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Support for TransformMap #46
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oesteban
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Feb 18, 2020
Implements two types of transforms mappings: 1. a general one for any internal transform, and 2. an optimized mapping for lineart transforms (head-motion correction). Since scipy's interpn only accepts linear and nearest interpolations, these mappings will not support for simultaneous slice-timing correction (STC) for the time being. The addition of 4D tensor B-Spline basis for interpolation would allow for simultaneous STC, HMC and SDC of functional time-series. Not sure we should keep supporting Lanczos interpolation, very much less if we want to do all three corrections at the same time. Closes nipy#46
oesteban
added a commit
to oesteban/nitransforms
that referenced
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Feb 18, 2020
Implements two types of transforms mappings: 1. a general one for any internal transform, and 2. an optimized mapping for lineart transforms (head-motion correction). Since scipy's interpn only accepts linear and nearest interpolations, these mappings will not support for simultaneous slice-timing correction (STC) for the time being. The addition of 4D tensor B-Spline basis for interpolation would allow for simultaneous STC, HMC and SDC of functional time-series. Not sure we should keep supporting Lanczos interpolation, very much less if we want to do all three corrections at the same time. Closes nipy#46
oesteban
added a commit
to oesteban/nitransforms
that referenced
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Feb 19, 2020
Implements two types of transforms mappings: 1. a general one for any internal transform, and 2. an optimized mapping for lineart transforms (head-motion correction). Since scipy's interpn only accepts linear and nearest interpolations, these mappings will not support for simultaneous slice-timing correction (STC) for the time being. The addition of 4D tensor B-Spline basis for interpolation would allow for simultaneous STC, HMC and SDC of functional time-series. Not sure we should keep supporting Lanczos interpolation, very much less if we want to do all three corrections at the same time. Closes nipy#46
oesteban
added a commit
to oesteban/nitransforms
that referenced
this issue
Feb 19, 2020
Implements two types of transforms mappings: 1. a general one for any internal transform, and 2. an optimized mapping for lineart transforms (head-motion correction). Since scipy's interpn only accepts linear and nearest interpolations, these mappings will not support for simultaneous slice-timing correction (STC) for the time being. The addition of 4D tensor B-Spline basis for interpolation would allow for simultaneous STC, HMC and SDC of functional time-series. Not sure we should keep supporting Lanczos interpolation, very much less if we want to do all three corrections at the same time. Closes nipy#46
oesteban
added a commit
to oesteban/nitransforms
that referenced
this issue
Feb 19, 2020
Implements two types of transforms mappings: 1. a general one for any internal transform, and 2. an optimized mapping for lineart transforms (head-motion correction). Since scipy's interpn only accepts linear and nearest interpolations, these mappings will not support for simultaneous slice-timing correction (STC) for the time being. The addition of 4D tensor B-Spline basis for interpolation would allow for simultaneous STC, HMC and SDC of functional time-series. Not sure we should keep supporting Lanczos interpolation, very much less if we want to do all three corrections at the same time. Closes nipy#46
oesteban
added a commit
to oesteban/nitransforms
that referenced
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Feb 20, 2020
Implements two types of transforms mappings: 1. a general one for any internal transform, and 2. an optimized mapping for lineart transforms (head-motion correction). Since scipy's interpn only accepts linear and nearest interpolations, these mappings will not support for simultaneous slice-timing correction (STC) for the time being. The addition of 4D tensor B-Spline basis for interpolation would allow for simultaneous STC, HMC and SDC of functional time-series. Not sure we should keep supporting Lanczos interpolation, very much less if we want to do all three corrections at the same time. Closes nipy#46
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This would be a list of Affines, for instance, to correct for head motion. In this case, the TransformMap.map(x) method (with N transforms in the map) will generate N mappings of x.
The difference with concatenation is that transforms are not sequential in the map, they are applied in parallel.
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