Radiosurgical Planning Minimally invasive procedure that uses an - - PDF document

5/26/2010 1

Radiosurgical Planning

Radiosurgery Radiosurgery

Minimally invasive procedure that uses an intense, focused beam of radiation as an ablative surgical instrument to destroy tumors

Tumor = bad Brain = good Critical structures = good and sensitive

The Radiosurgery Problem The Radiosurgery Problem

Dose from multiple beams is additive

Treatment Planning for Treatment Planning for Radiosurgery Radiosurgery

• Determine a set of beam

configurations that will destroy a tumor by cross- firing at it

• Constraints:

Constraints:

– Desired dose distribution – Physical properties of the radiation beam – Constraints of the device delivering the radiation – Duration/fractionation of treatment

Critical Tumor

Prior After 10 weeks After 16 weeks

Conventional Radiosurgical Systems Conventional Radiosurgical Systems

• Isocenter-based treatments
• Stereotactic frame required

Gamma Knife

Luxton et al., 1993 Winston and Lutz, 1988

LINAC System Gamma Knife

5/26/2010 2 Isocenter Isocenter-

• Based Treatments

Based Treatments

• All beams converge at the isocenter
• The resulting region of high dose is

spherical Nonspherically shaped tumors are approximated by multiple spheres

– “Hot Spots” where the spheres overlap – “Cold Spots” where coverage is poor – Over-irradiation of healthy tissue

Stereotactic Frame for Stereotactic Frame for Localization Localization

• Painful
• Fractionation of

treatments is difficult

• Treatment of

extracranial tumors is impossible

The CyberKnife The CyberKnife

linear accelerator robotic gantry X-Ray cameras

CyberKnife (Accuray) CyberKnife (Accuray)

http://accuray.com/

Treatment Planning Treatment Planning Becomes More Difficult Becomes More Difficult

• Much larger solution space

– Beam configuration space has greater dimensionality – Number of beams can be much larger – Number of beams can be much larger – More complex interactions between beams

• Path planning

– Avoid collisions – Do not obstruct X-ray cameras Automatic planning required (CARABEAMER)

Inputs to CARABEAMER Inputs to CARABEAMER

(1) Regions of Interest:

Surgeon delineates the regions of interest

CARABEAMER creates

3D regions

5/26/2010 3 Inputs to CARABEAMER Inputs to CARABEAMER

(2) Dose Constraints:

Tumor Dose to tumor Falloff of dose around tumor

(3) Maximum number of beams

Critical around tumor Falloff of dose in critical structure Dose to critical structure

Basic Problem Solved by Basic Problem Solved by CARABEAMER CARABEAMER

• Given:

– Spatial arrangement of regions of interest – Dose constraints for each region: a ≤ D ≤ b – Max number of beams allowed: N (~100-400)

• Find:

– N beam configurations (or less) that generate dose distribution that meets the constraints.

• Position and orientation of the radiation

beam

Beam Configuration Beam Configuration

y z φ

• Amount of radiation or beam weight
• Collimator diameter

x θ

(x, y)

Find 6N parameters that satisfy the constraints

CARABEAMER’s Approach CARABEAMER’s Approach

1. Initial Sampling:

Generate many (> N) beams at random, with each beam having a reasonable probability of being part of the solution.

2. Weighting:

Use linear programming to test whether the beams can produce a dose distribution that satisfies the input produce a dose distribution that satisfies the input constraints.

3. Iterative Re-Sampling:

Eliminate beams with small weights and re-sample more beams around promising beams.

4. Iterative Beam Reduction:

Progressively reduce the number of beams in the solution.

Initial Beam Sampling Initial Beam Sampling

• Generate even distribution of target points on the

surface of the tumor

• Define beams at random orientations through these

points

Deterministic Beam Selection is Deterministic Beam Selection is Less Robust Less Robust

5/26/2010 4 Curvature Bias Curvature Bias

• Place more target points in regions of

high curvature

Dose Distribution Before Beam Weighting

50% Isodose Surface 80% Isodose Surface

CARABEAMER’s Approach CARABEAMER’s Approach

1. Initial Sampling:

Generate many (> N) beams at random, with each beam having a reasonable probability of being part of the solution.

2. Weighting:

Use linear programming to test whether the beams can produce a dose distribution that satisfies the input produce a dose distribution that satisfies the input constraints.

3. Iterative Re-Sampling:

Eliminate beams with small weights and re-sample more beams around promising beams.

4. Iterative Beam Reduction:

Progressively reduce the number of beams in the solution.

Beam Weighting Beam Weighting

• Assign constraints to

h ll f h

• Construct geometric arrangement of regions formed

by the beams and the tissue structures each cell of the arrangement: – Tumor constraints – Critical constraints T C B1 B2 B3 B4

Linear Programming Problem Linear Programming Problem

• 2000 ≤ Tumor ≤ 2200

2000 ≤ B2 + B4 ≤ 2200 2000 ≤ B4 ≤ 2200 2000 ≤ B3 + B4 ≤ 2200 2000 ≤ B3 ≤ 2200 2000 ≤ B1 + B3 + B4 ≤ 2200 2000 ≤ B1 + B4 ≤ 2200

T B1 T

2000 ≤ B1 + B4 ≤ 2200 2000 ≤ B1 + B2 + B4 ≤ 2200 2000 ≤ B1 ≤ 2200 2000 ≤ B1 + B2 ≤ 2200

• 0 ≤ Critical ≤ 500

0 ≤ B2 ≤ 500

C B2 B3 B4

Results of Beam Weighting Results of Beam Weighting

Before Weighting After Weighting

50% Isodose surfaces 80% Isodose surfaces

5/26/2010 5 CARABEAMER’s Approach CARABEAMER’s Approach

1. Initial Sampling:

Generate many (> N) beams at random, with each beam having a reasonable probability of being part of the solution.

2. Weighting:

Use linear programming to test whether the beams can produce a dose distribution that satisfies the input produce a dose distribution that satisfies the input constraints.

3. Iterative Re-Sampling:

Eliminate beams with small weights and re-sample more beams around promising beams.

4. Iterative Beam Reduction:

Progressively reduce the number of beams in the solution.

CARABEAMER’s Approach CARABEAMER’s Approach

1. Initial Sampling:

Generate many (> N) beams at random, with each beam having a reasonable probability of being part of the solution.

2. Weighting:

Use linear programming to test whether the beams can produce a dose distribution that satisfies the input produce a dose distribution that satisfies the input constraints.

3. Iterative Re-Sampling:

Eliminate beams with small weights and re-sample more beams around promising beams.

4. Iterative Beam Reduction:

Progressively reduce the number of beams in the solution.

Plan Review Plan Review

• Calculate resulting dose distribution
• Radiation oncologist reviews
• If satisfactory, treatment can be

y, delivered

• If not...

– Add new constraints – Adjust existing constraints

Treatment Planning: Extensions

Simple path planning and collision avoidance Automatic collimator selection

Critical Tumor

Evaluation on Sample Case

Linac plan 80% Isodose surface

CARABEAMER’s plan

80% Isodose surface

Another Sample Case Another Sample Case

50% Isodose Surface 80% Isodose Surface LINAC plan

CARABEAMER’s plan

5/26/2010 6 Evaluation on Synthetic Data Evaluation on Synthetic Data X

2000 ≤ DT ≤ 2400, DC ≤ 500 2000 ≤ DT ≤ 2200,X

X

10 n = 500 250 Constraint IterationX

X

2000 ≤ D ≤ 2200, DC ≤ 500 2000 ≤ DT ≤ 2100, DC ≤ 500

X X

random seeds n = 250 n = 100 Beam Iteration

X

Dosimetry Results Dosimetry Results

Case #1 Case #2

80% Isodose Curve 90% Isodose Curve 80% Isodose Curve 90% Isodose Curve 80% Isodose Curve 90% Isodose Curve 80% Isodose Curve 90% Isodose Curve

Case #3 Case #4

Average Run Times Average Run Times

2000-2400 n = 500 n = 250 n = 100 2000 2200

:20 :20 :20 :20 :35 :35 :41 :41 :40 :40 :51 :51 :03:30 :03:30 :03:32 :03:32 :04:28 :04:28 :05:23 :05:23 :05:33 :05:33 :07:19 :07:19 :04:36 :04:36 :04:11 :04:11 :05:03 :05:03 :06:45 :06:45 :07:19 :07:19 :07:06 :07:06 3:06:12 3:06:12 3:09:19 3:09:19 3:35:28 3:35:28 1:40:55 1:40:55 1:44:18 1:44:18 1:41:19 1:41:19 Case 1 Beam Constr Case 2 Beam Constr Case 3 Beam Constr Case 4 Beam Constr

2000-2200 n = 500 n = 250 n = 100 2000-2100

:32 :32 :29 :29 :43 :43 :01:34 :01:34 :01:28 :01:28 :02:21 :02:21 :50 :50 :59 :59 :01:02 :01:02 :01:41 :01:41 :01:31 :01:31 :02:38 :02:38 :05:50 :05:50 :05:50 :05:50 :08:53 :08:53 :48:54 :48:54 :40:49 :40:49 1:57:25 1:57:25 :08:37 :08:37 :08:42 :08:42 :10:43 :10:43 :27:39 :27:39 :24:43 :24:43 1:02:27 1:02:27 :23:51 :23:51 :24:44 :24:44 :33:02 :33:02 3:26:33 3:26:33 3:22:15 3:22:15 7:44:57 7:44:57 :13:05 :13:05 :12:16 :12:16 :21:06 :21:06 1:03:06 1:03:06 1:07:12 1:07:12 5:06:29 5:06:29 25:38:36 25:38:36 27:55:18 27:55:18 53:58:56 53:58:56 6:33:02 6:33:02 7:11:01 7:11:01 176:25:02 176:25:02 44:11:04 44:11:04 84:21:27 84:21:27

Evaluation on Prostate Case Evaluation on Prostate Case

50% Isodose surface 70% Isodose surface

Cyberknife Systems Cyberknife Systems

Contact Stanford Report

New s Service

Stanford Report, July 2 5 , 2 0 0 1

Patients gather to praise minimally invasive technique used in treating tumors

By MI CHELLE BRANDT When Jeanie Schmidt, a critical care nurse from Foster City, lost hearing in her left ear and experienced numbing in her face, she prayed that her first instincts were off. “I said to the doctor, ` I think I have an acoustic neuroma (a brain tumor), but I 'm hoping I 'm wrong. Tell me it's wax, tell me it's anything,'” Schmidt recalled. I t wasn't wax, however, and Schmidt – who wound up in the Stanford Hospital emergency room when her symptoms worsened – was quickly forced to make a decision regarding treatment for her tumor.

Service

/ Press

Releases

y p q y g g On July 13, Schmidt found herself back at Stanford – but this time with a group of patients who were treated with the same minimally invasive treatment that Schmidt ultimately chose: the CyberKnife. She was one of 40 former patients who met with Stanford faculty and staff to discuss their experiences with the CyberKnife – a radiosurgery system designed at Stanford by John Adler Jr., MD, in 1994 for performing neurosurgeries without incisions. “I wanted the chance to thank everyone again and to share experiences with other patients,” said Schmidt, who had the procedure on June 20 and will have an MRI in six months to determine its effectiveness. “I feel really lucky that I came along when this technology was around.” The CyberKnife is the newest member of the radiosurgery family. Like its ancestor, the 33-year-old Gamma Knife, the CyberKnife uses 3-D computer targeting to deliver a single, large dose of radiation to the tumor in an

• utpatient setting. But unlike the Gamma Knife – which requires patients to wear an external frame to keep

their head completely immobile during the procedure – the CyberKnife can make real-time adjustments to body movements so that patients aren't required to wear the bulky, uncomfortable head gear. The procedure provides patients an alternative to both difficult, risky surgery and conventional radiation therapy, in which small doses of radiation are delivered each day to a large area. The procedure is used to treat a variety of conditions – including several that can't be treated by any other procedure – but is most commonly used for metastases (the most common type of brain tumor in adults), meningomas (tumors that develop from the membranes that cover the brain), and acoustic neuromas. Since January 1 9 9 9 , m ore than

3 3 5 patients have been treated at Stanford w ith the CyberKnife.

May 2010

Number of CK installations: 182 Installation locations: 24 countries http://www.accuray.com/CyberKnifeCenters/index.aspx P ti t t t d t d t O 100 000? (40 000 i M 2008) Patients treated to date: Over 100,000? (40,000 in May 2008) Indications: Brain, spine, lung, prostate, liver, pancreas (most common)

5/26/2010 7

Meningioma affecting vision 208 beam positions. The patient was treated with 5 fractions over 5 days at 40 minutes per fraction.

After 2 months

Prostate Spine Kidney Pancreas Lung

http://accuray.com/