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Maximum a Posteriori Proton CT Reconstruction Using Anatomic Prior

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H Li

H Li1*, Y Lin2, D Hansen3, J Seco4, q li5, (1) Boston University, Boston, MA, (2) University of Southern California, Los Angeles, CA, (3) Aarhus University, Aarhus C, Danmark, (4) Radiation Oncology, Mass General Hospital; Harvard Medical, Boston , MA, (5) , Radiology, Mass General Hospital; Harvard Medical, Boston , MA

SU-E-J-50 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

It has been demonstrated that the information in one image modality can be used to significantly help the reconstruction of another image modality. Here we develop and evaluate a novel Maximum a Posteriori (MAP) approach to apply anatomical information in MRI or x-ray CT image to facilitate the estimate of Proton CT (pCT) image.

To incorporate anatomical information of MRI or x-ray CT into the reconstruction of pCT images through priors based on information theoretic similarity measures, we formulate a MAP optimization problem, which includes a least-square data-fitting term to account for the noise in the measurement and a penalty term to maximize the joint density of feature vectors extracted from the pCT image and anatomical image. We defined feature vectors that emphasize prominent boundaries in anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. We use a non-parametric, FFT-based Parzen window approach to speed up the computation of the density function of the feature vector, the joint density of feature vector, and the gradients of joint entropy. The optimization problem is solved using conjugate gradient and Armijo-backtracking line search. We performed realistic simulations using anthropomorphic phantom to validate our approach and compare its performance to MAP approach with a quadratic smoothness penalty function (without anatomic information from x-ray).

The Preliminary results demonstrate the proposed MAP approach with MRI or x-ray CT as prior can achieve better contrast recovery and bias-variance trade-off curves compared with MAP approach without anatomical prior.

The proposed MAP approach can achieve superior image quality by using anatomical information in the MRI/x-ray CT image. In the future we will investigate how much data reduction we can achieve using the proposed method while maintain the same image quality.

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