ct perfusion imaging - imedex slides/1635... · 1st commercial ct unitcommercial ct unit • in...
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CT Perfusion ImagingCT Perfusion Imaging
Ivan Yeung, Ph.D.
Dept of Radiation Physics, Princess Margaret Hospital
Dept of Radiation Oncology, University of Toronto
University of Toronto
11stst Commercial CT UnitCommercial CT Unit11 Commercial CT UnitCommercial CT Unit
•• In 1972, Sir Godfrey Hounsfield introduced the first EMIIn 1972, Sir Godfrey Hounsfield introduced the first EMIIn 1972, Sir Godfrey Hounsfield introduced the first EMI In 1972, Sir Godfrey Hounsfield introduced the first EMI CT scanner.CT scanner.
•• It takes 5 min to scan and 2.5 hours to reconstruct.It takes 5 min to scan and 2.5 hours to reconstruct.
•• Designed as a revolutionary Designed as a revolutionary anatomicalanatomical imaging tool.imaging tool.
EMI scanner
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Development of CTDevelopment of CTDevelopment of CTDevelopment of CT•• CT was quickly development, during the 80’s, CT CT was quickly development, during the 80’s, CT
has become a work horse in almost every radiology has become a work horse in almost every radiology y gyy gydepartment.department.
•• Due to presence of cables, the fastest CT scan was Due to presence of cables, the fastest CT scan was done every 4done every 4--6 seconds6 secondsdone every 4done every 4--6 seconds.6 seconds.
•• First attempt to use CT to measure function First attempt to use CT to measure function –– tissue tissue perfusion perfusion –– is with stable Xenonis with stable Xenon--133.133.
•• ‘Messy’ experiment: patient breathes in Xenon, it ‘Messy’ experiment: patient breathes in Xenon, it diffuses from lung to blood, then carry to tissuediffuses from lung to blood, then carry to tissue
•• Nice propertyNice property –– high diffusion coefficient acrosshigh diffusion coefficient acrossNice property Nice property high diffusion coefficient across high diffusion coefficient across capillary.capillary.
•• Can be described by oneCan be described by one--compartment model. compartment model. Hence less computational demand and sparseHence less computational demand and sparse
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Hence less computational demand and sparse Hence less computational demand and sparse sampling is ok.sampling is ok.
OneOne--Compartment ModelCompartment ModelOneOne Compartment ModelCompartment Model
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Slip Ring Technology (mid 90’s)Slip Ring Technology (mid 90’s)Slip Ring Technology (mid 90 s)Slip Ring Technology (mid 90 s)
•• Power and data transfer via Power and data transfer via contact around gantrycontact around gantry
•• Gantry moves in one Gantry moves in one di tidi tidirectiondirection
•• Gantry can move up to 1/3 Gantry can move up to 1/3 sec per rotationsec per rotationsec per rotationsec per rotation
•• High temporal resolutionHigh temporal resolution•• Data able to handle moreData able to handle moreData able to handle more Data able to handle more
complex model complex model –– exchange exchange of contrast agentof contrast agent
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Distributed ModelDistributed ModelDistributed ModelDistributed Model
•• F F –– Perfusion, blow flow through the capillaryPerfusion, blow flow through the capillary
•• PS PS –– Leakiness from intravascular to extravascular spaceLeakiness from intravascular to extravascular space
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•• MTT MTT –– Time to take bolus to travel across the capillaryTime to take bolus to travel across the capillary
Perfusion CTPerfusion CT
contrast injector
ModelAIF(t) Tissue(t)
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Tracer Kinetics ModelsTracer Kinetics ModelsTracer Kinetics ModelsTracer Kinetics Models
ModelAIF(t) Tissue(t)
GE P f iGE Perfusion …
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NonNon--linear Curve Fittinglinear Curve FittingNonNon linear Curve Fittinglinear Curve Fitting
ModelAIF(t) Tissue(t)Model( ) ssue( )
F PS VbF1, PS1, Vb1….
F2, PS2, Vb2….
F3 PS3 Vb3
Measured
Fitted
F3, PS3, Vb3….
.
Fn, PSn, Vbn…
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n, n, n
Patients received Sorafenib + RadPatients received Sorafenib + RadPt # 1Pt # 1
Baseline Day 7 Day 21
CT
0.0180.020.022
Perfusion Map
0.0020.0040.0060.0080.010.0120.0140.016
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0.002
Patients received Sorafenib + RadPatients received Sorafenib + RadPatients received Sorafenib + RadPatients received Sorafenib + RadPt # 2Pt # 2
Baseline Day 7 Day 21
CT
0.05
Perfusion Map
0.01
0.02
0.03
0.04
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0
Preclinical Cervix Tumor xenograft in micePreclinical Cervix Tumor xenograft in micePreclinical Cervix Tumor xenograft in micePreclinical Cervix Tumor xenograft in mice
PerfusionPerfusion
High
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Low
Perfusion CT on Mouse 1 (antiPerfusion CT on Mouse 1 (anti--vascular drug)vascular drug)Enhanced CT Blood Flow Blood Volume MTTEnhanced CT ood o Blood Volume MTT
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Advancement in Perfusion CTAdvancement in Perfusion CTAdvancement in Perfusion CTAdvancement in Perfusion CT
•• True 4D CTTrue 4D CT –– True 3D volume scan is possibleTrue 3D volume scan is possibleTrue 4D CT True 4D CT True 3D volume scan is possibleTrue 3D volume scan is possible
•• Dose reduction schemes: intermittent XDose reduction schemes: intermittent X--ray on, low ray on, low dose CT, less frequent samplingdose CT, less frequent samplingdose CT, less frequent samplingdose CT, less frequent sampling
•• Optimization of protocolOptimization of protocol
•• Limited speed coneLimited speed cone beam CT perfusionbeam CT perfusion•• Limited speed coneLimited speed cone--beam CT perfusionbeam CT perfusion
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Dynamic Contrast Enhanced Perfusion CT Dynamic Contrast Enhanced Perfusion CT C t t CBCTC t t CBCTvs Contrast CBCTvs Contrast CBCT
CT Acquisition over time Multiple CT images at different timedifferent time
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CBCT Single contrast enhanced CBCT image
Acquisition over time
DCEDCE--CBCTCBCTDCEDCE CBCTCBCT•• Assume each voxel behaves as a single compartment with its washAssume each voxel behaves as a single compartment with its wash--in in
and washout behaviourand washout behaviour
where Cb(t) where Cb(t) –– contrast enhancement in each voxelcontrast enhancement in each voxelt t –– timetime
I, I, αα, k , k –– unknown parametersunknown parameters
]222[)( 222
αααααα −+−= − tttkt
b eetetIetC
,, ,, pp
•• Reconstruct the kinetics behavior Reconstruct the kinetics behavior according to the formula for each according to the formula for each voxel by optimization to determine thevoxel by optimization to determine the
t=0s, 0o t=1s, 3o
t=2s,voxel by optimization to determine the voxel by optimization to determine the 3 unknown parameters (I, 3 unknown parameters (I, αα, k) with , k) with projection dataprojection data
, 6o
•• 640 projections were obtained in 2 640 projections were obtained in 2 min. The min. The netnet (i.e. contrast (i.e. contrast –– baseline) baseline) contrast projections were used for contrast projections were used for
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optimization in Matlab for a volume of optimization in Matlab for a volume of 128x128x40.128x128x40.
Results: CT and Results: CT and EstimatedEstimated CBCT Tissue CBCT Tissue E h t d P f iE h t d P f iEnhancement and PerfusionEnhancement and Perfusion
CT CBCT
N tNet Enhancement
Perfusion
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ConclusionsConclusionsConclusionsConclusions
•• CT originally designed for anatomyCT originally designed for anatomyCT originally designed for anatomyCT originally designed for anatomy
•• Functional measurement (F, PS, MTT) can be Functional measurement (F, PS, MTT) can be performed for clinical and preclinical studiesperformed for clinical and preclinical studiesperformed for clinical and preclinical studiesperformed for clinical and preclinical studies
•• OnOn--going Advancement in perfusion CTgoing Advancement in perfusion CT
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AcknowledgementAcknowledgementAcknowledgementAcknowledgement
Lab members (past/present)Lab members (past/present) CollaboratorsCollaborators(p p )(p p )
•• Sunmo KimSunmo Kim
•• Brian LimBrian Lim
•• Richard HillRichard Hill
•• Mike MilosevicMike Milosevic
•• Masoom HaiderMasoom Haider
•• Doug MoseleyDoug Moseley
•• Richard ClarksonRichard Clarkson
•• Qiulin TangQiulin Tang
•• Rob BristowRob Bristow
•• Anthony FylesAnthony Fyles
g yg y
•• YoungYoung--Bin ChoBin Cho
•• TingTing--Yim LeeYim Leegg
•• Bill QianBill Qian
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•• David HedleyDavid Hedley
•• David JaffrayDavid Jaffray
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Funding SourcesFunding Sources
•• Terry Fox FoundationTerry Fox Foundation
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•• Jeff SiewerdsenJeff Siewerdsen
•• YoungYoung--Bin ChoBin Cho
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•• Canadian Institute of Health ResearchCanadian Institute of Health Research
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