csmc artificial intelligence in medicine (aim) program
TRANSCRIPT
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CSMC Artificial Intelligence in Medicine (AIM) Program
Guido Germano, PhD
Quantitative gated nuclear imaging
Disclosure: receipt of software royalties from
Cedars-Sinai Medical Center
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Cardiac perfusion SPECT: quantitative analysis
Defect extent, severity
& reversibility
Categorical, summed
& normalized scores
TPD
LVEF
ESV and EDV
Diastolic function
RWM & RWT
Phase analysis
Lung/heart ratio
TID ratio
LV shape
LV mass
PERFUSION
QUANTITATION
FUNCTION
QUANTITATION
OTHER
QUANTITATION
INTEGRATED ANALYSIS
Projections (rest & stress)
Short axis (rest & stress)
Gated short axis
(rest & stress)
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Cardiac SPECT perfusion/function quantification
QGS/QPS/AutoQUANT (Cedars-Sinai)
Emory Toolbox(Emory Univ.)
4D-MSPECT(Univ. of Michigan)
Main commercially available software
Germano et al., J Nucl Cardiol 2007;14:433
Garcia et al., J Nucl Cardiol 2007;14:420
Ficaro et al., J Nucl Cardiol 2007;14:455
Validation / normal limits info: www.csaim.com/validation
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Quantitative measures of perfusion
Segment-based:
Categorical scores (0-4)
Summed scores (combine extent & severity)
Normalized summed scores (indep. of # segments in model)
Pixel-based:
Extent of defect [%]
Severity of defect
Total perfusion deficit (TPD) [Berman, JNC 2004]
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Quantitative measures of perfusion
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Gated perfusion SPECT quantitation
LVEF = (EDV-ESV)/EDV * 100
EDV= 3D endocardium at ED
ESV= 3D endocardium at ES
WM = endocardial excursion
WT mostly from partial volume effect
Diastolic function = derivative of T-V curve
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J Nucl Med 2009; 50:1418–1426Supported by NIH NHLBI R01 grant: R0HL089765
Visual contour QC Expert agreement
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Automatic “bad contour” detectionIncorrect LV Incorrect VP
SQC VPO-VQC VPU-VQC
ROC Area
P value
Sensitivity
Specificity
1.0±0.00
< 0.0001
100%
98%
0.91±0.01
< 0.0001
100%
71%
0.97±0.01
< 0.0001
100%
77%
JNM 2009 J Nucl Med. 2009 Sep;50(9):1418-26 Supported by NHLBI R01 grant: R0HL089765
High accuracy for LV
segmentation detection in
MPS demonstrates that
this algorithm may improve
automated and objective
analysis of MPS.
ROC -bad
contour
Detection
Area =1.0
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Abnormality thresholds of SPECT EF and volumes
Gender LVEF EDV ESV
Cedars QGS F 51% 102 ml 46 ml
8 fr, Tc-99m (60 ml/m2) (27 ml/m2)
(Sharir, JNC 2006) M 43% 149 ml 75 ml
(75 ml/m2) (39 ml/m2)
Emory ECT F+M 51% 171 ml 70 ml8 or 16 fr
(Garcia, JNC 2007)
4D-MSPECT F 56-60% 118-122 ml 44-42 ml
8-16 fr, Tc-99m (66-68 ml/m2) (25-24 ml/m2)
(Ficaro, JNC 2007) M 47-52% 183-197 ml 91-82 ml
(91-98 ml/m2) (46-41 ml/m2)
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LVEF measurement: 8- vs. 16-frame gating
EDV = 114 ml
ESV = 37 ml
LVEF = 68%
EDV = 111 ml
ESV = 41 ml
LVEF = 63%
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Diastolic function: normal limits (QGS)
Akincioglu, JNM 2005
90 normal patients
Mean values:
PFR: 2.62 ± 0.46 EDV/s
TTPF: 164.6 ± 21.7 ms
Abnormality thresholds:
PFR < 1.71 EDV/s
TTPF: > 216.7 ms
PFR
ESV
EDV
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Phase analysis in gated perfusion SPECT
The time-volume curve
can be broken down by
segment, wall, vessel, etc.
Example
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Phase analysis in gated perfusion SPECT
Van Kriekinge JNM 2008
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Phase analysis helps predict response to CRT
Boogers et al, JNM 2009
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Integration with CTA
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Kaufmann PA, Gaemperli O. J Nucl Cardiol 2009;16: 170-72.
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Complementary MPI-CTA ?
Slomka et al, Expert Rev Cardiovascular Therapy 2008 Jan;6(1):27-41
Estimate: 10-15% of patients need both
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J Nucl Med 2009;
50:1621–1630
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Automatically Match Cardiac Phase
•Evaluate cost functional across all phases
where k denotes phase number and n is total number of phase and E is the cost functional
*
1 2 3{1,..., }
arg min ( , , , ),kk n
k E c c c
Phase 9 Phase 16
where
Woo et al. Med Phy 2009 (accepted)
Automatically Match Cardiac
Phase
•Evaluate cost functional across all phases
where k denotes phase number and n is total number of phase and E is the cost functional
*
1 2 3{1,..., }
arg min ( , , , ),kk n
k E c c c
Phase 9 Phase 16
where
Woo et al. Med Phy 2009 (accepted)Woo et al. Med Phys. 2009;36:5467-79.
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Automated MPS/CTA image fusion
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Translations
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Accuracy of automated CTA-MPI fusion
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Slomka
et al
JNM2009
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QPS analysis: original contours
TPD =1%
SSS =1
#3
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QPS analysis with CTA-guided contours
RCA TPD
7 %
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SPECT/CTA volume/surface fusionCATH: proximal RCA 100%, no significant LAD, LCX disease
#3
RCARCA
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CTA guided
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CTA-guided MPS quantification
LCX RCA
N=35
patients
J Nucl Med 2009; 50:1621–1630
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CTA-MPS fusion: possible applications
comparison of CTA and MPS perfusion imaging
Cedars-Sinai Medical Center
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Normal ROI, plaque lesion limits, fast luminal centerline within limits
Automatic CTA measurements/annotation
NCPNCP volume 112 mm3
Dey et al, ACC 2010
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Quantitative PET
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Motion-frozen perfusion imaging
Images obtained at Cedars-Sinai Project PI: Dan Berman
Lantheus clinical trial 18F- flurpiridaz (BMS747158)
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EF stress QPET/QGS Rb-82 vs. post-stress CTA
EF QPET vs. CTA
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10 20 30 40 50 60 70 80 90
EF-CT
QP
ET
EF
-str
Identity
Difference Plot
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Mean of All
Dif
fere
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QP
ET
EF
-str
- E
F-C
T)
Identity
Bias (-3.1)
95% Limits of agreement
(-17.2 to 11.1)
Bias = -3%
r =0.90
EF QGS vs CTA
10
20
30
40
50
60
70
80
90
10 20 30 40 50 60 70 80 90
EF-CT
QG
S E
F-s
tr
Identity
Difference Plot
-30
-25
-20
-15
-10
-5
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10 20 30 40 50 60 70 80 90
Mean of All
Dif
fere
nce (
QG
S E
F-s
tr -
EF
-CT
)
Identity
Bias (-13.6)
95% Limits of agreement
(-28.0 to 0.9)
Bias= -14%
r =0.87
Slomka, Germano, Bengel, J Nucl Med. 2009; 50 (Supplement 2):1167
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PET/CT fusion ED
Slomka, Germano, Bengel et al, SNM 2009
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PET/CT fusion ES
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Gated PET vs. Gated CTA
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GATED SPECT FOR DIASTOLIC
PERFUSION ASSESSMENT
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“Motion frozen” gated myocardial perfusion SPECT
Summed images (8 frames)
Conventional summation Summation after morphing
to ED template
Slomka et al., JNM 2004
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“Motion frozen” gated myocardial perfusion SPECT
Slomka et al., JNM 2004
Displacement
vectors are used for
warping ES onto
ED
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MF accuracy in obese population
0102030405060708090
100
Sensitivity Specificity Accuracy0
102030405060708090
100
Sensitivity Specificity Accuracy
S-TPD
MF-TPD
P = NS P = NS
92% 92%
59%
82% 82%88%
P < 0.05P < 0.05
P < 0.05
P < 0.05
93% 95%
55%
77%80%
89%
A B≥50% Stenosis ≥70% Stenosis
N=90
Suzuki Y et al. J Nucl Med. 2008 Jul;49(7):1075-9
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QUANTITATIVE RVEF
FROM PERFUSION SPECT
Case example