I am working on the DicomToNRRD converter program, adding the ability to
convert DICOM series to FSL DWI datasets, and FSL datasets to NRRD.
There is a problem with respect to the numeric precision in the NRRD
header.
The DicomToNrrdConverter regression tests converts a DICOM DWI Dataset to
NRRD, the same data set to FSL which comprises a 4D NIfTI file, a BValues
text file, and a BVectors FSL file.
In a perfect world, my FSLToNrrd converter would take that FSL set and
compare it to the NRRD file created from the same original DWI data, and
they would match.
This actually works if I compare the BValues/BVectors, and do
voxel-by-voxel comparison.
But I was trying to be clever, and used a pipeline of a
SubtractImageFilter and ImageStatisticsFilter, which is where I ran into
this problem:
ERROR: SubtractImageFilter(0x7fe6c48081f0): Inputs do not occupy the same
physical space!
InputImage Origin: [-1.3101199e+02, -1.5218300e+02, 4.9731600e-01,
0.0000000e+00],
InputImageIndexedDataObject1 Origin: [-1.3101200e+02, -1.5218300e+02,
4.9731600e-01, 0.0000000e+00]
Tolerance: 1.0000000e-06
InputImage Spacing: [1.0000000e+00, 1.0000000e+00, 2.4000039e+00,
1.0000000e+00],
InputImageIndexedDataObject1 Spacing: [1.0000000e+00, 1.0000000e+00,
2.4000000e+00, 1.0000000e+00]
Tolerance: 1.0000000e-06
The problem is that the NRRDImageIO uses a different print precision for
outputing floating point numbers than the DicomToNrrdConverter, and the
difference is enough to make the origin X values off by 1.0E+5, and the
space-checking code in the Image filters uses a tolerance of 1.0E+6.
I can try and fix DicomToNrrdConverter to use the same precision, but this
is maddening. Any other suggestions? Is there a better way to compute
tolerances in the Image Filters?
--
Kent Williams [email protected]
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