1. 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

2. Background Utah Projects in MRI guided HIFU
Large animal MRgHIFU system (Siemens/IGT)
Small animal MRgHIFU system (IGT)
Breast MRgHIFU system
(UofU/IGT/Siemens)
See poster 4.8 by
Allison Payne
We appreciate the opportunity to present at this NCIGT workshop. During the past 10 years our involvement in MRI guided HIFU has increased and we currently have three functioning HIFU systems: Our original large animal

3. Background: Utah Projects
MR guided HIFU
Breast:
Develop the Utah Breast MRgHIFU system
Brain
Develop 3D MRI Temperature measurements for MRI guided Brain HIFU
Temperature measurement requirements
glandular tissues AND fat
Near-field protection
Brain:
cover entire skull volume
high temporal and spatial resolution
MRI scanner
Tumor

4. MR Temperature Basics - = Proton Resonance Frequency Shift (PRF).
MR signal frequency depends on local chemical environment of water Hydrogen.
Temperature changes affect this environment.
Current Time Frame
Reference
Difference
Temperature Map
Frequency changes measured as image phase changes. - =

5. Breast: Temperature Measurements
3-point Dixon Images
Requirements:
Control treatment in glandular tissue
Avoid fat necrosis
Coverage, speed, and resolution
Temperature in water and fat?
Hybrid PRF/T1 method
2D GRE
2D/3D Segmented EPI
Fat
Water
PRF temperature map
T1 signal change map
fat
agar

6. MRI Thermometry - Breast
Hybrid PRF/T1
Signal from Spoiled GRE sequence:
Image sequence: 2 alternating flip angles
PRF from phase of each image
T1 from two images
Deoni, Rutt, Peters. Magn Reson Med :

7. Breast: Temperature Measurements
A) 3-Pt Dixon Water Image
B) 3-Pt Dixon Fat Image
C) PRF/T1 Magnitude Image
D) PRF Temperature Map
Pork Muscle
Breast Fat
Targeted Area
Transducer

8. Breast: Temperature Measurements
T1 Percent Change in Breast Fat
PRF Temperatures in Pork B)
C) PRF/T1 Magnitude Image
D) PRF Temperature Map
Pork Muscle
Breast Fat
Targeted Area
Transducer

9. Transcranial MRI guided HIFU
Funding:
Focused Ultrasound Surgery Foundation
NIH R01 EB013433

10. Transcranial MRI guided HIFU
Cover all heated regions: Skull + within
Resolution Speed Coverage (FOV)
1mm isotropic 1s Full head/breast
205x160x100 TR=35, ETL=7: 80s
205x160x33 (1x1x3mm), TR=35, ETL=7: 27s
Image Volume

11. Required Values Brain Spatial Resolution: 1 x 1 x 3 mm
Temporal Resolution:
Volume Coverage:
Signal - to - Noise:
1 x 1 x 3 mm
2 seconds per image
256 x 162 x 72 mm
Brain
Image Volume:
256 x 162 x 72 mm
Image Volume

12. 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): (2009).
3: N. Todd, A. Payne, D. L. Parker, Magn Reson Med 63:1269–1279 (2010)

13. Data Acquisition & Reconstruction
F = Fourier Transform
m = Image Estimate
d’ = Undersampled Data ~
Data Space (k-space)
Image Space
Inverse Fourier Transform
256 x 162 x 24 pixels
256 x 162 x 24 pixels

14. Constrained Reconstruction
F = Fourier Transform
m = Image Estimate
d’ = Undersampled Data
= Gradient in time ~
is iteratively updated subject to constraints:
Image must match acquired data
Image must change smoothly in time
iteration = 5
iteration = 25
iteration = 50
iteration = 100

15. TCR: Constrained Reconstruction
Sequence Parameters
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
Data Undersampling ky
For a typical HIFU heating, kz
Constrained Reconstruction
Scan Time:
1.8 s / time frame
25 s / full data set
Not real time

16. Constrained Reconstruction Results
Validation Tests:
“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
Full Data
2.8 s
“Truth”
5.4 s
10.1 s
16.2 s
Constrained Reconstruction
6X data
reduction
2.8 s
“Truth”
0.9 s
1.7 s
2.7 s

17. Model Predictive Filtering
Thermal Model
Artifact-free Temperature maps
Undersampled k-space
Goal: real time
N. Todd, A. Payne, D. L. Parker, MRM 63:1269–1279 (2010)

18. Model-Predictive Filtering
Segment tissues
Determine tissue-specific thermal and acoustic properties
TCR + Modeling
Use tissue-specific properties in dynamic MPF temperature measurements
Realtime, 3D, large FOV
From highly undersampled 3D segmented EPI PRF

19. Tissue Segmentation Breast tissue segmentation
Hierarchal Support Vector Machine algorithm
Non-FS T1
FS T2-w
FS PD-w
3pt Dixon
H2O only
3pt Dixon
Fat only
Another aspect to the grant is to improve the planning, monitoring and assessment of HIFU treatments. In this example Yi Wang has implemented a h-SVM algorithm to segment the breast into multiple tissue types. This segmentation aids both the thermometry and US modeling efforts needed to accurately plan a treatment. The algorithm requires five multi-parametric images as inputs to the algorithm. The results show that using the h-SVM algorithm after interpolating the data give a more accurate segmentation than c-SVM, FCM or h-SVM with no interpolation. This accuracy was confirmed both visually and statistically by a breast radiologist.
h-SVM w/ Zero-Filled-Interpolation

20. Tissue property estimation: Acoustic parameters
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.

21. Tissue property estimation: Thermal parameters
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

22. Hybrid Angular Spectrum (HAS): Pressure Modeling

23. HAS SAR prediction

24. HAS: Head Model
Courtesy: Guido Gerig, University of Utah

27. Model Predictive Filtering
Multi-step, recursive algorithm 3 2
Phase (n+1) 1 3
Temp (n)
Temp (n+1, model)
K-space (n+1) 5
Magnitude (n)
Step 1: Use model to predict temperature at time n+1.
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 k-space.
Temp (n+1, model and data)

28. Model Predictive Filtering
Use the Pennes Bioheat Equation, tissue properties, and a pre-treatment heating to determine the thermal model.
Full Data
Model Only
T = temperature
r = density
C = tissue and blood heat capacity
k = thermal conductivity
Wb = blood perfusion
Q = heat applied

29. 2-D MPF Results
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

30. 3D (R=12) vs 2D (R=1) MPF Temperatures
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)

31. 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:

32. Summary: Work in Progress
Brain requires:
Large FOV: Cover insonified volume
High speed: 1s/volume
High resolution: < 1 x 1 x 3 mm3
Our solutions:
PRF: Highly undersampled (>8) 3D segmented EPI
TCR:
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)
Requires
tissue segmentation
estimate of tissue acoustic and thermal properties
Property estimates:
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
Can be used to supplement TCR or MPF
Difficult with currently used HIFU coils
Just to briefly summarize:

33. Acknowledgments Thank You People: Funding: Yi Wang Dennis Parker
Urvi Vyas
Bob Roemer
Emilee Minalga
Joshua de Bever
Doug Christensen
Leigh Neumayer
Chris Dillon
Allison Payne
Joshua Coon
Justin Tidwell
Nick Todd
Rock Hadley
Lexi Farrer
Nelly Volland
Robb Merrill
Mahamadou Diakite
Henrik Odeen
Funding:
Focused Ultrasound Surgery Foundation
Siemens Medical Solutions
NIH grants
F31 EB A1,
R01 EB013433, and R01 CA