Optimal and robust PET data sinogram restoration based on the response of the system

Proceeding of: 2006 IEEE Nuclear Science Symposium Conference Record, San Diego, CA, Oct. 29 - Nov. 1, 2006We present an optimal and robust technique for the restoration of positron emission tomography (PET) data. It is based on an iterative deconvolution of Fourier Rebinned (FORE) sinograms employing the EM-ML algorithm regularized with MAP. The deconvolution kernel is related to the System Response Matrix (SRM) and the axial point spread function(PSF) caused by FORE. This method is able to deblur the acquired data whitout the introduction of additional noise and enhancing the quality (resolution, contrast) of the images reconstructed using FBP.Support from UCM grant. J.M. Udias acknowledges support from MEC (FPA2006- 07393) and CENIT (Ministerio de Industria, Spain). Part of the computations of this work were done at the “High capacity cluster for physical techniques” of the Faculty for Physical Sciences of UCM, funded in part by the UE under the FEDER program and in part by the UCM

Mammographic image restoration using maximum entropy deconvolution

An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.Comment: 18 pages, 10 figure

SVD-Based Evaluation of Multiplexing in Multipinhole SPECT Systems

Multipinhole SPECT system design is largely a trial-and-error process. General principles can give system designers a general idea of how a system with certain characteristics will perform. However, the specific performance of any particular system is unknown before the system is tested. The development of an objective evaluation method that is not based on experimentation would facilitate the optimization of multipinhole systems. We derive a figure of merit for prediction of SPECT system performance based on the entire singular value spectrum of the system. This figure of merit contains significantly more information than the condition number of the system, and is therefore more revealing of system performance. This figure is then compared with simulated results of several SPECT systems and is shown to correlate well to the results of the simulations. The proposed figure of merit is useful for predicting system performance, but additional steps could be taken to improve its accuracy and applicability. The limits of the proposed method are discussed, and possible improvements to it are proposed

バイナリ・トモグラフィのための連続時間画像再構成

Binary tomography is the process of reconstructing a binary image from a finite number of projections. We present a novel method for solving binary tomographic inverse problems using a continuous-time image reconstruction (CIR) system described by nonlinear differential equations based on the minimization of a double Kullback- Leibler divergence. We prove theoretically that the divergence measure monotonically decreases in time. Moreover, we demonstrate numerically that the quality of the reconstructed images of the nonlinear CIR system is better than those from an iterative reconstruction method