Digital Image Processing-CR

1. Introduction to CR / DR Digital Image Processing Eamer T&D, Stephane Alric

2. The Digital World Today Media & Entertainment Traffic & transportation Defense & security Medical imaging Education & training Corporate AV Industries & processes Infrastructure & utilities

3. How you get your digital way ? • Computed Radiography • Digital Radiography • Digital Fluoroscopy • Ultrasound • DSA • CT Scanning • MRI • Nuclear Medicine • Computerized Thermal Imaging • etc

4. Analogue & Digital Image Capture General X-ray Mammography Panoramic Mobile X - ray

5. Kodak DirectView CR Systems • No new x-ray unit needed • Workflow is similar to screen film • Accommodate for all radiography applications; GR, mammography, ER, ICU, or other 5

6. What is Computed Radiography? CR is a term used to describe projection radiography Eliminates the need for film as a recording medium. Examinations are acquired using a storage phosphor plate instead of screen/film. ( Photostimulable Luminescence )

7. Computed Radiography (CR) Desired Goals: • Near-Zero Repeated Exams -due to exposure factors (Technique) • Consistency of Results- Image to Image, Day to Day • Multiple sizes of CR Plates -can be used in a conventional Bucky or for Portable exams • Image Accessibility- Multiple Destinations Simultaneously, Always Available • Productivity - Images routed automatically on network Minimal USER INTERVENTION

8. CR Benefits • More latitude to compensate for varying techniques. • Consistentimage density and contrast. • Decreased repeat exams caused by over/under exposure. • Distributed image access. • Ability to produce multiple prints • Reduction in lost films/lost revenue • Image preview for positioning verification.

9. Storage Phosphor CR Cassette Durable cassettes with rigid screen mounting # • Lightweight • No mechanical contact of screen • No screen bending during transport and scanning Designed for long life to reduce operational costs - tested up to 45,000 cycles Available in sizes for a variety of applications: • 35 x 35 cm, 35 x 43 cm, 18 x 24 cm, 24 x 30 cm, 15 x 30 cm (dental panoramic) and 14 x 33 in. (long-length) Available with a variety of screen types: • PQ for general radiography • GP Plus for high resolution chest exams • HR • EHR-M2 for mammography with high-resolution scans with a 50-micron pixel pitch

10. Amplifiers Vs Detector • Storage Phosphor CR • RbCl, BaFBr:Eu2+, BaF(BrI):Eu2+, BaFI:Eu2+, BaSrFBr:Eu2+ • Storage - Conversion • Photostimulable Luminescence • Detective Quantum Efficiency • Pixel – DQE – SNR – MTF, EI • Noise • Read Out - Erase • etc • Intensifying Screen • Calcium tungstate, Gadolinium Oxysulfides, Lanthanum, yttrium • Intensification / Amplification • Phosphorescence - Afterglow • Screen Conversion Efficiency • Speed • Cross Over Exposure • Green or Blue Emitting • etc

11. Storage Phosphors and Screen/Film • Both systems use screens to absorb x-rays • Both systems’ screens have similar structure (small phosphor particles suspended in a binder) • Both systems emit light promptly upon absorption of x-rays (photoluminescence) • Both systems’ screens can be used for thousands of diagnostic exposures • ONLY storage phosphor screens can retain a portion of the absorbed x-ray energy for later read out

12. Attractiveness of Storage Phosphor CR • Extended Exposure latitude (Broad exposure flexibility with a single detector) • Reusable Detector (Reduction in costs for consumables) • Compatibility with current exposing equipment (No major changes to equipment /exposure techniques) • Digital Output (Gateway into the digital Image and Information Management)

13. Patient Radiography Using Computed Radiography Exposure X-Rays Storage Phosphor Screen/Plate Latent image in form of trapped electrons where the photons hit the phosphor plate.

14. Imaging Cycle

15. Photostimulable Luminescence

16. 1.00 0.90 0.80 0.70 0.60 Response 0.50 0.40 0.30 0.20 0.10 0.00 1000 200 400 300 600 700 800 900 500 Wavelength (nm) The spectra of the stimulating and released light used in the phosphor screens. Because these peaks are separate, they enable the stimulated laser light to be filtered from the phosphor screen luminescence. Light into the screen – light out of the screen

17. The Imaging Cycle • Image Processing 1001011010011101

18. Why Image Processing? • Maintain the familiar image characteristics • Provide a similar tonal rendering. • Restore sharpness. • Beyond the familiar • Automatically adjust for x-ray exposure. • Automatically accommodate changes in exposure dynamic range. • Enhance sharpness. • Increase the range of exposures visualized without loss of contrast. (EVP) • Automatically mask collimated areas.

19. Image Processing for Image Quality

20. Image Processing for Image Quality

21. Scan/Erase Aerial Image Hardcopy Lightbox luminance Minification MIM configuration Calibration Pixel value interpretation Softcopy Device MIM configuration Calibration Pixel value interpretation PACs application Ambient Downsampling/resizing Grid detection/suppression (GDS) Stitching (LLI) Perceptual Tone-scale (PTS) Unsharp masking (USM) Enhanced Visualization Processing (EVP) Stitching Noise suppression Laser PMT(s) & Collector Galvo. (fast scan) Transport (slow scan) Electronics Calibration Lin to log conversion Plate Time since exposure Time since last erase Erase lamps Generator Energy spectrum Focal spot Beam modifiers (filters, collimators) Technique Grids Positioning subject CR Image Quality Components Acquire Process Image Display Calibrated pixel values: cv12 = 1000 X log(exposure in mR) + 2000 = 1000 X [log10 (ADC16/65535)+4] -linear from 0.01 to 100 mR (4 decades of exposure) -calibration achieved by adjusting system gain to produce desired response (655 ADC counts/mR).

22. Digital Imaging - CR and DR Digital Image Acquisition Digital Image Processing Digital Image Display

23. Menu Image processing steps • Segmentation • Grayscale remapping • Sharpness restoration • Image Equalization • Collimation masking • Display

24. Why Image Processing ? “Raw” Image Data Four decades of log exposure have been mapped through the “native” response of the display.

25. Digital Image Processing steps Automatic Processing Direct Exposure LUT Collimation Blades 12-bit Image Data Region-of-Interest Segmentation Grayscale Remapping Edge enhancement Body Part Projection EVP Application (Equalization) Black Surround Masking Display Compensation 12-bit DICOM Image “New automatic tone scale method for computed radiography,” L. Barski, R. Van Metter, D. Foos, H-C. Lee, X. Wang, Proc SPIE 3335, 164, 1999.

26. Digital Image Processing steps Automatic Processing Direct Exposure LUT Collimation Blades 12-bit Image Data Region-of-Interest Segmentation Grayscale Remapping Edge enhancement Body Part Projection EVP Application (Equalization) Black Surround Masking Display Compensation 12-bit DICOM Image “New automatic tone scale method for computed radiography,” L. Barski, R. Van Metter, D. Foos, H-C. Lee, X. Wang, Proc SPIE 3335, 164, 1999.

27. Region-of-Interest Segmentation Recognizing the diagnostically relevant regions in x-ray images • Collimation detection • Exclude Foreground • Direct exposure detection • Estimating the anatomical region of interest Direct Exposure

28. Region-of-Interest Segmentation Collimation Detection • Collimation boundary pixels. • Edge profile analysis. • Classify transition segments. • Candidate collimation blades. • Hough Transform. • Analysis (figures of merit): • Candidate configurations. • Select “best” configuration.

29. Region-of-Interest Segmentation Direct Exposure detection • Transition segment analysis • Analysis of line profiles • Background transitions characterized by slope and extent • Spatial correlation of exposure variations • Analysis of background pixel histogram • Accommodate • Radiation field non-uniformity • X-ray scatter • Multiple exposures DIRECT EXPOSURE

30. Region-of-Interest Segmentation Finding the ROI Activity Analysis arm arm lung lung 2 3 4 5 7 8 6 1 spine 1 Activity Histogram Pixel Value Histogram 0.8 0.6 0.4 0.2 0 1100 1300 1500 1700 1900 2100 2300 2500 Pixel Value

31. Region-of-Interest Segmentation Finding the ROI

32. Digital Image Processing steps Automatic Processing Direct Exposure LUT Collimation Blades 12-bit Image Data Region-of-Interest Segmentation Grayscale Remapping Edge enhancement Body Part Projection EVP Application (Equalization) Black Surround Masking Display Compensation 12-bit DICOM Image “New automatic tone scale method for computed radiography,” L. Barski, R. Van Metter, D. Foos, H-C. Lee, X. Wang, Proc SPIE 3335, 164, 1999.

33. Render the diagnostically relevant regions for display • Grayscale remapping • Sharpness restoration (edge enhancement) • Signal equalization • Collimation masking

34. Image Histogram Bones Direct exposure Collimation Soft tissues • The image processing uses several histograms to calculate specific values in the image • Code Value Histogram #Pixels Code value histogram Code values 0 (White) 4095 (black)

35. Perceptually linear grayscale remapping • Render ROI so that… • equal changes in log (exposure) gives equal perceived brightness differences for independent of surrounding brightness . • use a human visual system model Perceived contrasts are equal

36. Grayscale remapping

37. CR/DR GUI – Grayscale remapping

38. CR/DR GUI – Grayscale remapping

39. Automated Grayscale Remapping dmax dmin rp lp • Body Part & Projection • Patient thickness • Contrast • Grids • Technique • Collimation • Positioning • Pathology • Observer Preferences

40. Digital Image Processing steps Automatic Processing Direct Exposure LUT Collimation Blades 12-bit Image Data Region-of-Interest Segmentation Grayscale Remapping Edge enhancement Body Part Projection EVP Application (Equalization) Black Surround Masking Display Compensation 12-bit DICOM Image “New automatic tone scale method for computed radiography,” L. Barski, R. Van Metter, D. Foos, H-C. Lee, X. Wang, Proc SPIE 3335, 164, 1999.

41. Edge enhancement • Compensate for blurring introduced by image capture and display • Enhance image details for improved visibility • Detection of normal or abnormal anatomical structures in a medical image depends largely on the visibility of edges • Edge enhancement increases the contrast of (relevant) edges to aid the viewer in detecting targets • Kodak current method : Unsharp masking

42. Edge Enhancement

44. Digital Image Processing steps Automatic Processing Direct Exposure LUT Collimation Blades 12-bit Image Data Region-of-Interest Segmentation Grayscale Remapping Edge enhancement Body Part Projection EVP Application (Equalization) Black Surround Masking Display Compensation 12-bit DICOM Image “New automatic tone scale method for computed radiography,” L. Barski, R. Van Metter, D. Foos, H-C. Lee, X. Wang, Proc SPIE 3335, 164, 1999.

45. The Contrast / Latitude dilemna EVP GOOD CONTRAST FOR BONE DETAIL AND SOFT TISSUES! • Good contrast for bone details • Soft tissuesdifficult to see due to limited display latitude • Soft tissues well visualized • Bone detail difficult to see due to limiteddisplay contrast How to maximize the range of visible anatomy WITHOUT simultaneously lowering its display contrast?

46. The Contrast / Latitude dilemna EVP GOOD CONTRAST FOR BONE DETAIL AND SOFT TISSUES!

47. The Contrast / Latitude dilemna EVP Low-Freq. Tone Scale EVP High-Freq. local contrast • EVP resolves the contrast-latitude dilemma • Lower frequencies (large areas!) get lower contrast • Higher frequencies (edges, details!) get higher contrast PTONE Tone Scale CVD 4000 3000 2000 1000 0 CVE 2000 3000

48. Equalization (EVP) • Extend range of image exposures that are rendered with full detail contrast • Implement as a spatial frequency decomposition and reconstruction

49. EVP Example (C-Spine)

50. EVP Example (T-Spine)