IMPROVED
MRI TEMPERATURE IMAGING
USING A SUBJECT-SPECIFIC BIOPHYSICAL MODEL
Nick Todd, Allison Payne,
Douglas A. Christensen, Henrik Odeen, Dennis L. Parker
Utah Center for Advanced Imaging Research, University of Utah
Background

UCAIR
Utah Center
For Advanced
Imaging Research
Utah Projects in MRI
guided HIFU

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Large animal MRgHIFU
system (Siemens/IGT)
Small animal MRgHIFU
system (IGT)
Breast MRgHIFU system


(UofU/IGT/Siemens)
See poster 4.8 by
Allison Payne
Background: Utah Projects

Utah Center
For Advanced
Imaging Research
MR guided HIFU

Breast:

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Develop the Utah Breast MRgHIFU system
Brain


UCAIR
Develop 3D MRI Temperature measurements for MRI guided
Brain HIFU
Temperature measurement requirements

Breast:

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glandular tissues AND fat
Near-field protection
Brain:


cover entire skull volume
high temporal and spatial resolution
Tumor
MR Temperature Basics
UCAIR
Utah Center
For Advanced
Imaging Research
Proton Resonance Frequency Shift (PRF).
MR signal frequency depends on local
chemical environment of water
Hydrogen.
Temperature changes affect this
environment.
Current Time Frame
Frequency changes
measured as image
phase changes.
Reference
-
Difference
=
Temperature Map
Breast: Temperature Measurements
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Requirements:
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
Utah Center
For Advanced
Imaging Research
3-point Dixon Images
Control treatment in
glandular tissue
Avoid fat necrosis
Coverage, speed, and
resolution
Temperature in water
and fat?

UCAIR
Fat
Water
Hybrid PRF/T1 method
2D GRE
2D/3D Segmented EPI
fat
agar
PRF temperature
map
T1 signal change
map
MRI Thermometry - Breast
Hybrid PRF/T1
Signal from Spoiled GRE sequence:
1- E ) sin a
(
S =C
×e ( )
1
if T
1- E1 cos a
E1  exp  TR T1 T  
Image sequence: 2 alternating flip angles
PRF from phase of each image
T1 from two images
S
S
=
E1 + C (1- E1 )
sin a tan a
T1 = -TR ln ( m)
Deoni, Rutt, Peters. Magn Reson Med 2003 49:515-526.
UCAIR
Utah Center
For Advanced
Imaging Research
Breast: Temperature Measurements
A) 3-Pt Dixon Water Image
C) PRF/T1 Magnitude Image
Pork
Muscle
Breast
Fat
Targeted Area
Transducer
B) 3-Pt Dixon Fat Image
D) PRF Temperature Map
UCAIR
Utah Center
For Advanced
Imaging Research
Breast: Temperature Measurements
T1 Percent Change in Breast Fat
PRF Temperatures in Pork
B)
C) PRF/T1 Magnitude Image
Pork
Muscle
Breast
Fat
Targeted Area
Transducer
D) PRF Temperature Map
UCAIR
Utah Center
For Advanced
Imaging Research
UCAIR
Utah Center
For Advanced
Imaging Research
Transcranial
MRI guided HIFU
Funding:
Focused Ultrasound Surgery Foundation
NIH R01 EB013433
Transcranial MRI guided HIFU


UCAIR
Utah Center
For Advanced
Imaging Research
Cover all heated regions: Skull + within
Resolution Speed
Coverage (FOV)
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1mm isotropic
1s
Full head/breast
205x160x100 TR=35, ETL=7: 80s
205x160x33 (1x1x3mm), TR=35, ETL=7: 27s
Image Volume
Required Values
UCAIR
Utah Center
For Advanced
Imaging Research
1 x 1 x 3 mm
Spatial Resolution:
Temporal Resolution: 2 seconds per image
Volume Coverage:
256 x 162 x 72 mm
Signal - to - Noise:
Brain
Image Volume:
256 x 162 x 72 mm
Image Volume
Transcranial MRI guided HIFU

How to go faster:

2D Spatially selective RF excitation

Prefer full FOV

Parallel imaging + UNFOLD1

Temporally Constrained Reconstruction (TCR)2

Model Predictive Filtering (MPF)3

1: Chang-Sheng Mei, et al. Magnetic Resonance in Medicine 66:112–122 (2011)
2: N. Todd et al. Magn Reson Med 62(2):406-419 (2009).

3: N. Todd, A. Payne, D. L. Parker, Magn Reson Med 63:1269–1279 (2010)

UCAIR
Utah Center
For Advanced
Imaging Research
Data Acquisition & Reconstruction
~
Fm  d '
Data Space (k-space)
UCAIR
Utah Center
For Advanced
Imaging Research
F = Fourier Transform
~ = Image Estimate
m
d’ = Undersampled Data
Image Space
Inverse
Fourier
Transform
256 x 162 x 24 pixels
256 x 162 x 24 pixels
UCAIR
Constrained Reconstruction
2
~
~
min
m t md '2 
s.t. F
Utah Center
For Advanced
Imaging Research
F = Fourier Transform
~ = Image Estimate
m
d’ = Undersampled Data
t = Gradient in time

2
2
~
~
m  arg min WF m  d ' 2    t mi 2 
i


~ is iteratively updated subject to
m
constraints: Image must match acquired data
Image must change smoothly in time
iteration = 5
iteration = 25
iteration = 50
iteration = 100
TCR: Constrained Reconstruction
Sequence Parameters
•
•
•
•
•
UCAIR
Utah Center
For Advanced
Imaging Research
Data Undersampling
1.5 x 2 x 3 mm
288 x 216 x 108 mm
192 x 108 x 36 matrix
EPI Factor: 7 lines per excitation
TR/TE = 35 / 9 ms
ky
kz
Scan Time:
1.8 s / time frame
Constrained
Reconstruction
25 s / full data set
Not real time
Constrained Reconstruction Results
Validation Tests:
Utah Center
For Advanced
Imaging Research
Full Data
“Truth”
2.8 s
5.4 s
10.1 s
16.2 s
Constrained
Reconstruction
6X data
reduction
“Truth”
2.8 s
0.9 s
1.7 s
2.7 s
“Truth”:
Full Data used
1.5 x 1.5 x 3.0 mm
2.8 seconds per image
288 x 162 x 24 mm
Test Cases:
288 x 162 x 48 mm
288 x 162 x 90 mm
288 x 162 x 144 mm
UCAIR
Model Predictive Filtering
UCAIR
Utah Center
For Advanced
Imaging Research
¶T
rC = kÑ2T -WbCv (T - Ta ) + Q
¶t
æ k 2 WbCv
Qj ö
T j+1 = T j + ç Ñ T j T j - Ta, j + ÷ Dt
rC
rC ø
è rC
(
)
Artifact-free
Temperature maps
Goal: real time
N. Todd, A. Payne, D. L. Parker, MRM 63:1269–1279 (2010)
Model-Predictive Filtering

Segment tissues
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Determine tissue-specific thermal and
acoustic properties
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UCAIR
Utah Center
For Advanced
Imaging Research
TCR + Modeling
Use tissue-specific properties in dynamic MPF
temperature measurements
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Realtime, 3D, large FOV
From highly undersampled 3D segmented EPI
PRF
Tissue Segmentation
UCAIR
Utah Center
For Advanced
Imaging Research
Breast tissue segmentation
•
Hierarchal Support Vector Machine algorithm
Non-FS T1
FS T2-w
FS PD-w
3pt Dixon
H2O only
h-SVM saggital
h-SVM w/ Zero-Filled-Interpolation
3pt Dixon
Fat only
Tissue property estimation:
Acoustic parameters
UCAIR
Utah Center
For Advanced
Imaging Research
Segment treatment
volume into a small
number of tissue types
4-8 low power
pulses cover
targeted volume
TCR – reconstruct
temperature images
In-vivo estimates of the change in the attenuation coefficient
with log10 of thermal dose using the iterative parameter
estimation technique . Urvi Vyas et al. ISTU 2011
MR temps to get
SAR patterns
Use ultrasound model (HAS) to determine
absorption and speed of sound
to match measured pattern
Tissue acoustic values for
Model Predictive Filtering.
Tissue property estimation:
Thermal parameters
UCAIR
Utah Center
For Advanced
Imaging Research
Segment treatment
volume into a small
number of tissue types
4-8 low power
pulses cover
targeted volume
TCR – reconstruct
temperature images
MRI temps during
cooling
Determine thermal
diffusivity using
cooling temperature
curves
Cheng et al., JMRI 16(5), 2002
 r2 
T r , t   At  exp 2 
 R t 
 2
4k
R t  
t
c
Hybrid Angular Spectrum (HAS):
Pressure Modeling
UCAIR
Utah Center
For Advanced
Imaging Research
HAS SAR prediction
UCAIR
Utah Center
For Advanced
Imaging Research
HAS: Head Model
Courtesy: Guido Gerig, University of Utah
UCAIR
Utah Center
For Advanced
Imaging Research
UCAIR
Utah Center
For Advanced
Imaging Research
UCAIR
Utah Center
For Advanced
Imaging Research
Model Predictive Filtering
UCAIR
Utah Center
For Advanced
Imaging Research
Multi-step, recursive algorithm
Phase (n+1)
1
Temp (n)
Temp (n+1,
model)
Df
DT =
ag B0TE
Step 1: Use model to predict temperature at time n+1.
Magnitude (n)
K-space (n+1)
5
Step 2: Convert temperature map to phase map for time n+1.
Step 3: Use this phase and the magnitude from time n to create k-space for
time n+1.
Step 4: Insert any actually acquired k-space lines.
Step 5: Recalculate the temperature for time n+1 using the data updated kspace.
Temp (n+1, model
and data)
Model Predictive Filtering
UCAIR
Utah Center
For Advanced
Imaging Research
Use the Pennes Bioheat Equation, tissue
properties, and a pre-treatment heating to
determine the thermal model.
¶T
rC
= kÑ2T -WbCv (T - Ta ) + Q
¶t
Full Data
T = temperature
 = density
C = tissue and blood heat capacity
k = thermal conductivity
Wb = blood perfusion
Q = heat applied
Model Only
2-D MPF Results
UCAIR
Utah Center
For Advanced
Imaging Research
Fully sampled k-space data sets: 288x288x20mm FOV, 2.3x2.3x4mm res, 8.3 sec/scan.
25% of k-space used in reconstruction.
Power = 36W (Model Id data set)
Mean and STD of error over an ROI
MPF
Power = 42W
Mean and STD of error over an ROI
MPF
Power = 48W
Mean and STD of error over an ROI
MPF
3D (R=12) vs 2D (R=1) MPF Temperatures
UCAIR
Utah Center
For Advanced
Imaging Research
Common:
Ultrasound pulse = 36 W/58.1 sec
3-D GRE:
FOV = 256x256x32 mm3,
Matrix = 128x128x16
Resolution = 2.0x2.0x2.0 mm3
TR/TE = 25/8 ms
Tacq = 76.8 s/image volume (R=1)
= 6.4 s/image volume (R=12.1)
2D GRE:
FOV = 256x256x20 mm (sl = 3mm)
Matrix = 128x128
Resolution = 2.0x2.0x3.0 mm3
TR/TE = 65/8 ms;
8.3 sec per scan (R=1)
Scans repeated 8x for variability
N. Todd, A. Payne, D. L. Parker, MRM 63:1269–1279 (2010)
Model Predictive Filtering Results
Phantom Heating
2.0 x 2.0 x 2.0 mm
0.5 seconds per image
256 x 162 x 48 mm
σT < 1°C
Transverse:
Sagital:
Coronal:
UCAIR
Utah Center
For Advanced
Imaging Research
Summary: Work in Progress

Utah Center
For Advanced
Imaging Research
Brain requires:




UCAIR
Large FOV: Cover insonified volume
High speed: 1s/volume
High resolution: < 1 x 1 x 3 mm3
Our solutions:
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PRF: Highly undersampled (>8) 3D segmented EPI
TCR:
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Does not require tissue thermal and acoustic properties
Achieves high spatial and temporal resolution, large FOV, LOW NOISE!
Cannot (yet) be performed in real time
Model-predictive Filtering (MPF)
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Requires
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Property estimates:
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tissue segmentation
estimate of tissue acoustic and thermal properties
SAR: Hybrid Angular Spectrum (HAS)
Diffusivity/Perfusion: MRI during cooling
Also achieves high spatial and temporal resolution, large FOV, LOW NOISE!
Potential real time application
Parallel imaging
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Can be used to supplement TCR or MPF
Difficult with currently used HIFU coils
Acknowledgments
Thank You
People:
Yi Wang
Urvi Vyas
Dennis Parker
Emilee Minalga
Bob Roemer
Joshua de Bever
Doug Christensen
Chris Dillon
Leigh Neumayer
Joshua Coon
Allison Payne
Justin Tidwell
Nick Todd
Lexi Farrer
Rock Hadley
Robb Merrill
Nelly Volland
Mahamadou Diakite Henrik Odeen
Funding:
Focused Ultrasound Surgery Foundation
Siemens Medical Solutions
NIH grants
F31 EB007892-01A1,
R01 EB013433, and R01 CA134599.
UCAIR
Utah Center
For Advanced
Imaging Research