[Neuroimaging] JSON-LD and DICOM?
satra at mit.edu
Mon Jul 3 13:58:03 EDT 2017
sorry, ohbm was busy, just catching up with this! it seems there are a few
threads in here that i will attempt to summarize and comment.
*1. the original question: is there a json-ld context that can be used or
included in a nifti-header extension. *
this can be easily created based on both the work done by david clunie to
expose each dicom term but also the extensive curation by karl helmer. we
can create a simple json-ld context definition that behaves as a lexicon.
there are pieces of dicom where this will get complicated, but for our
needs a vocabulary may be a good starting point.
here are the terms in neurolex: http://neurolex.org/wiki/Category:DICOM_term
they are being transitioned off to scicrunch/interlex and i'm sure karl and
i can put together a basic context for us.
*2. nibabel addons and the metadata extension in nifti. *
for those who are unfamiliar, we have been using a non-standardized
extension based on dcmstack in nifti for several years now in the heudiconv
tool. there is an opportunity to make this part of nibabel and create a
standard extension. as with many extensions, most software tools may choose
to ignore an extension, but the value of this extension to keep dicom
metadata around with the raw converted nifti file is immense. currently, we
simply discard this information (wait till point 3 for the dicom-nifti
dimension). as we create this standard, it would be good to leverage
json-ld to simply point to a context file such as this:
we don't have to expand this out in each embedded header.
*3. the dicom-nifti dimension:*
*a. state of the field.*
this dicom-nifti dimension reflects the reality of our field in many ways.
most of us neuroimagers live in a research/exploratory space and mostly do
not have any clinical applications that need to be embedded into hospital
systems. the clinical imaging community is trying to make their algorithms
work for clinical decision systems in the clinical enterprise, hence their
need for dicom operators. much of cognitive neuroscience is not applicable
to the clinic hence very little incentive for people to think about dicoms.
*b. the variations in dicom and nifti*
as nate noted there are some big differences in scanners as they apply to
research institutions. trying to standardize dicom output across scanners
is itself a big undertaking and not in the interest of many centers. i'm
not even talking about metadata standardization here, i'm simply saying let
all dicom scanners output multi-frame dicom. if this is something the
community can achieve it would be a big step towards a common framework.
however, if it requires every center to change their mode of operation,
it's a huge barrier at present. nifti on the other hand is a compact format
and fits easily into current filesystem views.
*c. software support *
as has been well noted in this thread, the brain imaging community for most
relies on a set of software packages that support nifti extensively.
updating these tools to support dicom i/o is a resource intensive
undertaking. if magically, through a week long hackathon, every software
supported dicom objects, i don't think we would be having this
in addition better dicom support in nibabel could be very useful to a
subset of the community developing tools in python. for example, from a
memory representation perspective, it doesn't matter what the disk file
format is as long as there is a nice api to read it.
we view dicom in the same lens that we saw it through in the nineties.
perhaps we can be educated on the diffs in the last 20 years.
*d. metadata maintenance*
as an algorithm developer, one would have to decide what metadata to keep
and what new pieces of metadata to add to the dicom object. i know andrey,
steve, and others have done this for segmentation objects and structured
reports, but the field would have to do this for connectomes, surfaces, and
blindly copying dicom metadata is analogous to blindly copying nifti header
extensions. so in both spaces, one has to decide what to keep and what to
modify. while we can be careful about this for new algorithms, doing so for
the existing ones is a lot of work.
*e. a view of information that reduces cognitive load*
as algorithm developers we care about the view, the minimal set of
information that is needed to write a function/solve a problem. nifti-1 was
an agglomeration of those views when it was created, together with some
backward compatibility decisions with analyze. people were not thinking of
large databases, diffusion imaging, and other areas that we now consider
important. and hence nifti is a view of the underlying information that is
already out of date. yes the extensions were part of the solution, but how
many people use the diffusion extension over bvecs and bval files (a la
the dicom object stores much more information, but it is also a view. it
does not store the raw sensor data (think nikon RAW vs JPEG) in most cases,
because people thought it was excessive. as we now seem to find with
simultaneous multislice seqeunces in fMRI and dMRI, the reconstruction
algorithm has a huge impact on the SNR of the combined channel data. hence
more people are preserving k-space data in projects that use such sequences.
at some point neither dicom nor nifti will be the appropriate format. we
are not there yet but there are many pointers in that direction as
connected information aggregates (genetics, imaging, behavior, ehr, etc).
at present, in our resource constrained development environments, perhaps
we can preserve information and make it useful when we can.
On Mon, Jul 3, 2017 at 7:15 AM, Matthew Brett <matthew.brett at gmail.com>
> Hi Steve,
> On Mon, Jul 3, 2017 at 2:03 PM, Steve Pieper <pieper at isomics.com> wrote:
> > To avoid repetition, I'll just point to my comments 3 years ago on this
> > topic . Since then we've continued to work on DICOM in research
> >  and we still think it's a neat idea. The software is coming along
> > nicely too .
> I am sure there's a place for sticking entirely to DICOM, but, as you
> know, nearly all standard imaging software reads and writes NIfTI, for
> preference, and often only writes NIfTI. So, a solution that works
> with NIfTI is a lot closer to hand than switching to using DICOM at
> all stages of processing.
> Neuroimaging mailing list
> Neuroimaging at python.org
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