PET image reconstruction: A stopping rule for the MLEM algorithm based on properties of the updating coefficients

Revisiting stopping rules for iterative methods used in emission tomography

“…The removal of the negligible count data does not completely delete the information from small measurements. This data is still included in the first term of (5) but can be totally removed from (6) if this equation, and consistently also (9), is divided by m i=1 p ij on the right hand side.…”

“…While both theory and experiment indicate the monotonic increase of the likelihood function in the EM method, it is well-known that stopping at a smaller likelihood value, i.e. not at the maximum for the likelihood, can give a higher quality solution [6][7][8][9]. Stopping the iteration before convergence in this way is necessary but appears to conflict with the appropriate notation for PET imaging is provided.…”

Expectation maximization (EM) algorithms using polar symmetries for computed tomography (CT) image reconstruction

“…Noise in the iterative images can be reduced by about 60% compared to the FBP results without compromising spatial resolution [9]. Tomographic imaging systems have been greatly improved in the last few years [10] whereas several methods have been proposed to accelerate the convergence of iterative methods [11][12][13][14][15]. Great improvements have been obtained by reducing the system matrix size taking into account the geometry of the system [16], and consequently reducing the computational complexity.…”

Development of computational small animal models and their applications in preclinical imaging and therapy research

“…These are commonly combined with simulation tools accounting for all physical aspects involved in the image acquisition process and characteristics of the imaging system to generate a simulated dataset that closely mimic clinical and experimental studies. The known features of computational models and simulated datasets provide precise information enabling to evaluate the impact of physical degrading factors inherent to the imaging process, [131][132][133][134][135] assess different design concepts and performance of medical imaging systems, [136][137][138][139][140][141][142][143][144][145][146][147][148][149][150] and advance the development and validation of new image segmentation, [151][152][153][154][155] registration, 156-161 reconstruction, [162][163][164][165][166][167] and processing techniques. [168][169][170][171][172][173][174][175] Likewise, the Digimouse and MOBY models served as optically heterogeneous virtual subjects for light propagation calculations to assess the impact of various parameters involved in optical molecular imaging techniques [176]…”