University College London, Gower Street, London, UK, WC1E 6BT {g.gao, s.tarte, p.chinchapatnam, d.hill,

Size: px

Start display at page:

Download "University College London, Gower Street, London, UK, WC1E 6BT {g.gao, s.tarte, p.chinchapatnam, d.hill,"

Barbara Harper
6 years ago
Views:

1 Validation of the use of photogrammetry to register preprocedure MR images to intra-procedure patient position for image-guided cardiac catheterization procedures Gang Gao 1, Segolene Tarte 1, Andy King 2, Yingliang Ma 2, Phani Chinchapatnam 1, Tobias Schaeffter 2, Reza Razavi 2, Dave Hawkes 1, Derek Hill 1 and Kawal Rhode 2 1 University College London, Gower Street, London, UK, WC1E 6BT {g.gao, s.tarte, p.chinchapatnam, d.hill, d.hawkes}@cs.ucl.ac.uk 2 Kings College London, Strand, London, UK, WC2R 2LS {andrew.king, y.ma, tobias.schaeffter, reza.razavi, kawal.rhode}@kcl.ac.uk ABSTRACT A hybrid X-ray and magnetic resonance imaging system (XMR) has been proposed as an interventional guidance for cardiovascular catheterisation procedure. However, very few hospitals can benefit from the XMR system because of its limited availability. In this paper we describe a new guidance strategy for cardiovascular catheterisation procedure. In our technique, intra-operative patient position is estimated by using a chest surface reconstructed from a photogrammetry system. The chest surface is then registered with the same surface derived from pre-procedure magnetic resonance (MR) images. The catheterisation procedure can therefore be guided by a roadmap derived from the MR images. Patients were required to hold the breath at end expiration during MRI acquisition. The surface matching accuracy is improved by using a robust trimmed iterative closest point (ICP) matching algorithm, which is especially designed for incomplete surface matching. Compared to the XMR system, the proposed guidance strategy is low cost and easy to set up. Experimental data were acquired from 6 volunteers and 1 patient. The patient data were collected during an electrophysiology procedure. In 6 out of 7 subjects, the experimental results show our method is accurate in term of reciprocal residual error (range from 1.66m to 3.75mm) and constant (closed-loop TREs range from 1.49mm to 3.55mm). For one subject, trimmed ICP failed to find the optimal transform matrix (residual = 4.89, TRE = 9.32) due to the poor quality of the photogrammetry-reconstructed surface. More studies are being carried on in clinical trials. Keywords: cardiac catheterization, photogrammetry, MRI, ICP 1. INTRODUCTION Cardiac catheterisation procedures are routinely carried out using x-ray fluoroscopic guidance. Procedures that require the accurate positioning of catheters within the heart, such as electrophysiology studies, have a clear requirement for the integration of patient-specific anatomical information. This can be illustrated by the widespread use of electroanatomical mapping systems that use a tracked roving catheter to reconstruct chamber morphology that serves as a roadmap for catheter positioning. Several strategies have also been developed to integrate three-dimensional cardiac anatomy derived from either MR or CT imaging into the guidance strategy of these procedures [1][2]. Rhode et al. have previously described one such approach that fuses cardiac MR images with real-time x-ray fluoroscopy using a hybrid x- ray/mr (XMR) imaging system [3]. These systems are not commonplace and we wanted to develop an image fusion strategy that could be implemented on separate MR and x-ray imaging systems. One of the key steps in such a strategy is the alignment of pre-procedure cardiac MR data to the catheter laboratory coordinate system during the procedure. Our aim was to evaluate the use of photogrammetry to register the MR scanner coordinate system to the catheter laboratory coordinate system by matching of the chest wall skin surface. A photogrammetry system is a measurement technology in which the three-dimensional (3D) coordinates of points on an object are determined by measurements made from two or more photographic images taken from different positions. With the currently available technology (VisionRT, UK), such measurements can be made in real time enabling the reconstruction of surface data at 10 frames per second [4]. The intra-procedure position of a patient can be measured using photogrammetry by imaging the chest wall skin surface. This surface can be registered to pre-procedure Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, edited by Michael I. Miga, Kevin Robert Cleary, Proc. of SPIE Vol. 6918, 69181Q, (2008) /08/$18 doi: / SPIE Digital Library -- Subscriber Archive Copy Proc. of SPIE Vol Q-1

In this study, we evaluated the use of our photogrammetry system for the alignment of MR image data and the catheter laboratory coordinate system.

2 chest wall surfaces derived from either MR image data. Therefore, MR data can be aligned to the intra-procedure patient position. This strategy has been used for radiation therapy for alignment of pre-treatment plan images to the intratreatment environment [5]. In this study, we evaluated the use of our photogrammetry system for the alignment of MR image data and the catheter laboratory coordinate system. We have tested our approach on 6 volunteers and 1 patient undergoing a cardiac electrophysiology procedure. Both volunteer and patient experiments were carried out using a Philips 1.5T MR imaging system (Philips Medical Systems, Best, Netherlands) and a VisionRT photogrammetry system. We evaluated our solution in terms of residual error in the surface matching and target registration error (TRE) for points in the region of the heart using the closed loop validation method [6]. 2. METHOD 2.1 MR Imaging Multislice volumes were acquired using a 3D GRE sequence (typically, 256x256 matrix, 80 slices, TR = 3.25ms, TE = 1.47ms, Flip angle = 50 ) with voxel dimensions of 1.80mm in the transverse plane and a slice thickness of 3mm. The field of view was such that the chest and both shoulders were included in each scan. The scans were carried out at endexpiration breath-hold. Chest surfaces were segmented from the MR images manually by selecting points on the chest wall surface (Figure 1). In principle, the chest surface can also be segmented by using an automatic or semi-automatic algorithm to reduce the labour cost and the subjective errors. (a) Figure 1 (a) In the transverse MR images, over a thousand points were manually defined on the chest surface (indicated by arrows), building a (b) 3D chest model. (b) 2.2 Chest surface reconstruction A photogrammetry system is a measurement technology in which the three-dimensional (3D) coordinates of points on an object are determined by measurements made from two or more photographic images taken from different positions. With the currently available technology (VisionRT, UK), such measurements can be made in real time enabling the reconstruction of surface data at more than 10 frames per second. After MR imaging, the volunteer/patient was positioned in the catheter laboratory environment and a model of their chest surface was estimated by the VisionRT photogrammetry system. The VisionRT system consists of two video cameras, acquiring video sequences from different angle (Figure 2). 3D surfaces were constructed from these video sequences using software supplied with the system. The VisionRT system was fixed above the x-ray table at the head end at a range of approximately 1.5m where there was clear line of sight for the cameras. The reconstructed surfaces consist of approximately points with 3.5mm separation, covering the shoulders and the chest. One significant advantage of the VisionRT system is its capability to reconstruct 3D surfaces during respiratory cycles. With the current research model, it is possible to acquire more than 10 surfaces per second. During surface acquisition, the subjects were asked to breathe normally. Typically, more than 30 surfaces can be reconstructed during a respiratory cycle, recording the position change of the chest surfaces (Figure 3). Those surfaces could be used to partly compensate the errors caused by respiratory motion. Further details will be introduced in section 2.4. Proc. of SPIE Vol Q-2

Figure 2 (a) Photogrammetry system (indicated by arrow) in the operating room and (b) chest surface acquired by the photogrammetry system.

3 Surface matching ICP matching algorithm proposed by Besl and McKay [7] is a standard solution to surface matching.

A robust extension of ICP, trimmed ICP [8], was adopted to improve the accuracy of the surface matching. The trimmed ICP algorithm is outlined as follow: 1.

Select N least value from the sorted d i = 1... n. For the N selected point pairs, compute the transform matrix that minimise mean square error. 4.

3 Figure 2 (a) Photogrammetry system (indicated by arrow) in the operating room and (b) chest surface acquired by the photogrammetry system. RpiratOry Phase a Figure 3 By using the VisionRT system, it is possible to acquire multiple surfaces during respiratory cycles, recording the position change of the chest surface. 2.3 Surface matching ICP matching algorithm proposed by Besl and McKay [7] is a standard solution to surface matching. ICP assumes each point in the source surface S has a corresponding point in the target surface T. However, surfaces extracted from MR images and VisionRT frequently violate this assumption. A robust extension of ICP, trimmed ICP [8], was adopted to improve the accuracy of the surface matching. The trimmed ICP algorithm is outlined as follow: 1. For each point Pi in the source surface S, find its closest point p` in the target surface, calculate distance di between Pi and p` 2. Sort d i = 1... n in ascending order 3. Select N least value from the sorted d i = 1... n. For the N selected point pairs, compute the transform matrix that minimise mean square error. 4. Repeat the first step until the termination condition is met. In this study, N was set to be 0.7 which assumed a 70% overlap between the MRI-derived surface and the VisionRT surface. 2.4 Respiratory motion compensation The chest surface is considered to be a rigid body. However, the respiratory motion will change the position and the shape of the chest. Although breath hold was required during the MR scan, the position of the chest surface remains unknown due to the variability of the breath hold position. To achieve the best possible result, there is always necessary Proc. of SPIE Vol Q-3

4 to identify the breath hold position during MRI. Using the VisionRT system, it is possible to acquire multiple surfaces T i (i [1, n]) during a respiratory cycle, recording the positions of the chest surface in various respiratory phases. The MRIextracted surface S is registered with those surfaces using the technique described in section 2.3. The residual errors are E i (i [1, n]). Figure 4 shows E i (i [1, n]) has a strong linear correlation with the amplitude of the respiratory signal. The best matched surface can be considered to have the same chest position as the MR-extracted surface. The error caused by respiratory motion can therefore be partly compensated. Amplitude of the respiratory signal vs. Residual error Signal Amplitude Residual Error Surface id 3 Respiratory signal Residual error Figure 4 A volunteer was asked to breath normally during data acquisition. 210 surfaces were reconstructed by VisionRT to cover three respiratory cycles, starting from end expiration. The respiratory signal was computed by measuring the anterior-posterior displacement of a control point which was manually selected from the chest surface. All the 210 surfaces were registered to an MR derived surface (breath-hold at the end expiration). The residual error has strong linear correlation with the amplitude of the respiratory signal (r=0.81). Typically, the VisionRT system is able to reconstruct more than 30 surfaces during a respiratory cycle. An exhaustive search for the best matched surface can be rather time-consuming. To increase the speed of the registration, a fast dichotomic searching algorithm is employed. The searching algorithm is outlined as follow: 1. T i (i [1, n]) are the VisionRT surfaces acquired to cover the respiratory cycle. Surface S was derived from MRI. 2. Register T 1 with S. Set E = Residual error after registration. 3. Set start = 1, end = n. 4. Register surface S with surface T (end + start)/2. Set E' = Residual error. 5. If E' > E, set start = 1, end = (n 1) / 2 6. If E' <= E, set start = (n 1) /2, end = n 7. Set E = E' 8. Repeat 3 until start = end. In practice, respiratory motion affects not only the position of the chest surface but also the position of myocardium. In this study, only the effect on the position of the chest surface is considered. The effect on cardiac position is being examined in a separated study. Proc. of SPIE Vol Q-4

5 2.5 Reciprocal residual error and closed loop validation Due to lack of ground truth, the registration results were evaluated by calculating the residual errors and the target registration errors (TRE) using a closed-loop method. The coverage of the two surfaces is not identical. The residual errors were calculated reciprocal method, which is outlined as follow: For each point Pi in the source surface S 1. Find its closest point p` in the target surface T, calculate the distance di between Pi and p` 2. For the point p`, find its closest point p``i [1, m] in the source surface S, calculate their distance d` 3. if d d` > ε, delete the point Pi from S Calculate the residual error from the remaining points in the source surface S to the target surface T. The threshold ε in step 3 was 4mm in our study. This reciprocal residual error eliminates points on one surface (i.e. the MRI-derived surface) that have no anatomical correspondence to the target surface (i.e. the photogrammetryreconstructed surface) due to incomplete coverage. TVRT ` VRT LSLaG 1 VRT ` MRI TVRT MRI VbI T Figure 5 The closed-loop evaluation method. In addition, we evaluated the consistency of the registration by using a closed-loop method (Figure 5). Given two VisionRT surfaces, VRT and VRT` which were acquired at different times and an MRI-derived surface, the closed-loop method calculates TRE by using the following equation: TRE [( TVRT MR ) ( TMR VRT `) ( TVRT VRT ) S MR ] S MR ( S MR ) = ` where S MR is the position of the target, which is manually defined within the MR volume in the region of the heart. T is a homogeneous transformation matrix. The subscripts, for example VRT MR indicate the registration direction. Between the acquisition of VRT and VRT`, the subjects were instructed to move their body position. The clinical accuracy requirement is largely determined by the size of the structure in which catheterization manipulations take place, e.g the left atrium. Our principle clinical application is electrophysiology procedures, for which the registration accuracy of 5mm or better would be clinically acceptable. 3. RESULTS Data was evaluated from 6 volunteers and 1 patient. The patient s data were acquired during an electrophysiology procedure. For each subject, two VisionRT surfaces acquired at different times were used in the validation. For the volunteer studies, the subjects were instructed to move the body position between the two VisionRT acquisitions. For the clinical trial, the chest surfaces of the patient were acquired 1.5 hours apart. For each subject, we manually defined Proc. of SPIE Vol Q-5

around 18 target points in clinically relevant regions of the heart. TREs were calculated by using the closed-loop method introduced in section 2.5. Subject Reciprocal Residual (mm) Table 1.

32 Poor, miss one shoulder 3 2.96 1.56 2.09 2.57 Good 4 6.16 4.94 3.51 3.03 Good 5 4.00 3.9 3.01 2.20 Good 6 3.87 3.42 3.56 3.24 Good Patient 5.04 8.98 4 3.

6 around 18 target points in clinically relevant regions of the heart. TREs were calculated by using the closed-loop method introduced in section 2.5. Subject Reciprocal Residual (mm) Table 1. The reciprocal residual errors and TREs ICP TRE (mm) Reciprocal Residual ((mm) Trimmed ICP TRE (mm) * VisionRT surface quality Good Poor, miss one shoulder Good Good Good Good Patient Good The VisionRT surfaces were aligned to the MRI-derived surfaces by using trimmed ICP, which was introduced in section 2.3. For comparison, traditional ICP is also used. Results were summarized in table 1. Although trimmed ICP does not show generally better TREs, it is more robust than ICP. Table 1 shows ICP failed to align the surfaces in two occasions (TRE > 5.00mm) while trimmed ICP only failed once due to the poor quality of the reconstructed surface. Some of the registration results are shown in Figure 6 and Figure 7. Trimmed ICP sacrifices computational time to achieve generally smaller residual errors. Running in the same PC under the same condition, our study shows trimmed ICP could take up to 24 seconds longer than the time taken by ICP to align the same pair of surfaces. The computational time of trimmed ICP depends on the size and the initial position of the source surface. In our study, for all the 7 subjects, trimmed ICP completes in one minute (<= 47s), which is acceptable by the electrophysiology procedure. By using trimmed ICP, data from 6 subjects (5 volunteers and 1 patient) were registered successfully (residual < 5mm, TRE < 5mm). The reconstruction of the shoulders provides an important guideline for the surface matching. For one volunteer, the registration failed (TRE = 9.32mm) because a shoulder was missed from the reconstructed surface. The VisionRT system is sensitive to the lighting conditions. The change of the lighting conditions could affect the quality of the reconstructed surface and cause registration failure. (a) (b) Figure 6 (a) Traditional ICP failed to align two partial-overlapped data sets. (b) With a robust outlier selection algorithm, ICP finds the optimal transform matrix. * The quality of the VisionRT surface was decided by an expert Proc. of SPIE Vol Q-6

For (b) and (c), the reciprocal residual errors are 4.

7 Trimmed ICP ICP (a) (b) (c) (d) Figure 7 Each of the two rows show a pair of surfaces were registered by using trimmed ICP and ICP. The first column shows the results of trimmed ICP while the second column shows the results of ICP. The reciprocal residual errors of (a) and (c) are 3.73mm and 3.01mm. For (b) and (c), the reciprocal residual errors are 4.65mm and 4mm. 4. DISCUSSION AND FUTURE WORKS One of our main aims in this work is to register the MR scanner coordinate system to the catheter laboratory coordinate system by matching of the chest wall skin surface. However, the overall goal is to use the surface registration to register the position of the heart from MR images to live fluoroscopy images. Without a proper ground truth, the major difficulty in this study is to validate the accuracy of the registration. Our experiments show encouraging results in terms of reciprocal residual errors and consistency. In the future, a more thorough evaluation of the registration accuracy of the photogrammetric method will be carried out using patient data. In this evaluation we can use a fixed catheter positioned in places such as the coronary sinus to validate the registration accuracy. The photogrammetry method is an attractive solution for the integration of pre-procedure anatomical information for image-guided cardiac catheterisation, especially electrophysiology procedures. It is low cost and easy to setup. As well as the ability to register image data, it also has the potential to allow measurement of respiratory phase. Typically, the VisionRT system is able to reconstruct more than 25 surfaces during a respiratory cycle, measuring the positions of chest surface over time. Potentially, this feature could be used for a respiratory-motion compensated image guidance strategy. 5. CONCLUSION We have described a new solution to register pre-procedure patient position to intra-procedure position. This method has an important role in the design of a new guidance strategy for cardiac catheterization procedure. In the paper we have shown that a MRI-derived surface can be registered with the same surface reconstructed by a photogrammetry system accurately in terms of reciprocal residual errors by using a trimmed ICP algorithm. We have also found that the result of the alignment is consistent using the closed loop validation method. Experimental results show that the closed-loop TREs are within 5mm for 6 out of 7 subjects. However, due to lack of the ground truth, the registration accuracy is not Proc. of SPIE Vol Q-7

8 fully investigated. We aim to carry out future work that will investigate the registration accuracy using clinical image data. ACKNOWLEDGMENTS The authors acknowledge the contributions of Vision RT Ltd. and Philips Medical Systems. They would also acknowledge EPSRC (EP/D061471/1) and DTI (17352) for funding the project. REFERENCES 1. Serfaty, JM., Yang, X., Aksit, P., et al.,: Toward-Guided Coronary Catheterization: Visualization of Guiding Catheters, Guidewires, and Anatomy in Real Time. Journal of Magnetic Resonance Imaging, 12: , Schalla, S., Saeed, M., Higgins, C., et al.,: Magnetic resonance guided cardiac catheterization in a swine model of atrial septal defect. Circulation, 108: , Rhode, K., Sermesant, M., Brogan, D., et al.,: A System for Real-Time XMR Guided Cardiovascular Intervention. IEEE Transaction on Medical Imaging. 24: , Tarte, S., McClelland, J., Hughes, S.,: A Non-Contact Method for the Acquisition of Breathing Signals that Enable Distinction Between Abdominal and Thoracic Breathing. Radiotherapy and Oncology, 81(1); p. S209, Bert, C., Metheany, K.G., Doppke, K.P., et al.,: Clinical experience with a 3D surface patient setup system for alignment of partial-breast irradiation patients. International Journal of Radiation Oncology Biology Physics, 64: , Jannin, P., Fitzpatrick, J.M., Hawkes, D.J., Pennec, X., Shahidi, R., Vannier, M.W.,: Validation of Medical Image Processing in Image-Guided Therapy. IEEE Transaction on Medical Imaging, 21: , Besl, P.J., McKay, N.,: A method for registration of 3-D shapes. IEEE PAMI, 14: , Bert, C., Metheany, K.G., Doppke, K.P., et al.,: Clinical experience with a 3D surface patient setup system for alignment of partial-breast irradiation patients. International Journal of Radiation Oncology Biology Physics, 64: , 2006 Proc. of SPIE Vol Q-8

Intraoperative Prostate Tracking with Slice-to-Volume Registration in MR

Intraoperative Prostate Tracking with Slice-to-Volume Registration in MR Sean Gill a, Purang Abolmaesumi a,b, Siddharth Vikal a, Parvin Mousavi a and Gabor Fichtinger a,b,* (a) School of Computing, Queen