Deep Boosted Regression for MR to CT Synthesis
This work addresses attenuation correction in PET-MRI imaging, offering a more accurate alternative to existing methods for medical quantification, though it is incremental as it builds on deep learning approaches.
The paper tackles the problem of MRI-based CT synthesis for PET attenuation correction by proposing a deep fully convolutional neural network that generates synthetic CTs recursively, reducing Mean Absolute Error from 131HU to 68HU and PET reconstruction error from 14.3% to 7.2%.
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%.