Role of image guidance in thermal therapy
Thermal Dosimetry Issues
In Thermal Therapy
• Facilitate more optimized treatment
Spinal cord
– planning
– targeting/localizing
VX-2
– monitoring/control
– verification
R. Jason Stafford, PhD
Department of Imaging Physics
• Imaging information synergistic with
integration of model based simulation
HIFU Ablation in Rabbit Paraspinal
Muscle @1.5T
• Endgame
– increase safety + efficacy
– facilitate minimally invasive approaches
previously not considered possible/safe
AAPM Ultrasound Symposium 2011 (Vancouver, BC)
Thermal therapy energy sources
— Treatment prescription
 Prescribed sonication point
Thermal dose (point)
— Thermal dose (total)
Hazle JD, Stafford RJ, Price RE ,JMRI 15 (2): 185-94., 2002.
Challenges for real-time monitoring
Multi-element U/S applicators
• Cryotherapy
HIFU
Interstitial/Intracavitary
Externally focused high-intensity ultrasound kernal
Water cooled interstitial or transurethral ultrasound applicator
• Radiofrequency
• Microwave
• Laser
• Ultrasound
3 mm
30
mm
High intensity focused ultrasound
(FUS or HIFU)
4
mm
Focused ultrasound beam
Faster delivery
Dt ~resolution
10 sec
High
Small Volume
25
Slower delivery
Dt ~ 10mm
minutes
Lower resolution
Larger Volume
1
Modalities for image-guided thermal therapy
Modalities for image-guided thermal therapy
T 2 P re
• US
T 2 T reat
T 2 P o st
T 2 P o st
T 1 T reat
T 1 + C P o st
T 1+ C P ost
T issu e P ath o lo g y
• US
A p p lica to r
• CT
• CT
• MRI
• MRI
T 1 P re
Multi-element U/S applicator therapy
Non-invasive
Non-ionizing
Arbitrary oblique plane orientation
Near real-time acquisition speeds
Multiple contrast mechanisms for
- anatomy
- function
- metabolism
P ath & D o se
—
Isodose: t 4 3 = 50 m in.
- temperature …
Example: MRgLITT in canine brain
MR Temperature Imaging (MRTI)
• Diffusion
Predicting outcomes using temperature history
• Proton resonance frequency (PRF) of
water shifts linearly with temperature
• Thermal damage is cumulative effect
• Sensitivity: -0.01 ppm/°C (water)
• Damage as function of exposure can be
modeled as an Arrhenius rate process (W)
• T1-relaxation
• PRF Shift
Tissue Type
(Canine)
• Advantages
Brain
Temp. Range
(ºC)
Temp. Sensitivity
(ppm/ºC)
25-59
-0.0102 + 0.0005
– Prostate
Reasonable temperature
sensitivity
32-59
-0.0099 + 0.0004
– Relatively
independent
of tissue
type+ 0.0006
Kidney
35-54
-0.0103
– Fast,
Liver gradient echo
35-51based acquisitions
-0.0098 + 0.0002
Bone (femur)
Amplitude (A.U.)
17-57
– Less sensitive at low field strengths
– Lipid is insensitive to temperature
– Sensitive to background field changes
• Motion, susceptibility, etc
water
(-OH)
t
W  A e
 Ea
R T ( )
d
-1.2
0
1.2
Frequency (ppm)
DT
R = Universal Gas Constant
A = Frequency Factor (3.1 x 10 98 s-1)
Ea = Activation Energy (6.3 x 10 5 J)
5ºC
Normal Canine Brain
(Henriques FC, Arch Pathol, 1947; 43: p. 489.)
0
• Cumulative Equivalent minutes @ 43°C (CEM43)
– Empirically derived from isoeffects observed in low
temperature hyperthermia work:
n D t
C E M 43 ( t n ) 
R
t0
( 43  T n )
 0.25 T n  43  C
 D t , w ith R = 
 0.50 T n  43  C
Sapareto SA, Dewey WC Int. J. Rad. Onc. Bio. Phys. 10: 787-800, 1984.
-2.4
laser fiber
30ºC
– Isotherm characterization of bioeffects limited
-0.0109 + 0.0002
• Disadvantages
aliased
lipid (-CH2)
12s HIFU
sonication
MRgFUS
isodose models
W>1
T > 57ºC
CEM43 > 240 min
2.4
Modern Review: Rieke, V. & Butts Pauly, K, JMRI, 27:376-90 (2008);
2
Arrhenius rates and a connection between A and E
Imaging versus Histology
30ºC
15s FUS exposure in vivo
(skeletal muscle)
ln(A*Dt)=ln(W)-E/RT
25 W
ln(A*Dt)
Dt
T for W=1
1s
60.1°C
5s
57.2°C
30s
54.1°C
240m 43.8°C
50 W
DT
75 W
(from regression)
100 W
E (Joules)
85 points plotted from literature reports of Arrhenius fits
1cm
125 W
A variety of tissues, cells, biological macromolecules and endpoints
and heating rates over the range of 43°C-80°C are represented
Doubling Dt results in the familiar ~1°C change for equivalent
damage of CEM43
0ºC
T2-W
(PRF-MRTI)
T2-W FSE
(post)
T2W-FSE
(t43>240min)
Wright NT, J Biomech Eng. 2003 Apr;125(2):300-4
Dice Similarity Coefficient
Arrhenius Damage (W)
Bias: 54.0+299.4 mm 3
DT (°C)
canine prostate
Regression and Bland-Altman Analysis
Pathology/H&E
(Cogaulation,Edema, t43>240min)
canine brain
Arrhenius Dose (W>1) ()
Outer rim of T1+C enhancement ()
Bias: 7.35+20.1 mm 2
Subject
DSC
1
0.92
2
0.91
3
0.91
4
0.93
Dice similarity coefficient (DSC):
DSC 
# in te rse ctin g
a v e ra g e

2 X Y
X  Y
mean+s 0.92+0.01
(Yung, JP, et al, Med Phys. 2010 Oct;37(10):5313-21)
3
Logit dose analysis of ex vivo changes in tissue R2*
FCSI for Multiparametric Imaging Guidance
R2* (Hz)
fiber
ΔR2* (Hz)
ΔT1-W Amplitude (%)
Temperature (°C)
37°C
50 Hz
Arrhenius Dose {W/(1+W)}
2.5%
70°C
0%
0 Hz
W(LD50) = 0.77+ 0.40
W(LD50) = 1.10 + 0.32
W(LD50) = 0.85+ 0.49
W(LD50) = 0.97+ 0.30
W(LD50) = 1.10+ 0.25
W(LD50) = 1.00 + 0.14
T (C)
Isotherm lines for damage from the Arrhenius
dose rate model correlate well with areas where
inflection is seen in the slope of R2*(T).
Calculated Dice Similarity Coefficient (DSC) = 0.84
DR2* Derived
Arrhenius
Probit analysis of thermal dose: McDannold NJ, et al, MRM 2004; Above results: Taylor BA, et al, JMRI 2010.
Taylor, BA, Stafford, RJ, iMRI Symposium 2010 (submitted to NMR in Biomed)
Arrhenius rate processes for alternative isoeffects
Multi-element IUS heating
(15 min @ 7W)
Multi-element IUS heating
(15 min @ 7W)
Arrhenius rate processes for alternative isoeffects
T1+C (sub)
(15 min heating)
W>1
N-K pump
(Grinberg 2001)
DT> 54°C
(Isotherm for 30sec)
W>1
Vascular stasis
(Brown 1992)
W > 1 (skin)
(Pearce SPIE 2009)
T1+C (sub)
(15 min heating)
CEM43 > 240 min
FD > 0.5
(Sapareto-Dewey 1984)
(Pearce, SPIE 2009)
W>1
ATP synth e
(Wang 1993)
W > 1 (skin)
(Henriques 1943)
DT> 54°C
(Isotherm for 30sec)
W>1
Cyt C
(Liggins 1999)
W > 1 (skin)
(Henriques 1943)
W > 1 (skin)
(Pearce SPIE 2009)
CEM43 > 240 min
FD > 0.5
(Sapareto-Dewey 1984)
(Pearce, SPIE 2009)
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Multi-element IUS heating
(15 min @ 7W)
Arrhenius rate processes for alternative isoeffects
Fast Chemical Shift Imaging (FCSI) for MRTI
N
rf
F ID ( t ) 
C
Gf
H (z) 
16-echo MFGRE for fast CSI
W>1
(Pearce, SPIE 2009)
W > 1 (skin)
(Henriques 1943)
B(z)
A( z )
(Sapareto-Dewey 1984)
FD > 0.5
(Pearce, SPIE 2009)
n
z
n  
n
1
dn  

n
z
n
Cn 
n 1
Im  ln(  n ) 
Dt
R e  ln(  n ) 
Dt
B(n )
 n A (  n )
20
Amplitude (A.U.)
lipid peaks
15
10
water
Susceptibility induced error
corrected using lipid reference
5
0
water
-5
-10
ΔT(δW-L)
CSI (Difference)
ΔT(δW)
CSI (Water Only)
-15
5ºC
CEM43 > 240 min
 w (t )
MR Spectrum (1 voxel from image)
DT
W>1
ATP synth e
(Wang 1993)
  i n  d n  T E n
n0
Q

CSI-MRTI in a Canine Femur
30ºC
W>1
HSP70 Expression
(Beckham 2004)

Temperature Change (°C)
DT> 54°C
(Isotherm for 30sec)
e
P
GΦ
T1+C (sub)
(15 min heating)
n
n 1
Gs
CPDΔT(Δ
-20
-2.4
-1.2
Temperature Probe
for verification
0
1.2
Frequency (ppm)
2.4
φ)
Fluoroptic
Flouroptic
ProbeProbe
-25
0
100
200
300
400
500
600
700
800
Time (sec)
Taylor BA, et al., Medical Physics. 2008;35(2):793-803.; Taylor BA, et al., Medical Physics. 2009;36(3): 753-764.
PRF-MRTI of FUS in breast
Fast CSI for MRTI
3-Peak Lipid
3-Peak Lipid
Multi-planar, multi-shot EPI MRTI
facilitated real-time MRTI with
high spatiotemporal resolution,
high SNR and lipid suppression
Water
3-Peak Lipid Water and
Lipid
5
T1-W MRTI for Adipose Tissue
Fast CSI of FUS in canine prostate
Fluoroptic
Probe
Temperature (C)
Adipose
Tissue
Gel
Water PRF
Adipose Temperature Sensitivity Calibration
W-F T2*
30
T1-W Signal (R2* corrected)
Laser
25
20
15
10
TSC = -0.56 +/- 0.05 %/C
5
0
Arrhenius Dose (W)
Taylor BA, et al, NMR Biomed (2011)
Adipose R2* (Hz)
Temperature (C)
breakpoint at W=1
Temperature (C)
Hynynen K, McDannold N, Mulkern RV, Jolesz FA., MRM 2000 Jun;43(6):901-4.
Challenges for PRF MRTI in the Body
Coupling MRTI feedback to models
• Motion distorts both anatomy & magnetic field
– Need to correct both for integrated damage estimate
– Breath holds can provide reasonable results
c
• Background B0 correction
Challenges
Background field drift
Intra/inter scan motion
Through plane motion
Applicator susceptibility
Variable SNR (tissue/time)
Real-time
(instantaneous)
Gating, navigators, multi-baseline techniques
Internal reference techniques (i.e., lipid)
Referenceless techniques (Rieke V, et al 2004)
Real-time modeling and simulation with MR feedback
Real-time
(cumulative)
–
–
–
–
magnitude
Below: MR-guided laser
ablation in liver with
background phase-correction
temperature
(uncorrected)
temperature
(corrected) 77ºC
 T ( qi , t )
   [( k  T ( qi , t ))]   b CbV  k  1   T  Ta   P ( qi , t )
t
heat diffusion
heat convection
heat source
Pennes Bioheat Equation useful for modeling therapy:
HEAT SOURCES (laser, ultrasound, RF, etc) :
P = Power absorbed (W m-3)
42ºC
Arrhenius Dose Arrhenius Dose
mean magnitude (uncorrected)
(corrected)
HEAT CONDUCTION (diffusion):
T = temperature (Kelvin)
c = specific heat of material (J kg-1 ºC-1)
ρ = density (kg/m3)
k = thermal conductivity of tissue (W m -1 ºC-1)
HEAT CONVECTION (effects of perfusion):
b = blood density (kg m-3)
V = volume flow rate per unit volume (s -1)
Cb = specific heat of blood (J kg-1 ºC-1)
k = dimensionless convection scale factor
Pennes HH, J Appl Physiol. 1: 93–122 (1948)
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Dynamic Data Driven, Model Constrained MRTI
MRTI
(°C)
Uncertainty
(°C)
Simulated
Data Loss
MDACC
MRTI = Dynamic Input
Algorithm compares
uncertainty in input
verses uncertainty in
model and provides an
optimal estimate
Can help compensate
for noisy, missing or
corrupt data
Can help in optimal
interpolation or
retrospective filtering
Kalman Predicted MRTI
Measured MRTI
( 95% CI for 57°C Isotherm)
(°C)
Acknowledgements & Collaborators
Computation intensive,
requires highly
parallelized, high
performance computing
to perform in real-time
(work in progress)
Imaging Physics
•
John Hazle, PhD
•
Edward F. Jackson, PhD
•
R. Jason Stafford, PhD
•
Luc Bidaut, PhD
•
Ken-Pin Hwang, PhD
•
Jim Bankson, PhD
•
•
•
Andrew Elliot, PhD
David Fuentes, PhD
•
•
Brian Taylor
Joshua Yung
•
•
Krista McAlee, RT(R)MR
Brandy Reed, RT(R)MR
Marites Melancon, PhD
Experimental Diagnostic Imaging
•
Chun Li, PhD
•
Marites Melancon, PhD
Interventional Radiology MRI
•
Kamran Ahrar, MD
•
Judy Ahrar, MD
•
Sanaz Javdi, MD
•
Yvette Valenzuela, RT(R)MR
Neurosurgery BrainSUITE™
•
Jeff Weinberg, MD
•
Rice University
•
Naomi Halas, PhD
•
Jennifer West, PhD
•
Lee Hirsch, PhD
•
Scott Sershen, PhD
Van Luu, RT(R)MR
Urology
•
John Ward, MD
Veterinary Medicine & Surgery
•
Peggy Tinkey, DVM
•
Rajesh Uthamanthil, DVM, PhD
•
Agatha Bourne, DVM, PhD
•
Sherry Klumpp, PhD
University of Texas - Austin
•
Tinsley Oden, PhD
•
Kenneth Diller, PhD
University of Texas – San Antonio
•
Yusheng Feng, PhD
Biotex, Inc
•
Ashok Gowda, PhD
•
Roger McNichols, PhD
•
Anil Shetty, MD, ME
•
Kevin Pham
Nanospectra Biosciences, Inc
•
Don Payne
•
Jon Schwartz, PhD
Work supported in part by: NIH CA79282, NIH AG19276, NIH CA101573, NIH
5T32CA119930, NSF CNS-0540033, NSF IIS-0325550, NSF IIO-0548741
David Fuentes, PhD
The diseases which medicines cannot
cure, excision cures: those which excision
cannot cure, are cured by the cautery; but
those which the cautery cannot cure, may
be deemed incurable.
- Hippocrates Aphorisms (400 BCE)
Thank you for your time!
Email: jstafford@mdanderson.org
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