Novel segmentation method using a self-organizing neural network for spectral CT

Congress:

ECR 2020

Poster Number:

C-08185

Type:

Scientific Exhibit

Keywords:

Performed at one institution, Experimental, Prospective, Tissue characterisation, Image registration, Segmentation, Physics, Computer Applications-Detection, diagnosis, CT-Quantitative, CT, Radiation physics, Computer applications, Physics in Medical Imaging

Authors:

A. Yoneyama¹, R. Baba², M. Kawamoto³, T.-T. Lwin⁴; ¹Tosu, Saga/JP, ²Tokyo/JP, ³Tosu/JP, ⁴Sagamihara/JP

DOI:

10.26044/ecr2020/C-08185

DOI-Link:

https://dx.doi.org/10.26044/ecr2020/C-08185

Fig. 1: Spectrum of absorption coefficients of different elements. Coefficients change...

Fig. 2: Absorption map used in DECT for estimating atomic number and segmentation

Fig. 3: (a) SOM map composed of many neurons having property P. (b) Property of neuron...

Fig. 4: CT spectrum is composed of CT values of each pixel at different X-ray energies...

Purpose

Spectral (multi-energy) CT (SCT) is currently being developed and is expected to obtain precise pseudo monochromatic images, images free from metal artifacts and beam hardenings, images enhanced with contrast agents, and fine segmented images by using CT images acquired at different X-ray energies. However, an attenuation coefficient map used in dual-energy CT (DECT) for estimating atomic numbers and segmentation cannot be applied, because the dimensions of absorption maps can be increased to 3 and higher in SCT. To overcome this limitation, we developed a novel...

Methods and materials

1.Principle of multi-energy CT Every element, such as carbon, oxygen, and nitrogen, has an eigen attenuation (absorption) coefficient and absorption edges (energies), where the absorption coefficient changes rapidly as shown in Fig. 1 . Dual- andSpectral (multi-energy) CT (SCT)calculates precise pseudo monochromatic images with CT images acquired at different X-ray energies by utilizing the absorption properties of each element. The estimation of atomic numbers and segmentation in DECT are normally performed by using absorption maps, for which the absorption coefficients of X-ray energy E1 and...

Results

1.Feasibility test of phantom Fig. 6 shows a sectional (CT) image of the phantom obtained with 20-keV SR (a) and an illustration of the phantom (b). CT observations were performed by rotating the phantom vertically in the air over 360 degrees. The exposure time for each projection image was 1.0 s, and the projection number was 1000. Developed segmentation was tested by using CT spectrum data obtained with 20-, 25-, 30-, and 35-keV SR. Fig. 7 (a) shows the calculated SOM map, which shows three...

Conclusion

A novel segmentation method for SCT utilizing a self-organizing neural network was developed and tested with CT images of a phantom and rat tail obtained by using monochromatic SR. Fine segmented images of the phantom and the tail were obtained, and this novel method is therefore expected to be a powerful tool for segmentation.

Personal information and conflict of interest

A. Yoneyama; Tosu, SAGA/JP - nothing to disclose R. Baba; Tokyo/JP - Employee at Hitachi Ltd. M. Kawamoto; Tosu/JP - nothing to disclose T. T. Lwin; Sagamihara/JP - nothing to disclose

References

1. Willshaw DJ, von der Malsburg C., How patterned neural connections can be set up by self-organization, Proc R Soc Lond B Biol Sci. 194, 431-45 (1976) 2.McQueen J, Some methods for classification and analysis of multivariate observations, In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297 (1967) 3.https://somoclu.readthedocs.io/en/stable/ 4.Yoneyama A., Baba R., and Kawamoto M. Quantitative evaluation system of dual energy computed tomography using monochromatic and polychromatic, C-1218, ECR 2019 (2019)