Dual Energy CT for Density Measurements

Norbert J. Pelc, Sc.D. Departments of Radiology and Bioengineering Stanford University

What is in a voxel?

Acknowledgements Souma Sengupta, Uri Shreter and Robert Senzig: GE Healthcare Thomas Flohr, Bernhard Schmidt and Karl Krzymyk: Siemens Healthcare Gretchen House, Mark Olszewski and Michael Decklever: Philips Healthcare Dominik Fleischmann, Carolina Arboleda and Adam Wang: Stanford University Cynthia McCollough: Mayo Clinic

Attenuation coefficients depend on photon energy

CT number depends on: • inherent tissue properties (chemical composition, density) • x-ray spectrum • administered contrast media

attenuation coefficient

100

?

10

iodine 1

bone water

0.1 35

55

75

95

115

135

photon energy

Can we be more specific? www.uhrad.com/ ctarc/ct153b2.jpg

use CT measurements at multiple energies for material specificity and improved quantitation

Motivation “Two pictures are taken of the same slice, one at 100 kV and the other at 140 kV...so that areas of high atomic numbers can be enhanced... Tests carried out to date have shown that iodine (Z=53) can be readily differentiated from calcium (Z=20)”. G.N. Hounsfield, BJR 46, 1016-22, 1973. Genant et al, Inv Radiol, 1977.

Single energy CT:

“water” beam hardening correction

Beam hardening

14

Accurate for “water-like” materials of any density

14

12

water

12

10

Methods: MLE is accurate polynomials are fast: p = a1L + a2L2 + a3L3 + …

10

ln(I0/I)

8

6

ln(I0/I)

water

8

6

4

2 4

Provides accurate densities if Zeff is known and uniform

2

0 0

2

4

6

8

line integral

10

12

14

0 0

2

4

6

8

10

line integral

12

14

Single energy CT:

“Water” beam hardening correction

Beam hardening

14

Correction fails with materials of very different Zeff

water 10

ln(I0/I)

Results in local and distant errors and artifacts

12

8

calcium 6

4

2

0 0

2

4

6

8

10

12

14

80kVp

line integral

Courtesy of Uri Shreter GE Healthcare

Single energy CT:

“Water” beam hardening correction

140 kVp Conventional CT Hoxworth Courtesy of Dr. Joseph Mayo Clinic Arizona

Courtesy of Uri Shreter GE Healthcare

OUTLINE

14

Correction fails with materials of very different Zeff

12

water

ln(I0/I)

10

Results in density errors and artifacts

8

calcium 6

4

2

Multi-pass corrections can help if materials and distribution are known Can we do better?

0 0

2

4

6

8

10

line integral

12

14

• Physical principles of multi-energy x-ray measurements • Signal processing • Quantitation opportunities and challenges • Data acquisition • Summary

I01 I02 energy E1 E2 water bone

tb

unknown thickness two known materials

100.0

E1

I01 I02

E2

energy E1 E2

10.0

water

linear attenuation coefficient (cm-1)

linear attenuation coefficient (cm-1)

unknown thickness two known materials cortical bone

water

1.0

bone

tb

0.1 10

20

50

100

200

100.0

E1

water

I1 = I01 e-(w1tw+ b1tb) I2 = I02 e-(w2tw+ b2tb) solve for tw and/or tb

dual energy x-ray absorptiometry (DEXA)

cortical bone

1.0

•

•

0.1 10

photon energy (keV)

I1 I2

E2

10.0

20

50

100

200

photon energy (keV)

I1 I2

tb = A{ ln(I01/I1) - (w1/w2)(ln(I02/I2)} scale for lost bone signal

subtract water

makes the water contribution at E2 match that at E1

material analysis with absorptiometry • 2 energies

2 materials

• can we generalize this? N energies for N materials? • limitation: two strong interaction mechanisms

2 energies materials

2

Compton scattering and photoelectric absorption Barring a K-edge in the spectrum, the energy dependence of each is the same for all elements!!

basis material decomposition: • barring a K-edge:

Basis material decomposition 1000

any material acts like a combination of pure photoelectric and Compton any material can be modeled as a weighted sum of two other materials

100

Cu O Ca Cu Ca'

10

Ca 1

.61*O + .04*Cu

O 0.1 0

Basis material decomposition

20

40

60

80

100

120

140

basis material decomposition: • barring a K-edge:

I0 .04 M grams of Cu .61 M grams of O

I0 M grams of Ca

= I

I

Indistinguishable at any x-ray energy above their Kedge Common “basis materials”: iodine and water, aluminum and plastic

any material acts like a combination of pure photoelectric and Compton any material can be modeled as a weighted sum of two other materials “basis material” decomposition in any projection measurement, we can only isolate two materials

• Material parameters: effective atomic number and electron density amounts of two basis materials can be measured using 2 x-ray energies

linear attenuation coefficient (cm-1)

K-edge subtraction 100.0

• reconstruct images in the normal manner, and combine HU images

E1 E2

10.0

water

cortical bone

easy to implement iodine

• combine projection data prior to reconstruction

1.0

0.1 10

20

50

100

200

photon energy (keV)

Very specific material information 2 narrow spectra, or 3 spectra

Dual energy processing ~80 kVp

iodine contrast

“water” contrast

Dual-energy processing

somewhat more difficult requires aligned projections enables “exact” beam hardening correction

Dual energy processing ~80 kVp

monochromatic 55 keV simulation comparable to ~ 80 kVp

~140 kVp

iodine contrast

“water” contrast

monochromatic simulation comparable to 80/150 kVp

Dual energy processing ~140 kVp

100

attenuation coefficient

~80 kVp

E2

~80 kVp

~140 kVp

iodine image (water cancelled)

water image (iodine cancelled)

10

iodine 1

water

0.1 35

iodine image (water cancelled)

E1

Dual energy processing

55

75

photon energy

Iodine image = a • (Image80 - b • Image140) restore reduced iodine signal

95

Water image = c • (Image80 - d • Image140)

amplify 140 kVp water signal

Virtual non-contrast (VNC) image

Dual energy processing ~80 kVp

~140 kVp

optimal combination (“mixed” image)

iodine CNR=7.9

iodine CNR=3.8

iodine CNR=10

water SNR=67

water SNR=71

iodine image

water image (VNC)

combined image has high SNR

material cancelled images have increased noise SNR=3.4

SNR=37

Noise depends on dose allocation

Noise ~80 kVp

~140 kVp

iodine image

Iodine image = a • (Image80 - b • Image140) iodine image

SNR=3.4

water image

SNR=37



=

a2

•

(80

+

b2

•

140)

depends on dose allocation to the 80 kVp and 140 kVp images

water image

dose allocation that maximizes iodine SNR SNR=3.4

SNR=37

iodine image

water image

80 kVp dose, 140 kVp dose same total dose SNR=1.3

SNR=34

Dual energy processing

Dual energy Basis material decomposition ethanol

LL = ln(I0L/IL), LH = ln(I0H/IH) mA, mB = amounts of basis materials

acrylic

With monochromatic beams, L’s are linear functions of m’s, so mA = a0LL + a1LH, mB = b0LL + b1LH With polychromatic beams, functions are nonlinear. Approaches: 1) iterative solutions (e.g., MLE) accurate but slow 2) polynomial approximation mA = a0LL + a1LH + a2LL2 + a3LH2 + a4LLLH+… accuracy depends on polynomial order, dynamic range, etc.

Dual energy Basis material decomposition water image

Iso-intense @ 140 kVp

acquire 80 kVp

calcium image

ethanol + NaCl

Iso-intense @ 80 kVp

calcium image

140 kVp

basis material decomposition

water image

fully characterizes object

Image Based Methods  Calculation of Energy-Selective Images: Noise @ different energies

fully characterizes object

Monoenergetic: 55 keV

Monochromatic energy is a display parameter.

Monoenergetic: 68 keV

Similar to “mixed” image Contrast and SNR vary 1.25*Ca + .22*Water with selected energy.

.78*Ca + .19*Water

B: Heismann, B. Schmidt, T. Flohr

Short course 987 SPIE Medical Imaging Conference 2010

Application of monoenergetic images

Beam hardening correction If basis material assumption holds (e.g., no K-edge materials), • nonlinear decomposition exactly handles polychromaticity • exact beam hardening correction • projection domain processing is preferred

• image interpretation high SNR, tissue characterization • extrapolation to higher energies MV, SPECT, PET

Discovery CT750 HD

Monochromatic CT from HDCT projectionbased recon

Posterior Fossa Artifact Reduction

Potential for beam hardening streak-free images 80kVp

Clinical Value

140kVp

•Spectral or Monochromatic images have a reduced beam hardening effect vs polychromatic reconstructed images

Projection based

Image based

Water

Aluminum

Water

Aluminum

•Beam hardening artifacts can obstruct the the clinician’s interpretation of important brain anatomy

Monochromatic

•Notice the visualization improvement of the brain anatomy between the petrous bones 140 kVp Conventional CT

75 keV

Projection based MD reduces beam hardening Courtesy of Uri Shreter, GE Healthcare

Images Courtesy of Dr. Joseph Hoxworth Mayo Clinic Arizona

Posterior Fossa Streak Artifacts are Removed / Lesion Verification

Monochromatic CT from HDCT projectionbased recon Potential for beam hardening streak-free images 80kVp

140kVp

Projection based

Image based

Water

80 kVp

100 keV

Aluminum

Water

Aluminum

Monochromatic

MD Iodine

Projection based MD reduces beam hardening Images Courtesy of Dr. Amy Hara, Mayo Clinic, Scottsdale, AZ

Beam hardening correction If basis material assumption holds (e.g., no K-edge materials), • nonlinear decomposition exactly handles polychromaticity • exact beam hardening correction • projection domain processing is preferred • starting with image domain data is possible

Courtesy of Uri Shreter, GE Healthcare

Dual energy processing accurate beam hardening built-in

material cancelled images monoenergetic images Maab et al, Med Phys, 2011

Lehmann et al: Med Phys 8, 659-67, 1981.

Quantitation • Decomposition yeilds local density of basis materials • Can convert basis materials – i.e., calculate density of any two materials • Quantifies actual materials only if they match basis materials • Suppose bases are a and b, but voxel has a and c Truth: (ma, mc) Appearance: (ma,0)+ (ma’, mb’) = ([ma+ma’] , mb’) • (Local) errors results if the materials are wrong (e.g., tissue vs. water vs. fat) • Virtual non contrast ≠ precontrast image

Scatter ideal:

mA = a{LL – (BL/BH) LH}

w/ scatter:

mA’ = mA – a{ln(1+SPRL) - (BL/BH) ln(1+SPRH)}

20 cm water absorber

Ghafarian et al, IEEE Nuclear Science Symposium, 2007.

Scatter

three known materials water

bone

1 = w1fw + b1fb + I1fI iodine

2 = w2fw + b2fb + I2fI fw+ fb + fI = 1

D. Tran and D. Fleischmann

Vetter et al, Med Phys 1998.

solve for fw , fb , fI

three materials water

fat

bone

• Bone mass from single energy CT is inaccurate if fat fraction is unknown

Goodsitt et al: Inv Radiol 22, 799, 1987.

three materials water

fat

bone

• Bone mass from single energy CT is inaccurate if fat fraction is unknown • DE can be more accurate but is less precise

Three known materials dual energy CT Reliable separation requires large (R1 - R2)2 calcium iodine

where R = high/low, depends on material and energies Works best for one high Z and one lower Z material, and very different x-ray energies Kelcz et al: Med Phys 6, 418-25, 1979.

Choice of photon energies

Photon counting detectors

• DE, EL, EH • very critical for SNR efficiency, separation robustness, etc. • implementations

• main challenges:

– different kVp and/or filtration – layered detector – photon counting with energy analysis

count rate capability, translates to scan speed imperfect and count-rate dependent energy response

• very promising in the long term but not yet ready for clinical use

Spectral separation • very critical for SNR efficiency, separation robustness, etc. • implementations photon counting with K-edge filter* photon counting with energy analysis* different kVp and filtration different kVp layered detector

better spectral separation and dose efficiency

Dual energy implementations • Sequential scans at different kVp motion sensitivity > 50% Trot

• Two sources at 90º on the same gantry some motion sensitivity (~ 25% Trot ?)

• Switching kVp within a single scan • Energy discriminating detectors

better immunity to motion

layered detector, photon counting * assumes good energy response

contrast material sensitivity very low concentration CNR • optimized single energy CT advantage: highest CNR limitation: inhomogeneous background

• temporal subtraction (post - pre) advantage: perfect background suppression (w/o motion) limitation: motion misregistration lower CNR at the same dose

• rapid dual energy CT advantage: motion immunity limitations: nonuniform Z background? lowest CNR at the same total dose

Summary • more material specificity than single energy CT(e.g., average material properties, material cancelled images) • perfect beam hardening correction (prerecon) effective monoenergetic images, more accurate RTP and PET attenuation correction

• significant challenges but also many opportunities

Summary • virtual pre-contrast image

Thank You

perfectly registered and simultaneously acquired beware of noise propagation. Separate optimized scans probably have lower total dose

• isolate contrast media from calcified plaque difficult, especially for small amounts of either

• lower dose? not likely, compared to optimized protocols

• molecular imaging? I don’t think so

Dose • Two scans. Do we have to double the dose? Depends on the goal Start by splitting the dose to both energies

Dose higher Z task

thicker object

image quality at given dose

beam energy both spectra of DE system can’t be optimal!

Summary of commercial systems

Dose • Two scans. Do we have to double the dose? Depends on the goal Start by splitting the dose to both energies

• DE not likely to provide the same noise performance as optimized single energy protocols • penalty, if any, is low

Dose comparison water image optimal combination (virtual non-contrast image)(post contrast image)

SNR=37

pre-contrast dose ~ 0.15D

iodine CNR=10

postcontrast dose ~ 0.79D

DUAL ENERGY 55/80 keV acquisition ideal dose allocation (~1:1) total dose = D CONVENTIONAL 55 keV pre/post contrast scans total dose 0.94 D

Under ideal conditions, DE scan has slightly higher dose

• Siemens: two sources, different kVp and filtration, image-based processing • GE: single source with rapid switching, same filter for both kVps, projectionbased processing • Philips: sequential scans at different kVp, same filtration, image-based processing • Lots of R&D work

Dose comparison water image optimal combination (virtual non-contrast image)(post contrast image)

SNR=34

pre-contrast dose ~ 0.13D

iodine CNR=5.5

postcontrast dose ~ 0.23D

DUAL ENERGY 55/80 keV acquisition poor dose allocation (~1:8) total dose = D CONVENTIONAL 55 keV pre/post contrast scans total dose 0.36 D

Under non-ideal conditions, DE scan has much higher dose

Applications of Dual Energy CT Another image based application : characterization of kidney stones

Principle of Dual Energy CT – Image Based Evaluation Each material is characterized by its „Dual Energy Index“ x80 and x140 are the Hounsfield numbers at 80 kV and 140 kV, resp.

HU at 80 kV

high Z

low Z HU at 140 kV

Material

DEI

Bone

0.1148

Liver

0.0011

Lung

-0.0021

Soft Tissue

-0.0052

Skin

-0.0064

Proteins

-0.0087

Fat

-0.0194

Gall fluid

-0.0200

based on subtraction, noisy.

Dual energy CT can measure chemical composition!  Uric acid stones can be differentiated from other renal calculi Courtesy of University Hospital of Munich - Grosshadern / Munich, Germany

Image Based Methods

Image Based Methods

 Modified 2-material decomposition: Separation of two materials  Assume mixture of blood + Iodine (unknown density) and bone marrow + bone (unknown density) Separation line

600 Iodine pixels 500 400 Bone pixels Blood+Iodine 300 Marrow+bone 200 Soft 100 tissue Blood Marrow 0 -100 -100 0 100 200 300 400 50 600 HU at 140 kV 0 at low HU numbers Short course 987  Additional postprocessing to improve classification B: Heismann, B. Schmidt, T. Flohr SPIE Medical Imaging Conference 2010 HU at 80 kV

Courtesy of B. Krauss, B. Schmidt, and Th. Flohr, Siemens Medical Solutions

 Modified 2-material decomposition: Separation of bone and Iodine  Automatic bone removal without user interaction  Clinical benefits in complicated anatomical situations:  Base of the skull  Carotid arteries  Vertebral arteries  Peripheral runoffs

Courtesy of Prof. Pasovic, University Hospital of Krakow, Poland B: Heismann, B. Schmidt, T. Flohr

Short course 987 SPIE Medical Imaging Conference 2010

noise in processed images low energy

high energy

material 1

high, correlated noise material 2

usually high noise material cancelled images

Kalender, Computed Tomography, Publicis Corporate Publishing, 2005.

equivalent monoenergetic images

Dual kVp, dual filtration

Principle of Dual Energy CT Data acquisition with different X-ray spectra: 80 kV / 140 kV

85 kVp 0.1 mm erbium Mean Energy: 56 kV

can be low noise

76 kV

135 kVp 1.5 mm bronze

Tube 1 Tube 2

Different mean energies of the X-ray quanta

• switched filtration improves separation • different mA helps apportion dose

Courtesy of B. Krauss, B. Schmidt, and Th. Flohr, Siemens Medical Solutions

Lehmann et al: Med Phys 8, 659-67, 1981.

Dual Source Challenge: Inconsistent scans

syngo Dual Energy - Principle of Dual Energy

Moving Objects

SOMATOM Definition Flash

140kV kV + SPS SS11::80 80kV kV SS22::140

Moving Phantom Simulation Ideal

4

x 10 x 104

quanta of quanta number number of

15 15

80 80kV kV 140 140kV kV 140 kV + SPS

10 10 Does not see movement

Dual Source system 55

00 SPS= Selective Photon Shield

50 50

100 100

150 150

photon photonenergy energy(keV) (keV)

Dual energy implementations • Sequential scans at different kVp motion sensitivity > 50% Trot

Courtesy of R. Senzig, GE Healthcare

Rapid kVp switching Dual energy CT • Requires fast generator and detectors

• Two sources at ~90º on the same gantry some motion sensitivity (~ 25% Trot)

• Switching kVp within a single scan1, 2

• Dose allocation controlled by dwell time • Difficult to switch filters

1. Lehmann et al: Med Phys 8, 659-67, 1981. 2. Kalender et al, Med Phys 13, 334, 1986.

Courtesy of Uri Shreter, GE Healthcare

Layered detector

Multilayer Detector

• simultaneous dual energy sensing • relatively poor spectral separation

X-Rays Photons 100%

~50% SCINT1

SCINT2

Low Energy Raw data

~50%

E1 image

+ High Energy Raw data

E2 image

---------------------------------------

= Weighted combined Raw data

CT image Carmi R, Naveh G, and Altman A: IEEE NSS M03-367, 1876-78, 2005

81

Investigational device

Dose efficiency

Dual Layer Detectors - Challenges  Complex technical realization  Reduced dual energy performance compared to dual kV

3 component decomposition, each technique optimized Relative std dev

24.1

36.2

 Performance depends on scintillator types and thicknesses  Dual energy performance less than 70% compared to dual kV technology (same radiation dose!)

4X overall difference in dose efficiency Kelcz et al: Med Phys 6, 418-25, 1979.

Courtesy S. Kappler, Siemens Healthca Short course 987 Page 84

B: Heismann, B. Schmidt, T. Flohr

SPIE Medical Imaging Conference 2010

Photon Counting Spectral CT – Detector Principle Absorbed single X-ray photon

(CZT crystal: Cadmium Zinc Telluride)

High Voltage

Discriminating thresholds

Counter 4 Counter 3 Counter 2 Counter 1

Direct conversion material

Pixelated electrodes

Photon Counting Prototype* Early results

Electronics

• Two systems in Philips Research labs • Research system installed at Washington University-Saint Louis in Nov. 2008

Charge pulse Pulse height proportional to x-ray photon energy Stored counts of all energy windows, for each reading time period Counts

Photon energy

Direct- Conversion Detector efficiently translates X-ray photons into large electronic signals These signals are binned according to their corresponding X-ray energies

‘K-Edge Imaging’ w/ Univ Ulm (Radiology Oct 08)

Novel contrast agents targeting fibrin (clots) w/ Wash U

*Works-in-Progress: Pending commercial availability and regulatory clearance

Confidential Investigational device

Dual energy implementations • Sequential scans at different kVp

86

8

syngo Dual Energy - Principle of Dual Energy

SOMATOM Definition

SOMATOM Definition Flash

motion sensitivity > 50% Trot. Helical?

• Two sources at ~90º on the same gantry

Two X-ray tubes, one at 80 kVp, second at 140 kVp

attenuation and material identification

Image Based Methods  Calculate material specific images: 2 materials of unknown density

I0

 Noise amplification in the material specific images!

But what material is it?

2000

Bone

T

1500

HU at 80 kV

Image noise  1000

Bone image

500

Soft tissue

0

I = I0 e-T

-500

Soft tissue image -1000 -1000

-500

0 HU at 140 kV

500

1000 Short course 987 SPIE Medical Imaging Conference 2010

B: Heismann, B. Schmidt, T. Flohr

Three known materials dual energy CT water

bone HUlow

iodine

bone and iodine in water

water + iodine

water + bone identity

Can calculate both iodine and bone. HUhigh Requires known mixing properties mb = A{ HUlow - ( 1,I/  2,I) HUhigh} and consistent water density Kelcz et al: Med Phys 6, 418-25, 1979.

T = ln(I0/ I)

Even more confusing if T is unknown