2018/08/22 by Kerstin Kläser, Paweł Markiewicz, Kläser, Kerstin +22
Computer Science · Engineering · Mathematics · Medicine · Physics and Astronomy · #Advanced X-ray and CT Imaging #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #cs.AI #physics.med-ph #stat.ML
paper · pdf · doi:10.48550/arxiv.1808.07431
Accepted at SASHIMI2018
arxiv created 2018/08/22 · openalex publication_date 2018/08/22 · arxiv updated 2018/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction is particularly challenging for PET-MRI as neither PET nor magnetic resonance imaging (MRI) can directly image tissue attenuation properties. MRI-based computed tomography (CT) synthesis has been proposed as an alternative to physics based and segmentation-based approaches that assign a population-based tissue density value in order to generate an attenuation map. We propose a novel deep fully convolutional neural network that generates synthetic CTs in a recursive manner by gradually reducing the residuals of the previous network, increasing the overall accuracy and generalisability, while keeping the number of trainable parameters within reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT pairs and a four-fold random bootstrapped validation with a 80:20 split is performed. Quantitative results show that the proposed framework outperforms a state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE) from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction error from 14.3% to 7.2%.