1. 1 SCAR Annual Meeting 2004 Public meeting of NEMA CT-MR Taskforce Saturday, May 22 nd, 7:30 – 9:00 AM

2. 2 Advances in DICOM will Enhance the clinical operation of CT and MR Kees Verduin, Philips Medical Systems Co-Chair CT-MR Taskforce, chair DICOM WG16

3. 3 Agenda 1.Introduction, who are here? 2.Limitations of current DICOM CT & MR objects. 3.New DICOM standard: clinical benefits 3.New DICOM standard: clinical benefits (these will also be addressed in a Hot Topics lecture by David Clunie at 1:30 pm) 4.Technicalities of the solution 5.Industry and User Challenges, a need for concerted action 6.CT-MR test tool: capabilities and development plan 7.Current industry commitments for participation 8.Questions & Answers

4. 4 Invited parties Vendors for MR & CT Modalities Vendors of PACS systems Vendors of Clinical Workstations Vendors of DICOM toolsets Members of CT-MR Taskforce This Meeting is sponsored by NEMA and SCAR

5. 5 Why Enhanced CT ? New applications such as –Cardiac CT, –Gated studies, –Perfusion CT, –CT fluoroscopy, –Contrast tracking and –Post-processing (e.g. lung cancer screening, colonography); are not supported in an interoperable way by the current standard. Not only are technique attributes insufficient, but also, the organization of increasingly large datasets as single-frame objects is awkward, and limitations of the existing definitions of spatial and temporal attributes are apparent.

6. 6 Why Enhanced MR ? New applications such as –Functional MRI, –Diffusion scans, –Spectroscopy, –Post-processing (e.g. DWI, with color or flow studies with Quantitative results); are not supported an interoperable way by the current standard. Not only are technique attributes insufficient, but also, the organization of increasingly large datasets as single-frame objects is awkward, and limitations of the existing definitions of spatial and temporal attributes are apparent.

7. 7 What is new ? The current new object definitions The current new object definitions: –Enhanced CT Image –Enhanced MR Image –MR Spectroscopy –Raw Data to be followed by XA, PET ….

8. 8 A new set of DICOM objects What is the effect for end-users ? –interoperability at advanced applications level –dissemination of these capabilities into PACS Workstations (raising the users expectations)

9. 9 Clinical Examples Faster arrival of images at point of interest More focus on clinical information Modality sends preferred viewing order Modality adds context information, like image types Workstations/PACS can make use of this information and decide how to present it best.

10. 10 Diffusion Imaging “Diffusion b-values” from 0 to 8000 and an ADC image

11. 11 Diffusion Tensor Imaging data Reconstructed Fiber Maps in the colors as seen by the creator

12. 12 MR Perfusion Imaging time non perfused stroke area Signal delayed perfusion time-to-peak map Real World Value Slope (0040,9225) Real World Value Intercept (0040,9224) RW values Stor ed valu es Quantitative data with Real World Values

13. 13 Cardiac Cine Loops Enables automatic multi-slice / multi-phase display, even for standard workstations

14. 14 Total body Imaging Display the correct image at the correct spot using Stacks and In-stack positions 1 3 4 12354 2 5

15. 15 Functional Brain Imaging 10-60 slices all slices measured in one TR repeated 100-1000 times to get sufficient signal leading to > 60,000 images in one object Store thousands of images in one object and display them in a consistent way using Multi-frame Header and Dimension Module

16. 16 Relative NAA peak-height Ratio of Choline and Creatinine peaks Spectroscopy Imaging

17. 17 Spectroscopy Relations Relation: MR Image Metabolite map Ratio of Choline and Creatinine peaks Relative NAA peak-height

18. 18 The basics of the Enhanced Objects Support of newest applications by New attributes Less ambiguity through Stricter definitions and rules Clear relationships through Reference mechanism Header size reduction through Multi-frame technique Context information from Image Type and Dimensions File size flexibility through Concatenations Functional images with Real World Values Functional images with Palette Color LUT information ALL these improve Interoperability

19. 19 What is really new ? Multi-Frame module with its Shared and Non-Shared header parts Dimensions and Dimension Organizations Coded Object relationships All these provide CONTEXT INFORMATION ! Further: –Real World Values –Grayscale images with Color information

20. 20 Multi-stack Color DimensionsMulti-frame Major Functions Real World Values Concatenations

21. 21 DICOM Header Information Restrictive handling of parameters to ensure interoperability Detailed description of parameters e.g.: –meaning of table position for CT, –image flavor and image type definition, –acquisition type –device information Exposure information and X ray details for CT Bolus application included Synchronization model, e.g. ECG, Respiratory Handling of palette color Annotations encoded in Grayscale Softcopy Presentation State

22. 22 Geometry of CT Acquisition System

23. 23 The Multi-frame properties Whenever possible, all images of a scan-series shall become frames in one object. What is common to all frames, shall not be repeated: Elements that do not change vs. elements that change. These have a different place in the object header. Related elements are defined in functional groups.

24. 24 From Single Image to Multi-frame N Objects, N Headers Pixel data (not to scale) Non-Shared (per-frame) Header Shared Header N Frames, One Header HEADER SIZE REDUCTION = less MB, faster transfer

25. 25 From Single-frame to MultiFrame N Frames, One Header Pixel data (not to scale)Dimension data (not to scale) Per-frame header Fixed Header N Objects, N Headers

26. 26 Performance Opportunities (Single Frame vs MultiFrame) TCP connection & Association establishment is the same Common header information is not repeated –Negligible compared to pixel data size Elimination of the delay between storage requests Opportunity for inter-slice (3D) compression But..it may be extremely implementation-dependent

27. 27 Dataset (attributes+pixels) C-Store response (acknowledgement) C-Store request AssociationAssociation

28. 28 Dataset (attributes+pixels) C-Store response (acknowledgement) C-Store request UIDs Store, parse, check AssociationAssociation DB

29. 29 Dataset (attributes+pixels) C-Store response (acknowledgement) C-Store request UIDs Store, parse, check AssociationAssociation DB

30. 30 Dataset (attributes+pixels) C-Store response (acknowledgement) C-Store request UIDs Store, parse, check AssociationAssociation DB

31. 31 Dataset (attributes+pixels) C-Store response (acknowledgement) C-Store request UIDs Store, parse, check AssociationAssociation DB

32. 32 Dimensions CT/ MR images can have many attributes that change from frame to frame. Dimension ModuleWhen such a change in an attribute is fundamental for the object, this data element can be structured by the creator as part of the Dimension Module. Context InformationIt supplies the Context Information required for a specialized workstation. It also provides an explicit display ordering for the frames in the CT/MR object, that can be used by viewing systems without specific CT/MR application knowledge.

33. 33 Space 5 5 In-Stack Position Stack ID = 1 4 4 3 3 2 2 1 1 Start with a dimension of space. A set of contiguous slices through the heart. Start with a dimension of space. A set of contiguous slices through the heart. Dimensions

34. 34 Temporal Position Index 2 2 1 1 Trigger Delay Time 48 ms 0 ms Space Time 5 5 In-Stack Position Stack ID = 1 4 4 3 3 2 2 1 1 5 5 In-Stack Position Stack ID = 1 4 4 3 3 2 2 1 1 Add dimension of time (delay time from R-wave). Sets of contiguous slices throughout cardiac cycle. Add dimension of time (delay time from R-wave). Sets of contiguous slices throughout cardiac cycle.

35. 35 Temporal Position Index 2 2 1 1 Trigger Delay Time 48 ms 0 ms Space (1) Time (2) 1 1 \ \ 5 5 \ \ 2 2 Dimension Index Values Dimension Index Pointers: 1.Stack ID 2.In-Stack Position 3.Temporal Position Index Dimension Index Pointers: 1.Stack ID 2.In-Stack Position 3.Temporal Position Index 5 5 In-Stack Position Stack ID = 1 4 4 3 3 2 2 1 1 5 5 In-Stack Position Stack ID = 1 4 4 3 3 2 2 1 1

36. 36 Temporal Position Index 2 2 1 1 Trigger Delay Time 48 ms 0 ms Space (1) Time (2) 1 1 \ \ 5 5 \ \ 2 2 Dimension Index Values Dimension Index Pointers: 1.Stack ID 2.In-Stack Position 3.Temporal Position Index Dimension Index Pointers: 1.Stack ID 2.In-Stack Position 3.Temporal Position Index 5 5 1\5\1 In-Stack Position Stack ID = 1 4 4 1\4\1 3 3 1\3\1 2 2 1\2\1 1 1 1\1\1 5 5 1\5\2 In-Stack Position Stack ID = 1 4 4 1\4\2 3 3 1\3\2 2 2 1\2\2 1 1 1\1\2

37. 37 Temporal Position Index 2 2 1 1 Trigger Delay Time 48 ms 0 ms Space (2) Time (1) 2 2 \ \ 1 1 \ \ 5 5 Dimension Index Values Dimension Index Pointers: 1.Temporal Position Index 2.Stack ID 3.In-Stack Position Dimension Index Pointers: 1.Temporal Position Index 2.Stack ID 3.In-Stack Position 5 5 1\1\5 In-Stack Position Stack ID = 1 4 4 1\1\4 3 3 1\1\3 2 2 1\1\2 1 1 1\1\1 5 5 2\1\5 In-Stack Position Stack ID = 1 4 4 2\1\4 3 3 2\1\3 2 2 2\1\2 1 1 2\1\1

38. 38 Temporal Position Index 2 2 1 1 Trigger Delay Time 48 ms 0 ms Space (2) Time (1) 2 2 \ \ 1 1 \ \ 5 5 Dimension Index Values Dimension Index Pointers: 1.Trigger Delay Time 2.Stack ID 3.In-Stack Position Dimension Index Pointers: 1.Trigger Delay Time 2.Stack ID 3.In-Stack Position 5 5 1\1\5 In-Stack Position Stack ID = 1 4 4 1\1\4 3 3 1\1\3 2 2 1\1\2 1 1 1\1\1 5 5 2\1\5 In-Stack Position Stack ID = 1 4 4 2\1\4 3 3 2\1\3 2 2 2\1\2 1 1 2\1\1

39. 39 Object Relationships Referenced Image Sequence Source Image Sequence

40. 40 Multi-modality - Multi-frame Implementations for processing of the generated volumetric data will largely benefit fromimplementations of the concept.Implementations for processing of the generated volumetric data will largely benefit from multi-modality implementations of the multi-frame concept. Common structures may lead to common architecture, which in turn will speed up the implementations.Common structures may lead to common architecture, which in turn will speed up the implementations.

41. 41 The timing dilemma for the Enhanced CT/MR objects For the CT & MR vendorsFor the CT & MR vendors: –Why implement it, while nobody is ready to use it? For the Workstation vendors:For the Workstation vendors: –Why implement it, when no one is creating it? For the PACS vendorsFor the PACS vendors: –Support it, but how to deal with the mix of workstations that can yes/no receive it ?

42. 42 CT/MR Workstation Negotiate Archive Negotiate The negotiation scenario Decide to send New object Negotiate Decide to send its own Old object send its own Old object with private elements Must Decide to Cancel Supports Enhanced SOP Class Workstation Decide to send New object Negotiate

43. 43 Create User awareness If your organization wants to make use of the new capabilities provided by new modality systems, then you better prepare yourself for considerable effort in PACS workstations and CT/MR workstationsIf your organization wants to make use of the new capabilities provided by new modality systems, then you better prepare yourself for considerable effort in PACS workstations and CT/MR workstations

44. 44 Collective support is needed The benefits of enhanced objects as described in this presentation, will only be visible if and when : –CT/MR scanners (creators) –PACS/workstations (receivers) –Archives/PACS systems (intermediaries) will collectively support the new MR and CT DICOM objects will collectively support the new MR and CT DICOM objects.

45. 45 CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation:CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation: What should be done?

46. 46 CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation:CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation: –Prepare the color pipeline What should be done?

47. 47 CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation:CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation: –Prepare the color pipeline –Prepare your databases for large objects What should be done?

48. 48 CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation:CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation: –Prepare the color pipeline –Prepare your databases for large objects –Create your real-world values UI What should be done?

49. 49 CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation:CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation: –Prepare the color pipeline –Prepare your databases for large objects –Create your real-world values UI –Prepare for Dimensions and Dimension Organizations What should be done?

50. 50 CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation:CT/MR vendors, vendors of Workstations & PACS need to prepare for implementation: –Prepare the color pipeline –Prepare your databases for large objects –Create your real-world values UI –Prepare for Dimensions and Dimension Organizations What should be done?

51. 51 Summary The New DICOM MR and CT Objects:The New DICOM MR and CT Objects: –will enhance interoperability –will increase cross system functionality –will reduce transfer time –will provide more context of acquisition information for display systems –will ease extraction of navigation information Participants will benefit from early exposure and privileged testing opportunities before products are released.Participants will benefit from early exposure and privileged testing opportunities before products are released.

52. 52 Further Information More in-depth discussions and presentations about: Relationships between images Concatenations Dimension Organization Real World Value mapping Extended Image Type Color Mapping can be found at can be found at : http://medical.nema.org/dicom/presents.html A new CT Standard

53. 53 Acknowledgement and copyright Images for this presentation were provided by: –GE Healthcare –Philips Medical Systems –Siemens Medical Solutions The slides of this presentation may be quoted if reference and credit to DICOM WG-16 and WG-21 is properly indicated

54. 54 How can vendors get involved ? To get involved in the proposed NEMA Initiative Contact Stephen Vastagh at NEMA: ste_vastagh@nema.org Detailed information: See the Flyer in the back of the room