MedImages.jl

MedImage.jl

The filesystem and choice of metadata is loosly based on BIDS format [1]

This project was created to standardize data handling of 3D and 4D medical imaging. It is currently subject to change and I am open to suggestions that would improve the library in construction. I’ve included below in categories what needs to be done and some basic ideas on How to approach it. I will post it for consultations with the community and then process to create test cases where Python SimpleItk (or other) methods will be treated as a reference.

  1. Designing data structure - requirements (need to explicitly specify the most important and the rest will be just in an additional dictionary)
    • hold voxel data as a multidimensional array
    • keep spatial metadata - origin, orientation, spacing
    • type of the image - label/CT/MRI/PET (need to construct enum for this) - frequently will be needed to be supplied manually
    • subtype of the image for example if MRI ADC/DWI/T2 etc. (need to construct enum for this)
    • type of the voxel data (for example Float32)
    • Date of saving
    • Acquisition time
    • Patient ID if present
    • current device - (CPU, GPU)
    • Study UID
    • Patient UID
    • Series UID
    • Study Description
    • Original file name
    • Display data - set of colors for the labels; window value for CT scan - will provide set of defaults based on image type and I will modify MedEye3D for convenient visualizations.
    • Clinical data dictionary - age, gender …
    • Is contrast administered
    • The rest of the metadata loaded from a file to store in a dictionary
  2. Data loading
    • Nifti - start with Nifti.jl
    • Dicom - Dicom.jl
    • Mha - ?
  3. Modifying voxel data together with spacing data
    • Modifying orientation of all images to single orientation - for example, RAS (we should select some default orientation) - TODO with AxisArrays.jl plus keep track of origin
    • Changing spacing to a given spacing - TODO with AxisArrays.jl with Interpolations.jl plus keep track of origin; use nearest neighbor interpolator for the label; and for example b spline for others
    • Resampling to another grid - for example, resample PET image to CT image based just on spatial metadata keeping track of changed metadata of the moving image TODO with AxisArrays.jl and Interpolations.jl
    • Cropping and dilatating with adjusting of origin offset TODO with AxisArrays.jl, storing also positions of original image in metadata (some artifacts happen on the image edges)
  4. Adding persistency
    • store the data as array in HDF5 and metadata as its attributes
    • design efficient loading and saving irrespective of the device the array is on
    • add the possibility to save back as nifty and dicom files for exporting

Point 3 is the trickiest one to do as there are a lot of corner cases

to look for https://github.com/haberdashPI/MetaArrays.jl

References

1)https://www.nature.com/articles/sdata201644

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