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  • Poster presentation
  • Open Access

Improvement of knowledge-based automatic slice-alignment method for cardiac magnetic resonance imaging

  • 1,
  • 1,
  • 1,
  • 2,
  • 3,
  • 3,
  • 3,
  • 3 and
  • 4
Journal of Cardiovascular Magnetic Resonance201214 (Suppl 1) :P272

https://doi.org/10.1186/1532-429X-14-S1-P272

  • Published:

Keywords

  • Cardiac Magnetic Resonance
  • Cardiac Magnetic Resonance Imaging
  • Ventricular Outflow Tract
  • Left Ventricular Outflow Tract
  • Image Processing Technique

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.

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 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).
Table 1

Number of patients and datasets.

 

patients

datasets

Japanese healthy volunteer data

17

37

Japanese clinical data

35

35

American clinical data

18

34

all data

70

106

Results

The proposed method successfully detected the six planes in 106 datasets (Table 1). The processes were completed in approximately 1.5 s (3.0-GHz CPU), which is twice as fast as the conventional method. The angular error and accuracy scores are shown in Table 2. These results are more accurate than those obtained by the conventional method.
Table 2

The average angular errors and accuracy scores of the conventional[1] and proposed method.

 

methods

type of datasets

short-axis

4-chamber

2-chamber

3-chamber

angular error[degree]

conventional method[1]

Japanese healthy volunteer data

3.39±1.98

8.74±5.30

8.81±5.74

11.02±6.41

  

Japanese clinical data

5.21±7.48

10.03±7.01

10.92±8.94

10.87±8.01

  

American clinical data

5.75±8.69

9.90±8.59

8.83±7.33

9.55±4.94

  

all data

4.75±6.65

9.54±7.01

9.51±7.42

10.50±6.56

 

proposed method

Japanese healthy volunteer data

3.39±1.98

5.12±3.44

7.15±4.51

5.46±3.65

  

Japanese clinical data

4.21±2.91

7.79±4.10

11.50±7.90

6.76±4.66

  

American clinical data

4.83±3.43

5.90±4.24

9.64±8.25

5.14±3.04

  

all data

4.12±2.85

6.25±4.05

9.38±7.20

5.79±3.87

accuracy score[scale:1-4]

conventional method[1]

Japanese clinical data(18/35)

3.74±0.55

3.47±0.69

3.82±0.46

3.76±0.54

 

proposed method

Japanese clinical data(18/35)

3.84±0.37

3.79±0.41

3.82±0.39

3.87±0.34

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.

Funding

No funding was received for this research.

Authors’ Affiliations

(1)
Corporate Research & Development Center, Toshiba Corporation, Kanagawa, Japan
(2)
MRI Systems Division, Toshiba Medical Systems Corporation, Tochigi, Japan
(3)
Department of Radiology, Kyorin University, Faculty of Medicine, Tokyo, Japan
(4)
Advanced Diagnostic Imaging Center, Salinas Valley Memorial Hospital, Salinas, CA, USA

References

  1. Nitta , et al: ESMRMB. 2011, No. 726Google Scholar

Copyright

© Nitta et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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