IMAGE RECONSTRUCTION AND ARTIFACT...
Transcript of IMAGE RECONSTRUCTION AND ARTIFACT...
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IMAGE RECONSTRUCTION AND
ARTIFACT REDUCTION
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Jiang Hsieh, PhD
Chief Scientist, GE Healthcare
Adjunct Professor, Univ. of Wisconsin-Madison
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SINOGRAM
• Measurements from a single view form a horizontal line in a sinogram.
• Different views stack up to form a sinogram
• Sinogram is useful in identifying potential system issues
object cross-section
sinogram
single projection
Detector Channel
Pro
ject
ion
Vie
w
single projection
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Reconstructed
Image
INTUITIVE RECONSTRUCTION
• Each measurement in the sinogram is generated by x-ray attenuated along a line
• Every point along the path has equal probability of contributing to the measurement
• The entire path is “painted” evenly with the measured intensity
• The process is repeated for all views with overlay
• This process is called “backprojection”
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Filtered
Sinogram
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Reconstructed
Image
Original
Sinogram
FILTERED BACKPROJECTION
• Blurry effect can be compensated for by “deconvolution” in either image or sinogram
• Sinogram “deconvolution” is a ramp shaped filter in frequency domain
• Combined with backprojection, the process is called “filtered backprojection”
K(w)
w-w w
|w|actual filter
f(x,y,z)= 𝑹𝟐
𝑳𝟐 𝒙,𝒚,𝜷𝒘(𝜸, 𝜷, 𝜶) 𝒉 𝜸 − 𝜸′ 𝒑 𝜸,𝜷, 𝜶 𝒅𝜸𝒅𝜷
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w
K(w)
-w w
high-resolution
filter
w
K(w)
-w’ w’
low-resolution
filter
FILTER IMPLEMENTATION
• Rectangular window can be modified to shape the frequency response.
• Different cutoff frequencies to create different reconstruction kernels.
RECON FILTER IMPACT
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• Higher frequency generally lead to sharper but noisier image
• For FBP, there is a tradeoff between spatial resolution and noise
StandardBone +
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TARGETED RECONSTRUCTION
• Clinical interests sometimes focus only on a small region inside the object
• If the ROI is smaller than scan FOV, backprojection can be performed over a portion of the filtered projection to form a targeted reconstruction.
filtered projection
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TARGETED RECONSTRUCTION
• Image pixel size changes linearly on the reconstruction FOV.
• Targeted reconstruction can result in better spatial resolution.
Recon at 50cm FOV Interpolated to 10cm FOV Recon at 10cm FOV
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MODEL-BASED ITERATIVE RECONSTRUCTION (MBIR)
Closed-form Solution
FBP
f(x,y,z)= 𝑹𝟐
𝑳𝟐 𝒙,𝒚,𝜷𝒘(𝜸, 𝜷,𝜶) 𝒉 𝜸− 𝜸′ 𝒑 𝜸, 𝜷,𝜶 𝒅𝜸𝒅𝜷 ෝ𝒙𝑴𝑨𝑷 = 𝒂𝒓𝒈
𝒎𝒂𝒙𝒙
𝑷(𝒙/𝒚)
MBIR
Analytical Models
Compare
Measured Raw DataSynthesized Raw Data
Optimization
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CLINICAL EXAMPLEONCOLOGY ABDOMEN/PELVIS 0.61MSV*
MBIR (Veo) FBP
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MBIR
Analytical
Models
Compare
Measured Raw DataSynthesized Raw Data
Optimization
DLIR TrainingMeasured Raw Data
“Reference” Image
DEEP LEARNING IMAGE RECONSTRUCTIONDLIR Inferencing
Compare
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RECONSTRUCTION COMPARISON
FBP IR (ASiR-V) DLIR (TrueFidelity)
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Conventional Extended FOV DL-based Extended FOV
SFOV
Extended FOV
EXTENDED FOV RECONSTRUCTION FOV• For RT applications, desired FOV is larger than the detector SFOV• Conventional technology often fails to produce accurate patient skin line• DL-based algorithm can improve the outcome
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IMAGE ARTIFACTS
• Nature of the X-ray Physics– Beam Hardening– Scatter – …
• Limitations of CT Technology– Helical– Cone Beam– …
• Patient-induced– Motion– Photon Starvation– …
• Operator-induced– Suboptimal protocols– Patient positioning– …
operator
patient
scanner
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NATURE OF PHYSICS: BEAM HARDENING
• X-ray photons in CT are poly-energetic.
• The attenuation, m(E), is energy-dependent.
• The path length changes in objects
• The measured projection is not line integral of m. energy, E
m(E
)
0 cm water 15 cm water 30 cm water
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WATER BEAM HARDENING CORRECTION
• Water beam hardening can be corrected with polynomial expansion of the projections.
no correction with correction
N
n
nn pp
1
'
channel
inte
nsi
ty
ideal
measured
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BONE BEAM HARDENING
• The attenuation characteristics of bone is significantly
different from that of soft tissue.
• Similar observation can be made for contrast agents. 0.1
1
10
100
1000
10000
0 50 100 150
m (
1/c
m)
energy (keV)
Water BH Error Bone BH
Calcium
Water
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80 kVp
DUAL ENERGY CORRECTION
140 kVp DECT
• Dual energy scans the object with two different kVps.
• Use of projection-space material decomposition can effectively remove BH
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without MAR
with MAR
• Metal inside patient body is a major source of artifact
• Metal objects induce beam-hardening and photon starvation
• MAR algorithm is an iterative correction approach to restore
soft-tissue in the image
METAL ARTIFACT REDUCTION (MAR)
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primary photons
object
scattered
photons
collimator
detector
input x-ray photons
SCATTER• Scatter increases with exposure volume.• Post patient collimator can be used effectively to reject the scatter.• A small portion of the scattered radiation can reach the detector.
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PHANTOM TEST
160mm System with 1D Collimator
160mm System with Focusing 2D Collimator
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• The amount of attenuation increases exponentially with path length.
• At low signal level, the noise in the projection is no longer dominated by the x-ray photon.
• Convolution filtering operation further amplifies the noise and leads to streak artifacts.
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patient scan example
50cm FOVPHOTON STARVATION
LeI
I m-0
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original
adaptively filtered
FBP
MBIR
ARTIFACT REDUCTION
Algorithmic Correctiono Adaptive filtering for streak reduction
o Iterative reconstruction
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80kVp, 6.44mGy 140kVp, 6.26mGy
0.1
1
10
100
0 50 100 150
energy (keV)
m (
1/c
m)
PROTOCOL OPTIMIZATION
o Scan speed
o Helical pitch
o kVp selection
o Slice thickness
o Reconstruction kernel
o mA modulation
Calcium
Water
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VOLUNTARY MOTION
• Better patient training
• Better instruction
• Faster scan speed
• Advanced algorithm
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OPERATOR-INDUCED
• CT operator plays an important role in artifact reduction.
• Improperly secured patient can lead to motion artifact.
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centered
s=28.810cm up
s=53.8
PATIENT CENTERING
• Bowtie filter is employed in most scanners.
• It assumes centered oval shaped objects.
• Patient centering is important.
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Missing Frequency Z-truncation Redundant Data Mishandling
WIDE-CONE: MATHEMATICAL CHALLENGE
• Axial wide-cone reconstruction faces three major technical challenges:
– missing frequency (axial mode sampling pattern)
– z-truncation (cone-beam geometry)
– redundant data handling (half-scan to improve temporal resolution)
• Solution to these challenges requires extensive research and testing.
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Advanced Reconstruction
Traditional Reconstruction
ALGORITHM COMPARISON
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HIGH HELICAL PITCH IMPACT
Pitch 3.2
Pitch 1.2
• When helical pitch is too large, incomplete sample can induce image artifacts
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SUMMARY
• Three generations of reconstruction algorithmo Filtered backprojection (FBP)
o Iterative reconstruction (IR)
o Deep-learning image reconstruction (DLIR)
• Many sources of CT artifactso Nature of Physics
o New Technologies
o Patient
o Operator
• It takes a combined efforts from manufactures, CT operators, and patients to
obtain the best image quality.
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