Derivation of the tumor position from external respiratory surrogates with periodical updating of external/internal correlation

E Kanoulas1, JA Aslam1, GC Sharp2, RI Berbeco3, S Nishioka4, H Shirato5, SB Jiang2

(1)Northeastern University, Boston, MA, (2)Mass General Hospital and Harvard Medical School, Boston, MA, (3)Brigham and Women's Hospital and Harvard Medical School, Boston, MA, (4)Department of Radiology, NTT Hospital, Sapporo, Japan, (5)Hokkaido University, School of Medicine, Sapporo, Japan

Motivation Beam tracking or gating of moving tumors requires

precise real-time tumor localization

Fluoroscopic marker tracking is accurate however requires large imaging dose

Deriving tumor position from external surrogates is dose free however inaccurate due to the uncertainties in internal/external correlation

This work Derive tumor position using external surrogate Periodically image to update internal/external

surrogate correlation Study the minimum updating rate and optimal

updating approach

Data used for the study

X-ray imagers

Laser housing

NTT Hospital (Dr. Seiko Nishioka)

Mitsubishi RTRT system(track 3D tumor position)

AZ-733 “Resp-gate” systemMonitors abdominal surface

3-D internal tumor motion + 1-D abdominal motion

Berbeco et al., “Residual motion of lung tumours in gated radiotherapy with external respiratory surrogates”, PMB, (2005 Aug 21), 50(16), p. 3655-67

Updating the correlation function

During patient setup both external and

internal surrogate position at 30Hz

During treatment external surrogate

position at 30Hz periodically (at low

frequency) internal marker position

0 50 100 150 200 250-10

-5

0

5

10

15

External surrogate position (mm)

Mar

ker p

ositi

on o

n S

I (m

m)

Training data during patient setupActual data during treatmentUpdate PointLine fit to training data

y = -0.081 * e + 13.254

Update Methods

1. Aggressive update (through the update point)

a. Shift line through update point

b. Re-fit line and force it through update point

2. Conservative update (balance between update and training points)

a. Re-fit line with extra weight to update point

b. Minimize the distances to update point and previous line

Method 1a. Shift line through point

0 50 100 150 200 250-10

-5

0

5

10

15

External surrogate position (mm)

Mar

ker po

sitio

n on

SI (

mm

)Training data during patient setupActual data during treatmentUpdate PointLine fit to training dataLine after update

y = -0.081 * e + 9.784

y = -0.081 * e + 13.254

Method 1b. Re-fit line through point

0 50 100 150 200 250-10

-5

0

5

10

15

External surrogate position (mm)

Mar

ker po

sitio

n on

SI (

mm

)Training data during patient setupActual data during treatmentUpdate PointLine fit to training dataLine after update

y = -0.081 * e + 13.254

y = -0.101 * e + 11.884

0 50 100 150 200 250-10

-5

0

5

10

15

External surrogate position (mm)

Mar

ker po

sitio

n on

SI (

mm

)

Training data during patient setupActual data during treatmentUpdate PointLine fit to training dataLine after update

y = -0.081 * e + 13.254

y = -0.092 * e + 12.483

Method 2a. Re-fit line with extra weight to point

0 50 100 150 200 250-10

-5

0

5

10

15

External surrogate position (mm)

Mar

ker po

sitio

n on

SI (

mm

)

Training data during patient setupActual data during treatmentUpdate PointLine fit to training dataLine after update

y = -0.081 * e + 13.254

y = -0.0757 * e + 11.434

Method 2b. Minimize the distances to update point and previous line

Results

0 1 2 5 10 150

0.5

1

1.5

2

2.5

3

3.5

Image Acquiring Frequency (hz)

95%

Con

fiden

ce In

terv

al (m

m)

No update

1a.Shift Line through update point

1b.Re-fit line & force it through update point

2a.Re-fit line with extra weight to update point

2b.Min. dist. to update point & previous line

5 patients 25 data sets Large SI

motion only (>20 mm)

Conclusions

Tumor localization using external surrogates requires periodical update of the internal/external correlation

Update frequency down to 2Hz gives 2 mm motion error (95% confidence level)

The aggressive update methods outperform the conservative ones at high update frequencies while the opposite is true for low update frequencies