Dual Energy X-Ray Imaging for the ICUCheckpoint Presentation

Team Members: Yifu Ding, Bisakha RayMentors:Dr. Jeff Siewerdsen (Dept. of Biomedical Engineering)Dr. John Carrino (Dept. of Radiology)Dr. Mahesh Mahadevappa (Dept. of Radiology)

Project Recap

• Bedside ICU imaging of interventional tools (tubes, lines, catheters, needles, and other devices) is challenging due to low radiographic image quality.

• New digital radiographic technology could improve image functionality: DE imaging for enhanced visualization of implanted devices.

Conventional Radiograph

DE Image

• DE images suffer relatively high pixel noise (subtraction increases noise by sqrt(2)).

• Optimal x-ray technique selection is essential to maximizing contrast and reducing noise.

• Advanced “noise reduction” decomposition algorithms can reduce noise amplification.

• Together, optimal x-ray technique selection and advanced decomposition techniques can give high image quality without increase in dose.

Challenges

Key Dates and Assigned Responsibilities

Topic Task Status Estimated Delivery Date

New Delivery Date

Technique Optimization

Added Filtration End February

End February

Antiscatter Grid Mid March End April

kVp Pair End March End March

Dose Allocation Mid April End April

Iteration in Multivariate Optimization

End April Ongoing

Key Dates and Assigned ResponsibilitiesTopic Task Status Estimated

Delivery Date

New Delivery Date

Image Decomposition

Noise reduction algorithms•SLS•SSH•ACNR•GLNR•KCNR•NOC•EPAS•A1•A2• A2a• A2b

End February-

End of April

Mid-May

Automatic w(x,y) Bisakha End of April End of April

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Deliverables: Technique Optimization

 

Maximum

 Expected

  Minimum

Selection of optimal kVp pair and optimal dose allocation   Selection of optimal

antiscatter grid

Selection of optimal added filtration

Deliverables: Image Decomposition

 

Maximum

 Expected

  Minimum

Identify optimal decomposition algorithm and parameters therein.   Implementation of SSF,

ACNR, NOC, and adaptive noise reduction algorithms. 

Automatic parameter selection within each algorithm, as appropriate [for example, w(x,y)]

Implementation of simple log subtraction (SLS) algorithm.

Key Dependencies and Plan for Resolving• Equipment

– X-ray Imaging System – Delivery and Installation (Feb. 12) – Filters – Available in laboratory – Antiscatter grids: Available in the Laboratory – Computers: Week of Feb. 22 – Imaging phantoms: Feb. 22

• Mentor Availability (Drs. Siewerdsen, Carrino, and Mahesh) – Weekly meeting (Siewerdsen) – Wednesday 3 p.m.– Monthly meeting (Carrino and Mahesh)– Planning OR visitation

• X-Ray Technique Optimization– Multivariate optimization (multiple step univariate analysis): – SPEKTR (Open Source) – Iteration in optimal: filter / grid / kVp / dose allocation

• Image Decomposition – Image data – Quantitative analysis (CNR) : MATLAB implementation – Radiologist interpretation (availability of Dr. Carrino) : ( Depending on how far and

fast we get on the project)

**

Sample High and Low Energy Images

High Energy Image: kVp = 120 Low Energy Image: kVp = 50

kVp = kiloVolt potential

logH = log(Ihigh);logL = log(llow);Ibone=-logH+w*logL;

Bone Image for Tissue Cancellation Parameter w =1.2 for SLS

logH = log(Ihigh);logL = log(llow);Itissue=logH-w*logL;

Soft Tissue Image for Tissue Cancellation Parameter w =0.6 for SLS

Filter Study: Differential caseContrast for bone image Contrast for soft tissue image

x-axis = atomic numbery-axis = thickness (g/m^2) /10color = contrast

Filter Study: Non-differential caseContrast for bone image Contrast for soft tissue image

x-axis = atomic numbery-axis = thickness (g/m^2) /10color = contrast

Abstract Submitted for 2010 AAPM Annual Meeting at Philadelphia Convention Center

Phantom ImagesLow Energy: 40 kVp High Energy: 130 kVp