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8/12/2011 1 Developing 4D-MRI For 4D Radiation Therapy Jing Cai, PhD Department of Radiation Oncology Duke University Medical Center, Durham NC Disclosure: No conflict of interest Outline Past - MRI for motion imaging - 4D-MRI strategies Current - Sequences - Surrogates Future - Clinical implementation - Future directions Pros and cons of MRI Pros - Superior soft-tissue contrast to CT - No risk of radiation exposure (long time imaging) - Flexible in image plane selection - Functional/molecular imaging - Variety of image contrasts Cons - Poor spatial accuracy (image distortion) - Various image artifacts (ghost, susceptibility) - Signal not correlated to electron density MRI for Motion Imaging Sites: lung, esophagus, liver, spinal cord, H&N, pancreas, etc. Tumor motion ~ location/size/type of cancer, etc. Correlation: external motion ~ tumor motion Statistical tumor motion (PDF) 4D-CT pitfalls Lung deformation 4D tumor motion in patients (hemi-diaphragmatic paralysis)

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Page 1: Past Developing 4D-MRI For 4D Radiation Therapy · PDF fileDeveloping 4D-MRI For 4D Radiation Therapy Jing Cai, PhD Department of Radiation Oncology ... - MRI for motion imaging -

8/12/2011

1

Developing 4D-MRI For 4D Radiation Therapy

Jing Cai, PhD

Department of Radiation Oncology

Duke University Medical Center, Durham NC

Disclosure: No conflict of interest

Outline►Past

- MRI for motion imaging

- 4D-MRI strategies

►Current

- Sequences

- Surrogates

►Future

- Clinical implementation

- Future directions

Pros and cons of MRI

► Pros

- Superior soft-tissue contrast to CT

- No risk of radiation exposure (long time imaging)

- Flexible in image plane selection

- Functional/molecular imaging

- Variety of image contrasts

► Cons

- Poor spatial accuracy (image distortion)

- Various image artifacts (ghost, susceptibility)

- Signal not correlated to electron density

MRI for Motion Imaging

►Sites: lung, esophagus, liver, spinal cord, H&N,

pancreas, etc.

►Tumor motion ~ location/size/type of cancer, etc.

►Correlation: external motion ~ tumor motion

►Statistical tumor motion (PDF)

► 4D-CT pitfalls

► Lung deformation

► 4D tumor motion in patients (hemi-diaphragmatic

paralysis)

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Tumor Motion Probability2 2 2

, , , , , , , , , ,

, , , ,

1( ) ( ( ) ) (1 sgn( ))

2m

tumor

i j k m i j k i j k m i j k i j k

i j k lung i j k CTVlung

Vobj w d p d P d P

V

Tumor Motion PDF Evolution

► Large variation during the scan

►Tends to stabilize after certain time

Simulate 4D-CT using MRI

Respiratory signals from internal surrogate

1st couch 2nd couch 3rd couch

……

Image Acquisition

Couch Movement

MRI ‘4DCT’

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8/12/2011

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4DCT MIP under-

estimates tumor ITV

MRI ‘4DCT’

y = 91.703x

R2 = 0.7747

y = 55.104x

R2 = 0.8469

y = 36.334x

R2 = 0.8523

0

10

20

30

40

50

60

70

80

0.00 0.20 0.40 0.60 0.80

Breathing Varibility

ITV

Err

or

(%)

1cm

3cm

5cm

Linear (1cm)

Linear (3cm)

Linear (5cm)

y = 45.917x - 0.4786

R2 = 0.7558

0

10

20

30

40

50

60

70

80

0.00 0.50 1.00 1.50 2.00

vR/S

ITV

Err

or

(%)

4D-CT AIP: inaccurate probability?

‘4DCT’ MRI ‘4DCT’ MRI ‘4DCT’ MRI

4D-CT AIP: Patients Spinal Cord Motion

► 4 frames/sec, 20 sec continuous

►Cord motion generally < 0.5 mm

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8/12/2011

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Brain Pulsatile Motion

CC AP

► DENSE sequence

► Pulsatile motion originated from brain stem and radiates toward peripheral brain regions.

► CSF can be easily identified due to its opposite movement

Lung Deformation using HP Gas MRI

2D Dynamic Tagged Lung: Sagittal

0.0s 0.7s 1.4s 2.1s 2.8s

Strategies of 4D-MRI

►Real time 3D

- ultra-fast 3D MR sequence

- fast gradient, multi-channel coils

- parallel processing

- current: voxel 3-4mm, 1.5 sec/frame

►Retrospective-sorted 2D

- fast 2D MR sequence

- respiratory signals (external, internal, etc.)

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8/12/2011

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Retrospective 4D-MRI

► Fast 2D cine MR

► Multiple slices

► Cine duration > 1 cycle

► Frame rate: ~3 frames/sec

► Slice thickness: 3-5 mm

► In-plane pixel size: 1-2 mm

Image Acquisition

► Surrogates

- External

- Internal

- Image-based

► Signal processing

► Phase determination

Respiratory Signal

Aim: simple, robust, quickly implementable

Potential MR Sequences

► TrueFISP/FIESTA (balanced steady state gradient echo)

- T2*/T1, sensitive to fluid, band artifacts from long TR

► HASTE/SSFSE (single shot fast spin echo)

- T2, good CNR, signal decay from lung echo train, blurring

► FLASH/Fast SPGR (fast spoiled gradient echo)

- T1 (poor), tumor hypo-intensity

► EPI (echo-planner imaging)

- GE-EPI (T2*), SE-EPI (T2), IR-EPI (T1)

- susceptibility, ghosting, chemical shift, fat suppression

Examples: 2D cine-MRI

► HASTE: visualize parenchyma better, tumor blurred

► TrueFISP: visualize vascular better, motion artifact

HASTE v.s. TrueFISP

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8/12/2011

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Dependence on Cancer Type

► Heterogeneous structure of squamous cell carcinoma may

reduce its conspicuity in TrueFISP images.

0

20

40

60

80

100

Perc

en

tag

e (

%)

.

Adenocarcinoma Squamous Cell

CNR reduction in TrueFISP relative to HASTE

Summary►HASTE and TrueFISP both can monitor lung

tumor motion during free breathing.

►Tumor conspicuity and image artifacts depend upon tumor characterizations.

►HASTE images show better tumor conspicuity than TrueFISP images.

►HASTE has local blurring artifact; TrueFISP has motion artifacts in the phase encoding direction.

Surrogates: external

RPMBelt

SpirometerSurface Imaging

Surrogates: internal/image-based

• Implanted markers

• Diaphragm

• Air content

• Lung density

• Lung area

• Body area (axial, sagittal)

• Normalized cross correlation

• Deformable image registration

• Fourier transform (magnitude, phase)

Implanted Makers

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Erik Tryggestad, et al. 2011 Joint AAPM/COMP Meeting

Time (seconds)

Am

pli

tud

e (

A.U

.)

Acquire external respiratory signal(Physiological Monitoring Unit --

PMU)

PMU logging:

• synchronized with the image acquisition computer, auto started/stopped within sequence run sampled at 50 Hz

External surrogate: PMU

•TrueFISP in lung volunteer

(dark blood pulse on)

•Average

frames/bin/slice = 17

•HASTE in abdo volunteer

•Average

frames/bin/slice = 26

4D-MRI with PMU

Erik Tryggestad, et al. 2011 Joint AAPM/COMP Meeting

Slice 3/10 Slice 5/10 Slice 7/10

Slice 2/9 Slice 4/9 Slice 6/9

Internal surrogate: diaphragm

von Siebenthal, et al., "4D MR imaging of respiratory organ motion and its variability," Phys Med Biol 52, 1547-1564 (2007).

Image-based surrogate: Body Area

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AxialBA

Sagittal BA

Pre-sorted 4DCT Images (n=31)

Breathing signal

from RPM

Breathing signal

from BA

Phase Comparison

( R, D, DA )

4DCTBA 4DCTRPM

Image Quality Comparison

( SBA, SRPM )

Marker position (P)

Breathing period (T)

Breathing amplitude (A)

Period variability (VT)

Amplitude variability (VA)

Space-dependent phase shift (F)

Correlations

Validation of BA as Surrogate

Signal and phase: BA v.s. RPM4DCTBA

4DCTRPM

4DCT: BA v.s. RPM

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Patient P T (s) VT A (mm) VA R D DA F (s) SBA SRPM

Lung Cancer Patients

Mean L2 3.4 0.18 6.5 0.20 0.90 -5.1 13.8 0.47 3.1 2.6

Abdominal Cancer Patients

Mean L2 3.7 0.19 6.8 0.21 0.94 -1.3 8.5 0.32 2.8 2.6

All Patients

Mean L2 3.6 0.19 6.6 0.20 0.92 -3.3 11.4 0.40 2.9 2.6

p * 0.61 0.34 0.50 0.78 0.52 0.04 0.28 0.001 0.03 0.23 0.92

Significant differences in R, DA, and F between the two

groups of patients.

Summary of measurements Image quality evaluation

Image-based surrogate: FFT

FFT

FFT surrogate: patient example

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Validation of FFT surrogate

► 10 Subjects, 2 min MRI scan, sagittal / coronal

► Respiratory signal: ROI tracking v.s. FFT phase angle

► Small phase difference (-3.13±4.85%), high correlation (r2=0.97±0.02 )

Phantom Study

Fixed

MotorSpring

Bolus

Gel

► MRI-compatible phantom driven by a sinusoidal pattern

► Mimics tumor and body motion

► 1.5T GE clinical scanner

► FIESTA: ~ 3 frames/sec, 6 sec/slice

► BA is the area under bolus

Phantom: axial BA

Axial Coronal Sagittal Sagittal

4D-MRI Single slice cine-MRI

Axial Coronal Saggittal

Phantom: sagittal FFT

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XCAT: axial BA

XCAT: sagittal BA

4DCT

4DMRI

Single slice cine MRI

Clinical Implementation

End points:

• Tumor-to-tissue CNR (4DMRI v.s. 4DCT)

• Tumor ITV accuracy (4DMRI v.s. cine MRI)

• Dosimetric impact (4DMRI v.s. 4DCT)

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Future Directions► Study tumor CNR v.s. cancer characteristics (sequences)

► Optimize imaging parameters (CNR, irregularity)

► Improve respiratory surrogate accuracy and robustness

► Use contrast: SPIO (super-paramagnetic iron oxide, CNR)

► Real 4D (sequence programming, hardware)

► Functional 4DMRI (ventilation, perfusion)

► Clinical implementations

- Immediate needs

S. A. Schmitz, et al. Iron-Oxide-Enhanced MRI of the Liver, ACTA RADIOLOGICA, 2006; 634-642

MIP imaging without sorting

► Goal: to develop a simple MRI technique to generate MIP for treatment planning in radiation therapy

► Why: many cases only need MIP, no need of individual phases (respiratory-gated RT)

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

2D MIP

3D MIP

Phantom Study

SI = 8.0 cm

RL = 2.1 cm

AP

= 7

.6 c

m

V = 66cm3

CT sagittal slice

motion platform stringstring

weight

phantom

Sagittal cine-MRI

Preliminary Phantom Results

SS-MRI Cine-MRI 4DCT

MIP

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Summary of Phantom Results

Volume of Phantom in 3D MIP (cm3) Area of Phantom in Sagittal 2D MIP (cm2)

trajectory SS-MRI 4DCT

diff from

4DCT (%) SS-MRI 4DCT Cine-MRI

diff from

4DCT (%)

diff from

cine-MRI (%)

static 67.5 66.0 2.3% 45.6 47.4 44.2 -3.7% 3.3%

sin 94.5 98.6 -4.2% 46.1 48.4 45.3 -4.8% 1.9%

(1-cos)2 94.1 95.0 -1.0% 43.9 45.6 43.7 -3.7% 0.5%

patient 1 87.2 88.9 -2.0% 39.7 40.7 43.8 -2.5% -9.4%

patient 2 82.3 78.3 5.0% 41.5 41.1 40.8 0.8% 1.7%

patient 3 82.3 81.1 1.5% 29.6 31.6 29.2 -6.3% 1.5%

patient 4 82.1 88.1 -6.8% 46.5 45.3 47.2 2.6% -1.5%

patient 5 80.3 85.7 -6.4% 47.5 44.8 46.2 6.0% 2.8%

patient 6 77.2 82.6 -6.6% 45.9 43.7 45.7 5.2% 0.5%

patient 7 80.1 86.1 -6.9% 45.7 42.9 44.0 6.5% 3.9%

patient 8 80.2 88.3 -9.2% 46.2 44.1 44.7 4.6% 3.3%

Acknowledgements

Stanley Benedict, PhD James Larner, MD

Paul Read, MD, PhD David Schlesinger, PhDTalissa Altes, MD James Brookeman, PhD

G. Wilson Miller, PhD John Mugler III, PhD

University of Virginia

Research was supported by NIH grant R01-HL079077, grant IN2002-01, Siemens Medical Solutions, and University of Virginia Cancer Center

Fang-Fang Yin, PhD Jim Chang, PhD

Zhiheng Wang, PhD Paul Segars, PhD

Brain Czito, MD Chris Kelsey, MD

Irina Vergalasova, BS Raj Panta, BS

Duke University

Xiaodong Zhong, PhDKe Sheng, PhD

UCLA Siemens

Erik Tryggestad, PhD

Johns Hopkins University