Della Maggiora, G;
Castillo-Passi, C;
Qiu, W;
Liu, S;
Milovic, C;
Sekino, M;
Tejos, C;
... Irarrazaval, P; + view all
(2020)
DeepSPIO: Super Paramagnetic Iron Oxide Particle Quantification using Deep Learning in Magnetic Resonance Imaging.
IEEE Transactions on Pattern Analysis and Machine Intelligence
10.1109/TPAMI.2020.3012103.
(In press).
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Abstract
The susceptibility of Super Paramagnetic Iron Oxide (SPIO) particles makes them a useful contrast agent for different purposes in MRI. These particles are typically quantified with relaxometry or by measuring the inhomogeneities they produced. These methods rely on the phase, which is unreliable for high concentrations. We present in this study a novel Deep Learning method to quantify the SPIO concentration distribution. We acquired the data with a new sequence called View Line in which the field map information is encoded in the geometry of the image. The novelty of our network is that it uses residual blocks as the bottleneck and multiple decoders to improve the gradient flow in the network. Each decoder predicts a different part of the wavelet decomposition of the concentration map. This decomposition improves the estimation of the concentration, and also it accelerates the convergence of the model. We tested our SPIO concentration reconstruction technique with simulated images and data from actual scans from phantoms. The simulations were done using images from the IXI dataset, and the phantoms consisted of plastic cylinders containing agar with SPIO particles at different concentrations. In both experiments, the model was able to quantify the distribution accurately.
| Type: | Article |
|---|---|
| Title: | DeepSPIO: Super Paramagnetic Iron Oxide Particle Quantification using Deep Learning in Magnetic Resonance Imaging |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TPAMI.2020.3012103 |
| Publisher version: | https://doi.org/10.1109/TPAMI.2020.3012103 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Magnetic resonance imaging , Decoding , Distortion , Machine learning , Magnetic susceptibility , Convolution , Image reconstruction |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10108156 |
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