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8/12/2011
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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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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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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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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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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