Taking Imaging to Task in IG Interventions · Presented at the AAPM-PSOC "Horizons" Meeting (Nov....
Transcript of Taking Imaging to Task in IG Interventions · Presented at the AAPM-PSOC "Horizons" Meeting (Nov....
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 1
Taking Imaging to Taskin IG Interventions
Taking Imaging to Taskin IG Interventions
Jeff Siewerdsen, PhD FAAPMDepartment of Biomedical Engineering
Department of Computer ScienceRussell H. Morgan Department of Radiology
Armstrong Institute for Patient Safety and QualityJohns Hopkins University
Funding Support / DisclosuresNational Institutes of Health
Siemens HealthcareCarestream Health
Elekta Oncology
Guidance + InterventionPlanning Localization Verification
Image-Guided Interventions (IGI)
•T12•T11
Population Data
Interventional Device Models
Preoperative Images
AtlasInformation
Disease + Biomechanical
Models
PatientData
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 2
Hybrid OR
Imaging in the ORMR‐Guided
AMIGO
(siemens.com) (imris.com)
BWH (ncigt.org)
Planned Planned
DeliveredDelivered
3D C-Arm Guidance3D Image Quality3D Image Quality Image ReconstructionImage Reconstruction
Video Overlay
PA
Target
Fissure
System IntegrationSystem IntegrationImage RegistrationImage Registration
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 3
c
CBCT-Guided InterventionsHead & Neck / Skull Base Spine Orthopaedic Neurosurgery
Thoracic (VATS)Base of Tongue (TORS) Interventional Radiology
Patient‐specific priorsPreoperative images (CT, MR, PET)Planning information (trajectories and volume definitions)Intraoperative images (2D 3D multi‐modality)
Taking Imaging to Task
Intraoperative images (2D, 3D, multi‐modality)
Procedure‐specific priorsInterventional devices
Population, statistical, and model‐based priorsAtlases
Specification of the imaging taskSize, Spatial frequency contentContrast, LocationContrast, Location
fx
1 2F H x H x
Statistical Decision Theory (Hypothesis Testing)
Task FunctionWtask(f )Hypothesis 1
H1(x)Hypothesis 2H2(x)
fy
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 4
Understand how a specification of imaging task can be leveraged to drive the imaging processTask-based detectability Design, optimizationTask-driven imaging
Learning Objectives
Task driven imaging
Understand how model-based image reconstruction can propel application in IGIImprove image quality, reduce doseUse of prior information
Understand how intraoperative imaging offers not only a means of high-precision guidance, y g p g ,but also a system for improved safety and quality assurance.Streamlined workflowLocalization, verificationIndependent checks, QA
Image Quality
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 5
Image Quality… and Imaging Task
Prewhitening observer (PW)(PW)
2MTF f
Detectability Index (d’)
Spatial Resolution
222' taskNPS f
MTFdW
fd f
Image Noise Imaging Task
ICRU Report #54
DoseDetector Configuration
SamplingElectronic Noise
DetectionDiscriminationClassification
Image Quality… and Imaging TaskDetectability Index (d’)
Prewhitening observer (PW)Generalized (include anatomical background etc.)Non-prewhitening observer (NPW)
4
4
22 2
2
2 2int
'Q E
task
taskBS
EMTF f W
S f
dfd
MTF f W df
f
E f NfS f
p g ( )Anthropomorphic (e.g., eye filter, NPWEi)
Eye FilterInternal Noise
ICRU Report #54
Burgess et al., JOSA A (14) 1997
Wagner et al. Med Phys (4) 1977
also papers by Richard, Tward, Prakash, Gang (Med Phys 2005‐13)
Quantum NoiseElectronic Noise
AnatomicalBackground
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 6
Detectability IndexExample Application: Task‐Based Technique Selection
Siewerdsen, NIM Phys. Rev. A (2012)
Soft-Tissue ImagingFresh cadaver (non-fixed)
Model-Based CBCT Reconstruction
PL + Huber Penalty (PLH) vs FBPRecon parameters for matched resolution
80 HU contrastESF width σ = 1 mm
Wang et al. PMB (in review)Wang et al. PMB (in review)
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 7
6
8
10
12
14
16
CN
R
1.4× CNR
½ DoseLow Dose Comparison100 kVp (15 – 120 mAs)
PLH
FBP
Model-Based CBCT Reconstruction
ESF width = 1 mm
PLH
20 40 60 80 100 1200
2
4
mAs
PLH reconstructionMatched spatial resolution (ESF = 1 mm)Reduces noisePreserves high contrast structures
FBP
FBP
60 mAs30 mAs 120 mAs15 mAsWang et al. PMB (in review)Wang et al. PMB (in review)
Model-Based Image ReconstructionFor example: Penalized Likelihood (PL) Estimation
An objective function (L) with a roughness penalty (R) on noisy data:Improves low-dose image quality, robust to limited (sparse) dataN it ti l ith t l th bj tiNumerous iterative algorithms to solve the objective:
- EM, ordered subsets, paraboloidal surrogates, …
Log-Likelihood
Balances data fidelityversus the penalty
Measured Projections
Regularization / PenaltyDifference invoxel values
Penalty function(e.g., linear, quadratic, Huber, …)
Image Estimate
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 8
ˆ arg max[lo ; )g ]( TL y R
Projection Data Image Estimate Reconstruction
Penalized Likelihood (PL)
Imaging Performancein Model-Based Reconstruction
Object dependence through its projections
T 1 TD{ ( )[A ] A}A+y RLLocal linearization of the reconstruction algorithm
arg max[lo ; )g ](L y R
Quadratic PenaltyPenaltyStrength
Log-Likelihood
Covariance and Noise‐Power Spectrum
Tˆ[cov{ }] cov{ }j jy L LNoise in
Reconstructions
,NPS FT[cov{ } ]j Nj,N
Local stationarity
Noise in Projections
Covariance and Noise‐Power Spectrum
jth VoxelLocation Dependence
G. Gang et al. SPIE Physics of Medical Imaging (2013)G. Gang et al. SPIE Physics of Medical Imaging (2013)J. J. FesslerFessler et al. IEEEet al. IEEE‐‐TMI (1996)TMI (1996)
Image Noise in Model-Based Reconstruction
2.0
x10-6
1.0
NPS(NPS(ffxx,f,fyy))PL ReconstructionPL Reconstruction24 cm Ellipse24 cm EllipseMonoenergeticMonoenergetic beambeamNo bowtie filterNo bowtie filter360 views, 360360 views, 360oo
11 mGymGy
0
1 1 mGymGy
G. Gang et al. SPIE Physics of Medical Imaging (2013)G. Gang et al. SPIE Physics of Medical Imaging (2013)G. Gang et al. Med Phys (under review)G. Gang et al. Med Phys (under review)
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 9
Image Noise in Model-Based Reconstruction
2.0
x10-6
1.0
NPS(NPS(ffxx,f,fyy))
0.4
0.6d'(d'(x,yx,y))PL ReconstructionPL Reconstruction24 cm Ellipse24 cm EllipseMonoenergeticMonoenergetic beambeamNo bowtie filterNo bowtie filter360 views, 360360 views, 360oo
11 mGymGy
0
Imaging Task(e.g., Line‐Pair Discrimination)
0
0.2
1 1 mGymGy
G. Gang et al. SPIE Physics of Medical Imaging (2013)G. Gang et al. SPIE Physics of Medical Imaging (2013)G. Gang et al. Med Phys (under review)G. Gang et al. Med Phys (under review)
Detector
Task-Driven ImagingTask-Based Modeling
Task-Driven ImagingStart with a specification of task
Location in patientSpatial frequencies of interest
TraditionalCircular
Trajectory
X-raySource
TaskTask--DrivenDrivenTrajectoryTrajectory
p qTask-based image quality model
+ Model-based reconstruction Drive the image acquisition process
Exploit knowledge of the imaging system, patient, and reconstruction process
Define constraints of geometry, dose, Nproj
Acquire projections that maximize task performance
Preoperative CTPlanning Data
Task Definition
Task-DrivenAcquisition
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 10
Detectability Index (d’) and Trajectory ()
Task-Driven Imaging
22
22,
x y z
j
Taskj df df dWMTF fd
Local Predictor of NPS and MTF
2NPS
2,j
xj j kas yT zNPSd
df df fF W dMT
1 1, , , ,N N
0
x10-6
,NPS FT[cov{ } ]jN N
,MTF FT[PSF{ } ]jN N
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
Task-Driven ImagingDetectability Index (d’) and Trajectory ()
22
22,
x y z
j
Taskj df df dWMTF fd
Design Goal
1 1, , , ,N N “Standard”= 0° Orbit
Oblique0 Orbit
2,j
xj j kas yT zNPSd
df df fF W dMT
2ˆ arg max ,jd
Task-Driven
Orbit
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 11
d’2(, |{}) 1st Best Projection (1,1)d’2(, |{1,1}) 2nd Best Projection (2,2)3rd Best Projection (3,3)d’2(, |{1,1},{2,2})d’2(, |{1,1},{2,2},{3,3}) 4th Best Projection (4,4)5th Best Projection (5,5)d’2(, |{1,1},{2,2},…,{4,4}) 6th Best Projection (6,6)d’2(, |{1,1},{2,2},…,{5,5}) 7th Best Projection (7,7)d’2(, |{1,1},{2,2},…,{6,6}) 8th Best Projection (8,8)d’2(, |{1,1},{2,2},…,{7,7})d’2(, |{1,1},{2,2},…,{8,8}) 9th Best Projection (9,9)10th Best Projection (10,10)d’2(, |{1,1},{2,2},…,{9,9}) 15th Best Projection (15,15)d’2(, |{1,1},{2,2},…,{14,14})d’2(, |{1,1},{2,2},…,{19,19}) 20th Best Projection (20,20)25th Best Projection (25,25)d’2(, |{1,1},{2,2},…,{24,24}) 30th Best Projection (30,30)d’2(, |{1,1},{2,2},…,{29,29}) 35th Best Projection (35,35)d’2(, |{1,1},{2,2},…,{34,34})-30
-20
-10
0
degrees)
Detectability Map
Task-Driven Imaging
0 50 100 150 200
10
20
30
(d
(degrees)
Ray Traversing Center of Nodule
21 1, arg max , |d
Objective Function
,
22 2 1 1
,, arg max , | ,d
23 3 1 1 2 2
,, arg max , | , , ,d
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
(deg)
-30
-10
“Bad” 220°Trajectory
d’2(, |{1,1},{2,2},…,{35,35})
Task-Driven Imaging
“Bad” 220° Trajectory Task-Based 220° Trajectory
50 100 150 200
(deg)
10
30
Task-Based 220°Trajectory
Lung Nodule
Surgical Tool
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 12
“Bad” 220° Trajectory Task-Based 220° Trajectory
Task-Driven Imaging
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
-30
-20
-10
d’2(, |{1,1},{2,2},…,{35,35})
g)
Preoperative CTPlanning Data
Detectability Map
Task-Driven Imaging
0 50 100 150 200
0
10
20
30
(d
e
(deg)
Planning Data
Task Definition
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 13
Standard Circular Short Scan Trajectory
Task-Driven Imaging
Task-Driven 215° Trajectory
StaymanStayman and and SiewerdsenSiewerdsen, Fully3D (2013), Fully3D (2013)
Improve geometric precisionTarget ablationAvoidance of normal tissues
Promote minimally invasive techniques
From Precision… to SafetyThe Role of Intraoperative Imaging
o o e a y as e ec quesReduced co-morbidity, faster recoveryTreatment of otherwise “untreatable” disease
Support innovation in advanced proceduresAdvanced delivery systems (e.g., robotics, …)Integration of therapies (e.g., IGRT + IGS)
P ti t f t d OR lit (ORQA)
High‐PrecisionSurgery
Patient safety and OR quality assurance (ORQA)Integration of information independent checksStreamlined localization and guidance (w/o trackers)Detect complications, RFBs in the ORAssessment of the surgical productQuantitative measurement of the surgical product
Expose fundamental factors determining outcome
Broader Utilization
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 14
CT (Prone)
3D-2D Registrationas a Safety Check vs. Wrong‐Level Surgery
CT (Supine)
OtakeOtake et al. Phys Med et al. Phys Med BiolBiol 57(17) (2012)57(17) (2012)
CC--arm Fluoroscopyarm FluoroscopyProne + SupineProne + SupineKyphosisKyphosis + + LordosisLordosisThorax, Abdomen, PelvisThorax, Abdomen, Pelvis
3D-2D Registrationas a Safety Check vs. Wrong‐Level Surgery
LevelCheck
T12
Failure criterion 5mm
L5
L4
L3
L5
L4
L3
L2
L1
T12
L5
L4
L3
L2
L1
L3
L2
L1
T12
T11
T10
T9
mPDE
Th
orax
Abd
ovi
s
2.4
2.0
1.7
2.4
2.0
1.7
2.4
OtakeOtake et al. Phys Med et al. Phys Med BiolBiol 57(17) (2012)57(17) (2012)
Before registration
After registration
L3
Clinical Studies at Johns Hopkins Hospital(mm)Deformation Case
Pel
v
2.0
1.7
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 15
Joint Registration + ReconstructionKnown‐Component Reconstruction (KCR)Simultaneous registration and reconstructionSeparate the estimation process:
‐ Reconstruction of an unknown background A t
Unknown Background
Anatomy, ‐ Registration of a known component therein
Device models, I(n)
*
* *,
( )* ,ˆˆ , arg max ;n
IL y R
Known Component
I(n)
Registration Regularization
Data Fit (Likelihood)
Stayman et al IEEEStayman et al IEEE‐‐TMI (2012)TMI (2012)
Joint Registration + ReconstructionKnown‐Component Reconstruction (KCR)
True Volume FBP Penalized-Likelihood KCR
Reduced artifactsImproved image quality, reduced dose
Quality AssuranceQuantitative Assessment / Verification
f th S i l P d tp g q y,
Extension to deformable components
Stayman et al SPIE (2013)Stayman et al SPIE (2013)Stayman et al IEEEStayman et al IEEE‐‐TMI (2012)TMI (2012)
of the Surgical Product
( )nI
J. H. Siewerdsen (Dept. of Biomedical Engineering, Johns Hopkins University)
11/8/2013
Presented at the AAPM-PSOC "Horizons" Meeting (Nov. 7, 2013) (Bethesda MD) 16
Understand how a specification of imaging task can be leveraged to drive the imaging process Task-driven imaging
Learning Objectives
Task driven imaging
Understand how model-based image reconstruction can propel application in IGI Penalized likelihood estimation (PLH) Joint registration-reconstruction (KCR)
Understand how intraoperative imaging offers not only a means of high-precision guidance, y g p gbut also a system for improved safety and quality assurance. 3D2D registration (LevelCheck) Verification of surgical product (ORQA)
AcknowledgmentsI‐STAR LaboratoryImaging for Surgery, Therapy, and Radiology
www.jhu.edu/istar
Clinical CollaboratorsJay Khanna (Ortho Spine Surgery)Ziya Gokaslan (Neuro Spine Surgery)Gary Gallia (Neurosurgery)Gary Gallia (Neurosurgery)Doug Reh (Otolaryngology)Jeremy Richmon (Otolaryngology)Marc Sussman (Thoracic Surgery)John Carrino (MSK Radiology)Peter Pronovost (Armstrong Institute)
BMEJ Web Stayman, Wojciech ZbijewskiAdam Wang, Yoshito OtakeSebastian SchaferSajendra NithiananthanSajendra NithiananthanGrace Gang, Jen XuHao Dang, Steve Tilley
Comp SciRuss TaylorGreg HagerAli UneriDan MirotaSureerat Reaungamornrat
ECEJerry Prince
RadiologyKen TaguchiM Mahesh
Rad OncJohn WongJunghoon Lee