Tuesday, Dec 3rd 10:15 to 11:00 IHE Classroom at InfoRAD at RSNA 2002.

Transcription

1 Tuesday, Dec 3rd 10:15 to 11:00 IHE Classroom at InfoRAD at RSNA

2 Prepared by: DICOM Working Group 16: Magnetic Resonance Presented by: Kees Verduin, Philips Medical Systems Bob Haworth, General Electric Medical Systems David Clunie, PixelMed Publishing 2

3 Dimensions Raw Data Multi-frame Spectroscopy Multi-stack Color Real World Values 3

4 New applications have emerged, which could not be supported before e.g. diffusion, fmri... Too many private elements hamper interoperability Data explosion in multi image acquisitions >60,000 gives huge overhead in image headers Functional images: dynamic images, viability of cardiac walls, mapping to color. Spectroscopy: Spectra and their interpretation need to be shared in an interoperable way. Also to be stored in standard archives. Raw Data: needs to be archived in standard archives. 4

5 Members Participants MR Imaging Institutes: UCSF: Mark Day NIH: Ronald Levin Medical Companies: Esaote: Luigi Pampana General Electric: Bob Haworth Philips: Kees Verduin Bas Revet Sensor Systems: Yaman Aksu Siemens: Elmar Seeberger Matthias Drobnitzky It took: three and a half years of working time, 19 face-to-face meetings, 13 teleconferences and 49 versions of the supplement. 5

6 Clinical examples of the benefits of the new standard Concepts of the supplement Details of the solution Implementation tool Questions and Answers Future presentations and workshops 6

7 Support for latest MR applications through recognition of modern MR parameters and context information Increased interoperability in multi-vendor situations (less private elements ) Color Images displayed as on the creating system Increased clinical performance through: Easier and automated post-processing based on transmitted values Context information that allows display of images in the order defined by the creator (only overruled by application knowledge) Context information from the structure of the header 7

8 Diffusion Imaging Use of Color for Diffusion and Functional imaging Functional Brain Imaging Cardiac Imaging Spectroscopy 8

9 Diffusion b-values from 0 to 8000 and ADC image 9

10 Reconstructed Fiber Maps in the colors as seen by the creator 10

11 10-60 slices all slices measured in one TR repeated times to get sufficient signal leading to > 60,000 images in one object 11

12 Post-processing with results in color 12

13 No STRESS STRESS Enables automatic multi-slice / multi-phase display, even for standard workstations 13

14 14

15 Make clear that it is worth participating. Motivate the parties involved, to support the new standard: such that all will change their implementation the users benefits must provide a clear incentive TOOLS are needed for a Proof of Concept 15

16 Support of newest applications by new attributes Less ambiguity through stricter definitions and rules Clear Relationships between Referenced images Header size reduction through Multi-frame technique File size flexibility through Concatenations Context information from Dimension organization Functional images with Real World Values Support Functional images with Color interpretation these benefits are equally applicable to CT imaging 16

17 Diffusion b-value and related elements Parallel Acquisition Technique Cardiac Tagging attributes Coil Name and Multi-coil configurations Acquisition Duration and other timing elements Spectroscopy data and related elements 17

18 Prior Image 0 - n 0 - n Ref. Image Sequence required if planned on prior images Ref. Image Sequence if planned on prior image 0 - n Prior Spectroscopy Source Image Sequence for derived images Ref. Image Sequence required if planned on prior images 0 -n Image Raw Data Ref. Raw Data Sequence 0-n Ref. Image Sequence Only if image type is METABOLITE MAP 0-n Ref. Raw Data Sequence Spectroscopy 0-n Source Image Sequence for derived spectroscopy data Ref. Image Sequence required if planned on prior spectroscopy data 18

19 Whenever possible, all images of a scan become frames in one object Do not repeat what is common to all frames Group related elements 19

20 N Objects, N Headers HEADER SIZE REDUCTION N Frames, One Header Fixed Header Per-frame header Pixel data (not to scale) 20

21 N Objects, N Headers N Frames, One Header Fixed Header Per-frame header Dimension data (not to scale) Pixel data (not to scale) 21

22 Data elements that are common to all images in a series can be shared and do not have to be repeated (the concept of shared and non-shared headers) Related elements can be grouped, and as such indicate by their position in the header the type of acquisition that was performed (e.g: cardiac trigger time: if this differs per frame it is cardiac multi-phase scan) This packaging leads to an over-all reduction of header size 22

23 Dimensions: frame organization Concatenations: splitting large acquisitions Real World Values: numerically significant pixels Color: functional data fused with anatomical data 23 BH

24 What are the important organizational elements for a set of frames? Groups of slices (Stacks) Slices within a group (In-Stack Position) Time/Phase ordering (Temporal Position Index) Echo, Cardiac phase, b-value, stimulus, Z-score Who best knows the important data organizational indexes? Image object creator Must the frames be in some specific order? No! Frame order is not relevant. Presentation/Usage should be driven by the user/application. Mechanism to specify order? Images contain a Dimension Module specifying organizational indexes 24 BH

25 Frame Number In-Stack Position 2 1 Stack ID3 Frame Number In-Stack Position 2 1 Stack ID2 Frame Number BH In-Stack Position 2 1 Stack ID1 25

26 time Slice Order for phase 1 Temporal position Index Phase order for slice 2 In-stack Index Frame number 1-6 Frame number Frame number 7-12 Image frames can be sorted/displayed independent of implicit frame order 26

27 Why do we need concatenations? An image may be too big for DICOM indexes, media or database storage A pseudo real-time transfer of a stream of images What is a concatenation? set of image objects in the same series with the same dimension indexes uniquely identified with a UID contained image objects have the same Instance Number 27

28 Legend: Fixed Header Per-frame header Dimension data (not on scale) Pixel data (not on scale) BH An object may be split up into two or more SOP Instances, keeping the same concatenation UID 28

29 Relates the pixel value to the actual value and unit it represents (e.g., velocity in mm/sec) Modality LUT VOI LUT P LUT Display Stored Values Real Value LUT Real world value Value Unit Real World Value LUT Data (0040,9212) or Real World Value Intercept and Slope attributes Measurement Units Code Sequence (0040,08EA) 29 BH

30 non perfused stroke areal time Stor ed valu es Real World Value Slope (0040,9225) Real World Value Intercept (0040,9224) RW values signal delayed perfusion BH time-to-peak map 30

31 Functional Color on Anatomic Grayscale Images Range of Stored Values to be mapped to grayscale Modality LUT VOI LUT P- LUT Mapped to gray level RGB values by display device Largest Monochrome Pixel Value R G B + Color Display Range of Stored Values to be mapped to color Palette Color Number of entries... Grayscale image base with functional color (Palette Color) New image display pipeline Allows gray-scale window/level without changing colors Frame can be displayed as just grayscale if pipeline/color not supported 31 BH

32 Value Mapping: VOI, Color (by paradigm), Real World (Z) Pixel Values Anatomic Reference VOI LUT Grayscale Window/Level Z=5.1 No Z Z=4.9 Z=5.1 Left Motor Paradigm Right Motor Paradigm Color Map Color Map Z-score Map Z-score Map Language Paradigm Color Map Z-score Map Mappings to show Colors and Real World values is extensive 32 BH

33 NEMA Committee for the Advancement of DICOM authorized in May 2002 the development of a test/demonstration tool implementing portions of Supplement 49 funded by MR vendors and others Basic Goals: Education of the user community Feasibility of implementation Promotion of early implementation and adoption Demonstration of reference software and a test tool Find areas of the standard that needed to be clarified Provide newly encoded reference images for discussion and early testing Allow vendors to test out their implementations (send/receive) before testing with other vendors PixelMed Publishing was awarded the contract after an RFP/Bid cycle 33 BH

34 Sample images and spectroscopy objects, including: Single original and derived images Images with minimal attributes Images with shared and per-frame varying attributes Images with stacks, dimensions and concatenations Viewing Tool, including Read images, spectroscopy objects and DICOMDIR from files Receive and send images and spectroscopy objects across the network Query and retrieve images and spectroscopy objects across the network Display multi-frame images and spectra in implicit and by dimension order Display values of common, shared and per-frame varying attributes Validation Tool To check whether or not objects conform to the standard 34 DC

35 100% Pure Java Portable across Windows, Linux, Solaris, Mac OS X, etc. JRE or greater On a fast PC with adequate memory, sufficient to display multi-frame objects of 512x512 of several hundred MB in size Takes advantage of Java internationalization - supports all DICOM character sets Re-use of existing freely available pure Java components Hypersonic SQL database Sun XML pack PixelMed Publishing DICOM toolkit (parsing, network, display code) Validation tool New approach - XSLT rule set for validation of complex conditions, with rules derived (also using XSLT) from XML representation of DICOM object definitions 35 DC

36 The tool will become available to the funding participants in two phases: Jan / Feb 2003 phase 1 September 2003 phase 2 (full version including spectroscopy). The tool will also become available in the public domain in

37 New DICOM Objects for MR: will enhance interoperability increase cross system functionality reduce transfer time The benefits described in this presentation will only be visible if and when: MR (and CT) scanners DICOM workstations PACS systems will change to support the new MR (and CT) DICOM objects. Hospitals and Clinics need to prepare for this. 37

38 Implementation aspects will be discussed at SPIE 2003 Moderators: SPIE Medical Imaging All day workshop Monday, February 17th, 2003 San Diego Charles Parisot, Chair, DICOM Advancement Committee, NEMA Kees Verduin, Chair DICOM WG16 With the contribution of leading MR DICOM experts. 38

39 DICOM supplement 49 Enhanced MR Image Storage SOP Class is published by NEMA at: ftp://medical.nema.org/medical/dicom/final/sup49_ft.pdf It will be an integral part of the DICOM 2002 standard. This presentation is posted on: 39

40 Images for this presentation were kindly provided by: GE Medical Systems Philips Medical Systems Siemens Medical Systems The slides of this presentation may be quoted if reference and credit to DICOM WG-16 is properly indicated. 40