Treatment Time Reduction through Parameter Optimization in Magnetic Resonance Guided High Intensity Focused Ultrasound Therapy

Joshua CoonDecember 7, 2011

PART ONE: OVERVIEW AND THEORY

High Intensity Focused Ultrasound (HIFU): Overview

• Ultrasound energy used to heat/ablate tissue– Magnifying glass and light

• Clinical use as cancer therapy– Several advantages over traditional

therapies

• Area of active research– Extensive clinical trials in China and

Europe

Why Use HIFU?

• No poisonous chemicals– Chemotherapy

• No ionizing radiation– However, sometimes HIFU used

with radiation• Relatively non-invasive

– Compared to surgery• Shorter recovery time

– Outpatient procedure; short repetition time

HIFU Transducer

Safety, Efficacy and Treatment Time

Ultrasound Transducer

Safety• Reduce healthy tissue heating

Efficacy• Ensure entire tumor treated

Treatment Time• Long treatments reduce safety

and efficacy• Patient movement• Permanent tissue property

changes• Attenuation

coefficient• Cost

Treatment Parameters

Controllable• Transducer manufacturing

– Central beam frequency– Number of elements– Size and radius of curvature

• Transducer state– Power level– Power on and off times

• Characteristics of focal zones– Focal zone size(s)– Focal zone shape(s)

• Duty cycle for diluted focal zones– Spacing(s) between focal zones– Focal zone packing

• Path of focal zones through tumor– Axial and transverse

Non-Controllable• Physiological parameters

– Perfusion and conduction– Tissue composition– Tumor geometry– Tumor location

Ultrasound Transducer

Components of HIFU Treatment Simulations

• Ultrasound Attenuation Equation– Models how ultrasound energy is converted to

heat• Heat Flow Equation

– Models flow of heat through the body• Thermal Damage Equation

– Models how much tissue is damaged due to heating

Treatment Time

• Objective function for optimization routines• Has additional constraints to ensure treatment

efficacy and patient safety

𝒕 𝒕𝒓𝒆𝒂𝒕𝒎𝒆𝒏𝒕=∑𝒏=𝟏

𝑵𝒕𝒉𝒆𝒂 𝒕𝒏+𝒕𝒄𝒐𝒐𝒍𝒏

Treatment Time Optimization

• Run computer simulated treatments to optimize user controllable parameters – to minimize treatment time– Large number of possible treatments ~ – Well in excess of 200,00 computer hours lifetime

• Confine investigations to parameters likely to realize the greatest gains– Trajectory of focal zone– Focal zone size– Focal zone spacing

PART TWO: MY RESEARCH

First Paper: Treatment Time Reduction through Treatment Path Optimization

• Coon J, Payne A, Roemer R. HIFU treatment time reduction in superficial tumours through focal zone path selection. International Journal of Hyperthermia. 2011;27(5):465-81.

Study Motivation

• Reduce MRgHIFU treatment times– Strategies for treatment path selection

• Develop a model of the physics behind treatment time reductions– Role of thermal superposition

• Tumor• Normal tissue

– Role of non-linear rate of thermal damage

11.2 cm

3.3 cm

3.3 cm3.3 cm

x y

z

Normal tissue constraintsat +/- 1cm

Simulation Geometry Tumor = 1.8cm x 1.8cm x 0.8cm

43 °C Normal tissue limit

37 °C Region boundary

Pennes equation

Homogeneous\constant tissue properties

240 CEM in tumor

Ultrasound Modeling

• Ultrasound beam from transducer modeled via Hybrid Angular Spectrum (HAS) method

• Modeled with parameters taken from a 256 element phased array used in experiments

• Developed by Dr. Christensen of the Bioengineering department

Thermal and Tissue Damage Modeling

• Thermal modeling via finite difference time domain approximation of bioheat equation1. Region broken into small cubes with constant physical and

acoustic properties2. Cubes start with temperature at time 3. Conduction, perfusion, and heat deposition (via calculated for

each cube4. and started again

• Tissue damage (thermal dose) integrated from generated temperature maps

Treatment Path

• Tumor ablated using three treatment planes– Conservative spacings of 3mm for planes– Planes 15 or 36 positions each

• Paths divided into two major categories– Axially Stacked– Non-Axially Stacked

• Transverse paths were investigated with the best path from the first part of the study

Simulation Region

Back

Middle

Front

Tumor

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

25 26 27 28 29 30

31 32 33 34 35 36

37 38 39 40 41 42

43 44 45 46 47 48

49 50 51 52 53 54

55 56 57 58 59 60

61 62 63 64 65 66

67 68 69 70 71 72

73 74 75 76 77 78

79 80 81 82 83 84

85 86 87 88 89 90

91 92 93 94 95 96

97 98 99 100 101 102

103 104 105 106 107 108

PL (BMF); XY Ra AS (MBF); XY Ra 2 5 8 11 14 17

20 23 26 29 32 35

38 41 44 47 50 53

56 59 62 65 68 71

74 77 80 83 86 89

92 95 98 101 104 107

1 4 7 10 13 16

19 22 25 28 31 34

37 40 43 46 49 52

55 58 61 64 67 70

73 76 79 82 85 88

91 94 97 100 103 106

3 6 9 12 15 18

21 24 27 30 33 36

39 42 45 48 51 54

57 60 63 66 69 72

75 78 81 84 87 90

93 96 99 102 105 108

Results

AS (MFB)XYRa

AS (MBF) XYRa

PL (MFB)XYRa

AS (FBM) XYRa

AS (FMB) XYRa

3D Max Last

3D Max First

3D Kn

PL (FBM) XYKn

AS (BFM) XYRa

AS (BMF) XYRa

PL (BFM)XYKn

Pl (BMF) XYRa

Pl (BMF) XZRa

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Focal Zone Path

Trea

tmen

t Tim

e (s

)

58%

0%0%

38%30%

32%

50%

63%47%

Treatment Path

Conclusion:

• Treatment path selection reduces treatment time

Additional Path Studies

• Also ran for subset of paths and several perfusion and transducer power levels

• The ordering of the paths remained unchanged

0 5 10 15 20 250

2

4

6

8

10

12

14

16

18

20

Time (s)

Tem

pera

ture

Ris

e (C

)

Middle

Back

Front

Adjacent

Single Pulse Heating: Middle Plane

17 18 19 20 21 22

36 5 6 7 8 23

35 16 1 2 9 24

34 15 4 3 10 25

33 14 13 12 11 26

32 31 30 29 28 27

1 22 34 8 29 16

33 13 30 15 23 7

21 2 9 6 17 28

12 31 14 24 35 5

25 20 3 10 27 18

32 11 26 19 4 36

1 2 3 10 11 12

8 9 4 17 18 13

7 6 5 16 15 14

19 20 21 28 29 30

26 27 22 35 36 31

25 24 23 34 33 32

1 2 3 4 5 6

14 15 16 17 18 7

13 12 11 10 9 8

19 20 21 22 23 24

32 33 34 35 36 25

31 30 29 28 27 26

Transverse PathsExtensions:

• Take best axial stack and study transverse paths

Inner-Middle-Outer (IMO) Knight Jumps (Kn)

Small Squares (Sq) Large Rectangles (Rec)

Trea

tmen

t Tim

e (s

)

Transverse Path

* *

*

*

*

10% 11% 29%39% 43% 43%

57%

72%

79%

6 Degree Constraint

5 Degree Constraint2500

3000

3500

1500

2000

1000

500

0Kn IOM IMO Ra MOI OMI OIM Rec MIO Sq

Transverse Path Study

Conclusions: • Adjacency of axial

stacks desirable for higher normal tissue temperature limit

• Adjacency of axial stacks undesirable for lower normal tissue temperature limit

Additional Studies

• Over 125 paths studied in total, including over 100 random paths (not shown)

• Two additional tumor models studied:– Large superficial tumor– Medium deep tumor

• Results consistent across several paths and tumor models

Conclusions• Treatment path selection can greatly reduce treatment

times• Axial stacking provides largest treatment time reduction• Middle-Front-Back stack ordering always fastest

– Effective use of thermal superposition• Transverse stack “adjacency” selection depends on normal

tissue constraints– High adjacency for higher temperature limit– Low adjacency for lower temperature limit

• Effects hold for range of perfusions, transducer power levels, and tumor sizes and depths

Second Paper: In Preparation

• HIFU Treatment Time Reduction through Optimal Scanning,

Coon J, Todd N, Roemer R.

Goals of Second Paper

• Compare “Concentrated” versus “Diluted” focal zone treatment strategies

• Study optimal focal zone spacing and packing• Verify concentrated vs. diluted results in

phantom model

9.5 cm

7.0 cm

7.0 cm7.0 cm

x y

z

Temperature/dose constraintsat +/- 1cm from tumor edge

Skin/Water Interface

Focal Zone Spacing

x

x

Tumor

Simulation Schematic

Axial Tumor Close-up1.0mm

16, 30mm

Simulation Region

Concentrated vs. Diluted Focal Zones

x

x

x

x

x

x

Next Position Next Position

100%

0%

0%

100% 50%

50%

Concentrated Diluted

Small Axial Tumor

0 2 4 6 8 10 12 1460

80

100

120

140

160

180

200

220

Distance between Focal Zone Centers (mm)

Trea

tmen

t Tim

e (s

ec)

69/31%

40/60%

40/60%

40/60% 43/57%

59/41%

74/26%

68/32%

66/34%

64/35%

73/27%

Conclusions:

• Optimal spacing around 8 or 10mm

• Concentrated focal zones faster than diluted focal zones

x xx x

x x x x

Multi-Position Axial Treatments

3 Position2 Position

4 Position 17 Position

Concentrated vs. Diluted Focal Zones: Small Axial Tumor

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1860

80

100

120

140

160

180

200

220

Number of Focal Zone Locations

Hea

ting

Tim

e

Conclusion:

• Concentrated focal zones treatments faster than diluted for treatments using wide range of focal zone packings

• Diminishing returns with increased packing in concentrated treatments

Transverse Spacing Optimization

x

x

x

x

Volume Treated

•Both stacks & volume between treated•Vary stack transverse distance•Compare ablation rates (mm3/sec) because treatment volumes unequal

Treatment Approach:1, 2, 3, 4, 5, 6mm

16mm

2 Adjacent Axial Stacks: Control Volume Ablation Rates

1 2 3 4 5 60.1

0.15

0.2

0.25

0.3

0.35

Distance Between Axial Stack Centers (mm)

Con

trol V

olum

e A

blat

ion

Rat

e (m

m3 /s

ec)

54/20/15/10%

31/30/13/25%33/30/9/26%

31/32/7/30%

43/41/0/16%

54/21/11/14%

25/25/25/25%

25/25/25/25%

25/25/25/25% 25/25/25/25%25/25/25/25%

25/25/25/25%

Conclusions:

• Concentrated treatments faster than diluted across range of transverse spacings

• Optimal transverse spacing at 3mm

Concentrated vs. Diluted Scanning: Agar Phantom

Concentric Circles

• 25 points• Circles with 1,8

and 16 points• Radii of 0mm,

2.25mm and 4.5mm

Cartesian Grid • 25 points• 5x5 grid• 2mm between

points

• Concentrated scans had 15 seconds of heating per point with one repeat

• Diluted scans had 0.1 seconds of heating per point with 150 repeats

Concentrated vs. Diluted

• MR temperature data used to calculate thermal dose

Concentrated Cartesian

Diluted Cartesian Concentrated Circles Diluted Circles

100

200

300

400

500

600

Phantom Treatment Type

Num

ber o

f Vox

els

Trea

ted

to 2

40

YZ Plane

XZ Plane

XY Plane

Phantom Experiment: Dose Comparison

Conclusions:

• Concentrated treatments have higher ablation rates than diluted treatments

Simulation to Phantom Matching

1.Simulate treatments with variable transducer power and conduction

2. Match dose 240/30 CEM dose contour lines between simulation/phantom

3. Use matched power/conduction to treat volume with different dwell times at each position

Method:

Distance (mm)

Dis

tanc

e (m

m)

20 40 60 80 100

20

40

60

80

100

Simulation/Phantom Matching

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.720

40

60

80

100

120

140

160

180

Simulation Conduction Coefficient [W/(m*C)]

Vox

els

Diff

eren

t Bet

wee

n S

imul

atio

n/P

hant

om

3.02.3

3.22.72.62.5 2.8 2.8

240 CEM Dose Line

30 CEM Dose Line

Conclusions:

• Reasonable match between simulation/phantom dose possible

• The best match for the 30 CEM line corresponds to “literature value” for agar phantom conduction

Dwell Time Study Method

• Use data from simulation/phantom matching study to set transducer power/conduction coefficient

• Reproduce phantom treatments modified to treat a small control volume

• Compare treatments with different dwell times per position

Dwell Time Results

Diluted FZ 0.1 Sec 1.0 Sec Non-Optimed Concentrated FZ

Optimized Concentrated FZ

200

210

220

230

240

250

260

270

280

290

Hea

ting

Tim

e

Conclusion: Making treatments increasingly concentrated shortens treatment times

Dwell Time

Conclusions

• Concentrated focal zones treatments faster than diluted focal zones– Verified in agar phantom– Verified in simulations matched to phantom

• Optimal axial spacing has small amount of overlap between focal zones

• Optimal transverse spacing with small gap between axial stacks

• Diminishing returns with increased packing

Future Work

• Verify axial path, concentrated vs. diluted, and optimal spacing results in phantom/animal models

• Expand simulations to patient specific geometries and changing ultrasound attenuation and blood perfusion values– Preliminary work done with axial path and “worst-

case” attenuation change model