AN ACCURACY EVALUATION OF PRO- STATE BIOPSIES UNDER ENDOREC- TAL 2D ULTRASOUND USING A 3D ENDORECTAL ULTRASOUND-BASED REGISTRATION SYSTEM

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1 AN ACCURACY EVALUATION OF PRO- STATE BIOPSIES UNDER ENDOREC- TAL 2D ULTRASOUND USING A 3D ENDORECTAL ULTRASOUND-BASED REGISTRATION SYSTEM M. BAUMANN 1)3), P. MOZER 1)2), G. CHEVREAU 1)2), P. CONORT 2), E. CHARTIER-KASTLER 2), F. RICHARD 2), V. DAANEN 3), J. TROCCAZ 1) 1) TIMC laboratory Université Joseph Fourier/CNRS/INPG IN3S Faculté de Médecine Domaine de la Merci La Tronche Cedex France. 2) Urology department Pitié-Salpêtrière hospital Université Pierre et Marie Curie (Paris VI) Paris France. 3) Koelis SAS 5, av. du Grand Sablon F La Tronche ABSTRACT Prostate biopsies are in general carried out following a systematic, sector-based acquisition pattern under endorectal 2D ultrasound (US) guidance. The goal of this study is to evaluate the accuracy with which biopsy pattern targets can be reached under 2D US control. We therefore conceived a 3D US based prostate image registration system which makes it possible to project biopsy needle trajectories into a reference prostate volume in order to achieve a biopsy distribution map. The fully automatic system is robust (96% of correct registrations), has a root mean square (r.m.s.) accuracy of 1.41mm and requires about 5s to align two volumes. Analysis of the biopsy distribution showed that the planning target was reached for 63% of all biopsies, and that the average length of the needle trajectories in the sectors was 14mm for a 24mm needle. INTRODUCTION Prostate biopsies, typically realized by transrectal access under 2D US (TRUS) guidance, are currently the only way to confirm cancer diagnostics and to evaluate its prognostics. Unfortunately, US prostate tumor sensitivity is not

2 sufficient to define a biopsy protocol that targets suspicious US lesions. Biopsies are therefore acquired using a probabilistic systematic biopsy site pattern (Fig. 1a). The current standard, the 12-core systematic protocol, yields however a cancer detection sensitivity of only 32% overall, 75% for clinically significant cancers, and 11% for nonsignificant cancers [3]. The low sensitivity raises the question of whether the systematic biopsy sites are accurately reached under 2D TRUS guidance, since a lack of accuracy may result in under-sampling of prostate zones. The low correlation of cancer presence or absence in histological sections with theoretically corresponding tissue removed during radical prostatectomy supports this hypothesis [4]. To better evaluate the quality of the sampling distribution that can be achieved with 2D US we developed an image-based prostate biopsy tracking system based on 3D US. Modern 3D US probes make it possible to rapidly acquire high-quality volumes of the prostate. If one acquires a reference volume before biopsy acquisition and a tracking volume after each biopsy, it is possible to determine the transformation between the prostate in the reference and the tracking volume by aligning both images. The biopsy trajectories in the tracking images can then be projected into the reference volume, which makes precise biopsy distribution analysis possible. TRUS Prostate image alignment is, however, challenging since the US probe also serves as a needle guide and the prostate pose and position in the images thus considerably change between acquisitions. During intervention, the probe undergoes rotations of up to 360 degrees around its principal axis, tilting of up to 30 degrees and its head undergoes displacements of several centimetres. Patient movements and gland mobility relative to surrounding tissue introduce additional transformations, and make it impossible to derive biopsy locations simply from the probe position. It is therefore not sufficient to track relative probe movements using e.g. an infrared or magnetic localization system. We therefore chose an approach without probe tracking that performs alignment by an image-based registration algorithm. Image-based registration is basically an optimization problem on an image similarity measure [6]. The problem with existing similarity measures is that they are characterized by a limited capture range. This means that if the physical transformation between the compared images is too large, the optimization process will converge to a physically incorrect solution. Concerning TRUS prostate biopsy images, the transformation between the reference image and an image acquired during biopsy acquisition is in general beyond the capture range of common similarity measures. In this study we propose a fast and robust registration framework for endorectal prostate images acquired during a biopsy procedure. We solve the problem of estimating large transformations by using an endorectal probe movement model. We improve registration robustness and convergence speed with a multi-resolution approach and we extend the capture range by

3 defining a similarity measure on multiple aspects of the image. The registration system is used to compute the biopsy distribution map for 14 patients. We show that it is difficult to accurately reach the lateral prostate sectors defined in the standard protocol. METHOD AND MATERIAL 1. Biopsy Accuracy Evaluation Fig. 1a illustrates the 12-core systematic 2D biopsy pattern, which is the current clinical standard. To evaluate targeting accuracy, we use a sector representation of the standard protocol (Fig. 1b), which also incorporates the prostate height. It is thus composed of twelve right parallelepipeds. the average biopsy length inside a sector. 2. Biopsy Distribution Map The accuracy study is performed using a 3D US endorectal probe (RIC5-9 on a Voluson, both from GE Medical). An 18-gauge biopsy needle with a cutting length of 23 mm is used. A reference volume is acquired before the intervention. For biopsy targeting and acquisition, the probe is switched to 2D mode. After the biopsy gun shot, the needle is left inside the prostate the time necessary to acquire a 3D US volume containing the needle. The needle is then manually segmented in the 3D volumes and the biopsy volumes are registered with the reference volume. A biopsy distribution map is obtained after projection of the needle trajectories into the reference volume (See Fig. 2 and 3). (a) (b) Fig. 1: (a) illustrates the 12-core systematic standard protocol, which is defined on a schematic coronal cut of the prostate. (b) Coronal sector definition for the accuracy study (B=Base, M=Mid-Gland, A=Apex, L=Lateral, P=Parasagittal). Accuracy is evaluated by measuring the biopsy distribution in the sectors: for each sector we determine the percentage of planned biopsies that actually hit the corresponding sector. We also measure Figure 2: 3D biopsy distribution map.

4 Fig. 3: Biopsy distribution in the coronal cut that corresponds to the planning scheme. We finally fit the sector representation onto the reference volume and measure the hit percentages and needle penetration for each sector. 3. Prostate Image Registration The most challenging part of the study is the inter-image prostate registration. The current framework estimates the rigid transformation between the prostate in the reference and the tracking volumes by minimization of an intensity-based similarity measure [1]. The sole user intervention consists in the definition of a bounding box around the prostate in the reference image. This is necessary to obtain knowledge about the prostate location in the reference image. The probe head location and radius in the reference image is automatically detected using a variant of the Hough transform. A multi-resolution approach is used to accelerate registration and to make it more robust. A Gaussian resolution pyramid is computed for both the reference and the tracking image. The resolution is divided by two from level to level. Note that coarse levels are statistical aggregates of denser levels. Small deformations and the US specific speckle noise are thus averaged out on coarse levels. The similarity measure Sim used to compare the reference and the tracking volumes is derived from the attributevector approach presented in [2] and [5]. The principal idea is to consider different aspects of the volumes for comparison. We thus compute the Pearson correlation C of both volumes and also of their gradient magnitude volumes. The results are then multiplied. The justification to do so is that C a priori yields local minima at different locations for different aspects, but the minimum of the correct solution is a priori at the same location. When multiplying both correlations, local minima are attenuated while the capture range of the correct solution is accentuated. The capture range is, however, still not wide enough to estimate the transformation using a fast local optimization algorithm with guaranteed convergence (e.g. a conjugate gradient descent or Powell-Brent). We therefore need a method capable of finding a point inside the capture range. This could be done by systematic exploration of the 6D transformation space, and by picking the point with the lowest similarity measure value as start point for local optimization. The computational burden of this approach is, however, unacceptable. We therefore developed a 3D a priori model of endorectal probe movements during biopsy acquisition, which can be explored exhaustively in a reasonable time frame. The model is based on the following assumptions: 1) The probe head is always in contact with the prostate membrane, 2) the most important rotations occur around the

5 principal axis of the probe, and 3) all other rotations have a rotation point that can be approximated by a unique fix point FP rect. Starting from these assumptions it is possible to define a probe movement model based on a prostate surface approximation, the probe position in the US image (which is known) and a rotational fix point in the rectum. As shown in Fig. 4, the prostate surface is approximated by a bounding-box aligned ellipsoid. The ellipsoid is modelled using a 2D polar parameterization Pr Surf (α,β). The origin Pr Surf (0,0) of the parameterization corresponds to the intersection of the line from the prostate center C Pro to FP Rect. As illustrated in Fig. 5, Pr Surf (α,β) implements assumption 1) by determining plausible US transducer positions on the prostate surface. Assumption 3) is satisfied by requiring that the principal probe axis must always pass through FP Rect. Finally, a rotation about the principal probe axis implements assumption 2) and thus adds a third degree of freedom (See Fig. 6). The transformation T 0 that minimizes Sim on the model is found by systematic exploration of the model. T 0 is then used as start point for a local optimization of Sim. Note that systematic exploration is entirely performed on the coarsest pyramid level and its computational burden is hence comparable to local optimization. Fig. 4: Determination of the search model surface origin PR Surf (0, 0). Fig. 5: A 2D polar parameterization is used to determine a surface point Pr Surf (α,β). The probe is then rotated and translated such that its US origin OUS corresponds to Pr Surf (α,β). Fig. 6: The probe is rotated around its principle axis by an angle λ.

6 RESULTS 1. Registration Performances The registration method is validated on 237 3D images of the prostate acquired during biopsy of 14 different patients. The image resolution is The voxel side lengths vary from 0.33mm to 0.47mm. A five level resolution pyramid is used. Pre-search on the model is carried out on the coarsest resolution level. Local optimization is carried out from the coarsest to the third-finest level. Registration is performed on a Pentium 4 with 3GHz. To validate registration, we segmented clearly visible point-like fiducials (e.g. calcifications) in the volumes. The distances between corresponding fiducials after application of the registration transformation were used as gold standard for accuracy evaluation. Registration success was determined visually. The results are given in Tab. 1. Evaluation Result Registration Success 96.7% Avg. computation time 6.5s Fiducial reconstruction distance error (r.m.s.) 1.44mm Fiducial reconstruction distance error (max) 3.84mm Tab. 1: Registration performances. 2. Biopsy Targeting Accuracy Biopsy targeting accuracy is evaluated for 15 patients that underwent a 12-core systematic biopsy. A total of 140 biopsies were evaluated. 4 biopsies could not be evaluated due to poor image quality, which led the registration algorithm to failure. The results are given in Tab. 2. On average, the operator reached the target in 63% of all cases. The ratio decreases as the planning approaches the boundaries of the prostate (from 100% in MP to 79% in ML). At lateral base and apex sides the lowest ratios may be explained not only by the difficulty to reach the targets, but also by the low prostate presence in those sectors. The relatively low inner length ratios tend to prove the inadequacy between a theoretical regular and parallel planning pattern and the actual constrained transrectal access. DISCUSSION We presented a registration algorithm for endorectal US prostate volumes and used it to evaluate the accuracy with which systematic prostate targets can be reached using 2D TRUS ultrasound. The presented registration algorithm is fast and very robust, despite the fact that the transformations between the biopsy volumes lie in general beyond the capture range of intensity-based similarity measures. Almost all registration failures were caused by poor image quality due to only partial contact of the probe head with the membrane during acquisition or due to US shadows cause by air bubbles in the contact gel. We plan to further improve registration quality by adding elastic registration based on a bio-mechanical prostate model. The biopsy protocol accuracy study reveals that it is difficult to reach targets defined on a systematic planning precisely under 2D TRUS guidance. Lack of precision of TRUS prostate biopsies was already evident due to their weak correlation with tissue samples

7 Target Sectors Nb biopsies towards a target sector from radical prostatectomy; our approach, however, is more precise. An interesting application of the prostate registration system is the fusion of the distribution maps acquired from different biopsy series of a patient. This would make it possible to verify whether all zones were biopsied. TRUS prostate image registration could also be used to verify if a suspect zone detected in an MRI image was correctly targeted by fusing the reference US volume with the MRI volume, and by projecting the sampling trajectory. A long-term objective is to provide a prostate biopsy guidance system. Acknowledgements Nb biopsies inside the target sector This work was supported by grants from the Agence Nationale de la Recherche (TecSan program, SMI project), from the French Ministry of Industry (ANRT agency), from the French Ministry of Health (PHRC Program, Prostate-echo project) and from Koelis S.A.S., France. The clinical data used in this study was acquired at the urology department of the St. Pitié la Salpétrière hospital, Paris. % biopsies inside the target sector Average biopsy length inside the target sector (mm) % biopsied tissue inside the target sector BL % 13 61% BP % 14 62% ML % 14 64% MP % 16 71% AL % 7 33% AP % 13 61% Total/Average % 14 62% Tab. 2: Targeting accuracy evaluation result.

8 BIBLIOGRAPHY [1] BAUMANN M., MOZER P., DAANEN V. TROCCAZ J. Towards 3D Ultrasound Image Based Soft Tissue Tracking: A Transrectal Ultrasound Prostate Image Alignment System MICCAI 2007, to appear. [2] FOROUGHI P., ABOLMAESUMI P., HASHTRUDI-ZAAD K. Intra-subject elastic registration of 3D ultrasound images. MedIA 2006, 10: [3] ROCCO B., DE COBELLI O., LEON M.E., FENUTI M, MASTROPASQUA M., MATEI D.V., GAZZANO G., VERWEIJ F., SCARDINO E., MUSI G., DJAVAN B., ROCCO F. Sensitivity and Detection Rate of a 12- Core Trans-Perineal Prostate Biopsy: Preliminary Report. Eur Urol, 2006, 49(5): [4] SALOMON L., COLOMBEL M., PATARD J.J., LEFRERE- BELDA M.A., BELLOT J., CHOPIN D., ABBOU C.C. Value of ultrasound-guided systematic sextant biopsies in prostate tumor mapping. Eur Urol, 1999, 35(4): [5] SHEN D. DAVATZIKOS C. HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration. IEEE J MI 2002, 21(11): [6] ZITOVA B., FLUSSER J. Image registration methods: a survey. Image and Vision Computing 21., 2003,