slicebox / dcm4che-streams

Akka-streams integration with dcm4che for streaming DICOM IO in Scala.



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Migration to dicom-streams

This project has migrated to the project dicom-streams as is no longer maintained. dicom-streams aims to become a standalone (partial) DICOM implementation with a fully streaming API.


The purpose of this project is to integrate akka-streams with dcm4che. Features will be added as needed (mainly in the slicebox project) and may include streaming reading and writing of DICOM data, as well as streaming SCP and SCU capabilities.

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

  .via(tagFilter(_ => true)(tagPath => // no private attributes 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 attributes. 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 attributes in dataset
    TagModification.endsWith(TagPath.fromTag(Tag.PatientName), _ => padToEvenLength(ByteString("John Doe"), VR.PN), insert = false),
    TagModification.endsWith(TagPath.fromTag(Tag.MediaStorageSOPInstanceUID), _ => updatedSOPInstanceUID, insert = false),
    TagModification.endsWith(TagPath.fromTag(Tag.SOPInstanceUID), _ => updatedSOPInstanceUID, insert = true),
  .via(fmiGroupLengthFlow()) // update group length in meta information, if present

The next example materializes a stream as a dcm4che Attributes objects instead of writing data to disk.

val futureAttributes: Future[(Option[Attributes], Option[Attributes])] =
    .via(attributeFlow) // must turn headers + chunks into complete attributes before materializing
    .runWith(attributesSink) {
  case (maybeMetaInformation, maybeDataset) => ??? // do something with attributes here

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 and new flows are created using the DicomFlowFactory.create method. The DicomFlow interface 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: DicomPreamble): List[DicomPart]
  def onHeader(part: DicomHeader): List[DicomPart]
  def onValueChunk(part: DicomValueChunk): List[DicomPart]
  def onSequenceStart(part: DicomSequence): List[DicomPart]
  def onSequenceEnd(part: DicomSequenceDelimitation): List[DicomPart]
  def onFragmentsStart(part: DicomFragments): List[DicomPart]
  def onFragmentsEnd(part: DicomFragmentsDelimitation): List[DicomPart]
  def onSequenceItemStart(part: DicomSequenceItem): List[DicomPart]
  def onSequenceItemEnd(part: DicomSequenceItemDelimitation): List[DicomPart]
  def onFragmentsItemStart(part: DicomFragmentsItem): List[DicomPart]
  def onDeflatedChunk(part: DicomDeflatedChunk): List[DicomPart]
  def onUnknownPart(part: DicomUnknownPart): 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: DicomPreamble): List[DicomPart] = part :: Nil
  def onHeader(part: DicomHeader): 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() = DicomFlowFactory.create(new DeferToPartFlow with TagPathTracking {
    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. Note that flows with internal state should be defined as functions (def) rather than constants/variables val/var to avoid shared state within or between flows.