Registration of 3D Ultrasound to Computed Tomography Images of the Kidney

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1 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 1

2 Motivation Amongst Canadians, kidney cancer is the 6 th most common cancer in women and 10 th 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. 2

3 Anatomy of the Kidney 3

4 Overview 4

5 Thesis Objectives 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. 5

6 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) 6

7 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. 7

8 Comparison A B A) US of non-freshly excised porcine kidney with no contrast. C B) CT of non-freshly excised porcine kidney with no contrast. D C) US of freshly excised porcine kidney with contrast. D) CTA of freshly porcine excised kidney with contrast 8

9 Phantom Construction A A) Remove the renal capsule so that it does not trap air. B 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. 9

10 Phantom Construction Cont d C C) Artery and vein are separated. D D) Artery and vein are tied off to prevent leaking of contrast into the agar. 10

11 Phantom Construction Cont d A) Kidneys are positioned. B) Agar is poured over and cooled to set. 11

12 Comparison to Human CTA A B Can identify the vascular system, renal pyramids and the renal cortex. 12

13 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) 13

14 Acquisition of Patient Data 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. 14

15 Registration Methods Register 15

16 Simulation of US from CT Air z 1 z 2 Replace with: Wein et al.,

17 Simulated US 17

18 Pre-scan and Post-scan Converted US Scan Conversion Post-scan Converted Data Pre-scan Converted Data 18

19 Methods of Simulation 19

20 Rigid Registration Algorithm CMA-ES Optimizer Hansen et al.,

Kidney Phantom Experiment Details 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.

21 Kidney Phantom Experiment Details 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. 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. 21

22 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. 22

23 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, mm and mm. 23

24 Initial Alignment of Patient Dataset 24

25 Results: Renal Cancer Patient 25

26 Results: Renal Cancer Patient 26

27 Conclusion: Contributions 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. 27

28 Conclusion: Future Work 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. 28

29 Acknowledgements 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 29

30 Extra Slides 30

31 Effect of Stand-off Pad on Image Quality 31

32 Fiducials in Patient Data 32

33 PCA Segmented surfaces are aligned using the major and minor axes Concavity of the surface is used to determine the correct orientation. 33

34 P-curve 34

35 Formulation of the Simulated US 35

36 Scan Conversion 36

37 Scan Conversion Correction 37

38 Scan Conversion Correction 38

39 Scan Conversion Correction Slow Half of the Sweep Fast Half of the Sweep 39

40 Understanding Simulation in the Direction of the US Beam 40

41 Mutual Information? 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. 41

42 Stages of Simulation for Phantom Data A C D B E 42

43 Comparison of Similarity Metrics for Portal Imaging to CT Registration Khamene et al,

Handzettel 1. CARE Right Computed Tomography, committed to the right dose Ivo Driesser Austin, July 22 nd 2014

Handzettel 1. CARE Right Computed Tomography, committed to the right dose Ivo Driesser Austin, July 22 nd 2014 CARE Right Computed Tomography, committed to the right dose Ivo Driesser Austin, July 22 nd 2014 Answers for life. Answers for life. Is 1 msv the right dose for every patient? Female, 54 Abdominal CT Male,