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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3012103
|2 doi
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|a pubmed24n1044.xml
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|a (DE-627)NLM313260745
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|a (NLM)32750834
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Maggiora, Gabriel Della
|e verfasserin
|4 aut
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| 245 |
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|a DeepSPIO
|b Super Paramagnetic Iron Oxide Particle Quantification Using Deep Learning in Magnetic Resonance Imaging
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 10.01.2022
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|a Date Revised 10.01.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Ferric Compounds
|2 NLM
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|a ferric oxide
|2 NLM
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| 650 |
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|a 1K09F3G675
|2 NLM
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| 700 |
1 |
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|a Castillo-Passi, Carlos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Qiu, Wenqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Shuang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Milovic, Carlos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sekino, Masaki
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tejos, Cristian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Uribe, Sergio
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Irarrazaval, Pablo
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 1 vom: 15. Jan., Seite 143-153
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:44
|g year:2022
|g number:1
|g day:15
|g month:01
|g pages:143-153
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|u http://dx.doi.org/10.1109/TPAMI.2020.3012103
|3 Volltext
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