Kinetic Modeling
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Transcript of Kinetic Modeling
Kinetic Modeling
Edward Di BellaDmitri RiabkovHarshali BalSathya Vijayakumar
Introduction to kinetic modeling
Challenges1. Formulation/selection of physiological model2. Obtaining accurate input functions3. Noise issues - optimal data groupings4. Noise issues - optimal reconstruction, non-linear optimization, use of constraints
Applications
Outline
Examples of tracer kinetics:
•Intravascular (99mTc-albumin, MS-325)
•Extracellular (gadolinium-DTPA)
•Intracellular (99mTc-Sestamibi, 99mTc-Teboroxime)
•More complex uptake and washout characteristics, including changes of state, binding (18FDG)
Introduction
•Area under curve
•Upslope
•Percent Enhancement
Semi-quantitative Models
Two Compartments
Tissue - Interstitial (ve)
Vascular - redblood cells
Tissue - Intracellular
Vascular - plasmacapillary
k
)/exp()( evEFtEFth
E = extraction, F = flow, t =time
=volume of extravascular spaceev
Axial concentration gradient
Vascular - plasma (Cp)Vascular - redblood cells (Hct)
capillary
Tissue
Model with capillary transit time
cec
c
TtvTtEFFE
TtFth
)/)(exp()(
Canine Study (LAD occlusion)
28 beats later FIESTA imageEarly contrast 8 regions
Myocardial Perfusion with Contrast MRI
(a) (b) (c)
(e)
27
(d)Region number
Infarct - volume of distribution changes
Tissue - Intracellular
Vascular - plasma (Cp)
Tissue - ve
Vascular - redblood cells
Normal Infarct
Myocardial viability
MRI and PET Viability
(Upper panels) Non-viable apical region shown with FDG-PET, left, and contrast enhanced MRI, right. (Lower panels) Non-viable inferoposterior region shown with FDG-PET, left, and contrast enhanced MRI, right. Arrows indicate regions of non-viability.
• MRI
– Saturation– Flow effects
Input Function - challenges
• Dynamic PET and SPECT
– Blood binding
– Temporal sampling rate
• Solution: Blind estimation of kinetic parameters
Static SPECT:
Dynamic SPECT:
20-30s 90-100s 490-500s
290-300s
University of Utah
Cluster Analysis for segmentation
Segmentation and formation of parametric imagesfor dynamic cardiac SPECT
• Approach: segment 4D short-axis data with clustering andobtain parametric maps– clustering:
p Cnpn
p
a min
an is vector of values for location n
p is the average curve of membersof pth cluster Cp
Results
Clustered blood input more smoothcompared to manually chosen ROIs
Smoother blood input may result inless variance in parameter estimatesfrom compartment model fitting
Results
Clustered wash-in images Summed short-axis images
201Tl canine study
Teboroxime patient study
SUMMARYKinetic modeling is a very useful approach with many applications
Numerous important and interesting research areas:– Acquisition
– Automated robust processing
– Input function
– Modeling
– Visualization and use of parametric images