1 Registration-Based Regional Lung Mechanical Registration-Based Regional Lung Mechanical Analysis: Retrospectively Reconstructed Dynamic Analysis: Retrospectively Reconstructed Dynamic Imaging versus Imaging versus Static Breath-hold Image Acquisition Static Breath-hold Image Acquisition Kai Ding, Kunlin Cao, Gary E. Christensen Kai Ding, Kunlin Cao, Gary E. Christensen Eric A. Hoffman and Joseph M. Reinhardt Eric A. Hoffman and Joseph M. Reinhardt The University of Iowa, Iowa City, IA 52242 The University of Iowa, Iowa City, IA 52242
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Registration-Based Regional Lung Mechanical Analysis: Registration-Based Regional Lung Mechanical Analysis: Retrospectively Reconstructed Dynamic Imaging versus Retrospectively Reconstructed Dynamic Imaging versus
Kai Ding, Kunlin Cao, Gary E. ChristensenKai Ding, Kunlin Cao, Gary E. Christensen
Eric A. Hoffman and Joseph M. ReinhardtEric A. Hoffman and Joseph M. Reinhardt
The University of Iowa, Iowa City, IA 52242The University of Iowa, Iowa City, IA 52242
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MotivationMotivation
Regional lung function depends Regional lung function depends on the mechanical relationships on the mechanical relationships between the lungs, rib cage, between the lungs, rib cage, diaphragm, and abdomendiaphragm, and abdomen
Disease can change lung tissue Disease can change lung tissue material properties, e.g.,material properties, e.g., Emphysema (COPD)Emphysema (COPD)
Traditional pulmonary function measurements, such as spirometry, only give global information and are unable to show regional differencesTraditional pulmonary function measurements, such as spirometry, only give global information and are unable to show regional differences
Heterogeneity of lung functionHeterogeneity of lung function
New available technologies enable us to study pulmonary function in a regional levelNew available technologies enable us to study pulmonary function in a regional level Multi-detector-row CT (MDCT)Multi-detector-row CT (MDCT) Dynamic volume CT imagingDynamic volume CT imaging Respiratory Gating methodsRespiratory Gating methods
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Previous Work – Imaging MethodsPrevious Work – Imaging Methods
Nuclear medicine imaging (e.g. PET, SPECT)Nuclear medicine imaging (e.g. PET, SPECT) Provides good functional informationProvides good functional information Low spatial resolutionLow spatial resolution
Hyperpolarized gas MR imagingHyperpolarized gas MR imaging Provides dynamic ventilation and diffusion mapsProvides dynamic ventilation and diffusion maps Much less anatomical detail than MDCTMuch less anatomical detail than MDCT No radiationNo radiation
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Xe-CT vs. Volumetric CTXe-CT vs. Volumetric CT Xenon-enhanced CT (Xe-CT)Xenon-enhanced CT (Xe-CT)
Previous work - Image RegistrationPrevious work - Image Registration
MR images (Gee et al.)MR images (Gee et al.) Not much anatomical details Not much anatomical details Limited landmark validation (22 landmarks)Limited landmark validation (22 landmarks)
4D CT by optical-flow (Guerrero et al.)4D CT by optical-flow (Guerrero et al.) Matched well with segmented volume changeMatched well with segmented volume change No regional validationNo regional validation
Regional lung expansion from temporal CT Regional lung expansion from temporal CT (Christensen et al.)(Christensen et al.)
Matched well with spirometry dataMatched well with spirometry data Only global validationOnly global validation
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Our WorkOur Work
Retrospectively reconstructed dynamic imaging and Retrospectively reconstructed dynamic imaging and static breath-hold imagingstatic breath-hold imaging
Using Jacobian which measures the regional volume Using Jacobian which measures the regional volume changechange
Registration-based regional lung expansion vs. Xe-CT Registration-based regional lung expansion vs. Xe-CT specific ventilationspecific ventilation
Using semi-automatic reference standard to evaluate Using semi-automatic reference standard to evaluate registration accuracyregistration accuracy
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DiagramDiagram
Sheep
DynamicImaging
StaticImaging
XenonImaging
3D Image Registration
3D Image Registration
CompartmentModel
Regional Expansion
Regional Expansion
Regional Ventilation
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Consistent Linear Elastic RegistrationConsistent Linear Elastic Registration Inverse consistent linear elastic registration (Christensen et al.)Inverse consistent linear elastic registration (Christensen et al.)
Jointly estimating Jointly estimating hh & & g g helps reduce the inverse consistency errorhelps reduce the inverse consistency error
Cost minimizationCost minimization
h
g
Template, T Target, S
Christensen and Johnson, Consistent Image Registration, IEEE TMI 20(7), July 2001, pp. 568-582.
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Consistent Linear Elastic RegistrationConsistent Linear Elastic Registration Regional volume changeRegional volume change
Output: Displacement field Output: Displacement field u(x,y,z)u(x,y,z) that maps image to image that maps image to image Use the Jacobian determinant of the displacement fieldUse the Jacobian determinant of the displacement field
Present our result in a natural way in Lagrangian Coordinate:If Jacobian>1 local expansionIf Jacobian<1 local contractionIf Jacobian=1 no expansion or contraction
1I0I
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Xenon CT AnalysisXenon CT Analysis
Basic ModelBasic ModelDuring wash-in (wash-out), Xe increases (decreases) mean lung density(enhancement is roughly linear with Xe concentration)
Observed time-intensity data is fit to compartment model using least squares curve fit.
Pulmonary Analysis Software SuiteIowa Comprehensive Lung Imaging Center
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Experimental MethodsExperimental Methods
Siemens Sensation 64 MDCT scannerSiemens Sensation 64 MDCT scanner Four sheep at supine positionFour sheep at supine position Anesthetized and Anesthetized and mechanically ventilatedmechanically ventilated
Three types of dataThree types of data Dynamic scans Dynamic scans
0, 25, 50, 75 and 100% phase points of inspiration portion 0, 25, 50, 75 and 100% phase points of inspiration portion and 75, 50 and 25% of the expiration portion, denoted as and 75, 50 and 25% of the expiration portion, denoted as the T0, T1, T2 … and T7 imagesthe T0, T1, T2 … and T7 images
Registration applied pairwise to estimate local expansionRegistration applied pairwise to estimate local expansion
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Three types of dataThree types of data Static scans Static scans
Acquired at 0, 5, 10, 15, 20 and 25 cm HAcquired at 0, 5, 10, 15, 20 and 25 cm H22O airway O airway
pressure (We use P10, P15, P20 and P25 images)pressure (We use P10, P15, P20 and P25 images) Registration applied pairwise to estimate local expansionRegistration applied pairwise to estimate local expansion
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Three types of dataThree types of data Xenon CT scans Xenon CT scans
Acquired at the end expiratory point during the respiratory cycle Acquired at the end expiratory point during the respiratory cycle (about 45 breaths)(about 45 breaths)
Triggered at PEEP of 10 Triggered at PEEP of 10 cm Hcm H22OO Slice thickness = 2.4 mm (about 3.2 times thicker than the volumetric Slice thickness = 2.4 mm (about 3.2 times thicker than the volumetric
CT slices)CT slices) 12 contiguous slices = 3 cm of coverage along the axial direction12 contiguous slices = 3 cm of coverage along the axial direction
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Registration AccuracyRegistration Accuracy Automatic landmark detection (Murphy et al.)Automatic landmark detection (Murphy et al.)
K. Murphy, B. van Ginneken, J. Pluim, S. Klein, and M. Staring, Semi-automatic reference standard construction for quantitative evaluation of lung CT registration, MICCAI 2008, 5242, pp. 1006-1013.
Note: 2D projection view. The landmarks are inside lung in 3D view.
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Registration AccuracyRegistration Accuracy Semi-automatic system (Murphy et al.)Semi-automatic system (Murphy et al.)
New landmark is added to a thin-plate-spline System estimates the new landmark position for the user
200 landmarks are matched by one observer across all 200 landmarks are matched by one observer across all image pairsimage pairs
For each landmark, the actual landmark position was For each landmark, the actual landmark position was compared to the registration-derived landmark positioncompared to the registration-derived landmark position
Dynamic scans mean landmark distance: Dynamic scans mean landmark distance: Before registration: on the order of 8 mmBefore registration: on the order of 8 mm After registration: on the order of 2 mmAfter registration: on the order of 2 mm
Static scans mean landmark distance: Static scans mean landmark distance: Before registration: on the order of 12 mmBefore registration: on the order of 12 mm After registration: on the order of 2 mmAfter registration: on the order of 2 mm
K. Murphy, B. van Ginneken, J. Pluim, S. Klein, and M. Staring, Semi-automatic reference standard construction for quantitative evaluation of lung CT registration, MICCAI 2008, 5242, pp. 1006-1013.
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Registration AccuracyRegistration Accuracy
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Registration JacobianRegistration Jacobian Registration-based estimates of regional expansion and Registration-based estimates of regional expansion and
Inspiration phase: T0 to T1 Jacobian>1 local expansion
Expiration phase: T4 to T5 Jacobian<1 local contraction
Note: Color scale is different. Each color bar is set as the min & max J in the slice
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Jacobian vs. Spec. VentilationJacobian vs. Spec. Ventilation Comparison between registration-based Jacobian map Comparison between registration-based Jacobian map
(whole lung) and Xe-CT estimates of specific ventilation (whole lung) and Xe-CT estimates of specific ventilation (limited axial coverage)(limited axial coverage)
Affine transform is applied to find the axial location of Xe-CT scan in volumetric CT scan
z Base
Apex
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Jacobian vs. Spec. VentilationJacobian vs. Spec. Ventilation Comparison between registration-based Jacobian map Comparison between registration-based Jacobian map
(whole lung) and Xe-CT estimates of specific ventilation (whole lung) and Xe-CT estimates of specific ventilation (limited axial coverage)(limited axial coverage)
z
y
Deformed slabs
0%IN 100%IN
Lu
ng
Heig
ht
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Jacobian vs. Spec. VentilationJacobian vs. Spec. Ventilation
Linear regression of averaged Jacobian and the sVLinear regression of averaged Jacobian and the sV
Linear regression with 95% confidence interval for T0 T1of AS70078
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CorrelationCorrelation
Correlation of the average Jacobian and the sV in dynamic Correlation of the average Jacobian and the sV in dynamic scansscans
Correlation coefficient r square from the linear regression of J and sV for each phase change pair and for each animal, T2-T3 (50%IN-75%IN) r2 = 0.85
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CorrelationCorrelation
Correlation of the average Jacobian and the sV in static Correlation of the average Jacobian and the sV in static scansscans
Correlation coefficient r square from the linear regression of J and sV for each phase change pair and for each animal, P20-P25 r2 = 0.84
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Jacobian Change Across PhaseJacobian Change Across Phase Different regions reach their maximum expansion at different Different regions reach their maximum expansion at different
points in respiratory cycle – coronal viewpoints in respiratory cycle – coronal view
AS70077 AS70078 AS70079 AS70080
Apex
Base
100%IN-75%EX
75%EX-50%EX
50%EX-25%EX
Base
Apex 0%IN-25%IN
25%IN-50%IN
50%IN-75%IN
75%IN-100%IN
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Jacobian Change Across Phase Jacobian Change Across Phase Different regions reach their maximum expansion at different Different regions reach their maximum expansion at different
points in respiratory cycle – sagittal viewpoints in respiratory cycle – sagittal view
AS70077 AS70078 AS70079 AS70080
Apex
Base
100%IN-75%EX
75%EX-50%EX
50%EX-25%EX
Base
Apex 0%IN-25%IN
25%IN-50%IN
50%IN-75%IN
75%IN-100%IN
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DiscussionDiscussion
Same level of correlation in both dynamic and static scansSame level of correlation in both dynamic and static scans Similar mouth pressureSimilar mouth pressure
Xe-CT measurements of sV have large Xe-CT measurements of sV have large (30% of the (30% of the mean)mean) Is measurement noisy?Is measurement noisy? Is underlying physiology variable?Is underlying physiology variable?
Registration estimates of Jacobian: smoothRegistration estimates of Jacobian: smooth How small is the smallest abnormal tissue we can detect?How small is the smallest abnormal tissue we can detect? Registration model includes smoothness constraintRegistration model includes smoothness constraint
Application to human dataApplication to human data Works for human data in tidal breathingWorks for human data in tidal breathing Low dose screening scans?Low dose screening scans?
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SummarySummary
Registration derived estimates of regional lung expansionRegistration derived estimates of regional lung expansion Both dynamic and static scans can be used for assessmentBoth dynamic and static scans can be used for assessment Registration J can be analyzedRegistration J can be analyzed Dynamic scans: reveal better heterogeneity, more dose, subjects Dynamic scans: reveal better heterogeneity, more dose, subjects
need trainingneed training Static scans: improved spatial resolution, single pair of breath-hold Static scans: improved spatial resolution, single pair of breath-hold
images, lower radiation doseimages, lower radiation dose Complementary to Xe-CT ventilation: provides local lung Complementary to Xe-CT ventilation: provides local lung
expansion informationexpansion information Expansion, strain, lung-rib cage interaction, etc.Expansion, strain, lung-rib cage interaction, etc.
Fast, low-cost, functional lung imaging protocolFast, low-cost, functional lung imaging protocol Method can provide information about lung mechanicsMethod can provide information about lung mechanics
