Difference between revisions of "2011 Winter Project Week:Breakout Multi-Image Engineering"
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Session Leaders: Jim Miller, Steve Pieper, Alex Yarmakovich, Junichi Tokuda, Demian Wasserman | Session Leaders: Jim Miller, Steve Pieper, Alex Yarmakovich, Junichi Tokuda, Demian Wasserman | ||
| + | |||
| + | =Background= | ||
| + | |||
| + | Increasingly, we have data in slicer which are multi something. Examples are: | ||
| + | *multi-channel (T1, T2, flair, dual echo, etc.) | ||
| + | *DWI | ||
| + | *rgb and other multi-channel images | ||
| + | *time series | ||
| + | |||
| + | =Current State= | ||
| + | We have part of the ability to handle such multi-dimensional volume in the DWI infrastructure: | ||
| + | *Dicom to nrrd, the slider in the volumes module to look at any of the individual volumes in DWI. | ||
| + | *Junichi's 4D mrml node takes a different approach but ultimately also allows to look at a time point at a time through a slider. | ||
| + | *Brainsfit has the ability to register such multi data sets to each other and Gtract has a first solution which is engineered for DWI | ||
| + | *Compareview would allow to show multi data in compareviewers. When we have a few volumes, then we can display all. If its a large number of volumes, we will only be able to display a few. | ||
| + | |||
| + | Multi volumes can be processed in specialized ways. Existing examples are: | ||
| + | *Parametric analysis: | ||
| + | **DTI estimation from DWI | ||
| + | **Tofts parameter estimation from DCE | ||
| + | *Segmentation: | ||
| + | **EM segmentation from multi-channel morphology data | ||
| + | |||
| + | =The Need= | ||
| + | All of the current examples are special cases. If we can generalize those into a single architecture for multi data, there would be a lot of potential for cross-benefits. | ||
| + | ==I/O== | ||
| + | *We should have a single module to organize the data. DICOM to nrrd is a good start, but we need to be able to handle separate T1 and T2 acquisitions as well. We also need to be able to handle non-dicom data. Perhaps something like: load all data into slicer and associate them inside slicer in a special module. Write out as a single nrrd file. | ||
| + | ==Display== | ||
| + | *we should create a single visualization infrastructure to handle multi data: compare viewers, rgb channels, time series movies: equivalent slice viewers and 3D viewers | ||
| + | ==Processing== | ||
| + | *common api for processing: EM segmentation, pharmacokinetic models, DWI filtering, tensor estimation should all plug into the data in the same way. | ||
Revision as of 11:49, 3 January 2011
Home < 2011 Winter Project Week:Breakout Multi-Image EngineeringBack to Project Week Agenda
Agenda breakout session: Multi-Image Engineering in slicer
Wednesday 8-10am
Session Leaders: Jim Miller, Steve Pieper, Alex Yarmakovich, Junichi Tokuda, Demian Wasserman
Background
Increasingly, we have data in slicer which are multi something. Examples are:
- multi-channel (T1, T2, flair, dual echo, etc.)
- DWI
- rgb and other multi-channel images
- time series
Current State
We have part of the ability to handle such multi-dimensional volume in the DWI infrastructure:
- Dicom to nrrd, the slider in the volumes module to look at any of the individual volumes in DWI.
- Junichi's 4D mrml node takes a different approach but ultimately also allows to look at a time point at a time through a slider.
- Brainsfit has the ability to register such multi data sets to each other and Gtract has a first solution which is engineered for DWI
- Compareview would allow to show multi data in compareviewers. When we have a few volumes, then we can display all. If its a large number of volumes, we will only be able to display a few.
Multi volumes can be processed in specialized ways. Existing examples are:
- Parametric analysis:
- DTI estimation from DWI
- Tofts parameter estimation from DCE
- Segmentation:
- EM segmentation from multi-channel morphology data
The Need
All of the current examples are special cases. If we can generalize those into a single architecture for multi data, there would be a lot of potential for cross-benefits.
I/O
- We should have a single module to organize the data. DICOM to nrrd is a good start, but we need to be able to handle separate T1 and T2 acquisitions as well. We also need to be able to handle non-dicom data. Perhaps something like: load all data into slicer and associate them inside slicer in a special module. Write out as a single nrrd file.
Display
- we should create a single visualization infrastructure to handle multi data: compare viewers, rgb channels, time series movies: equivalent slice viewers and 3D viewers
Processing
- common api for processing: EM segmentation, pharmacokinetic models, DWI filtering, tensor estimation should all plug into the data in the same way.
