MRI signal intensity based B-Spline nonrigid registration for pre- and intraoperative imaging during...

MRI Signal Intensity Based B-Spline Nonrigid Registration for Pre-and Intraoperative Imaging During Prostate Brachytherapy

Sota Oguro, MD1,2, Junichi Tokuda, PhD1, Haytham Elhawary, PhD1, Steven Haker, PhD1,Ron Kikinis, MD1, Clare M.C. Tempany, MD1, and Nobuhiko Hata, PhD1,*1Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston,Massachusetts, USA2Department of Diagnostic Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo,Japan

AbstractPurpose—To apply an intensity-based nonrigid registration algorithm to MRI-guided prostatebrachytherapy clinical data and to assess its accuracy.

Materials and Methods—A nonrigid registration of preoperative MRI to intraoperative MRIimages was carried out in 16 cases using a Basis-Spline algorithm in a retrospective manner. Theregistration was assessed qualitatively by experts’ visual inspection and quantitatively by measuringthe Dice similarity coefficient (DSC) for total gland (TG), central gland (CG), and peripheral zone(PZ), the mutual information (MI) metric, and the fiducial registration error (FRE) betweencorresponding anatomical landmarks for both the nonrigid and a rigid registration method.

Results—All 16 cases were successfully registered in less than 5 min. After the nonrigidregistration, DSC values for TG, CG, PZ were 0.91, 0.89, 0.79, respectively, the MI metric was −0.19± 0.07 and FRE presented a value of 2.3 ± 1.8 mm. All the metrics were significantly better than inthe case of rigid registration, as determined by one-sided t-tests.

Conclusion—The intensity-based nonrigid registration method using clinical data wasdemonstrated to be feasible and showed statistically improved metrics when compare to only rigidregistration. The method is a valuable tool to integrate pre- and intraoperative images forbrachytherapy.

Keywordsprostate brachytherapy; signal intensity-based nonrigid registration; B-Spline

MRI-GUIDED BRACHYTHERAPY is a method to treat localized prostate cancer (1–3). Itenables treatment planning on site through its delineation of prostate substructures andsurrounding tissue, especially when using an endorectal (ER) coil. D’Amico et al and Landiset al demonstrated the feasibility of this approach by performing 248 cases of MRI-guidedprostate brachytherapy using a 0.5 Tesla (T) open-configuration MRI scanner, and comparingthe clinical outcomes with other forms of brachytherapy (4,5). Their study reported that MR-guided prostate brachy-therapy presented fewer complications and a better quality of life thanwhen using ultrasound for image guidance. One of the drawbacks of the MR-guided procedure,

© 2009 Wiley-Liss, Inc.*Address reprint requests to: N.H., Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 FrancisStreet Boston, MA 02115. [email protected].

NIH Public AccessAuthor ManuscriptJ Magn Reson Imaging. Author manuscript; available in PMC 2010 January 4.

Published in final edited form as:J Magn Reson Imaging. 2009 November ; 30(5): 1052–1058. doi:10.1002/jmri.21955.

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however, is that the open-configuration 0.5T scanner had significantly lower field strength andless gradient performance than standard high-field scanner cylindrical magnets, resulting in alower signal-to-noise ratio and low contrast and resolution. Image registration betweenpreoperative MRI obtained in a high-field scanner and intraoperative images used for guidancein the open magnet can help contribute information from the diagnostic scan into the procedure(6).

Once registration is performed at the beginning of the procedure, the fused image is useful tobetter comprehend the prostate substructures and suspicious foci that would otherwise be moredifficult to detect using intraoperative MRI alone. Hirose et al found that a significant prostateshape change occurred between preoperative 1.5T ER coil images and intraoperative 0.5Timaging due to postural change from a supine to lithotomy position and the absence of an ERcoil at 0.5T. In particular, in the preoperative images, the prostate gland appeared to have asmaller anterior–posterior dimension and wider transverse dimension than in the intraoperativeimages. Furthermore, the shape change of the peripheral zone was greater than that of thecentral gland between pre- and intraoperative images (7).

Nonrigid registration, as opposed to rigid registration is, therefore, necessary to compensatefor the deformation of the prostate between pre- and intra-operative imaging. Bharatha et alreported nonrigid registration of ER coil-based 1.5T preoperative MRI with 0.5T intraoperativeimages using segmented data sets. The results from this study indicated that the method wasfeasible and significantly improved the accuracy of registration when compared to its rigidcounterpart (8,9). The method, however, requires manual segmentation of the prostate as apreprocessing step, which can introduce intra- and interobserver variability. Furthermore, themanual segmentation step considerably prolongs the registration process, which impacts theprocedure time of the brachytherapy intervention. Intensity-based nonrigid registration whichdoes not require manual segmentation would allow the mapping of high-resolution images tothe intraoperative ones in less time. In response to this need, Wang et al used such an approachto effectively track anatomical variations within CT images (10). While intensity-basedregistration was documented in this study, the method was tested in phantom trials and only afew clinical data sets, and was not tested for MR images.

Our objective is to test and validate the feasibility of intensity-based nonrigid registrationbetween preoperative MRI and intraoperative MRI obtained during MRI-guided brachytherapyof the prostate. Specifically, we tested the accuracy of the intensity-based registration methodusing a 1.5T preoperative MRI taken with an ER coil and a 0.5T intraoperative MRI takenwithout the ER coil.

MATERIALS AND METHODSPatient Selection and Imaging Protocols

This study included 16 men (mean age, 63.4 years; range, 54 to 72 years) who were randomlyselected from MRI-guided prostate brachytherapy procedures performed at our institutionbetween December 1998 and August 2002. Informed consent was obtained in each case afterdescription of the nature of the procedures. The entry criteria for patients into the MRI-guidedbrachytherapy program at the institution has been described elsewhere (1). All patientsunderwent preoperative 1.5T MRI using an ER coil with integrated pelvic phased multi-coilarray (Signa LX, GE Healthcare, Milwaukee, WI). The ER coil was a receive-only coil mountedinside a latex balloon with a diameter of 4–6 cm when inflated inside the patient’s rectum. Thepatients were placed in the supine position in the closed-bore magnet for the imagingexamination that included T2-weighted fast spin echo (FSE) images (repetition time/echo time[TR/TE] of 4050/135 ms; field of view of 12 cm, section thickness of 0.3 cm, section gap of0 cm, matrix of 256 × 256, 3 signal averages). Typical acquisition times were 5–6 min.

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Intraoperative imaging was performed in the open-configuration 0.5T MR scanner (Signa SP,GE Healthcare, Milwaukee, WI) (11). Each patient was placed in the lithotomy position tofacilitate prostate brachy-therapy by means of a perineal template. The template was fixed inplace by a rectal obturator (2-cm diameter). T2-weighted FSE images (TR/TE 6400/100 ms,field of view of 24 cm, section thickness of 0.5 cm, section gap of 0 cm, matrix 256 × 256, 2signal averages) were acquired in the scanner using a flexible external pelvic wrap-round coil,with typical acquisition times of 6 min. An example of pre- and intra-operative images ispresented in Figure 1.

Image RegistrationWe used the 3D Slicer (http://www.slicer.org/) and ImageJ (http://rsbweb.nih.gov/ij/) softwarepackages for image registration. Both are free open source software for visualization andimage-based computing. A specific module for 3D Slicer has been developed to automate theprocess of rigid and then nonrigid registration of image data sets. A rigid registration step wasnecessary before applying nonrigid registration, to center the pre- and intraoperative imageseries around the prostate.

The nonrigid registration was done using a B-Spline method, a well-known nonrigidregistration method (12). This method works by placing a uniformly spaced three dimensional(3D) grid over the volume to be registered, the lattice points of which act as control points forthe displacement of tissue. Displacement of each control point results in a deformation of theregion surrounding the point, in a way that makes the overall deformation as smooth as possible.The displacement is easily calculated using cubic polynomials. For our application, we used alattice with 5 × 5 × 5 control points. For our 3D application, this allowed for 125 × 3 = 375degrees of freedom in the determination of the B-Spline registration. This choice in the numberof control points was made based on experimenting with trial cases; we found that a 5 × 5 × 5grid provided a reasonable amount of control and acceptable processing time. To guide theregistration, a measure of image similarity based on mutual information (MI) was used (13).Informally, this similarity measure is maximized when the information in one image volume“best explains” the information content of the other. This measure has a precise meaning withinthe field of information theory. The B-Spline registration algorithm thus adjusts the controlpoints until the resulting deformation aligns the image volumes in a way that maximizes theinformation–theoretic similarity between them.

The registration method involved the following steps: (i) both images were cropped for sizeusing the ImageJ software to make the field of view the same on each image, (ii) the 1.5Tpreoperative images were rigidly registered with the 0.5T intraoperative images using thecorresponding function in 3D Slicer, and (iii) the 1.5T images were nonrigidly registered withthe 0.5T images using the nonrigid B-Spline registration module in 3D Slicer. We thenmeasured the required processing time and core computation time for the registrationprocedure.

Accuracy AssessmentThe volume of the prostate gland in the preoperative MRI was measured using 3D Slicer andthe distribution of the prostate volume was compared with similar studies previously reportedin the literature (14–16).

Evaluating the accuracy of a registration method is difficult because there is no gold standardwith which to compare it. Therefore, accuracy or validation of the nonrigid registration methodwas evaluated both qualitatively and quantitatively in several different ways to include bothlocal and global measures of registration success. A qualitative measure included a checker-board comparison between the registered image datasets. Quantitative methods involved a 3D

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http://www.slicer.org/

http://rsbweb.nih.gov/ij/

registration error map based on contour alignment between datasets using Hausdorff distance(HD). We also obtained the Dice similarity coefficient (DSC) between the segmentedregistered prostate images, measured the MI value of the images before and after registration,and finally calculated fiducial registration error (FRE) among common anatomical landmarks.

The registered images from the two data sets were merged for comparison in a checkerboardpattern, to qualitatively evaluate the difference between the pre-and intraoperative images(17,18). The contour of the total gland (TG), central gland (CG), and peripheral zone (PZ) ofthe prostate were drawn by an experienced radiologist (S.O.) under the supervision of a seniorradiologist (C.T.). The radiologists then assessed the continuity of the contours of the differentregions of the prostate gland in the checkerboard images.

A 3D distance map indicating the HD were created to assess the accuracy of registration acrossthe prostate gland surface, and investigate if the registered images correctly separate theprostate gland from the surrounding critical structure so that the radiation dose to the glanddoes not damage those surrounding structures. A similar assessment were performed in therelated study (19). The HD between two images were measured by extracting the edges of thesegmented prostate from the intraoperative MR images and from the registered preoperativeMR images and calculating the distance from each point on the contour to the nearest point onthe contour of the registered prostate (20). Perfect alignment between contours will give a HDof zero at every point.

The DSC was calculated to evaluate the coincidence between the segmented registeredpreoperative prostate volume and the segmented intraoperative one (8,21,22). The DSC isclinically relevant to MRI-guided brachytherapy because the standard radiation plan in thebrachytherapy suggests higher dose on the peripheral zone to avoid overdose to urethra causingurinary complications (23). Thus, having a correct volumetric map of peripheral zone in thefused (registered) images, i.e. higher value in DSC, enables more accurate and safer doseplanning in the brachytherapy. For two images with segmented volumes (voxels) S1 and S2,the DSC is defined as the ratio of the volume of their intersection to their total volume as,

where Volume (S1) and Volume (S2) represent the volumes of the segmented tissue region ineach image and Volume (S1∩S2) is the volume of the intersection between the two. The DSChas a value of 1 for perfect agreement of S1 and S2, and 0 when there is no overlap. A DSCvalue greater than 0.7 has been reported to indicate good match between two regions inassessing image segmentation (24). In our study, the DSC values were calculated betweendifferent substructures of the prostate in the preoperative images and those same structures inthe intraoperative images, first using only rigid registration and then using both rigid followedby nonrigid registration. The DSC values calculated in both cases were quantified andcompared to measure any improvement. To this end, the radiologists segmented TG, CG, andPZ in all images for each data set.

Calculating the MI value of the images before and after registration gives another measure ofimage alignment. In this study, we used the implementation by Mattes et al to calculate the MIvalues to evaluate the similarity between the registered datasets (25,26). The MI value isnegative and a smaller value indicates better similarity.

The validation using FRE consists of identifying well-defined corresponding anatomicalfeatures on registered images and measuring the 3D distance between them. The FRE is moredirectly linked to the accuracy of dose planning in the substructure of the prostate. Based on a

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related study, it was decided to verify the registration accuracy of the urethra (27). It is internalto the prostate, and passes through the prostate from base to apex. We selected the urethra atthe level of the verumontanum, where the ejaculatory duct opens into the urethra (28,29).Perfect agreement corresponds to an FRE of 0 mm. Unlike DSC, FRE tells the level of accuracyin a more intuitive manner using the metric system, in millimeters.

Summary statistics include the mean and standard deviation of the DSC values for TG, CG,PZ, MI, and FRE with and without nonrigid image registration were applied. A one-sidedpaired t-test was then performed to analyze the DSC values MI and FRE to see if the proposednonrigid registration method significantly improved the level of image matching between pre-and intraoperative images compared with rigid registration.

In addition, a grid motion model was made to estimate the local deformation of the prostatictissue during registration. A volume made of 3D grid lines was constructed, and then the samedeformable transform calculated in step 3 of the nonrigid registration method was applied tothe grid lines. Finally, the deformed grid lines were superimposed on the deformed preoperativeimage, and the anteroposterior (AP) distances of the deformed grid lines were assessedproviding a 3D deformation vector field of the registered prostate image.

RESULTSProcessing time for the registration, including preprocessing, was less than 5 min. The meancore computation time for nonrigid registration in 3D Slicer was 24.3 s (range, 15–51 s) on amid-end scientific computer (CPU, XEON 4 × 2.8 GHz; RAM memory, 8 GB). The meanprostatic volume measured in preoperative MRI was 40.0 mL (range, 21.0–107.5 mL), whichis similar to that found in other studies (14–16). Figure 2 illustrates the checkerboard imagesand the prostate images before and after nonrigid registration was performed. The checkerboarddisplay shows improved matching of TG, CG, and PZ structures. Figure 3 shows a 3D distancemap indicating the HD at each contour point that is a measure of the magnitude of misalignmentof the contours of the registered prostates. Larger errors were shown at the anterior part of thebase and bilateral side of the apex (Figure 3).

Mean DSC values and standard deviations for both rigid and nonrigid registration are presentedin Table 1, which shows a significant improvement of the coefficient with nonrigid registration(P < 0.05). Mean MI value was −0.19 ± 0.07. For the FRE, the verumontanum at the urethrawas observed in only 10 of the cases. We therefore computed the metric on only these 10 cases,leading us to conclude that mean FRE at urethra was 2.3 ± 1.8 mm. A box-plot of MI and FREare shown in Figure 4, in which nonrigid registration showed significant improvement ofmatching accuracy compared with rigid registration (P < 0.05).

Figure 5 shows the deformed preoperative image superimposed with grid lines indicating themagnitude of the deformation in the different areas of the gland. The AP distance of the gridlines at PZ was larger than at CG at mid-gland and apex levels of the prostate, indicating alarger deformation at PZ. Furthermore, in the 3D deformation vector field, increaseddeformation was observed at the posterior part of the prostate (Figure 6).

DISCUSSIONWe tested and validated the feasibility of intensity-based nonrigid registration between 1.5Tpreoperative MRI acquired with an ER coil and 0.5T intraoperative MRI acquired without theER coil during MRI-guided brachytherapy of the prostate. Using B-Spline registrationavailable in the open source software package 3D Slicer, these data series were successfullyregistered in clinically acceptable time frames. In the qualitative evaluation, the checkerboarddisplay showed improved matching between images. In quantitative evaluation, DSC values

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were improved when nonrigid registration was applied in the TG, CG, and PZ, as were FREand MI metrics.

Our experiments suggest that MI, the similarity measure used in the B-Spline registrationalgorithm, is likely to be maximized when the DSC value of the segmented total prostate glandis at or near its maximum theoretical value. In this sense, it seems the two measures arecorrelated. In fact, the mean DSC values obtained in this study are comparable to those obtainedin Bharatha’s segmentation-based nonrigid registration method (0.94, 0.86, and 0.76 for TG,CG, and PZ, respectively) (8). This demonstrates that equally good results can be obtainedusing an intensity-based method, which requires only a fraction of the time.

The deformed grid lines and 3D deformation vector field showed increased deformation in theperipheral zone of the prostate compared with the central zone, due to the presence of the ERcoil in the preoperative MRI of the prostate, which was then removed during the intervention(7). The calculated DSC values in the CG and PZ regions are higher than in Bharatha’ssegmentation-based method. This is because our B-Spline technique is combined withoptimization of likelihood functions that measure the similarity between the intensities withinthe two images, providing a better coincidence in regions with large deformation for the CGand PZ.

To show clinical relevancy of the registration, we measured the HD at each point of theregistered prostate’s surface, and created a 3D color map indicating the HD, which showedonly small errors, quantified at less than 2.6 mm in almost every part of the prostate except atthe anterior part of the base and bilateral side of the apex with a maximum distance of 5.2 mm.The largest error present in the base is indicative of the large amount of deformation, theprostate undergoes due to the presence of a urethral balloon catheter during the intervention.The registration method, based on B-Spline deformation, and thus presenting limiteddeformation, struggles to deform the preoperative MR image of the prostate the requiredamount in this area. The large error at the bilateral side of the apex also could be found in theFRE measurement in our study. This finding also coincides with other studies investigatingdeformable registration of the prostate (30–32). In those studies, maximum registration errorswere from 4.4 mm to 7 mm at the apex. We give rise to the view, as did the study by Reynieret al (30), that an accuracy of up to 3 mm at mid-gland is clinically acceptable in providingpreoperative MRI for treatment planning in brachytherapy. There remains an argument whethercurrent accuracy of deformable registration near the apex and base is acceptable inbrachytherapy planning.

The processing time of our method took less than 5 min, including preprocessing. By notrequiring manual segmentation of the prostate gland, our processing time is considerablyshorter than in other studies (8,10,33–35). We believe that 5 min is short enough to makeregistration feasible during a clinical intervention.

Several limitations are evident in this study. First, it involved analyzing only 16 cases in aretrospective manner. Further validation is needed to demonstrate that this method can beapplied during clinical interventions in a robust manner and without considerable interruptionto the workflow. Second, a DSC value was calculated to evaluate the coincidence between thedeformed preoperative image and the intraoperative one, which requires manual segmentationof specific regions of the prostate. The segmentation process is subject to potential error frominterobserver variability (36). A method that does not require segmentation would be lesssusceptible to observer error.

In this report, we retrospectively tested the feasibility of our intensity-based method on patientdata. Future work involves applying registration during the actual clinical brachytherapyprocedure and to quantify the impact this information has on clinical outcomes. The ultimate

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goal of such methodologies is to identify the target in a highly accurate manner using registeredpreoperative images and to deliver highly localized therapy under real-time guidance.

In conclusion, this study provides new and important information on the intensity-basednonrigid registration between preoperative high-field MR image of the prostate andintraoperative MR images obtained during brachytherapy. The registration methodsignificantly improved the matching between images and could be performed within aclinically acceptable time frame.

AcknowledgmentsThe authors acknowledge advisory comments from Dr. Sachio Kuribayashi, Department of Radiology, KeioUniversity School of Medicine. The contents of this study are solely the responsibility of the authors and do notnecessarily represent the official views of the NIH.

Contract grant sponsor: NIH; Contract grant numbers: 5U41RR019703, 5P01CA067165, 1R01CA111288,5U54EB005149, 5R01CA109246, 3P41RR013218. Part of this study was funded by Intelligent Surgical InstrumentsProject of METI, Japan.

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Figure 1.a,b: An example of preoperative axial T2-weighted image using an endorectal coil in a 1.5Tclosed bore scanner (a), and intraoperative axial T2-weighted image in a low-field 0.5Tinterventional scanner (b).

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Figure 2.a,b: In the checkerboard display between the preoperative image (before deformation) andintraoperative image, a black line indicates a contour of the total gland, whereas a white lineindicates a contour of the central gland. a: The contours showed discrepancy at white arrows.b: In a checkerboard display between deformed preoperative and intraoperative images, thecontours match closely after registration has been performed. A slight discrepancy remainedat the posterior part of the prostate (black arrow).

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Figure 3.a,b: An example of a 3D distance map obtained from measured Hausdorff distance (HD) ateach contour point of the registered prostates. The colored distance map indicating the HD wassuperimposed on the prostate model based on intraoperative MR image (blue). Red indicatesa larger distance between intraoperative and deformed preoperative MR images, and dark bluemeans smaller distances. Discrepancy was less than 2.6 mm in almost all surface of the prostate.Larger errors up to 5.2 mm were shown at the anterior part of the base and bilateral side of theapex. The 3D distance map was obtained using the ParaView (http://www.paraview.org/)software package, which is a free open source software for visualization and image-basedcomputing.

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http://www.paraview.org/

Figure 4.a,b: Boxplot of the mutual information (MI) metric and the fiducial registration error (FRE)for rigid and nonrigid registrations. Median values with lower and upper quartile (box) and 1.5times the interquartile range (whiskers) were shown in each figure. The mean MI value fornonrigid registration (−0.19) was significantly better than rigid registration (−0.14) (P < 0.05),and the mean FRE (2.3 mm) was significantly better than for rigid registration (4.5 mm) (P <0.05).

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Figure 5.Preoperative axial image after nonrigid registration has been applied. The grid lines indicatingthe image deformation are superimposed. Anteroposterior distance of the grid line near theperipheral zone was larger than near the central gland (white arrows), showing increaseddeformation in the area.

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Figure 6.Three-dimensional representation of deformation vector field, shown with normal pelvicanatomical models (prostate [dark blue], bladder [light blue], seminal vesicle [brown], andneurovascular bundle [red]). Yellow arrows’ size describes magnitude of deformation. Largerdeformations are present at the posterior part of the prostate. The 3D deformation vector mapwas obtained using the ParaView software package.

Oguro et al.


NIH


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Oguro et al.

Table 1

Mean DSC Values and Standard Deviations for Both Rigid and Nonrigid Registration*

DSC Rigid DSC Nonrigid

Region Mean SD Mean SD

TG 0.82 ±0.07 0.91 ±0.02

CG 0.78 ±0.08 0.89 ±0.03

PZ 0.64 ±0.13 0.79 ±0.07

*When comparing the underlying mean DSC values for rigid and nonrigid registration for all areas of the prostate, one-sided t-tests showed that the

DSC values improved with statistical significance (P < 0.05) in the total prostate gland and its substructures.

DSC = Dice similarity coefficient; TG = total gland; CG = central gland; PZ = peripheral zone; SD = standard deviation.


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