/dl) was injected twice into the right atrium at a high injection rate (8.0 ml/sec), and two CT scans, starting at 20 sec after the first injection and at 35 sec after the second injection, respectively, were performed. All CT images were obtained using an 8- or 16-detector CT scanner at a slice thickness of 0.625 mm. The AKA was defined as the largest radiculomedullary artery with a characteristic hairpin turn, and with continuity from the aorta to the ASA. The AKA with hairpin turn was detected in all patients (100%), and continuity from the aorta to the ASA was confirmed in 99 of the 110 patients (90.0%). The AKA arose between Th8 and L1 in 86 of these patients (86.8%), and originated from the left side in 71 patients (71.7%). RA-CT angiography may be useful for visualizing the AKA and the arterial continuity from the aorta to the ASA in patients with aortic aneurysm, although the use of more advanced CT machines will provide safe and easy identification of the AKA and arterial continuity with a small amount of contrast medium and a single scan.

Kisaki M.,Kyushu Institute of Technology | Yamamura Y.,Kyushu Institute of Technology | Kim H.,Kyushu Institute of Technology | Tan J.K.,Kyushu Institute of Technology | And 2 more authors.
2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 | Year: 2014

A medical image fusion technique provides insight into lesion diagnosis and spatial relationships among anatomical structures. In radiotherapy planning for CyberKnife treatment, for instance, the accurate area being irradiated is determined and calculated based on a fusion image of CT and MR images. Hence the risk of the normal tissues being irradiated could be brought down. However, commonly used mouse-based registration yields variations in the accuracy of the results due to manual operations. In addition, there are some pending issues such as the increased physical burdens to the operators and the requirement for a substantial investment of time. Therefore, in recent years many researches have attempted to automate the registration for multimodality images. In this paper, a CAD (Computer Aided Diagnosis) system is developed to assist radiotherapy planning for CyberKnife treatment. We propose a new automatic image registration technique for image fusion of head CT (Computed Tomography) and MR (Magnetic resonance) images. In our method, the minimization of ratio image uniformity and the maximization of normalized mutual information on VOIs (Volumes Of Interest) are performed based on Levenberg-Marquardt algorithm. We evaluated our proposed method by 5 clinical cases and discussed the accuracy of the registration results with computational time. © 2014 IEEE.

Komatsu M.,Kyushu Institute of Technology | Li G.,Kyushu Institute of Technology | Kim H.,Kyushu Institute of Technology | Tan J.K.,Kyushu Institute of Technology | And 3 more authors.
Proceedings of the 16th International Symposium on Artificial Life and Robotics, AROB 16th'11 | Year: 2011

In the medical image processing field, segmentation from the CT image is one of the most important problems for analyzing the abnormalities and diagnosis on visual screening. Many related segmentation techniques have been developed for automatic extraction of ROI. It is however, there are still no fully automatic segmentation methods that are generally applicable to ROI based on CT image set. In this paper, we present a technique for automatic extraction of liver region on the MDCT images employing automatic construction of tree-structural image transformation (ACTIT). We propose a new technique for extraction of organs employing ACTIT with non-contrast and contrast image set in order to introduce temporal change information. We apply the proposed technique to three abdominal image set and satisfactory segmentation results are achieved. © 2011 ISAROB.

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