Difference between revisions of "Project Week 25/CNN for PseudoCT Generation from T1T2 MR"
From NAMIC Wiki
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==Project Description== | ==Project Description== | ||
| + | This tool allows the user to generate an HU map (Pseudo-CT) from T1/T2 input MRI. The tool is still in the early development stages with good early results in terms of Mean Absolute Error and Bias. | ||
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|<!-- Objective bullet points --> | |<!-- Objective bullet points --> | ||
| − | + | The main object is to understand which topology of CNN is the most suited for the Pseudo-CT generation task. | |
|<!-- Approach and Plan bullet points --> | |<!-- Approach and Plan bullet points --> | ||
| − | + | Test of different structures with training on low-res images in order to speed-up computational time. | |
| + | Analysis of the different output with MAE and Bias metrics | ||
|<!-- Progress and Next steps (fill out at the end of project week), bullet points --> | |<!-- Progress and Next steps (fill out at the end of project week), bullet points --> | ||
| − | + | TBD | |
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Revision as of 15:28, 22 June 2017
Home < Project Week 25 < CNN for PseudoCT Generation from T1T2 MR
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Key Investigators
- Giampaolo Pileggi (Magna Graecia University, Italy/German Cancer Research Center (DKFZ), Germany)
- Paolo Zaffino (Magna Graecia University, Italy)
- Salvatore Scaramuzzino (Magna Graecia University/ASL Vercelli, Italy)
- Maria Francesca Spadea (Magna Graecia University, Italy)
- Gino Gulamhussene (Universität Magdeburg, Germany)
- Anneke Meyer (Universität Magdeburg, Germany)
Project Description
This tool allows the user to generate an HU map (Pseudo-CT) from T1/T2 input MRI. The tool is still in the early development stages with good early results in terms of Mean Absolute Error and Bias.
| Objective | Approach and Plan | Progress and Next Steps |
|---|---|---|
|
The main object is to understand which topology of CNN is the most suited for the Pseudo-CT generation task. |
Test of different structures with training on low-res images in order to speed-up computational time. Analysis of the different output with MAE and Bias metrics |
TBD |
Illustrations
