Registration of 3D Ultrasound to Computed Tomography ...jingx/docs/Masters_Thesis_Slides.pdf · Registration of 3D Ultrasound to Computed Tomography Images of the Kidney Jing Xiang

Registration of 3D Ultrasound to Computed Tomography Images of the

Kidney

Jing Xiang

Supervisors: Dr. Robert Rohling

Dr. Purang Abolmaesumi

Electrical and Computer Engineering University of British Columbia

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Motivation

• Amongst Canadians, kidney cancer is the 6th most common cancer in women and 10th most common cancer in men (Canadian Urological Association).

• Incidence rate of kidney cancer has increased by approximately 1.3% per year for males and females since late 1990s (Canadian Cancer Statistics 2010).

• For diagnosis, patients receive CT angiography (CTA).

• Partial nephrectomy is preferred over radical nephrectomy.

Anatomy of the Kidney

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Overview

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• Provide a suitable validation platform for registration. This entails developing a soft tissue phantom that can create realistic images in both US and CT and provide a gold standard for alignment.

• Develop a fully automatic registration method to align US and CT images of kidney using rigid-body registration.

• Examine two approaches to US simulation-based registration that differ in how the ultrasound simulation is created.

• Acquire clinical data and demonstrate how the registration performs with the additional challenges that arise with real subjects.

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Thesis Objectives

Acquisition of Phantom Data

US: Sonix RP machine with 3-7 MHz convex curvilinear abdominal probe (4DC7-3)

CT: Aquilion 64-slice CT scanner (Toshiba Medical Systems)

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Goals of Phantom Construction

• High quality images in CTA and US.

• Depict the surface boundaries of the kidney.

• Define the vascular and pyramid anatomy of the kidney in both modalities.

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Comparison

A) US of non-freshly excised porcine kidney with no contrast.

B) CT of non-freshly excised porcine kidney with no contrast.

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C) US of freshly excised porcine kidney with contrast.

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B

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D) CTA of freshly porcine excised kidney with contrast

Phantom Construction

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A) Remove the renal capsule so that it does not trap air.

B) Inject contrast agent

(Omnipaque iohexol):

• 1 to 40 dilution in water to highlight the cortex.

• 1 to 5 dilution in gelatin solution to highlight the arteries.

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B

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Phantom Construction Cont’d

C) Artery and vein are separated.

D) Artery and vein are tied off to prevent leaking of contrast into the agar.

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D

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Phantom Construction Cont’d

• A) Kidneys are positioned.

• B) Agar is poured over and cooled to set.

• Can identify the vascular system, renal pyramids and the renal cortex.

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Comparison to Human CTA

A B

Acquisition of Patient Data

US: Sonix RP machine with 3-7 MHz convex curvilinear abdominal probe (4DC7-3)

CT: Somatom Sensation 64-slice CT scanner (Siemens Medical Systems)

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• Patient is placed in flank position with a cushion beneath the abdomen.

• The preoperative US and CT are taken with the patient mimicking the position during surgery.

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Acquisition of Patient Data

Registration Methods

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Register

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Simulation of US from CT

Replace with:

Wein et al., 2008

Air

z1

z2

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Simulated US

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Pre-scan and Post-scan Converted US

Scan Conversion

Pre-scan Converted Data

Post-scan Converted Data

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Methods of Simulation

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Rigid Registration Algorithm

CMA-ES Optimizer Hansen et al., 2003

• Initial alignment obtained by finding the fiducial markers in both CT and US using Horn’s method (Horn, 1987) to determine the six rigid registration parameters.

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Kidney Phantom Experiment Details

• For each test, the CT was perturbed by a transform where each parameter was selected from a range of ±10° rotation about each axis and ±10 mm translation along each axis. • 50 registration tests were run. • 7 phantoms in total.

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Results: Kidney Phantoms

• Method A: mean TRE ranged from 1.8 to 3.9 mm. • Method B: mean TRE ranged from 1.4 to 4.2 mm.

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Renal Cancer Patient Experiment Details

• Bronze standard alignment was obtained using Principal Components Analysis (PCA) on manually segmented surfaces of the US and CT.

• For the registration tests, a set of transforms was selected randomly with different misalignment errors.

• 20 registration tests were performed for each range of misalignment errors: 0 – 5 mm, 5 – 10 mm, 10 – 15 mm and 15 – 20 mm.

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Initial Alignment of Patient Dataset

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Results: Renal Cancer Patient

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Results: Renal Cancer Patient

• Developed a detailed recipe for constructing soft tissue phantoms to evaluate CTA to US registration on kidneys.

• Validated simulation-based registration with the CMA-ES optimizer on phantom data using two different simulation approaches.

• Tested simulation-based registration on patient data and initial results demonstrate an improvement in registration accuracy by using a directionally simulated US.

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Conclusion: Contributions

• Validation on Patient Data. – Finding a method of acquiring a gold standard alignment

between US and CT.

– Extensively validate registration on more clinical data sets.

• GPU acceleration of the registration method. – Implement with parallel processing such that registration can

be close to real-time.

• Investigate need for deformable registration.

• Compare simulation-based registration to other approaches such as feature-based approaches.

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Conclusion: Future Work

• Funding NSERC

• Supervision Dr. Robert Rohling and Dr. Purang Abolmaesumi

• Clinical Support Dr. Chris Nguan Vickie Lessoway and Chris Eddy

• Data Acquisition Jack Bell Research Center Canada Diagnostics Centers Vancouver General Hospital

Acknowledgements

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Extra Slides

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Effect of Stand-off Pad on Image Quality

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Fiducials in Patient Data

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PCA

• Segmented surfaces are aligned using the major and minor axes • Concavity of the surface is used to determine the correct orientation.

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P-curve

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Formulation of the Simulated US

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Scan Conversion

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Scan Conversion Correction

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Scan Conversion Correction

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Scan Conversion Correction

Slow Half of the Sweep Fast Half of the Sweep

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Understanding Simulation in the Direction of the US Beam

• For multimodality problems, a suitable similarity metric is mutual information (MI).

• However, according to Wein et al., 2008, there are too many possible configurations where the joint entropy is minimal.

• At the correct alignment, there is only a small local optimum.

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Mutual Information?

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Stages of Simulation for Phantom Data

A

B

C

D

E

• Khamene et al, 2006

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Comparison of Similarity Metrics for Portal Imaging to CT Registration