Klaser, K;
Varsavsky, T;
Markiewicz, P;
Atkinson, D;
Thielemans, K;
Hutton, B;
Cardoso, M;
(2019)
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning.
In: Burgos, N and Gooya, A and Svoboda, D, (eds.)
Simulation and Synthesis in Medical Imaging. SASHIMI 2019. Lecture Notes in Computer Science, vol 11827.
Springer: Cham.
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Abstract
The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2 − norm between the ground truth CT and the pCT. However, given that the ultimate purpose is often to use the pCT as an attenuation map (µ-map) in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily optimal. The main objective should be to predict a pCT that, when used as µ-map, reconstructs a pseudo PET (pPET) which is as close as possible to the gold standard PET. To this end, we propose a novel multi-hypothesis deep learning framework that generates pCTs by minimising a combination of the pixel-wise error between pCT and CT and a proposed metric-loss that itself is represented by a convolutional neural network (CNN) and aims to minimise subsequent PET residuals. The model is trained on a database of 400 paired MR/CT/PET image slices. Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69.68HU) compared to a baseline CNN (66.25HU), but lead to significant improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u.
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