Impact of tracer distribution, count level, iterations and post-smoothing on PET quantification using a variously weighted least squares algorithm

Abstract

This work is a task-oriented quantitative analysis to assess the best range of reconstruction algorithms parameters which are most suited for quantitative PET imaging of the brain. Starting from a general iterative weighted-least squares objective function, well-known methods such as the Maximum Likelihood Expectation Maximization (MLEM), the Image Space Reconstruction Algorithm (lSRA) and related techniques are obtained. A detailed analysis was done for single-frame imaging as well as a comprehensive study on the impact of post-smoothing by applying a Gaussian convolution kernel at each and every possible post-reconstruction iteration number in order to achieve the lowest mean absolute error (MAE). It has been clearly demonstrated that an appropriate choice of iteration number and post-smoothing level makes most algorithms deliver similar MAE within an interval of 5%, with a considerable reduction of the MAE in comparison to non-optimized reconstructions. Also, reconstruction method performance was influenced by the total number of counts and the activity distribution, meaning that each radiopharmaceutical is not always best reconstructed by the same method. Broadly speaking, MLEM with a point spread function (PSF), [SRA PSF with smoothed expected data as weighting factors and filtered backprojection (FBP), when applied with a suitable level of post smoothing, were the methods offering the best quantitative images and from these three methods, FBP offered the most robust performance for a broad range of post-smoothing levels. An appropriate choice of iteration number and post-smoothing level for a task-oriented analysis significantly lowered the MAE in comparison with the standard practice of using default fixed numbers of iterations and/or post-smoothing.

Hardware Implementation of Reconfigurable 1D Convolution

“…Convolution operation has been widely used in denoising [1,2], extraction [3], structure smoothing [4], filtering [5,6], detection [7][8][9], image enhancement [10] and many other image processing applications [11][12][13][14][15].…”