TY - CHAP
T1 - Improved MR to CT synthesis for pet/mr attenuation correction using imitation learning
AU - Kläser, Kerstin
AU - Varsavsky, Thomas
AU - Markiewicz, Pawel
AU - Vercauteren, Tom
AU - Atkinson, David
AU - Thielemans, Kris
AU - Hutton, Brian
AU - Cardoso, M. Jorge
AU - Ourselin, Sébastien
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075687457&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32778-1_2
DO - 10.1007/978-3-030-32778-1_2
M3 - Conference paper
AN - SCOPUS:85075687457
SN - 9783030327774
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 21
BT - Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Burgos, Ninon
A2 - Gooya, Ali
A2 - Svoboda, David
PB - SPRINGER
T2 - 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -