exini / dicom-streams   4.0.2

Apache License 2.0 GitHub

A streaming and non-blocking API for reading and processing DICOM data

Scala versions: 2.13 2.12


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The purpose of this project is to create a streaming API for reading and processing DICOM data using akka-streams.

Advantages of streaming DICOM data include better control over resource allocation such as memory via strict bounds on DICOM data chunk size and network utilization using back-pressure as specified in the Reactive Streams protocol.

The logic of parsing and handling DICOM data is inspired by dcm4che which provides a far more complete (albeit blocking and synchronous) implementation of the DICOM standard.


The dicom-streams library is deployed to Sonatype. You need to include the Sonatype resolvers to find the package.

resolvers ++= Seq(Resolver.sonatypeRepo("releases"), Resolver.sonatypeRepo("snapshots"))

The library is included by

libraryDependencies += "com.exini" %% "dicom-streams" % "x.y.z"

Data Model

Streaming binary DICOM data may originate from many different sources such as files, a HTTP POST request, or a read from a database. Akka Streams provide a multitude of connectors for streaming binary data. Streaming data arrives in chunks (ByteStrings). In the Akka Stream nomenclature, chunks originate from sources, they are processed in flows and and folded into a non-streaming plain objects using sinks.

This library provides flows for parsing binary DICOM data into DICOM parts (represented by the DicomPart interface) - small objects representing a part of a data element. These DICOM parts are bounded in size by a user specified chunk size parameter. Flows of DICOM parts can be processed using a series of flows in this library. There are flows for filtering based on tag path conditions, flows for converting between transfer syntaxes, flows for re-encoding sequences and items, etc.

The Element interface provides a set of higher level data classes, each roughly corresponding to one row in a textual dump of a DICOM files. Here, chunks are aggregated into complete data elements. There are representations for standard tag-value elements, sequence and item start elements, sequence and item delimitation elements, fragments start elements, etc. A DicomPart stream is transformed into an Element stream via the elementFlow flow.

A flow of Elements can be materialized into a representation of a dataset called an Elements using the elementSink sink. For processing of large sets of data, one should strive for a fully streaming DICOM pipeline, however, in some cases it can be convenient to work with a plain dataset; Elements serves this purpose. Internally, the sink aggregates Elements into ElementSets, each with an asssociated tag number (value elements, sequences and fragments). Elements implements a straight-forward data hierarchy:

  • An Elements holds a list of ElementSets (ValueElement, Sequence and Fragments)
  • A ValueElement is a standard attribute with tag number and binary value
  • A Sequence holds a list of Items
    • An Item contains zero or one Elements (note the recursion)
  • A Fragments holds a list of Fragments
    • A Fragment holds a binary value.

The following diagram shows an overview of the data model at the DicomPart, Element and ElementSet levels.

Data model

As seen, a standard attribute, represented by the ValueElement class is composed by one HeaderPart followed by zero, one or more ValueChunks of data. Likewise, ecapsulated data such as a jpeg image is composed by one FragmentsPart followed by, for each fragment, one ItemPart followed by ValueChunks of data, and ends with a SequenceDelimitationPart.


The following example reads a DICOM file from disk, validates that it is a DICOM file, discards all private elements and writes it to a new file.

  .via(tagFilter(tagPath => tagPath.toList.map(_.tag).exists(isPrivate))) // no private elements anywhere on tag path

Care should be taken when modifying DICOM data so that the resulting data is still valid. For instance, group length tags may need to be removed or updated after modifying elements. Here is an example that modifies the PatientName and SOPInstanceUID attributes. To ensure the resulting data is valid, group length tags in the dataset are removed and the meta information group tag is updated.

val updatedSOPInstanceUID = padToEvenLength(ByteString(createUID()), VR.UI)

  .via(groupLengthDiscardFilter) // discard group length elements in dataset
      TagModification.endsWith(TagPath.fromTag(Tag.PatientName), _ => padToEvenLength(ByteString("John Doe"), VR.PN)),
      TagModification.endsWith(TagPath.fromTag(Tag.MediaStorageSOPInstanceUID), _ => updatedSOPInstanceUID)
      TagInsertion(TagPath.fromTag(Tag.SOPInstanceUID), _ => updatedSOPInstanceUID)
  .via(fmiGroupLengthFlow) // update group length in meta information, if present

Custom Processing

New non-trivial DICOM flows can be built using a modular system of capabilities that are mixed in as appropriate with a core class implementing a common base interface. The base interface for DICOM flows is DicomFlow. It provides an associated logic class DicomLogic that should be similarly extended and is where the state and logic of the flow should reside. The DicomLogic base class defines a series of events, one for each type of DicomPart that is produced when parsing DICOM data with DicomParseFlow. The core events are:

  def onPreamble(part: PreamblePart): List[DicomPart]
  def onHeader(part: HeaderPart): List[DicomPart]
  def onValueChunk(part: ValueChunk): List[DicomPart]
  def onSequence(part: SequencePart): List[DicomPart]
  def onSequenceDelimitation(part: SequenceDelimitationPart): List[DicomPart]
  def onFragments(part: FragmentsPart): List[DicomPart]
  def onItem(part: ItemPart): List[DicomPart]
  def onItemDelimitation(part: ItemDelimitationPart): List[DicomPart]
  def onDeflatedChunk(part: DeflatedChunk): List[DicomPart]
  def onUnknown(part: UnknownPart): List[DicomPart]
  def onPart(part: DicomPart): List[DicomPart]

Default behavior to these events are implemented in core classes. The most natural behavior is to simply pass parts on down the stream, e.g.

  def onPreamble(part: PreamblePart): List[DicomPart] = part :: Nil
  def onHeader(part: HeaderPart): List[DicomPart] = part :: Nil

This behavior is implemented in the IdentityFlow core class. Another option is to defer handling to the onPart method which is implemented in the DeferToPartFlow core class. This is appropriate for flows which define a common behavior for all part types.

To give an example of a custom flow, here is the implementation of a filter that removes nested sequences from a dataset. We define a nested dataset as a sequence with depth > 1 given that the root dataset has depth = 0.

  def nestedSequencesFilter() = new DeferToPartFlow[DicomPart] with TagPathTracking[DicomPart] {
    override def createLogic(attr: Attributes): GraphStageLogic = new DeferToPartLogic with TagPathTrackingLogic {
      override def onPart(part: DicomPart): List[DicomPart] = if (tagPath.depth > 1) Nil else part :: Nil

In this example, we chose to use DeferToPartFlow as the core class and mixed in the TagPathTracking capability which gives access to a tagPath: TagPath variable at all times which is automatically updated as the flow progresses.


The plugin sbt-ci-release is used to manage releasing to Sonatype. The library is automatically released on each push to the master branch. The version is derived from the tag closet to the latest commit to master. Given this, the following procedure should be used:

  1. Merge develop into master via a new branch
  2. Tag the new release as vx.y.z, e.g. using git tag v3.2.1, following semantic versioning recommendations
  3. Push tags to origin by git push --tags
  4. Push release commit to origin by git push, this will trigger the release using the tagged version
  5. From the Github project page, create a new release using the existing release tag describing your changes
  6. Merge master into develop via a new branch


This project is released under the Apache License, version 2.0.