Based on a paper which will appear on Med. Phys. Issue of Dec. 05 Introduction Contrast cancellation algorithm Experimental setup Experimental images: acquisition and correction Simulated images: MCNP Image analysis and SNR calculation Results and discussion Conclusions Contrast cancellation technique applied to digital X-ray imaging using silicon strip detectors

Mammography and dual energy radiology Digital mammography: Allows image post-processing Allows use of dual-energy techniques DUAL ENERGY = exploit the different energy dependence of the absorption coefficients of different materials Limitations in mammographic images: Small signal from pathologic tissues superimposed on the high contrast from non-uniform structure of healthy (glandular+fat) tissues. Large number of false positives. Dual energy techniques allow to suppress the cluttered background Not yet evolved into routine clinical examination due to limitations of conventional imaging systems. Dual kVp and double detector approaches require high doses Synchrotron not available in hospitals

Dual energy projection algorithm GOAL: cancel the contrast between 2 chosen materials Suited for dual energy mammography: remove the contrast between the two healthy tissues (glandular and fat), enhancing the contrast of the pathology PROCEDURE: Express the mass attenuation coefficient μ of a material  at a given energy E as a combination of the coefficients of two basis materials  and  Measure the logarithmic attenuation M = μ ξ t ξ of the material  of thickness t ξ at two different energies: low (E l ) and high (E h )  A 1 and A 2 represent the thicknesses of the two basis materials which would provide the same X-ray attenuation as material ξ.

C  C 90°  M1M1 R 1   M2M2 2 If a monochromatic beam of intensity I 0 goes through material ξ which is partly replaced by another material ψ … I0I0 I1I1 I2I2 ξ ψ … then the vertices of vectors M 2 (material ξ ) and M 1 (mat. ξ + ψ ) lie on a line R defined only by the properties of materials α, β, ξ and ψ. Projecting along direction C, orthogonal to R : … both M 1 and M 2 will project to the same vector, canceling the contrast between materials ξ and ψ (projection angle  = contrast cancellation angle) A2A2 A1A1 Dual energy projection algorithm Logarithmic attenuation M in a given pixel represented as a vector of: components A1 and A2 in the basis plane modulus proportional to the gray level of that pixel & Alvarez, Macovski, Phys. Med. Biol. 21 (1976) 733 ; Lehmann et al., Med. Phys. 8 (1981) 659

Experimental setup To implement dual energy imaging we need: a dichromatic beam a position- and energy-sensitive detector Quasi-monochromatic beams ordinary X-ray tube + mosaic crystal monochromator Advantages with respect to dual kVp imaging Dose reduction Single exposure Advantages with respect to truly monochromatic synchrotron radiation Cost, dimensions Avilability in hospitals Linear array of silicon microstrips + electonics for single photon counting Detector exposed directly to X-rays best spatial resolution Full custom ASIC for single photon counting Does not add noise to the signal Infinite dynamic range Dual energy performance: 2 discriminators and counters per channel Scanning required to build 2D image

Experimental setup: mammographic beam Bragg Diffraction on Highly Oriented Pyrolitic Grafite Crystal W anode tube 1st and 2nd Bragg harmonics  E and 2E are obtained in the same beam Collimator

Experimental setup: mammographic beam 3 energy pairs used (16-32, and keV): Beam spectrum after mosaic crystal Beam spectrum after 3 cm of PMMA absorber

Experimental setup: detecting system Chip RX64 → counts incident photons on each strip of the detector 4 cm 6.4 mm 10 strip = 1 mm micro-bondings Silicon microstrip detector each strip is an independent detector which gives an electric signal when an X- ray photon crosses it and interacts with a silicon atom Knowing from which strip the electric signal comes from, the position of the incoming X-ray photon is reconstructed.

Detector efficiency Front geometry Strip orthogonal to the beam 70  m of Al light shield Edge-on geometry Strip parallel to the beam 765  m of inactive Si Better efficiency for E > 18 keV Efficiency calculation X-ray absorbed if interacts in passive regions X-ray detected if makes photoelectric effect in active regions

Front-end electronics Detector Full custom ASIC for single photon counting, in 2 versions: 1)RX64: Single threshold 1 discriminator and 1 counter for each channel 2)RX64DTH: Double threshold 2 discriminators and 2 counters for each channel

Cu K  Mo K  Ge K  Rb K  Ag K  Sn K  Ag K  Mo K  Sn K  SystemTpTp GAIN  V/el. ENCEnergy resolution 6 x RX640.7  s64≈170 el.≈0.61 keV 6 x RX64DTH0.8  s47≈ 200 el.≈0.72 keV 241 Am source with rotary target holder (targets: Cu, Rb, Mo, Ag, Ba) Cu-anode X-ray tube with fluorescence targets (Cu, Ge, Mo, Ag, Sn) System calibration

Mammographic phantom  S. Fabbri et al., Phys. Med. Biol. 47 (2002) 1-13 Three component phantom: polyethylene (PE), PMMA and water to mimic adipose, glandular and cancerous tissues in the breast E  _fat  _gland  _canc E μ_PEμ_PMMAμ_water

Data taking conditions Exposures: RX64: double exposure each exposure with a different discriminator threshold RX64DTH: single exposure high and low energy withtin the same exposure Detector thresholds: set on the basis threshold scans of the dichromatic beam 40 profiles (20 in each of the 2 halves of the phantom)

Image processing Low thr.High thr. Measured (raw) 16 keV32 keV HE and LE imagesCorrect for: 1.Pixels with huge n. of counts (bad counter conversion) 2.Dead pixels 3.X-ray beam fluctuations 4.Subtract high threshold image from low threshold one 5.Correct for spatial inhomogeneities of beam and detector extracted from flat-field profiles

1= PMMA 2=water 3=PE 4=(water+PE) Experimental images at the 2 energies Image at 16 keV water polyethylene PMMA Image at 32 keV PMMA polyethylene Water not visible

Simulation with MCNP 1=detector 2=PMMA 3=water 4=PE MCNP-4C simulation with ENDF/B-VI library Generate monochromatic (instead of quasi- monochromatic) X-ray beam Track photons and electrons through the phantom and the detector (including the inactive region in front of the strips) Store the energy deposition in each strip Fill histograms of counts vs. strip number Top View Side View

Image at 32 keVImage at 16 keV MCNP images at the 2 energies 1= PMMA 2=water 3=PE 4=(water+PE) water polyethylene PMMA polyethylene Water not visible

Experiment vs. simulation water polyethylenePMMA Phantom profiles at 16 keV Water not visible Phantom profiles at 32 keV

Contrast and SNR calculation Contrast between 2 materials: Areas of 5x5 pixels considered Noise contrast: divide the PMMA area in sub-images of 5x5 pixels 5x5= compromise beteeen large number of photons in each subimage and large number of samplings in the PMMA area SNR (Signal to Noise Ratio): SNR = 5: detectability threshold for human obserevers

SNR for projected images Calculate pixel-by-pixel hybrid images with the formula: Plot SNR vs. projection angle  ExperimentSimulation Contrast cancellation angle: value of  which makes SNR=0 Same shape of SNR vs  for experimental and MCNP images SNR values slightly higher in the simulated images

Projection angle with max. SNR The projection at the contrast cancellation angle do not maximize SNR Best visibility may be achieved projecting at angles where the drawbacks of some residual cluttered background are compensated by a larger SNR At ≈29° the noise has a minimum and SNR a maximum ExperimentMCNP

PMMA-water cancellation ExperimentMCNP Only PE (fat) details are visible Angle (deg)SNR TheoMCExp RX64 Exp DTH MCExp RX64 Exp DTH

PE-water cancellation Only PMMA (glandular) details are visible Angle (deg)SNR TheoMCExp RX64 Exp DTH MCExp RX64 Exp DTH ExperimentMCNP

PE-PMMA cancellation Only water (cancer) details are visible Angle (deg)SNR TheoMCExp RX64 Exp DTH MCExp RX64 Exp DTH ExperimentMCNP

SNR vs. size of sampling area for noise Larger sampling area:  smaller noise fluctuations (more photons in each area)  larger SNR Minimal size of a water (cancer) detail which can be detected in the hybrid image ≈ 30 pixels Detectability threshold (SNR>5)

Conclusions Single exposure dual energy mammographic imaging performed using: Compact source of quasi monochromatic X-rays Silicon strip detectors with single photon counting capability 3 component phantom Experimental images: Demonstrate the feasibility of contrast cancellation between 2 materials, thus enhcancing the visibility of small details in the third one Suited as a second examination in the cases where the conventional mammography leads to an uncertain diagnosis Simulated images: Reproduce the main features observed in the data Possibility of using MCNP to investigate the performance of our imaging system in more realistic cases (more realistic phantoms, biological tissues…) Future: Optimize X-ray source and detector to guarantee clinical viability of this technology: Increase counting statistics, especially at high energy Study tube heating limitations, parallax errors Dose evaluation Introduce beam energy smearing in the MCNP simulations

Dual energy mammography

E  keV: Signal from cancer tissue deteriorated by the adipose tissue signal E  keV Cancer tissue not visible, image allows to map glandular and adipose tissues Dual energy mammography