양전자 방출 단층 촬영을 위한 3차원 최대사후 영상재구성에서 평활 파라미터의 적응형 미세 조정 (Adaptive Fine-Tuning of Smoothing Parameters in 3-D MAP Image Reconstruction for Positron Emission Tomography)

서지정보

· 발행기관 : 대한의용생체공학회
· 수록지 정보 : 의공학회지 / 45권 / 5호 / 205 ~ 216페이지
· 저자명 : 이재훈, 엄소연, 이수진

초록

The maximum a posteriori (MAP) approach has played an important role in developing model-based iter- ative reconstruction methods for positron emission tomography (PET). In this approach, the objective function con- sists of the likelihood and prior terms, with the balance between these two terms controlled by a positive smoothing parameter. When the prior term takes a quadratic form as a regularizer, the objective function can be easily optimized using the method of iterated conditional modes (ICM). However, the quadratic regularizer has the disadvantage of being unable to preserve the edges of the reconstructed image. In this study, we propose a new method for enhancing edges even with a quadratic regularizer within the ICM framework by adaptively fine-tuning the smoothing param- eter. To adjust the smoothing parameter, we use a method that transforms the histogram, obtained from the pixel- wise standard deviation of the estimated image in the previous iteration, into a mapping function for assigning an optimal value of the smoothing parameter for each pixel. The simulation results demonstrate that the proposed method improves the reconstruction accuracy in terms of the percentage error as well as the bias calculated from independent noise trials by efficiently enhancing the edges.

영어초록

The maximum a posteriori (MAP) approach has played an important role in developing model-based iter- ative reconstruction methods for positron emission tomography (PET). In this approach, the objective function con- sists of the likelihood and prior terms, with the balance between these two terms controlled by a positive smoothing parameter. When the prior term takes a quadratic form as a regularizer, the objective function can be easily optimized using the method of iterated conditional modes (ICM). However, the quadratic regularizer has the disadvantage of being unable to preserve the edges of the reconstructed image. In this study, we propose a new method for enhancing edges even with a quadratic regularizer within the ICM framework by adaptively fine-tuning the smoothing param- eter. To adjust the smoothing parameter, we use a method that transforms the histogram, obtained from the pixel- wise standard deviation of the estimated image in the previous iteration, into a mapping function for assigning an optimal value of the smoothing parameter for each pixel. The simulation results demonstrate that the proposed method improves the reconstruction accuracy in terms of the percentage error as well as the bias calculated from independent noise trials by efficiently enhancing the edges.

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