Smoothing-parameter tuning for regularized PET image reconstruction using deep learning

Jaehun Lee; Soo-Jin Lee

doi:10.1117/12.2653152

7 April 2023 Smoothing-parameter tuning for regularized PET image reconstruction using deep learning

Jaehun Lee, Soo-Jin Lee

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124632C (2023) https://doi.org/10.1117/12.2653152
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

In regularized PET image reconstruction, the performance of a reconstruction algorithm depends crucially on the smoothing parameter that controls the balance between the likelihood term and the regularization term. In this work, we propose a new method of tuning the smoothing parameter using deep learning. Unlike the traditional estimation-theoretic approach where the smoothing parameter is estimated directly from the observed noisy data, the deep learning-based method requires large amounts of prior training pairs, which are usually unavailable in routine clinical practice. To overcome this problem, we extend our previous work on the training-set approach which provides a mathematical formula to calculate the smoothing parameter for the simple quadratic spline regularizer. We note that this training-set approach is effective only when the noiseless representative exemplars are available. For deep learning, however, collecting large amounts of such noiseless exemplars for a training dataset is unrealizable. Therefore, instead of collecting them, we generate diverse images similar to the exemplars of the ground truth radiotracer distribution using Gibbs sampling followed by post-processing and calculate the smoothing parameter value for each image. For our deep learning model, we use the residual network architecture that allows deeper layers with higher efficiency than the typical convolutional neural network. The experimental results show that our deep learning method provides optimal values of the smoothing parameter, which are comparable to the accurate values calculated from noiseless exemplars, for a wide range of underlying images.

Citation Download Citation

Jaehun Lee and Soo-Jin Lee "Smoothing-parameter tuning for regularized PET image reconstruction using deep learning", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124632C (7 April 2023); https://doi.org/10.1117/12.2653152

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