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What is the ideal solution between the absolute or relative resampling in PET ?
In PET images, we do not recommend to use the relative resampling. As explained in https://www.ncbi.nlm.nih.gov/pubmed/26669541, relative resampling induces a high correlation between texture indices and metabolic volume, and we proposed to use the absolute resampling instead. In PET, we resample with 64 number of grey levels (size bin equals to 0.3) between 0 and 20 SUV units (because this is the standard range of SUVs in oncology with FDG). We think the same effect is observed in CT images, but if using the absolute resampling (which we recommend), you have to set yourself the range of HU and the number of bins (or bin size) for your data, as a function of the data content. Basically, the bin size has to be set as a function of the meaning of HU difference. If a HU difference of 10 does not mean much, then the bin size could be set to 10 for instance, using a smaller value is useless, and a range of HU between -1000HU and 3000HU for example (including all HU possible variations).
What is the ideal solution between the absolute or relative resampling in MR ?
In MR, the impact of different resampling schemes has been studied in http://iopscience.iop.org/article/10.1088/1361-6560/aabd21. Based on that paper, we recommend using a fixed bin width, which corresponds to absolute resampling.
In PET, the DICOM fields that have to be properly filled for correct SUV correction are:
DICOM fields to SUV conversion :
(0008,0032) TM #6  Acquisition Time
(0010,1020) DS #6  Patient’s Size
(0018,1074) DS #10  Radionuclide Total Dose
(0018,1075) DS #12  Half Life
(0018,1072) TM #6  Radiopharmaceutical Start Time
(0054,1001) CS #4  Units
(0028,1052) DS #2  Rescale Intercept
(0028,1053) DS #8  Rescale Slope
About the Spatial resampling XYZ spacing.
Actually which value i should input ?
The main point of the spatial resampling XYZ spacing is to make sure that all calculations are performed with the same voxel size whatever the voxel size of the input image. This is important because textural feature values depend on voxel size. If all input images have the same voxel size, the user does not have to worry about this spacing, as it will be set by default to the voxel size so will always be the same. If the voxel size is not always the same for the different images processed in a study, then a fixed voxel size should be chosen (eg 2 x 2 x 2 mm in PET), and then used that for all images. This will remove the variability in texture values due to different voxel size.
I would like to ask You about parameters in "Intensity of Discretization" in MR images. How to determine this values? And how this value effect to our results.
We think some of our conclusions will probably hold for other organs, but the min and max values to be used for quantization might depend on the organ. Ideally, a similar study should be conducted for the organ or MR sequence of interest. to determine the most suited values. For MRI, we think that a standardization of intensities is first necessary. This standardization would require to define a reference tissue (which will depend on the field of view and the organ). This is a difficult part of the procedure.