Automated planning of breast radiotherapy using cone beam CT imaging Guy Amit and Thomas G. Purdie Citation: Medical Physics 42, 770 (2015); doi: 10.1118/1.4905111 View online: http://dx.doi.org/10.1118/1.4905111 View Table of Contents: http://scitation.aip.org/content/aapm/journal/medphys/42/2?ver=pdfcov Published by the American Association of Physicists in Medicine Articles you may be interested in A study of respiration-correlated cone-beam CT scans to correct target positioning errors in radiotherapy of thoracic cancer Med. Phys. 39, 5825 (2012); 10.1118/1.4748503 Thoracic target volume delineation using various maximum-intensity projection computed tomography image sets for radiotherapy treatment planning Med. Phys. 37, 5811 (2010); 10.1118/1.3504605 Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy Med. Phys. 36, 4 (2009); 10.1118/1.3026602 IMRT planning and delivery incorporating daily dose from mega-voltage cone-beam computed tomography imaging Med. Phys. 34, 3760 (2007); 10.1118/1.2779127 Cone-beam CT with megavoltage beams and an amorphous silicon electronic portal imaging device: Potential for verification of radiotherapy of lung cancer Med. Phys. 29, 2913 (2002); 10.1118/1.1517614
Automated planning of breast radiotherapy using cone beam CT imaging Guy Amit Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G2M9, Canada
Thomas G. Purdiea) Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G2M9, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada; and Techna Institute, University Health Network, University of Toronto, Toronto, Ontario M5G 1P5, Canada
(Received 25 June 2014; revised 7 December 2014; accepted for publication 14 December 2014; published 16 January 2015) Purpose: Develop and clinically validate a methodology for using cone beam computed tomography (CBCT) imaging in an automated treatment planning framework for breast IMRT. Methods: A technique for intensity correction of CBCT images was developed and evaluated. The technique is based on histogram matching of CBCT image sets, using information from “similar” planning CT image sets from a database of paired CBCT and CT image sets (n = 38). Automated treatment plans were generated for a testing subset (n = 15) on the planning CT and the corrected CBCT. The plans generated on the corrected CBCT were compared to the CT-based plans in terms of beam parameters, dosimetric indices, and dose distributions. Results: The corrected CBCT images showed considerable similarity to their corresponding planning CTs (average mutual information 1.0 ± 0.1, average sum of absolute differences 185 ± 38). The automated CBCT-based plans were clinically acceptable, as well as equivalent to the CT-based plans with average gantry angle difference of 0.99◦ ± 1.1◦, target volume overlap index (Dice) of 0.89 ± 0.04 although with slightly higher maximum target doses (4482 ± 90 vs 4560 ± 84, P < 0.05). Gamma index analysis (3%, 3 mm) showed that the CBCT-based plans had the same dose distribution as plans calculated with the same beams on the registered planning CTs (average gamma index 0.12 ± 0.04, gamma < 1 in 99.4% ± 0.3%). Conclusions: The proposed method demonstrates the potential for a clinically feasible and efficient online adaptive breast IMRT planning method based on CBCT imaging, integrating automation. C 2015 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4905111] Key words: cone-beam computed tomography, breast cancer, radiotherapy planning, automation, image processing
1. INTRODUCTION Kilovoltage (kV) cone-beam computed tomography (CBCT) imaging has been evaluated for setup reproducibility and target localization in breast IMRT.1,2 CBCT imaging is also a promising technology for adaptive radiotherapy as images can be acquired rapidly over an entire volume, with low radiation doses.3 However, CBCT imaging does not produce accurate CT numbers/Hounsfield units (HU) and suffers from artifacts and radio-density inconsistencies, which can affect both accurate dose calculation and automated segmentation algorithms.4 The clinical utilization of CBCT images in radiotherapy therefore requires image adjustment methods. One approach of CBCT adjustment is using phantom-based HU calibration to derive HU to electron density (ED) conversion curves.5 However, this calibration process differs between scanner vendors and highly depends on phantom material and size.6,7 It has been shown that the patient size and geometry have considerable influence on CBCT values and the use of patient-independent CBCT HU to ED calibration is therefore inaccurate.8 Investigations of the clinical feasibility of CBCT for dose calculation have shown that the scatter and artifacts in CBCT 770
Med. Phys. 42 (2), February 2015
were severe surrounding inhomogeneous tissue, nevertheless, CBCT-based treatment plans were found to be dosimetrically comparable to CT-based plans.6 Multiple researchers have suggested advanced image processing and enhancement methods aiming to improve the CBCT image quality and facilitate its use in radiotherapy. Image enhancement methods typically attempt to estimate the scattered radiation using, for example, deconvolution technique,9,10 Monte Carlo simulations,11 or a posteriori probability estimation.12 In the radiotherapy domain, a major portion of the CBCT processing methods addressed the problem of CT-CBCT deformable registration, utilizing CT information for automated CBCT segmentation.13–16 Most of the previously suggested methods4,17,18 rely on information from the patient’s planning CT for improving the quality of CBCT images and adapting them for radiotherapy procedures. However, as modern linear accelerators are equipped with CBCT imaging capabilities, it is possible to envision an online IMRT treatment model, where the planning is performed automatically on the CBCT images, at the treatment unit.19,20 In this work, we describe a method for CBCT-based automated breast IMRT treatment planning. By having planning and treatment performed in the same session,
0094-2405/2015/42(2)/770/10/$30.00
© 2015 Am. Assoc. Phys. Med.
770
771
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
we can avoid replanning due to changes in patient shape and anatomy, which may be seen between the CT simulation and the patient’s first treatment fraction. An adaptive model like that would allow patients to get a new treatment plan without any delays in treatment or rebooking appointments. We describe a database-matching method that automatically applies intensity corrections to CBCT image sets, based on identified similarity with planning CT image sets from the database. Our goal was to evaluate the applicability of this method in the clinical environment. To that end, the corrected CBCT image sets were used for automated tangential breast IMRT treatment planning, demonstrating the potential for an online adaptive treatment planning model based on CBCT imaging. 2. METHODS 2.A. Reference library construction
A CT-CBCT reference library was used as a database for mapping the relationship between corresponding CT and CBCT image sets. The library consisted of 38 paired CT and CBCT image sets obtained for each patient. CT images were registered onto the CBCT images, using 3D intensity-based rigid registration.21 The registration was implemented using matlab’s image processing toolbox (The Mathworks, Inc., Natick, MA). The lungs, heart, and body contours were auto-
771
matically delineated on the CT images using a combination of image threshold methods and model-based segmentation.22 Using the calculated CT-CBCT rigid registration transform, the delineated regions of interest were aligned onto the CBCT images. The gray-level histograms of each region-of-interest (ROI) were calculated on the CT and CBCT images. Two field tangential treatment plans were automatically generated using the CT image sets,22 and used as ground-truth reference plans.
2.B. CBCT image processing
Two different image processing approaches were applied to the CBCT images. Both approaches attempt to identify a “similar” library entry, and then to use the library data to transform the CBCT images into “CT-like” images, on which automated planning can be applied. The images are transformed by histogram matching, a standard image processing technique, aiming to change the gray-level histogram of the input image to resemble a reference histogram.23 The first method is employing global histogram matching of the CBCT image, correcting all voxels within the body volume. The second method is assuming availability of ROI delineation in the CBCT images and is applying histogram matching separately for each ROI (Fig. 1). The processed CBCT images produced by these two methods are denoted CBCT-GLB and CBCT-ROI, respectively.
F. 1. The CBCT processing pipeline. The input CBCT image is preprocessed (a) by a noise-reduction filter, the body volume is delineated and optionally the lungs and heart are segmented. The gray-level histogram is calculated (b) either for the entire body volume or for each ROI. The CBCT image is matched against the reference CT-CBCT library to identify the “closest” library CBCT and obtain its paired library CT (c). The selected CT is used to perform histogram matching (d) on the CBCT. The processed CBCT is then analyzed by the automated planning algorithm (e) to produce a two-field tangential breast treatment plan. The output plan is compared to the ground-truth plan, generated automatically from the planning CT. Medical Physics, Vol. 42, No. 2, February 2015
772
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
2.C. Global histogram matching
Given a new CBCT image set, the following processing steps are applied to produce the output CBCT-GLB image set: (a1) Preprocessing by a noise reduction filter. An anisotropic diffusion filter24 is applied to reduce scatter noise while preserving edges and other semantically meaningful details of the image. (a2) Body contour segmentation. The scanned body volume is automatically delineated using image thresholding, following detection and removal of the couch from the image. (a3) Library search using registration-based similarity. The segmented body volume is registered against each library CBCT, using 3D intensity-based rigid registration. Mutual information (MI) is used as a similarity metric for choosing the closest library entry.25 The paired library CT is then selected for histogram matching. MI between images A and B is defined by: H(A) + H(B) − H(A,B), where H(X) = − x p X (x)log p X (x) is the entropy of image X, and H(A,B) = − a,b pAB(a,b)log pAB(a,b) is the joint entropy. Intuitively, MI measures how much knowing one image reduces uncertainty about the other image. (a4) Histogram matching of the CBCT body ROI against the corresponding ROI from the selected library CT. The histogram-matching algorithm aims to find a monotonic grayscale transformation T, to minimize |(Cin(T(k))) −Cref (k)| for all intensity values k ∈ [0, 255], where Cin and Cref are the cumulative graylevel histograms of the input and reference images, respectively. 2.D. ROI-based histogram matching
In the case where ROI segmentation information is available for the CBCT images, histogram matching can be applied separately to the lungs, heart, and the remaining body volume. In this work, ROI-based histogram matching was evaluated using the “ground-truth” ROIs aligned from the registered CT images. This allowed the evaluation to be independent of the accuracy of the segmentation algorithm. In practice, when the corresponding CT is not available, a designated CBCT segmentation algorithm can be applied for delineating the ROIs. One possible approach is using a two-phase processing, where the CBCT images are first transformed by global histogram matching, as described in Sec. 2.C, and the CT-like image is then segmented and refined by ROI-based histogram matching. Given a new CBCT image set, the following processing steps are applied to produce the output image set CBCT-ROI image set: (b1) Preprocessing, as described above in step (a1). (b2) Histogram calculation. Gray-level histograms (256 bins) are calculated for the lungs, heart, and the remaining body volume. (b3) Library search using histogram-based similarity. The closest library CBCT is chosen by calculating ChiMedical Physics, Vol. 42, No. 2, February 2015
772
square distance between the gray-level histograms of the ROIs, defined by (hin(k) − href (k))2 , d(hin,href) = k (hin(k) + href (k))/2 where hin and href are the histograms of the input and reference ROIs. The distance between the images is taken to be the average distance over all ROIs. (b4) Histogram matching. For each ROI, histogram matching is performed as described above in step (a4). 2.E. Automated planning
The automated technique for tangential breast IMRT planning has been previously described. Briefly, the method emulates all the manual steps required for generating a treatment plan including beam placement, segmentation, IMRT optimization, dose calculation, and plan documentation.22,26 It detects radio-opaque anatomical markers in the CT images, used to drive automatic model-based segmentation of the breast target and organ at risk volumes and then applies a heuristic algorithm to define the tangential beam geometry for treatment. The automated method is focused on optimizing the segmentation for sections of the organ at risk volumes that are in close proximity to the breast volume. The heuristic algorithm is entirely based on the absolute volume irradiated in each of the organs at risk; therefore, the sections of the organs at risk distant from the breast are not considered or evaluated. 2.F. Evaluation experiments
The analyzed dataset consisted of 38 planning CT images with scan resolution of 1 × 1 × 2 mm3 and bit depth of 12 bits, obtained from patients undergoing whole breast irradiation at our institution (12 right-breast patients and 26 left-breast patients). The standard prescription dose was 4240 cGy in 16 treatment fractions, prescribed as the mean dose to the whole breast volume. The corresponding CBCT images were acquired at the treatment units with typical scan resolution of 1 × 1 × 2 mm3. CBCT-GLB and CBCT-ROI images were produced for each patient in the dataset, using the remaining 37 patients as the reference library (leave-one-out). The quality of output CBCT image sets was initially evaluated by measuring its similarity to the ground-truth CT image set of the same patient, using conventional image similarity measures including MI, average sum of absolute differences (SAD), and Chi-square histogram distance. A test subset of 15 patients was randomly selected from the analyzed dataset. The output CBCT images of these patients were imported into the treatment planning system (pinnacle,3 Philips Radiation Oncology Systems, Madison, WI), and were processed by the breast automated planning algorithm that produced CBCT-based treatment plans. The resulting CBCTbased plans were compared to the CT-based ground-truth plans by examining their delineated ROIs, target volumes, beam angles, the defined clinical protocol dosimetric indices, and adherence to our institutional clinical acceptability guidelines.
773
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
The quality of the CBCT-based treatment plans was determined by the performance of two main components of the automated planning tool. The first component is the automated segmentation and gantry angle selection, which delineates the volumes of the lungs and the heart on the CBCT image, and uses these volumes to choose an optimal beam angle for adequate target coverage. The second component is IMRT optimization and dose calculation, which uses HU to ED conversion curves to calculate dose from the intensity values of the CBCT image. In order to separate the contribution of these two components to the final resulting plans, a CT(CBCT-GLB) hybrid plan was generated for each test case by placing the CBCT-derived beams on the CT image set. To ensure accurate dose-grid alignment, the CT and CBCT images were registered using deformable image registration with a multiscale Horn–Schunk optical flow algorithm.27,28 The CBCT-derived beams were recalculated on the registered CT image set, to generate a dose distribution based on the CT geometry. 3D gamma-index evaluation29,30 was used to compare the dose distributions of the CBCT-based plans and the CT-(CBCT-GLB) hybrid plans.
3. RESULTS The intensity histograms of the library CBCT images showed large interpatient variability, compared to CT images (Fig. 2). This variability was quantified by calculating the average Chi-square distance between all pairs of body volume histograms. The average pairwise distance for the CT images was 0.09 ± 0.06 [with most of the variability originating from the heart histograms, Fig. 2(a)]. For the unprocessed
773
CBCT images, the average pairwise distance was considerably higher at 0.22 ± 0.19 [Fig. 2(b)]. When each CBCT image set was compared against its closest library CBCT, the average Chi-square distance was 0.03 ± 0.02, indicating the feasibility of the database-matching approach. In the same way, the average pairwise MI similarity between any two library coregistered CBCT images was 0.54 ± 0.25, while the MI similarity between the closest image pairs was 57% higher, at 0.85 ± 0.2. The effect of applying histogram matching on the CBCT images is visualized in Figs. 2(c) and 2(d) and quantified in Fig. 3. Following processing, the gray-level histograms of the CBCT images resembled the histograms of the CT images. The use of the library CT for the histogram matching was shown to be comparable to using the paired planning CT: The Chi-square histogram distances between the unprocessed CBCT images and the library-selected CT images were similar to the distances between the CBCTs and their corresponding planning CTs (0.55 ± 0.16 vs 0.55 ± 0.15, P = NS). Following processing, the Chi-square distances between CBCT-GLB and CT reduced to 0.20 ± 0.12, for either library CT or paired planning CT (P < 10−6 vs unprocessed distances). Since histogram similarity may not necessarily imply contextual image similarity, the effect of the CBCT processing was also quantified using image similarity measures. Figure 4(a) shows the MI image similarity between the processed CBCT and its corresponding registered planning CT, using different processing variations. Prior to processing, the average MI similarity between the registered CBCT-CT pairs was 0.97 ± 0.09. Processing by global histogram matching increased the average MI similarity by 3% to 1.0 ± 0.1 (P < 10−6, compared to unprocessed). Performing histogram
F. 2. Gray-level histograms of all 38 image sets, calculated for different ROIs (whole body, lungs, heart, and body without lungs and heart). The interpatient variability of the CT images (a) is smaller than the unprocessed CBCT images (b). Following processing of the CBCT images by global (c) or ROI-based (d) histogram matching, the interpatient variability is reduced, and the histograms become more similar to the reference CT histograms. Medical Physics, Vol. 42, No. 2, February 2015
774
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
F. 3. Chi-square histogram distances between CBCT images and their corresponding planning CTs (paired CT) and between CBCT images and their library-selected CTs (library CT). The distances are significantly reduced, following histogram matching (CBCT-GLB and CBCT-ROI), compared to the unprocessed images.
matching with the ground-truth planning, CT had a consistent small advantage over using library-selected CT (P < 0.001). A further increase of 3% in similarity was achieved by ROIbased histogram matching, with an average value of 1.03 ± 0.1 (P < 10−6, compared to global histogram matching), but with no significant difference between processing with planning CT and library-selected CT. Figure 4(b) shows the average SAD between voxels of the body volume in the unprocessed CBCT and its paired planning CT (384 ± 218), which was significantly reduced with both the processed CBCT-GLB (185 ± 38, relative reduction 44% ± 21%) and the processed CBCT-ROI (202 ± 60, relative reduction 39% ± 28%)(P < 10−6, compared to unprocessed). The ability of the automated planning algorithm to correctly delineate the lungs and the heart in the processed CBCT images was evaluated on the 15 test cases, for which automated CBCT-based treatment plans were generated. The quality of the lung and heart segmentation was compared to the CTbased segmentation by calculating the Dice coefficient D(A,B) = 2| A ∩ B|/(| A| + |B|). The automatically segmented CBCTROI images had average Dice values of 0.94 ± 0.01, 0.92 ± 0.04, and 0.85 ± 0.2 for the right lung, left lung, and
774
heart, respectively. The average Dice coefficients were lower for the segmented CBCT-GLB images: 0.92 ± 0.02 for the right lung, 0.87 ± 0.05 for the left lung, and 0.56 ± 0.23 for the heart (P < 0.001 compared to CBCT-ROI results). For the lung and heart, segmentation errors were consistently at the posterior edge of the contours, whereas the anterior segmented sections adjacent to the breast were closely aligned. The results for each test plan are detailed in Table I. The two-field tangential treatment plans that were automatically generated using the processed CBCT images were similar to the reference CT-based plans. The average difference in gantry angle of the medial beam was 0.99◦ ± 1.1◦ for CBCTGLB plans and 0.85◦ ± 0.8◦ for CBCT-ROI plans (P = NS). The difference in the anterior–posterior location of the beam’s isocenter was 0.23 ± 0.24 cm and 0.20 ± 0.18 cm for the two CBCT processing methods, respectively (P = NS). The consequent target volume, which is directly determined by the placement of the beams, was similar to the CT-based plans, and the Dice coefficient of the overlap between the CT and CBCTGLB target volumes was 0.89 ± 0.04. Table II outlines the derived dosimetric indices of the CT-based and CBCT-based automated plans. Among these indices, the CBCT-based plans had higher maximum target doses (4482 ± 90 vs 4560 ± 84, P < 0.05). The CT, CBCT-GLB, and the CT-(CBCT-GLB) hybrid plans were reviewed for clinical acceptability (TGP). The CBCT-GLB and CT-(CBCT-GLB) plans were scored, based on target coverage, organ at risk sparing, dose homogeneity, and maximum dose relative to the CT plans. All plans were clinically acceptable; however, the most significant discrepancies were as follows: one of the CT-(CBCT-GLB) hybrid plans had a higher mean target dose (4323 ± 70 cGy) than the corresponding CBCT-GLB plan (4255 ± 76 cGy) and CT plan (4240 ± 59 cGy). In addition, one CT-(CBCTGLB) hybrid plan had a higher maximum plan dose (+7.0%) compared with the corresponding CBCT-GLB plan (+6.3%) and the CT plan (+4.5%). All other plans were dosimetrically equivalent. The displacement between the pairs of CBCT and CT image sets was estimated from the deformation vector fields, computed by the CT-CBCT deformable registration. The average magnitude of the deformation vectors within the body volume was 6.2 ± 1.2 mm (range 4.2–8.0 mm). The dose
F. 4. Mutual information image similarity (a) and average SAD (b) between CBCT images and their corresponding planning CTs before processing (unprocessed), following global histogram matching (CBCT-GLB) with reference histograms from either the planning CT (paired-CT) or the library-selected CT (library-CT), and following ROI-based histogram matching (CBCT-ROI). Medical Physics, Vol. 42, No. 2, February 2015
775
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
775
T I. Evaluation of processed CBCT images and their derived treatment plans (all values are compared to the corresponding planning CT). Image similarity (planning CT) Plan No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Avg. ±Std.
Plan type
MI
CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI
1.08 1.15 1.05 1.07 0.98 1.01 0.85 0.87 1.13 1.12 0.80 0.82 1.08 1.09 1.00 1.00 1.10 1.10 1.11 1.11 1.16 1.19 0.91 0.93 0.99 1.04 1.02 1.06 1.03 1.06 1.00 ± 0.1 1.03 ± 0.1
SAD
Segmentation overlap (planning CT) Target
Ipsilat. lung
Beam comparison (CT plan)
Heart
Beam angle diff. (deg)
167 0.83 0.93 0.57 2.3 255 0.86 0.95 0.90 1.4 189 0.88 0.90 0.57 1.1 200 0.88 0.91 0.94 1.1 200 0.91 0.89 0.76 0.0 163 0.91 0.92 0.92 0.0 175 0.92 0.94 0.80 0.0 228 0.91 0.94 0.91 0.0 159 0.89 0.90 0.36 3.2 181 0.91 0.94 0.70 2.4 171 0.89 0.93 0.88 0.3 167 0.89 0.94 0.92 0.3 173 0.92 0.92 0.22 0.0 196 0.92 0.95 0.92 0.0 271 0.93 0.94 0.69 0.2 153 0.94 0.95 0.96 0.2 156 0.94 0.89 0.59 0.0 162 0.94 0.95 0.91 0.0 166 0.92 0.95 0.28 0.0 181 0.91 0.96 0.93 0.9 164 0.88 0.90 0.10 1.6 200 0.90 0.94 0.11 1.0 139 0.83 0.80 0.78 0.4 150 0.82 0.94 0.93 0.4 182 0.84 0.92 0.52 2.9 170 0.88 0.94 0.87 2.3 203 0.84 0.89 0.87 1.4 315 0.85 0.96 0.94 1.0 148 0.92 0.91 0.46 1.3 220 0.91 0.94 0.90 1.8 185 ± 38 0.89 ± 0.04 0.91 ± 0.04 0.56 ± 0.23 0.99 ± 1.1 202 ± 60 0.90 ± 0.03 0.94 ± 0.01 0.81 ± 0.20 0.85 ± 0.80
distributions of the CBCT-GLB plans were very similar to the CT-(CBCT-GLB) hybrid plans. Using values of 3% and 3 mm for the dose difference and distance-to-agreement criteria, respectively, the mean gamma index of all test plans was
Dose comparison (hybrid plan, 3%/3 mm)
Isocenter diff. (cm)
Dose diff (%)
Gamma index
Gamma < 1 (%)
0.49 0.30 0.31 0.31 0.00 0.00 0.00 0.00 0.81 0.61 0.15 0.15 0.00 0.00 0.02 0.02 0.00 0.00 0.00 0.20 0.50 0.30 0.12 0.12 0.52 0.42 0.31 0.20 0.29 0.39 0.23 ± 0.24 0.20 ± 0.18
0.56 ± 0.52 0.59 ± 0.59 0.65 ± 0.46 0.54 ± 0.54 0.64 ± 0.43 0.51 ± 0.51 0.75 ± 0.54 0.62 ± 0.62 0.76 ± 0.53 0.68 ± 0.68 0.71 ± 0.46 0.59 ± 0.59 0.66 ± 0.44 0.53 ± 0.53 0.53 ± 0.48 0.47 ± 0.47 0.54 ± 0.50 0.50 ± 0.50 0.92 ± 0.63 0.90 ± 0.90 0.98 ± 0.62 0.66 ± 0.66 0.50 ± 0.48 0.53 ± 0.53 0.52 ± 0.42 0.56 ± 0.56 0.57 ± 0.44 0.48 ± 0.48 0.46 ± 0.41 0.55 ± 0.55 0.65 ± 0.14 0.58 ± 0.10
0.12 ± 0.37 0.24 ± 0.33 0.10 ± 0.17 0.18 ± 0.30 0.11 ± 0.15 0.11 ± 0.21 0.16 ± 0.22 0.23 ± 0.35 0.14 ± 0.16 0.25 ± 0.34 0.12 ± 0.16 0.22 ± 0.30 0.10 ± 0.14 0.14 ± 0.18 0.10 ± 0.19 0.11 ± 0.16 0.12 ± 0.19 0.11 ± 0.17 0.18 ± 0.19 0.38 ± 0.45 0.20 ± 0.23 0.26 ± 0.30 0.10 ± 0.23 0.12 ± 0.21 0.08 ± 0.14 0.18 ± 0.38 0.09 ± 0.21 0.13 ± 0.16 0.06 ± 0.13 0.16 ± 0.40 0.12 ± 0.04 0.19 ± 0.07
98.5 97.6 99.6 97.7 99.6 99.6 99.6 98.2 99.7 95.6 99.5 97.1 99.7 99.3 99.3 99.6 99.4 99.5 99.7 92.6 99.3 97.4 98.9 99.0 99.7 98.4 99.5 99.5 99.7 99.0 99.4 ± 0.3 98.0 ± 1.8
0.12 ± 0.04, and the ratio of gamma-index
Automated planning of breast radiotherapy using cone beam CT imaging Guy Amit Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G2M9, Canada
Thomas G. Purdiea) Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G2M9, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada; and Techna Institute, University Health Network, University of Toronto, Toronto, Ontario M5G 1P5, Canada
(Received 25 June 2014; revised 7 December 2014; accepted for publication 14 December 2014; published 16 January 2015) Purpose: Develop and clinically validate a methodology for using cone beam computed tomography (CBCT) imaging in an automated treatment planning framework for breast IMRT. Methods: A technique for intensity correction of CBCT images was developed and evaluated. The technique is based on histogram matching of CBCT image sets, using information from “similar” planning CT image sets from a database of paired CBCT and CT image sets (n = 38). Automated treatment plans were generated for a testing subset (n = 15) on the planning CT and the corrected CBCT. The plans generated on the corrected CBCT were compared to the CT-based plans in terms of beam parameters, dosimetric indices, and dose distributions. Results: The corrected CBCT images showed considerable similarity to their corresponding planning CTs (average mutual information 1.0 ± 0.1, average sum of absolute differences 185 ± 38). The automated CBCT-based plans were clinically acceptable, as well as equivalent to the CT-based plans with average gantry angle difference of 0.99◦ ± 1.1◦, target volume overlap index (Dice) of 0.89 ± 0.04 although with slightly higher maximum target doses (4482 ± 90 vs 4560 ± 84, P < 0.05). Gamma index analysis (3%, 3 mm) showed that the CBCT-based plans had the same dose distribution as plans calculated with the same beams on the registered planning CTs (average gamma index 0.12 ± 0.04, gamma < 1 in 99.4% ± 0.3%). Conclusions: The proposed method demonstrates the potential for a clinically feasible and efficient online adaptive breast IMRT planning method based on CBCT imaging, integrating automation. C 2015 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4905111] Key words: cone-beam computed tomography, breast cancer, radiotherapy planning, automation, image processing
1. INTRODUCTION Kilovoltage (kV) cone-beam computed tomography (CBCT) imaging has been evaluated for setup reproducibility and target localization in breast IMRT.1,2 CBCT imaging is also a promising technology for adaptive radiotherapy as images can be acquired rapidly over an entire volume, with low radiation doses.3 However, CBCT imaging does not produce accurate CT numbers/Hounsfield units (HU) and suffers from artifacts and radio-density inconsistencies, which can affect both accurate dose calculation and automated segmentation algorithms.4 The clinical utilization of CBCT images in radiotherapy therefore requires image adjustment methods. One approach of CBCT adjustment is using phantom-based HU calibration to derive HU to electron density (ED) conversion curves.5 However, this calibration process differs between scanner vendors and highly depends on phantom material and size.6,7 It has been shown that the patient size and geometry have considerable influence on CBCT values and the use of patient-independent CBCT HU to ED calibration is therefore inaccurate.8 Investigations of the clinical feasibility of CBCT for dose calculation have shown that the scatter and artifacts in CBCT 770
Med. Phys. 42 (2), February 2015
were severe surrounding inhomogeneous tissue, nevertheless, CBCT-based treatment plans were found to be dosimetrically comparable to CT-based plans.6 Multiple researchers have suggested advanced image processing and enhancement methods aiming to improve the CBCT image quality and facilitate its use in radiotherapy. Image enhancement methods typically attempt to estimate the scattered radiation using, for example, deconvolution technique,9,10 Monte Carlo simulations,11 or a posteriori probability estimation.12 In the radiotherapy domain, a major portion of the CBCT processing methods addressed the problem of CT-CBCT deformable registration, utilizing CT information for automated CBCT segmentation.13–16 Most of the previously suggested methods4,17,18 rely on information from the patient’s planning CT for improving the quality of CBCT images and adapting them for radiotherapy procedures. However, as modern linear accelerators are equipped with CBCT imaging capabilities, it is possible to envision an online IMRT treatment model, where the planning is performed automatically on the CBCT images, at the treatment unit.19,20 In this work, we describe a method for CBCT-based automated breast IMRT treatment planning. By having planning and treatment performed in the same session,
0094-2405/2015/42(2)/770/10/$30.00
© 2015 Am. Assoc. Phys. Med.
770
771
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
we can avoid replanning due to changes in patient shape and anatomy, which may be seen between the CT simulation and the patient’s first treatment fraction. An adaptive model like that would allow patients to get a new treatment plan without any delays in treatment or rebooking appointments. We describe a database-matching method that automatically applies intensity corrections to CBCT image sets, based on identified similarity with planning CT image sets from the database. Our goal was to evaluate the applicability of this method in the clinical environment. To that end, the corrected CBCT image sets were used for automated tangential breast IMRT treatment planning, demonstrating the potential for an online adaptive treatment planning model based on CBCT imaging. 2. METHODS 2.A. Reference library construction
A CT-CBCT reference library was used as a database for mapping the relationship between corresponding CT and CBCT image sets. The library consisted of 38 paired CT and CBCT image sets obtained for each patient. CT images were registered onto the CBCT images, using 3D intensity-based rigid registration.21 The registration was implemented using matlab’s image processing toolbox (The Mathworks, Inc., Natick, MA). The lungs, heart, and body contours were auto-
771
matically delineated on the CT images using a combination of image threshold methods and model-based segmentation.22 Using the calculated CT-CBCT rigid registration transform, the delineated regions of interest were aligned onto the CBCT images. The gray-level histograms of each region-of-interest (ROI) were calculated on the CT and CBCT images. Two field tangential treatment plans were automatically generated using the CT image sets,22 and used as ground-truth reference plans.
2.B. CBCT image processing
Two different image processing approaches were applied to the CBCT images. Both approaches attempt to identify a “similar” library entry, and then to use the library data to transform the CBCT images into “CT-like” images, on which automated planning can be applied. The images are transformed by histogram matching, a standard image processing technique, aiming to change the gray-level histogram of the input image to resemble a reference histogram.23 The first method is employing global histogram matching of the CBCT image, correcting all voxels within the body volume. The second method is assuming availability of ROI delineation in the CBCT images and is applying histogram matching separately for each ROI (Fig. 1). The processed CBCT images produced by these two methods are denoted CBCT-GLB and CBCT-ROI, respectively.
F. 1. The CBCT processing pipeline. The input CBCT image is preprocessed (a) by a noise-reduction filter, the body volume is delineated and optionally the lungs and heart are segmented. The gray-level histogram is calculated (b) either for the entire body volume or for each ROI. The CBCT image is matched against the reference CT-CBCT library to identify the “closest” library CBCT and obtain its paired library CT (c). The selected CT is used to perform histogram matching (d) on the CBCT. The processed CBCT is then analyzed by the automated planning algorithm (e) to produce a two-field tangential breast treatment plan. The output plan is compared to the ground-truth plan, generated automatically from the planning CT. Medical Physics, Vol. 42, No. 2, February 2015
772
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
2.C. Global histogram matching
Given a new CBCT image set, the following processing steps are applied to produce the output CBCT-GLB image set: (a1) Preprocessing by a noise reduction filter. An anisotropic diffusion filter24 is applied to reduce scatter noise while preserving edges and other semantically meaningful details of the image. (a2) Body contour segmentation. The scanned body volume is automatically delineated using image thresholding, following detection and removal of the couch from the image. (a3) Library search using registration-based similarity. The segmented body volume is registered against each library CBCT, using 3D intensity-based rigid registration. Mutual information (MI) is used as a similarity metric for choosing the closest library entry.25 The paired library CT is then selected for histogram matching. MI between images A and B is defined by: H(A) + H(B) − H(A,B), where H(X) = − x p X (x)log p X (x) is the entropy of image X, and H(A,B) = − a,b pAB(a,b)log pAB(a,b) is the joint entropy. Intuitively, MI measures how much knowing one image reduces uncertainty about the other image. (a4) Histogram matching of the CBCT body ROI against the corresponding ROI from the selected library CT. The histogram-matching algorithm aims to find a monotonic grayscale transformation T, to minimize |(Cin(T(k))) −Cref (k)| for all intensity values k ∈ [0, 255], where Cin and Cref are the cumulative graylevel histograms of the input and reference images, respectively. 2.D. ROI-based histogram matching
In the case where ROI segmentation information is available for the CBCT images, histogram matching can be applied separately to the lungs, heart, and the remaining body volume. In this work, ROI-based histogram matching was evaluated using the “ground-truth” ROIs aligned from the registered CT images. This allowed the evaluation to be independent of the accuracy of the segmentation algorithm. In practice, when the corresponding CT is not available, a designated CBCT segmentation algorithm can be applied for delineating the ROIs. One possible approach is using a two-phase processing, where the CBCT images are first transformed by global histogram matching, as described in Sec. 2.C, and the CT-like image is then segmented and refined by ROI-based histogram matching. Given a new CBCT image set, the following processing steps are applied to produce the output image set CBCT-ROI image set: (b1) Preprocessing, as described above in step (a1). (b2) Histogram calculation. Gray-level histograms (256 bins) are calculated for the lungs, heart, and the remaining body volume. (b3) Library search using histogram-based similarity. The closest library CBCT is chosen by calculating ChiMedical Physics, Vol. 42, No. 2, February 2015
772
square distance between the gray-level histograms of the ROIs, defined by (hin(k) − href (k))2 , d(hin,href) = k (hin(k) + href (k))/2 where hin and href are the histograms of the input and reference ROIs. The distance between the images is taken to be the average distance over all ROIs. (b4) Histogram matching. For each ROI, histogram matching is performed as described above in step (a4). 2.E. Automated planning
The automated technique for tangential breast IMRT planning has been previously described. Briefly, the method emulates all the manual steps required for generating a treatment plan including beam placement, segmentation, IMRT optimization, dose calculation, and plan documentation.22,26 It detects radio-opaque anatomical markers in the CT images, used to drive automatic model-based segmentation of the breast target and organ at risk volumes and then applies a heuristic algorithm to define the tangential beam geometry for treatment. The automated method is focused on optimizing the segmentation for sections of the organ at risk volumes that are in close proximity to the breast volume. The heuristic algorithm is entirely based on the absolute volume irradiated in each of the organs at risk; therefore, the sections of the organs at risk distant from the breast are not considered or evaluated. 2.F. Evaluation experiments
The analyzed dataset consisted of 38 planning CT images with scan resolution of 1 × 1 × 2 mm3 and bit depth of 12 bits, obtained from patients undergoing whole breast irradiation at our institution (12 right-breast patients and 26 left-breast patients). The standard prescription dose was 4240 cGy in 16 treatment fractions, prescribed as the mean dose to the whole breast volume. The corresponding CBCT images were acquired at the treatment units with typical scan resolution of 1 × 1 × 2 mm3. CBCT-GLB and CBCT-ROI images were produced for each patient in the dataset, using the remaining 37 patients as the reference library (leave-one-out). The quality of output CBCT image sets was initially evaluated by measuring its similarity to the ground-truth CT image set of the same patient, using conventional image similarity measures including MI, average sum of absolute differences (SAD), and Chi-square histogram distance. A test subset of 15 patients was randomly selected from the analyzed dataset. The output CBCT images of these patients were imported into the treatment planning system (pinnacle,3 Philips Radiation Oncology Systems, Madison, WI), and were processed by the breast automated planning algorithm that produced CBCT-based treatment plans. The resulting CBCTbased plans were compared to the CT-based ground-truth plans by examining their delineated ROIs, target volumes, beam angles, the defined clinical protocol dosimetric indices, and adherence to our institutional clinical acceptability guidelines.
773
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
The quality of the CBCT-based treatment plans was determined by the performance of two main components of the automated planning tool. The first component is the automated segmentation and gantry angle selection, which delineates the volumes of the lungs and the heart on the CBCT image, and uses these volumes to choose an optimal beam angle for adequate target coverage. The second component is IMRT optimization and dose calculation, which uses HU to ED conversion curves to calculate dose from the intensity values of the CBCT image. In order to separate the contribution of these two components to the final resulting plans, a CT(CBCT-GLB) hybrid plan was generated for each test case by placing the CBCT-derived beams on the CT image set. To ensure accurate dose-grid alignment, the CT and CBCT images were registered using deformable image registration with a multiscale Horn–Schunk optical flow algorithm.27,28 The CBCT-derived beams were recalculated on the registered CT image set, to generate a dose distribution based on the CT geometry. 3D gamma-index evaluation29,30 was used to compare the dose distributions of the CBCT-based plans and the CT-(CBCT-GLB) hybrid plans.
3. RESULTS The intensity histograms of the library CBCT images showed large interpatient variability, compared to CT images (Fig. 2). This variability was quantified by calculating the average Chi-square distance between all pairs of body volume histograms. The average pairwise distance for the CT images was 0.09 ± 0.06 [with most of the variability originating from the heart histograms, Fig. 2(a)]. For the unprocessed
773
CBCT images, the average pairwise distance was considerably higher at 0.22 ± 0.19 [Fig. 2(b)]. When each CBCT image set was compared against its closest library CBCT, the average Chi-square distance was 0.03 ± 0.02, indicating the feasibility of the database-matching approach. In the same way, the average pairwise MI similarity between any two library coregistered CBCT images was 0.54 ± 0.25, while the MI similarity between the closest image pairs was 57% higher, at 0.85 ± 0.2. The effect of applying histogram matching on the CBCT images is visualized in Figs. 2(c) and 2(d) and quantified in Fig. 3. Following processing, the gray-level histograms of the CBCT images resembled the histograms of the CT images. The use of the library CT for the histogram matching was shown to be comparable to using the paired planning CT: The Chi-square histogram distances between the unprocessed CBCT images and the library-selected CT images were similar to the distances between the CBCTs and their corresponding planning CTs (0.55 ± 0.16 vs 0.55 ± 0.15, P = NS). Following processing, the Chi-square distances between CBCT-GLB and CT reduced to 0.20 ± 0.12, for either library CT or paired planning CT (P < 10−6 vs unprocessed distances). Since histogram similarity may not necessarily imply contextual image similarity, the effect of the CBCT processing was also quantified using image similarity measures. Figure 4(a) shows the MI image similarity between the processed CBCT and its corresponding registered planning CT, using different processing variations. Prior to processing, the average MI similarity between the registered CBCT-CT pairs was 0.97 ± 0.09. Processing by global histogram matching increased the average MI similarity by 3% to 1.0 ± 0.1 (P < 10−6, compared to unprocessed). Performing histogram
F. 2. Gray-level histograms of all 38 image sets, calculated for different ROIs (whole body, lungs, heart, and body without lungs and heart). The interpatient variability of the CT images (a) is smaller than the unprocessed CBCT images (b). Following processing of the CBCT images by global (c) or ROI-based (d) histogram matching, the interpatient variability is reduced, and the histograms become more similar to the reference CT histograms. Medical Physics, Vol. 42, No. 2, February 2015
774
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
F. 3. Chi-square histogram distances between CBCT images and their corresponding planning CTs (paired CT) and between CBCT images and their library-selected CTs (library CT). The distances are significantly reduced, following histogram matching (CBCT-GLB and CBCT-ROI), compared to the unprocessed images.
matching with the ground-truth planning, CT had a consistent small advantage over using library-selected CT (P < 0.001). A further increase of 3% in similarity was achieved by ROIbased histogram matching, with an average value of 1.03 ± 0.1 (P < 10−6, compared to global histogram matching), but with no significant difference between processing with planning CT and library-selected CT. Figure 4(b) shows the average SAD between voxels of the body volume in the unprocessed CBCT and its paired planning CT (384 ± 218), which was significantly reduced with both the processed CBCT-GLB (185 ± 38, relative reduction 44% ± 21%) and the processed CBCT-ROI (202 ± 60, relative reduction 39% ± 28%)(P < 10−6, compared to unprocessed). The ability of the automated planning algorithm to correctly delineate the lungs and the heart in the processed CBCT images was evaluated on the 15 test cases, for which automated CBCT-based treatment plans were generated. The quality of the lung and heart segmentation was compared to the CTbased segmentation by calculating the Dice coefficient D(A,B) = 2| A ∩ B|/(| A| + |B|). The automatically segmented CBCTROI images had average Dice values of 0.94 ± 0.01, 0.92 ± 0.04, and 0.85 ± 0.2 for the right lung, left lung, and
774
heart, respectively. The average Dice coefficients were lower for the segmented CBCT-GLB images: 0.92 ± 0.02 for the right lung, 0.87 ± 0.05 for the left lung, and 0.56 ± 0.23 for the heart (P < 0.001 compared to CBCT-ROI results). For the lung and heart, segmentation errors were consistently at the posterior edge of the contours, whereas the anterior segmented sections adjacent to the breast were closely aligned. The results for each test plan are detailed in Table I. The two-field tangential treatment plans that were automatically generated using the processed CBCT images were similar to the reference CT-based plans. The average difference in gantry angle of the medial beam was 0.99◦ ± 1.1◦ for CBCTGLB plans and 0.85◦ ± 0.8◦ for CBCT-ROI plans (P = NS). The difference in the anterior–posterior location of the beam’s isocenter was 0.23 ± 0.24 cm and 0.20 ± 0.18 cm for the two CBCT processing methods, respectively (P = NS). The consequent target volume, which is directly determined by the placement of the beams, was similar to the CT-based plans, and the Dice coefficient of the overlap between the CT and CBCTGLB target volumes was 0.89 ± 0.04. Table II outlines the derived dosimetric indices of the CT-based and CBCT-based automated plans. Among these indices, the CBCT-based plans had higher maximum target doses (4482 ± 90 vs 4560 ± 84, P < 0.05). The CT, CBCT-GLB, and the CT-(CBCT-GLB) hybrid plans were reviewed for clinical acceptability (TGP). The CBCT-GLB and CT-(CBCT-GLB) plans were scored, based on target coverage, organ at risk sparing, dose homogeneity, and maximum dose relative to the CT plans. All plans were clinically acceptable; however, the most significant discrepancies were as follows: one of the CT-(CBCT-GLB) hybrid plans had a higher mean target dose (4323 ± 70 cGy) than the corresponding CBCT-GLB plan (4255 ± 76 cGy) and CT plan (4240 ± 59 cGy). In addition, one CT-(CBCTGLB) hybrid plan had a higher maximum plan dose (+7.0%) compared with the corresponding CBCT-GLB plan (+6.3%) and the CT plan (+4.5%). All other plans were dosimetrically equivalent. The displacement between the pairs of CBCT and CT image sets was estimated from the deformation vector fields, computed by the CT-CBCT deformable registration. The average magnitude of the deformation vectors within the body volume was 6.2 ± 1.2 mm (range 4.2–8.0 mm). The dose
F. 4. Mutual information image similarity (a) and average SAD (b) between CBCT images and their corresponding planning CTs before processing (unprocessed), following global histogram matching (CBCT-GLB) with reference histograms from either the planning CT (paired-CT) or the library-selected CT (library-CT), and following ROI-based histogram matching (CBCT-ROI). Medical Physics, Vol. 42, No. 2, February 2015
775
G. Amit and T. G. Purdie: Automated planning of breast radiotherapy using CBCT imaging
775
T I. Evaluation of processed CBCT images and their derived treatment plans (all values are compared to the corresponding planning CT). Image similarity (planning CT) Plan No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Avg. ±Std.
Plan type
MI
CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI CBCT-GLB CBCT-ROI
1.08 1.15 1.05 1.07 0.98 1.01 0.85 0.87 1.13 1.12 0.80 0.82 1.08 1.09 1.00 1.00 1.10 1.10 1.11 1.11 1.16 1.19 0.91 0.93 0.99 1.04 1.02 1.06 1.03 1.06 1.00 ± 0.1 1.03 ± 0.1
SAD
Segmentation overlap (planning CT) Target
Ipsilat. lung
Beam comparison (CT plan)
Heart
Beam angle diff. (deg)
167 0.83 0.93 0.57 2.3 255 0.86 0.95 0.90 1.4 189 0.88 0.90 0.57 1.1 200 0.88 0.91 0.94 1.1 200 0.91 0.89 0.76 0.0 163 0.91 0.92 0.92 0.0 175 0.92 0.94 0.80 0.0 228 0.91 0.94 0.91 0.0 159 0.89 0.90 0.36 3.2 181 0.91 0.94 0.70 2.4 171 0.89 0.93 0.88 0.3 167 0.89 0.94 0.92 0.3 173 0.92 0.92 0.22 0.0 196 0.92 0.95 0.92 0.0 271 0.93 0.94 0.69 0.2 153 0.94 0.95 0.96 0.2 156 0.94 0.89 0.59 0.0 162 0.94 0.95 0.91 0.0 166 0.92 0.95 0.28 0.0 181 0.91 0.96 0.93 0.9 164 0.88 0.90 0.10 1.6 200 0.90 0.94 0.11 1.0 139 0.83 0.80 0.78 0.4 150 0.82 0.94 0.93 0.4 182 0.84 0.92 0.52 2.9 170 0.88 0.94 0.87 2.3 203 0.84 0.89 0.87 1.4 315 0.85 0.96 0.94 1.0 148 0.92 0.91 0.46 1.3 220 0.91 0.94 0.90 1.8 185 ± 38 0.89 ± 0.04 0.91 ± 0.04 0.56 ± 0.23 0.99 ± 1.1 202 ± 60 0.90 ± 0.03 0.94 ± 0.01 0.81 ± 0.20 0.85 ± 0.80
distributions of the CBCT-GLB plans were very similar to the CT-(CBCT-GLB) hybrid plans. Using values of 3% and 3 mm for the dose difference and distance-to-agreement criteria, respectively, the mean gamma index of all test plans was
Dose comparison (hybrid plan, 3%/3 mm)
Isocenter diff. (cm)
Dose diff (%)
Gamma index
Gamma < 1 (%)
0.49 0.30 0.31 0.31 0.00 0.00 0.00 0.00 0.81 0.61 0.15 0.15 0.00 0.00 0.02 0.02 0.00 0.00 0.00 0.20 0.50 0.30 0.12 0.12 0.52 0.42 0.31 0.20 0.29 0.39 0.23 ± 0.24 0.20 ± 0.18
0.56 ± 0.52 0.59 ± 0.59 0.65 ± 0.46 0.54 ± 0.54 0.64 ± 0.43 0.51 ± 0.51 0.75 ± 0.54 0.62 ± 0.62 0.76 ± 0.53 0.68 ± 0.68 0.71 ± 0.46 0.59 ± 0.59 0.66 ± 0.44 0.53 ± 0.53 0.53 ± 0.48 0.47 ± 0.47 0.54 ± 0.50 0.50 ± 0.50 0.92 ± 0.63 0.90 ± 0.90 0.98 ± 0.62 0.66 ± 0.66 0.50 ± 0.48 0.53 ± 0.53 0.52 ± 0.42 0.56 ± 0.56 0.57 ± 0.44 0.48 ± 0.48 0.46 ± 0.41 0.55 ± 0.55 0.65 ± 0.14 0.58 ± 0.10
0.12 ± 0.37 0.24 ± 0.33 0.10 ± 0.17 0.18 ± 0.30 0.11 ± 0.15 0.11 ± 0.21 0.16 ± 0.22 0.23 ± 0.35 0.14 ± 0.16 0.25 ± 0.34 0.12 ± 0.16 0.22 ± 0.30 0.10 ± 0.14 0.14 ± 0.18 0.10 ± 0.19 0.11 ± 0.16 0.12 ± 0.19 0.11 ± 0.17 0.18 ± 0.19 0.38 ± 0.45 0.20 ± 0.23 0.26 ± 0.30 0.10 ± 0.23 0.12 ± 0.21 0.08 ± 0.14 0.18 ± 0.38 0.09 ± 0.21 0.13 ± 0.16 0.06 ± 0.13 0.16 ± 0.40 0.12 ± 0.04 0.19 ± 0.07
98.5 97.6 99.6 97.7 99.6 99.6 99.6 98.2 99.7 95.6 99.5 97.1 99.7 99.3 99.3 99.6 99.4 99.5 99.7 92.6 99.3 97.4 98.9 99.0 99.7 98.4 99.5 99.5 99.7 99.0 99.4 ± 0.3 98.0 ± 1.8
0.12 ± 0.04, and the ratio of gamma-index
