Automatic Detection of Coronary Stent Struts in Intravascular OCT imaging

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1 Automatic Detection of Coronary Stent Struts in Intravascular OCT imaging Kai Pin Tung a, Wen Zhe Shi a, Luis Pizarro a, Hiroto Tsujioka b, Hai-Yan Wang a, Ricardo Guerrero a, Ranil De Silva b, Philip Eddie Edwards a and Daniel Rueckert a a Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK; b Royal Brompton Hospital, Imperial College London, London, UK ABSTRACT Optical coherence tomography (OCT) is a light-based, high resolution imaging technique to guide stent deployment procedure for stenosis. OCT can accurately differentiate the most superficial layers of the vessel wall as well as stent struts and the vascular tissue surrounding them. In this paper, we automatically detect the struts of coronary stents present in OCT sequences. We propose a novel method to detect the strut shadow zone and accurately segment and reconstruct the strut in 3D. The estimation of the position of the strut shadow zone is the key requirement which enables the strut segmentation. After identification of the shadow zone we use probability map to estimate stent strut positions. This method can be applied to cross-sectional OCT images to detect the struts. Validation is performed using simulated data as well as in four in-vivo OCT sequences and the accuracy of strut detection is over 90%. The comparison against manual expert segmentation demonstrates that the proposed strut identification is robust and accurate. Keywords: OCT, Strut Shadow Detection, Strut Reconstruction 1. INTRODUCTION Atherosclerosis is a disease characterized by a deposit of plaque in the arterial wall over time. The formation of an atherosclerotic plaque is considered to be the cause of stenosis (narrowing). Its rupture can lead to occlusion of the arteries. 1 Stent deployment is currently the preferred vascular interventional procedure for stenosis treatment. The deployment procedures are guided with x-ray angiography and optical coherence tomography (OCT). OCT is an intravascular high resolution (< 20µm) imaging technique. It is similar to intravascular ultrasound (IVUS), 1, 2 but measures the intensity of back-reflected infrared light instead of acoustical waves. OCT as an intravascular modality can accurately differentiate the most superficial layers of the vessel wall as well as stent struts and the vascular tissue surrounding them. 3 With the ability for high-resolution cross-sectional imaging of the vessel, OCT could become a reference tool for following-up patients with stented coronaries longitudinally and to guide optimal antiplatelet therapy to prevent late stent thrombosis. Knowledge about stent strut properties is likely to be valuable in the management of cardiovascular diseases. Recent studies 4 8 have proposed several methods for detecting the vessel wall and stent strut by analyzing endovascular OCT sequences. One approach presented in 4, 6 is used to detect struts by calculating the increasing signal transitions of cumulative gradient component of the polar OCT images in 2D slices. Another method 5, 8 is proposed by using threshold and Catmull-Rom splines to estimate the shape of vessel wall and struts. Rotger et al. 7 proposed a strut detecting method based on a cascade of classifiers in intravascular ultrasound. In addition, Xu et al. 9 proposed an improved steerable filter for computing the local ridge strength and orientation to identify the stent struts. Ughi et al. 10 used an intensity profiles of the A-lines to segment the stent strut. However, these algorithms only focused on the detection of strut position without consideration of the weak intensity responses of stent struts and stent area. Stent area is used for predict angiographic in-stent restenosis Further author information: (Send correspondence to Kai-Pin Tung) Kai-Pin Tung: ktung@ic.ac.uk, Telephone: +44(0)

2 Figure 1: The illustration of an OCT image. The guide-wire shadow artifact is visible from 10 to 11 o clock position marked as ****. (ISR) after sirolimus-eluting stent implantation 11 and predict possible strut positions while the struts are not fully appeared in an image. Therefore, we propose an automatic algorithm for identification of the strut and its shadow zone, enabling the accurate and robust segmentation. An example of struts, strut shadow zones and stent area is shown in Fig MATERIALS AND METHODS Our approach consists of two steps: we first detect the strut shadow zone, and then segment the struts. In a few words, a cumulative intensity histogram is computed to detect the strut shadow zone. We then apply probability map and morphological operations to identify the stent struts. These steps will be described in detail in the following subsections. This two-step process is applied to each 2D slice of our OCT image sequences. It is important to mention that before any processing the enhancing and denoising of the OCT images needs to be carried out to improve the poor signal-to-noise ratio of the images. This preprocessing facilitates the detection of the shadows and struts. We utilise a Hessian-based filter 12 to enhance the shadow zone structures. In addition, the block matching 3D (BM3D) filter of Dabov et al. 13 (which is a state-of-the-art filter for image denoising) is used in the strut detection. 2.1 OCT Imaging Protocol The OCT image sets were acquired with a commercially available system (ImageWire C7 (St. Jude system), Lightlab imaging, Westford, MA, USA). This system is used for percutaneous coronary interventions and exploits time-domain OCT. Images were acquired during a pullback with a constant speed of 20.0 mm/s. A DICOM format video recording is then made of the coronary artery, including the stented segment. Four data sequences from patients were used in this paper. Each sequence has 268 frames and every frame has guide wire artifacts as shown in Fig. 1. All images have a field of view of 360 x 360 pixels. 2.2 Strut Shadow Zone Detection The strut can appear as a high contrast speckle with a variable length depending on the orientation of the OCT probe with respect to the stent. When the contrast of the strut is high, it produces a radial shadow zone that can be used to infer the presence or absence of a strut. Thus, to simplify the strut shadow zone detection, each OCT image is reformatted and interpolated into a polar coordinate system (θ, ρ). To identify the shadow zone, firstly we compute the cumulative intensity histogram along radius ρ for each angle θ to obtain the positions of strut shadow zone. This means we sum the intensity of each pixel along ρ for each θ. If a shadow exists in a colum in the θ direction, its cumulative intensity will be low. We assume that the peaks represent the shadow zones and that the presence of each peak corresponds to the proportion of the shadow zone in the θ-axis. However, a number of peaks might indicate false positive matches for the shadow zone because of the imaging noise and artifacts resulting from the guide-wire of the catheter.

3 Figure 2: Shadow zone detection. (a) The blue curve is the cumulative intensity curve of OCT image and the yellow curve is the first derivative of the diagram. The green dots represent the vessel wall estimation while the cyan circles represent the decreasing changes and red plus shows the width of shadow zone area. The detection results of shadow zone are shown in (b). We locate the peaks from the smoothed cumulative intensity histogram by using zero-crossing of the signal s gradient. The peaks are classified into two classes: positive peaks and negative peaks. Positive peaks are positive to negative zero-crossing and negative peaks are negative to positive zero-crossing. From the the classification, we can define the peaks and valleys in the cumulative intensity histogram. We use the positive to negative peak amplitude to define the hight sh hight and the negative to negative peak amplitude to define the width sh width of the shadow. After the candidates of shadow have been detected, we identify the shadow zone by thresholding the candidate s area. The threshold is trained using one frame of each dataset. In the training samples, the peak areas are manually classified as binary labels: L(P eakarea) = {L shadow, L non shadow }. The threshold is obtained by using Linear Discriminant Analysis (LDA). We apply the threshold to the entire datasets. Fig. 2 shows the results of the shadow zone detection algorithm. The blue curve in Fig. 2(a) is the cumulative intensity histogram. It can be clearly observed that if a shadow exists, a signal of the shadow zone protrudes. A flat area of the blue curve is a guide-wire artifact that is eliminated by vessel wall detection algorithm. 14 The cyan circles in the yellow signal (the gradient of cumulative intensity histogram) are the decreasing monotonic signal transitions which can be used to measure the width of the shadow zone (red cross). The shadow zone detecting boxes are shown in Fig. 2(b). We then use these shadow zone portions to detect the relative strut. The next section explains how these detections can be used to locate strut. 2.3 Strut Segmentation The appearance of a strut in an OCT image is not unique and uniform, so it is a challenging task is to locate the strut shape and strut positions. A strut in the OCT image is located between vessel wall and the shadow zone (covered) or between catheter and vessel wall (malaposed). The vessel wall can thus be used to differentiate between these two situations and can be estimated by the vessel wall detection algorithm. 14 We construct a search area consisting of a strut, a shadow, the vessel wall related to the shadow and caesium in the θ- and ρ-direction (Fig. 3 (a)). The task is to recognize the strut in the area since the components have the similar or higher intensity except the strut shadow. An example of wrong strut position (*) obtained by the highest intensity in the searching area is also shown in Fig. 3 (a). Therefore, prior information respect to the search area is essential. We build a prior probability map for each area to enhance and locate the strut position. We first compute the cumulative intensity of the search area in the θ- and ρ-direction. We estimate a potential strut location p(x, y) where y is the peak of cumulative intensity in the θ-direction and x is initialized as the middle of the searching area (between vessel wall and the shadow) in the ρ-direction. x is then iteratively updated using previous strut position. We use a 2D Gaussian filter G spatial (p(x, y), σ θ, σ ρ ) with σ θ = sh width /2

4 Figure 3: A prior probability map construction and strut position estimation. (a) An intensity image and the wrong strut position (*), (b) a prior probability map and (c) the strut probability and the correct strut position (*) obtained from the combination of (a) and (b) and σ ρ = sh hight /2 for this purpose. Fig. 3 (b) is the construction of the a prior probability map and its equation is shown below. G spatial (p(x, y), σ θ, σ ρ ), if n = 1 W n (p) = (1) G spatial (p(x pre strut, y), σ θ, σ ρ ), if n > 1 where x pre strut is the previous strut s location in the ρ-direction. Thus, a strut probability J (Fig. 3(c)) can be defined as J n (p) = I n (p) W n (p) (2) where I n is the normalized intensity of the searching area. The strut position (*) can be inferred as the highest probability of the output J (Fig. 3(c)). However, to detect the malaposed struts, we will need a different probability map. The stent area is a crossing section area of stent struts in a 2D image slice. The ideal case of the stent area is a circle or ellipse and we therefore estimate the possible strut positions by ellipse-fitting. 15 We then use the estimated elliptic strut position to initialize x in Eq. 1 and update x by previous strut position and the estimated elliptic strut position iteratively. Finally, we use region growing and mathematical morphology to segment the strut from the strut position (Eq. 2). Fig. 4 is an example of segmentation results and both the covered and the malaposed strut are fully segmented. In addition, we connect the stent struts in polar coordinate to generate the real stent area since the stent area is irregular in most cases. Fig. 5 is an example of stent area in different slice in one dataset. 3. RESULTS Our automatic strut detection method has been applied to all four OCT sequences. As a pre-processing step, we enhance the shadow zone structures and denoise the whole image sets for the struts. A Hessian-based filter 12 can be used to enhance line- or vessel- like structures and we can use its characteristics to enhance the shadow zone area since the structure of each shadow is line-like structure in polar coordinate. Another advantage of using this filter is that some shadows are difficult to recognize because of the image noise and the filter can help to distinguish small shadows which corrupted by image noise from an enhanced image. The BM3D denoising method 13 works well for images corrupted by Gaussian noises. It uses parameters σ to adjust the amount of smoothing, i.e. the larger σ the smoother the image. We apply a fixed σ to represent strut

5 Figure 4: Strut segmentation in different frame in one data set. Figure 5: Stent Area. Ideal shape of stent area is dot ellipse and the actual shape is a solid polygon. structures well and avoid oversmoothing. In practice, the σ is set to 15. In the region growing step of the strut segmentation, we use the strut position as the seed point and use dilation to segment the strut. Fig. 6 shows the results of strut segmentation in all data sets and Fig. 7 is a 3D stent reconstruction in one dataset. To evaluate our results, we choose several measures to assess the accuracy of the algorithm. We compute the precision (P), sensitivity (S) and the so-called F-measure. Precision is defined as P = T P T P + F P while sensitivity is defined as T P S = (4) T P + F N where T P is the number of true positives (struts), F P is the number of false positives (the number of regions that do not intersect any strut) and F N is the amount of false negatives. The F-measure can be used as a single measure of the performance and is computed as the harmonic mean of precision and sensitivity F = (2P S)/(P + S). Table 1 presents measurements of the F-measure in each dataset compared with the expert strut manual segmentation. Table 1: The measurement of the proposed method. Dataset 1 Dataset 2 Dataset 3 Dataset 4 Precision Sensitivity F-measure (3) Using all the patient datasets, we obtain an average F = 0.93 which is an improvement in comparison to the F = 0.81 obtained in Rotger s method 7 using ultrasound imaging. In addition, we use stent area to evaluate the

6 Figure 6: Strut segmentation in each data set. Figure 7: 3D strut reconstruction in one data set. shape of stent struts and predict possible stent struts positions even though they are not appear in an image. The evaluation of stent area of 100 slices of one dataset is shown in Fig. 8. It is obvious that the result is under-estimated. The reason is that the accuracy of this detecting algorithm depends on the quality of image. If the intensity between image background and strut shadows is ambiguous, false shadow detection will occur and this will lead to a failure of the strut segmentation. 4. CONCLUSIONS We presented a fully automated method based on a cumulative-intensity computation and probability maps of strut location to automatically detect individual stent struts in OCT. Our method has focused on the images which have ambiguous intensity between strut and the surrounding media and this would result in inaccurate strut detection. Our method can therefore distinguish the strut by enhancing strut positions and identify the struts accurately.

7 Figure 8: Stent Area Evaluation. 5. ACKNOWLEDGMENTS Dr. de Silva is supported by Imperial College London and NIHR Cardiovascular Disease Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust. REFERENCES [1] Unal, G. and Carlier, S. G., In-vivo optical coherence tomography image analysis, The IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (2010). [2] Jang, I. K., Bouma, B. E., Kang, D. H., Park, S. J., Park, S. W., Seung, K. B., Choi, K. B., S., M., Schlendorf, K., Pomerantsev, E., Houser, S. L., Aretz, H. T., and Tearney, G. J., Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound, Journal of the American College of Cardiology 39(4), (2002). [3] Pinto, T. L. and Waksman, R., Clinical applications of optical coherence tomography, Journal of Interventional Cardiology 19(6), (2006). [4] Dubuisson, F., Kauffmann, C., Motreff, P., and Sarry, L., In vivo oct coronary imaging augmented with stent reendothelialization score, Medical Image Computing and Computer-Assisted Intervention 5761, (2009). [5] Gurmeric, S., Isguder, G. G., Carlier, S., and Unal, G., A new 3-d automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks, Medical Image Computing and Computer-Assisted Intervention 5762, (2009). [6] Kauffmann, C., Motreff, P., and Sarry, L., In vivo supervised analysis of stent reendothelialization from optical coherence tomography, IEEE Transactions on Medical Imaging 29(3), (2010). [7] Rotger, D., Radeva, P., and Bruining, N., Automatic detection of bioabsorbable coronary stents in ivus images using a cascade of classifiers, IEEE Transactions on Information Technology in Biomedicine 14(2), (2010). [8] Unal, G., Gurmeric, S., and Carlier, S., Stent implant follow-up in intravascular optical coherence tomography images, The International Journal of Cardiovascular Imaging 26, (2010). [9] Xu, C., Schmitt, J., Akasaka, T., kubo, T., and Huang, K., Automatic detection of stent struts with thick neointimal growth in intravascular optical coherence tomography image sequences, Physics in Medicaine and Bioligy 56, (2011).

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