Improvement of knowledge-based automatic slice-alignment method for cardiac magnetic resonance imaging
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POSTER PRESENTATION
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Improvement of knowledge-based automatic slice-alignment method for cardiac magnetic resonance imaging Shuhei Nitta1*, Tomoyuki Takeguchi1, Nobuyuki Matsumoto1, Shigehide Kuhara2, Kenichi Yokoyama3, Masamichi Imai3, Rieko Ishimura3, Toshiaki Nitatori3, Timothy Albert4 From 15th Annual SCMR Scientific Sessions Orlando, FL, USA. 2-5 February 2012 Background Automatic slice alignment allows images of the six standard cardiac planes as defined in the SCMR Image Acquisition Protocols to be obtained by simple and quick operation. Our previously reported method can detect these planes using ECG-gated breath-hold axial multislice images [1]. Achieving higher accuracy and greater robustness for variation in clinical images will lead to improved usability and reliability, resulting in easier cardiac MR examinations. To achieve these goals, we have substantially refined our previously reported automatic slice-alignment method. A combination of knowledge-based recognition and image processing techniques is applied to multiple feature point search to reduce errors in automatic detection. Volunteer and clinical data were used to evaluate of the degree of improvement.
3-chamber views using the proposed method combined with knowledge-based recognition and image processing techniques. The angular error between the results and manual annotation of the normal vector of each view was measured for three subsets (Table 1). Eighteen Japanese clinical data subsets were scored for diagnostic accuracy by two physicians (1: unacceptable, 2: marginal, but diagnostically useful, 3: good, 4: excellent).
Methods ECG-gated 2D steady-state free precession (SSFP) axial multislice images were acquired using a 1.5-T MRI scanner (Excelart VantageTM powered by Atlas, Toshiba Medical Systems) during a single breath-hold. The scanning conditions were TR/TE = 4.2/2.1 and matrix = 198x256. The slice thickness was set to 7 mm with no gaps, resulting in a scanning time of less than approximately 20 s. The positions of the mitral valve, the cardiac apex, the left ventricular outflow tract, the tricuspid valve, and the right ventricular corner are detected to determine the long-axis and three short-axis orientations in order to define the 4-chamber, 2-chamber, and
Conclusions We have developed a sophisticated slice-alignment method employing knowledge-based recognition combined with image processing techniques to simplify cardiac scan planning. The experimental results showed that the proposed method can detect the cardiac planes more quickly and accurately than the conventional method and is more robust for data from a variety of ethnic groups.
1 Corporate Research & Development Center, Toshiba Corporation, Kanagawa, Japan Full list of author information is available at the end of the article
Author details 1 Corporate Research & Development Center, Toshiba Corporation, Kanagawa, Japan. 2MRI Systems Division, Toshiba Medical Systems Corporation, Tochigi,
Results The proposed method successfully detected the six planes i
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