Dual Energy X-Ray Imaging for the ICU Checkpoint Presentation
Transcript of Dual Energy X-Ray Imaging for the ICU Checkpoint Presentation
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)
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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