Image Image- -guided Radiotherapy using guided Radiotherapy using - - PowerPoint PPT Presentation

Image Image-

• guided Radiotherapy using

guided Radiotherapy using A ti Pi l T h l A ti Pi l T h l Active Pixel Technology Active Pixel Technology

John Osmond John Osmond1,3

1,3

with with with with

Emma Harris Emma Harris1, Andrew Clark , Andrew Clark2, Bob Ott , Bob Ott1, Andrew Holland Andrew Holland3 & Phil Evans & Phil Evans1 Andrew Holland Andrew Holland & Phil Evans & Phil Evans

1The Institute of Cancer Research & The Royal Marsden NHS Trust,

The Institute of Cancer Research & The Royal Marsden NHS Trust,

2STFC R th

f d A l t L b t STFC R th f d A l t L b t

3B

l U i it B l U i it

2STFC Rutherford Appleton Laboratory,

STFC Rutherford Appleton Laboratory, 3Brunel University Brunel University

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MI3 Project

• Multidimensional Integrated Intelligent Imaging

£4 4m RCUK Basic Technology Research Grant

• £4.4m RCUK Basic Technology Research Grant
• 11 Research Institutes
• Objectives:
• Objectives:

– To extend APS spectral response (e.g. γ-ray, IR, ionising) – To develop on-chip intelligence to improve performance To develop on chip intelligence to improve performance – To customise devices and demonstrate in novel scientific applications (e.g. Medical, Biological, Security, Space Imaging)

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Motivation Motivation

• Radiotherapy: Deliver x-ray dose to cancerous tissue whilst

minimising the dose to surrounding healthy tissue. minimising the dose to surrounding healthy tissue.

• Image data taken at planning, pre-treatment, during treatment:

– Position patient, – Verify treatment, Compensate for motion – Compensate for motion.

• Current systems (e.g. Video, a-Si) are prone to limitations:

Current systems (e.g. Video, a Si) are prone to limitations: – Substantial additional dose sometimes required, – Data not always acquired from beam’s eye view, – Readout, transfer and downstream processing slow.

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CMOS Active Pixel Sensor (APS) CMOS Active Pixel Sensor (APS)

• CMOS APS may address many of these limitations:

– Single pixel & Region-of-Interest (ROI) readout – Single pixel & Region-of-Interest (ROI) readout, – Intelligence in pixel & Field Programmable Gate Array (FPGA), – Radiation hardness,

• Aims:

– Build APS (520x520 25 µm pixels, ROI readout, >100 fps), I t APS i t M V i i t – Incorporate APS into MeV imaging system, – Assess imaging performance & compare to standard imager, Demonstrate simple logic to verify treatment (offline) – Demonstrate simple logic to verify treatment (offline).

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Experimental Set Experimental Set-

• Up

Up

• Mirror configuration used in camera EPIDs.

Sensor and electronics remain outside treatment beam

• Sensor and electronics remain outside treatment beam.

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Head Phantom Head Phantom

0.25s 0.5s 1s 2s 8s 4s

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Contrast to Noise Ratio vs Dose Contrast to Noise Ratio vs Dose

• CNR calculated in square
• CNR calculated in square

region of skull.

• CNR = 8.1 x Dose0.5.
• CNR = 5, Dose = 0.38 cGy.
• Time = 1/18 s.

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Relative MTF Relative MTF

• Calculated using the QC3
• Calculated using the QC3

phantom:

• Reasonable agreement with

comparison data (lower f ) comparison data (lower f50)

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IMRT IMRT

• Intensity Modulated Radiation

Therapy (IMRT): Therapy (IMRT): – Linear accelerator (Linac) produces MeV x-ray p oduces e ay radiation; – Multi-Leaf Collimator (MLC), an array of movable tungsten leaves, shapes the radiation field; radiation field; – Sequence of shaped fields are delivered to form spatially varying dose.

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Total IMRT Dose Total IMRT Dose

• Lung treatment delivered in 6 segments over 35 s.
• Good agreement between APS and a-Si flat-panel EPID.

APS EPID

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Area Area-

• Time Analysis

Time Analysis

• Area above threshold (50%
• f max) vs time
• f max) vs. time.
• Model based on:

– Prescribed leaf positions, Prescribed leaf positions, – Prescribed dose and measured dose-rate, – Leaf speed, acceleration and time delay. Si l l ith l l t d

• Simple algorithm calculated
• ffline in software, then

eventually online in FPGA. e e tua y o e G

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Edge Edge-

• Detection

Detection

• Threshold at 50% of max
• Mean scalar discrepancy in
• Mean scalar discrepancy in

leaf edges of 0.03 cm.

• Mean discrepancy in area of

p y < 1%

• ROI readout will greatly

i d ( 26) improve speed (x26)

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Summary and Future Work Summary and Future Work

• Current system can:

Resolve bone structure ( 2 cGy) and MLC leaf edges – Resolve bone structure (~2 cGy) and MLC leaf edges, – Verify IMRT to 0.12 s (time), 1 cm2 (area), 0.3 mm (leaves).

• Limitations:
• Limitations:

– Frame rate required for CNR > 5 is 18 fps (Linac ~ 400 Hz).

• Future work:

Future work: – Develop system using Large Area Sensor (x20 area), – Evaluate in contact configuration (greater light collection and g (g g resolution, less FOV), – Improve FPGA to utilise ROI, increased frame-rate, on- sensor intelligence.

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Questions?

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