Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
چکیده
چکیدهای برای این سند ثبت نشده است.
چکیدهای برای این سند ثبت نشده است.
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this
We present a new algorithm for penalized-likelihood emission image reconstruction. The algorithm monotonically increases the objective function, converges globally to the unique maximizer, and easily accommodates the nonnegativity constraint and nonquadratic but convex penalty fu
This paper presents a new class of algorithms for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. We derive the algorithms by applying to the transmission log-likelihood a version of the convexity technique developed by De Pierro for emi
In this paper, a method is introduced for incorporating perfectly registered MRI boundary information into a penalized likelihood emission reconstruction scheme. The boundary curve is modeled as a periodic spline whose coe cients are estimated from the MRI image. The resulting bo