This paper addresses the problem of reconstructing an image from low-count Positron Emission Tomography (PET) data. We build on recent advances combining deep neural networks with expectation-maximization algorithms. More specifically, we extend the recent DIPRecon approach [1] to handle various challenges linked to natively low-count Yttrium 90 data. To this end, we rely on the interpretation of the Deep Image Prior (DIP) in the light of approximate Bayesian inference. By introducing a stochastic gradient Langevin dynamics (SGLD) optimizer, we reduce the tendency of the algorithm to overfit the noisy maximum likelihood estimate while improving the contrast recovery figures. Moreover, as a by-product of the SGLD optimization, the method recovers an uncertainty value associated with every voxel in the estimated image. We qualitatively and quantitatively evaluate the proposed method on data acquired with the NEMA IEC body phantom achieving high-quality results.
Acquisition data and treatments for quality controls of gamma cameras and Positron Emission Tomography (PET) cameras are commonly performed with dedicated program packages, which are running only on manufactured computers and differ from each other, depending on camera company and program versions. The aim of this work was to develop a free open-source program (written in JAVA language) to analyze data for quality control of gamma cameras and PET cameras. The program is based on the free application software ImageJ and can be easily loaded on any computer operating system (OS) and thus on any type of computer in every nuclear medicine department.
Based on standard parameters of quality control, this program includes 1) for gamma camera: a rotation center control (extracted from the American Association of Physics in Medicine, AAPM, norms) and two uniformity controls (extracted from the Institute of Physics and Engineering in Medicine, IPEM, and National Electronic Manufacturers Association, NEMA, norms). 2) For PET systems, three quality controls recently defined by the French Medical Physicist Society (SFPM), i.e. spatial resolution and uniformity in a reconstructed slice and scatter fraction, are included. The determination of spatial resolution (thanks to the Point Spread Function, PSF, acquisition) allows to compute the Modulation Transfer Function (MTF) in both modalities of cameras. All the control functions are included in a tool box which is a free ImageJ plugin and could be soon downloaded from Internet. Besides, this program offers the possibility to save on HTML format the uniformity quality control results and a warning can be set to automatically inform users in case of abnormal results. The architecture of the program allows users to easily add any other specific quality control program.
Finally, this toolkit is an easy and robust tool to perform quality control on gamma cameras and PET cameras based on standard computation parameters, is free, run on any type of computer and will soon be downloadable from the net (http://rsb.info.nih.gov/ij/plugins or http://nucleartoolkit.free.fr).
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