Gradient maintenance: A new algorithm for fast online replanninga) Ergun E. Ahunbayb) and X. Allen Li Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226
(Received 14 October 2014; revised 16 April 2015; accepted for publication 24 April 2015; published 18 May 2015) Purpose: Clinical use of online adaptive replanning has been hampered by the unpractically long time required to delineate volumes based on the image of the day. The authors propose a new replanning algorithm, named gradient maintenance (GM), which does not require the delineation of organs at risk (OARs), and can enhance automation, drastically reducing planning time and improving consistency and throughput of online replanning. Methods: The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each OAR in daily anatomy can be maintained the same as that in the original plan, the intended plan quality of the original plan would be preserved in the adaptive plan. The algorithm requires a series of partial concentric rings (PCRs) to be automatically generated around the target toward each OAR on the planning and the daily images. The PCRs are used in the daily optimization objective function. The PCR dose constraints are generated with dose–volume data extracted from the original plan. To demonstrate this idea, GM plans generated using daily images acquired using an in-room CT were compared to regular optimization and image guided radiation therapy repositioning plans for representative prostate and pancreatic cancer cases. Results: The adaptive replanning using the GM algorithm, requiring only the target contour from the CT of the day, can be completed within 5 min without using high-power hardware. The obtained adaptive plans were almost as good as the regular optimization plans and were better than the repositioning plans for the cases studied. Conclusions: The newly proposed GM replanning algorithm, requiring only target delineation, not full delineation of OARs, substantially increased planning speed for online adaptive replanning. The preliminary results indicate that the GM algorithm may be a solution to improve the ability for automation and may be especially suitable for sites with small-to-medium size targets surrounded by several critical structures. C 2015 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4919847] Key words: adaptive radiotherapy, online replanning, image guided radiotherapy, dose deformation, deformable image registration
1. INTRODUCTION Interfraction variation, a major issue affecting radiation therapy (RT) for some tumor sites1,2 could result in suboptimal dose distributions and significant deviations from the originally optimized plan if it is not adequately accounted for. Recently, image guided RT (IGRT) has been widely used to correct (eliminate or reduce) the deteriorating effects of the interfraction variation.3 A dedicated new plan that is optimized based on the image of the day (online reoptimization) would eliminate all interfractional variations, both random and systematic.4 The main challenge of online replanning is that it needs to be performed very quickly while the patient is in the treatment setup position. If the replanning process takes significantly longer than the IGRT repositioning, the additional time can result in increased anatomical motion which would counteract the benefits obtainable from the process. Also the reduced time for planning, QA, and decision making may increase likelihood of error unless reliable algorithms and verification systems are employed. Offline replanning may be a possible alternative to online replanning, but this type of intervention 2863
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cannot account for random variations and can only account for a portion of systematic variation (depending on the number of fractions). In recent years, a great deal of effort has been spent to developing new RT planning algorithms and technologies allowing fast and automated replanning.5,6 However, none of these developments is mature enough for the routine practice of online replanning in the clinic. The two most time consuming processes in the current replanning technology are (1) the delineation of the volumes of interest on the daily image set and (2) the plan optimization (inverse planning). These processes are difficult to automate; therefore, they cannot be speeded up by simply increasing or parallelizing computer power. In this work, we propose a novel algorithm, gradient maintenance (GM), to specifically address these two major obstacles. The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each organ at risk (OAR) surrounding the target in the replanning process can be maintained the same as that in the original plan, the obtained (adaptive) plan from the replanning process would have the comparable plan quality as the original plan. The GM
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© 2015 Am. Assoc. Phys. Med.
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algorithm requires only the accurate delineation of the targets and does not require the delineation of OARs on the image of the day, dramatically reducing replanning time and human effort. Since only the target volume needs to be delineated, this algorithm would be especially beneficial for tumor sites with a large number of OARs and a small target volume (e.g., abdomen and pelvis). Another advantage is that compared to the conventional optimization, the GM optimization’s objective function (OF) would be less affected by variations in the daily anatomy (since it is based only on the dose gradients, not the dose–volume criteria of OARs). Therefore, the GM algorithm’s OF would need less tweaking and trial and error by the planner which could enhance the ability to automate the replanning process. The purpose of this work is to present the idea of the GM algorithm by testing if the daily plans generated by the GM algorithm without using OAR contours would be comparable to the daily plans that are optimized using the full sets of OAR contours. 2. MATERIALS AND METHODS 2.A. Implementation
The main idea of the GM algorithm is to reproduce the original dose gradients, as achieved in the original plan, during the replanning process on daily images. The algorithm starts to capture the dose gradients from the dose distribution in the original plan for each of the OARs in the vicinity of the target and then proceeds with a replanning optimization process aiming to maintain the originally achieved dose gradients on the anatomy of the day based on the daily image. The dose gradient in each OAR is represented by the dose distributions in a series of partial concentric rings (PCRs) (shells in 3D) that are generated automatically. Described below are the key steps of the GM algorithm. 2.A.1. Planning images
2.A.1.a. Generation of original plan. Generation of the original plan is done by using a regular, conventional planning technique, e.g., IMRT optimization, to achieve the desired dose–volume objectives for both targets and OARs based on the full sets of contours on the planning images. The obtained original plan can be the best generated plan possible since the planner is not under the time pressure as in the online replanning process. 2.A.1.b. Construction of PCRs. A number of concentric rings for a target are generated by expanding the target [e.g., clinical target volume (CTV)] outward by variable or equal thicknesses (e.g., 5 mm in present testing). Then, for each OAR(i) (i = 1,..., number of relevant OARs), and each concentric ring( j), j = 1: number of concentric rings, PCR(i, j) is formed by the intersection of the ring( j) with the projection of the OAR(i) [e.g., PCR(i,1): from 0 to 5 mm, PCR(i,2): from 5 to 10 mm, and so on toward OAR(i)]. The projection of OAR(i) on the concentric ring( j) is calculated by finding the points on the ring surface where the surface normal Medical Physics, Vol. 42, No. 6, June 2015
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vectors intersect with the OAR(i); however, the projections may further be modified based on the user’s discretion. The modification may be manual since automation is not critical for this offline part of the process. In this study, we uniformly expanded the projections by 5 mm in every direction to account for the daily variations of the position of the OARs or the size relative to the target volume. For the PCRs toward the rectum, we included the whole posterior surface of the prostate CTV. Figure 1 shows an example of the PCRs generated for the rectum and bladder for a prostate cancer case. The portions of the concentric rings covering the whole target which are not included in any of the PCR ( j = 1: number of OARs) structures (the regions that are not toward any particular OAR) are denoted as PCR-rest( j). The dose–volume properties of these PCR-rest are also extracted in the original plan (Sec. 2.A.1.c below) and used in the OF for the daily optimization (Sec. 2.A.2.c below). 2.A.1.c. Extraction of dose gradients. From the original plan DVHs of each PCR, a set of dose–volume-parameters, including mean dose, and/or equivalent uniform dose (EUD), are extracted. As an example, Fig. 2 shows the DVHs for (a) the planning target volume (PTV) and OARs and (b) a series of PCRs that are reconstructed from the dose distribution of the original plan for a prostate cancer case. The dose gradients are captured by the PCRs (and their DVH parameters) via two mechanisms: (1) interPCR variation: the dose drop-off from one PCR to the next is captured by gradual drop in dose values from central PCRs to peripheral ones and (2) intraPCR variation: the dose drop within the volume of the PCR is reflected in the shape of the DVH of the PCR. Therefore, by capturing the DVH parameters of the concentric rings and forcing the same parameters on the daily image, the same dose gradients will be maintained. Also, it should be noted that the PCR thickness does not need to be modified based on dose gradient steepness, since it would still be preserved within the DVH of a thick PCR. 2.A.1.d. Construction of objective functions. Construction of objective functions for online replanning is done by combining the dose–volume constraints of the target (the same as those used to generate the original plan) and PCRs. The dose–volume constraints that were extracted from the original plan DVH in step 2.A.1.d are used in the OF for the PCRs. A software tool was developed to carry out steps 2.A.1.c and 2.A.1.d. 2.A.2. Image of the day
2.A.2.a. Delineation of the target. Target structure is the only volume of interest that needs delineation (manually and/or automatic) on the daily image set. We typically use autosegmentation based on deformable image registration (DIR) with manual editing if necessary afterward. 2.A.2.b. Generation of PCRs on the daily image. This is accomplished by two automated steps: (i) determining the deformation field between the original and newly delineated targets, and (ii) populating the PCRs from the planning image to the daily images using the deformation field. A “contour based” DIR is run to generate the deformation field where
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F. 1. Examples of PCRs in the (a) transverse and (b) sagittal views for a prostate cancer case.
only the contoured shapes are used by the DIR with all the image intensity information removed from the image sets. All the volumes of interest other than the target volumes are removed and not included in the DIR process. The target volumes are masked with uniform intensity (1000 HU), and
the outside of the target volume is masked with 500 HU. The values of 500 and 1000 HU are completely arbitrary and any other two distinct numbers could be used. The only goal of this process is to find correspondence between the surface points of two already delineated target volumes; therefore,
F. 2. Dose–volume histograms and the EUD dose–volume constraints of (a) the target and OARs for the original plan of a prostate cancer case, and (b) the sample PCRs of the rectum and bladder. Medical Physics, Vol. 42, No. 6, June 2015
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F. 3. Demonstration of deformation field between surface points of the CTV (prostate + seminal vesicles) on plan CT [(a) and (b)] and daily CT [(c) and (d)]. Matching points by the calculated deformation field have the same color on both surfaces, which shows that the contour based deformable registration was adequately accurate in matching the points on the planning surface with corresponding regions on the daily target surface. This deformation field allows the surface region toward the rectum defined on the planning target surface (e) to be transferred to its matching surface region on the daily target volume (f).
the DIR process is very simple and robust. A regular image intensity based DIR on the other hand has to solve the difficult problem of finding correspondence between image intensities of two image sets; therefore, it is typically confounded by erroneous results. The accuracy of this process is demonstrated in Fig. 3, in a case where there is a large variation between the planning and daily CTV (prostate + seminal vesicles) shapes. The figure demonstrates that the surface points on the planning target volume [Figs. 3(a) and 3(b)] are accurately matched to the corresponding ones in the daily target volume [Figs. 3(c) and 3(d)] by the contour-based deformable image registration. Medical Physics, Vol. 42, No. 6, June 2015
The corresponding points on both surfaces are assigned the same colors. It can be observed that approximately same anatomical locations on the plan CT [Figs. 3(a) and 3(b)] and daily CTs [Figs. 3(c) and 3(d)] are matched with each other. This deformation field is used to transfer the projection area for each OAR [for example, rectum shown in figure] from the plan CT (e) to daily CT (f). Multiple concentric rings are generated from these projection surfaces to be used for GM optimization. 2.A.2.c. Reoptimization. Reoptimization is done by using the objective functions determined in Sec. 2.A.1.d. To reach the objective function (dose gradients) faster, the original plan
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(Sec. 2.A.1.a) may be used as the starting point during the reoptimization process (also called a “warm start”). 2.B. Validation
In this proof-of-principle study, the effectiveness of the GM algorithm was demonstrated and tested on five prostate and five pancreas patients for 35 and 25 fractions total for prostate and pancreas cases, respectively. For the one prostate cancer case, we used 14 fractions, and for another prostate case, we used six fractions, while for the rest of the cases, we used five randomly picked fractions. All daily CTs were acquired during the routine IGRT using a CT-on-rails (CTVision, Siemens). The original plans were designed to deliver 75.6 Gy in 42 fractions for the prostate cases and 50.4 Gy in 28 fractions for the pancreas case based on appropriate dose–volume constraints for both target and OARs. A CTV–PTV margin of 3 mm was used on both the original and the daily targets, to generate the PTV3mm target volume which was used both as the target volume during optimization and for evaluating the daily dose distributions. The 3 mm margin was used to account for all the uncertainties other than the interfractional errors (intrafractional uncertainties, delineation uncertainties, mechanical uncertainties, etc.). The daily dosimetric evaluations of tumor coverage were performed using the PTV3mm volume instead of the CTV because uncertainties that require the 3 mm PTV margin will still be present but unseen on the daily image and will still have effect on the actual dose that CTV receives. The adequate coverage of CTV can only be ascertained with the adequate coverage of the PTV3mm in the daily plan. The PCRs on the planning CTs and their dose–volume parameters from the original plans were obtained following the method described above (Secs. 2.A.1.b and 2.A.1.c). Plans generated for each daily CT set: 1. The repositioning plan (to mimic regular IGRT): The original plan is applied to the daily CT via rigid body alignment (translation only) based on the best visual alignment of the original GTV with the daily GTV (as a replication of the clinical IGRT process). This is achieved by copying the GTV from the original plan to the daily CT and manually/visually seeking an alignment that maximizes the overlap between the planning and daily GTVs. The original plan is copied to the daily plan based on this alignment, and the doses are recalculated on the daily CT without any changes to the original plan parameters (MLC shapes, beam angles, monitor units, etc.). Note that the actual clinical practice of IGRT alignment may vary based on physician preference; however, for our analysis, we used alignment based on maximizing GTV overlap for objectivity and simplicity purposes. There could always be better alignment that prioritizes a particular OAR sparing, but any deviation from our alignment would result in worsening of target coverage and likely increase of other OAR doses. 2. The optimization plan: A new optimization plan using the whole set of CTV and OAR contours on the daily Medical Physics, Vol. 42, No. 6, June 2015
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CT and the same dose–volume constraints as used for the original plan. PTV3mm is used for the target structure (CTV + 3 mm margin). 3. The GM plan: A plan optimized using the dose–volume constraints of the CTV and PCRs based on the daily CT. Neither contours nor dose–volume constraints of the OARs were used. PTV3mm is used for the target structure (CTV + 3 mm margin). 4. The segment aperture morphing (SAM) plan: Segment aperture morphing algorithm5 as implemented by Prowess (Panther, CA) as realART online planning tool as applied to the original plan. SAM adjusts the segment shapes via taking into account the variation of the target shape from the beam’s eye view. SAM only uses the target contour and is a fast online replanning tool, therefore, was included in this study for comparison purposes. All plans including the original plans based on the planning CT and the three daily plans based on the daily CT were generated using an in-house tool in conjunction with a planning system (Panther, Prowess, Inc.). The plan qualities including dose coverage of target and sparing of OARs for the three daily plans were compared using the full set of contours on daily images. Since GM method aims at preserving the original dose gradients toward each OAR, large variations in the OARs’ position relative to the target may potentially cause GM method to deliver suboptimal results. To address this issue, we investigated the effect of variation in the (a) relative OAR distance to target and (b) the relative change in the OAR projection area on the target surface. For (a), we calculated the minimum and average distances of all voxels in the OARs to target surface for each daily CT. In the superior–inferior direction, we only considered the region of the OARs that is in the same CT slices with the target volumes. For (b), we calculated two different surface regions on the daily target volumes. (1) S-daily: the projection area of daily OARs on the surface of the daily target; (2) S-deformed: the projection of the original plan OAR area on the planning target, deformed to the daily target volume/surface. Note that the S-deformed is the surface that is used to generate the PCRs for the GM method on the daily image. The S-daily is the ideal surface of the daily target that faces a particular daily OAR volume. This S-daily is not available during the online process since the daily contours are not generated online. The fraction of S-daily that is covered by the S-deformed is an indication of what portion of the daily OAR will be covered by the daily PCRs that are generated from S-deformed. We calculated the coverage amount both with and without the 5 mm surface expansion which was applied to account for this type of uncertainty. We plotted the OAR dose ratios of GM method to the optimization and repositioning plans as a function of the minimum and average OAR distance change (a) and coverage of S-daily by the
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T I. Average dosimetric parameters for the four scenarios (reposition, optimization, GM, and SAM) for the prostate cases are presented. The p-values of paired two-tailed Wilcoxon signed-rank tests for the comparison amongst the GM method (scenario 1) and reposition (scenario 3), and optimization (scenario 2) and SAM (scenario 4) are also presented. Results from prostate cases Reposition: 1
Optimization: 2
GM: 3
SAM: 4
p: 1–3
p: 2–3
p: 3–4
Bladder
Mean (Gy) V 70 Gy (%) V 45 Gy (%)
26.4 ± 11.4 3.9 ± 4.7 12.6 ± 13.4
25.4 ± 12.0 2.6 ± 3.9 10.7 ± 13.0
23.0 ± 9.9 2.1 ± 2.9 9.4 ± 10.3
26.7 ± 12.0 3.7 ± 5.1 12.2 ± 14.1
(Received 14 October 2014; revised 16 April 2015; accepted for publication 24 April 2015; published 18 May 2015) Purpose: Clinical use of online adaptive replanning has been hampered by the unpractically long time required to delineate volumes based on the image of the day. The authors propose a new replanning algorithm, named gradient maintenance (GM), which does not require the delineation of organs at risk (OARs), and can enhance automation, drastically reducing planning time and improving consistency and throughput of online replanning. Methods: The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each OAR in daily anatomy can be maintained the same as that in the original plan, the intended plan quality of the original plan would be preserved in the adaptive plan. The algorithm requires a series of partial concentric rings (PCRs) to be automatically generated around the target toward each OAR on the planning and the daily images. The PCRs are used in the daily optimization objective function. The PCR dose constraints are generated with dose–volume data extracted from the original plan. To demonstrate this idea, GM plans generated using daily images acquired using an in-room CT were compared to regular optimization and image guided radiation therapy repositioning plans for representative prostate and pancreatic cancer cases. Results: The adaptive replanning using the GM algorithm, requiring only the target contour from the CT of the day, can be completed within 5 min without using high-power hardware. The obtained adaptive plans were almost as good as the regular optimization plans and were better than the repositioning plans for the cases studied. Conclusions: The newly proposed GM replanning algorithm, requiring only target delineation, not full delineation of OARs, substantially increased planning speed for online adaptive replanning. The preliminary results indicate that the GM algorithm may be a solution to improve the ability for automation and may be especially suitable for sites with small-to-medium size targets surrounded by several critical structures. C 2015 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4919847] Key words: adaptive radiotherapy, online replanning, image guided radiotherapy, dose deformation, deformable image registration
1. INTRODUCTION Interfraction variation, a major issue affecting radiation therapy (RT) for some tumor sites1,2 could result in suboptimal dose distributions and significant deviations from the originally optimized plan if it is not adequately accounted for. Recently, image guided RT (IGRT) has been widely used to correct (eliminate or reduce) the deteriorating effects of the interfraction variation.3 A dedicated new plan that is optimized based on the image of the day (online reoptimization) would eliminate all interfractional variations, both random and systematic.4 The main challenge of online replanning is that it needs to be performed very quickly while the patient is in the treatment setup position. If the replanning process takes significantly longer than the IGRT repositioning, the additional time can result in increased anatomical motion which would counteract the benefits obtainable from the process. Also the reduced time for planning, QA, and decision making may increase likelihood of error unless reliable algorithms and verification systems are employed. Offline replanning may be a possible alternative to online replanning, but this type of intervention 2863
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cannot account for random variations and can only account for a portion of systematic variation (depending on the number of fractions). In recent years, a great deal of effort has been spent to developing new RT planning algorithms and technologies allowing fast and automated replanning.5,6 However, none of these developments is mature enough for the routine practice of online replanning in the clinic. The two most time consuming processes in the current replanning technology are (1) the delineation of the volumes of interest on the daily image set and (2) the plan optimization (inverse planning). These processes are difficult to automate; therefore, they cannot be speeded up by simply increasing or parallelizing computer power. In this work, we propose a novel algorithm, gradient maintenance (GM), to specifically address these two major obstacles. The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each organ at risk (OAR) surrounding the target in the replanning process can be maintained the same as that in the original plan, the obtained (adaptive) plan from the replanning process would have the comparable plan quality as the original plan. The GM
0094-2405/2015/42(6)/2863/14/$30.00
© 2015 Am. Assoc. Phys. Med.
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E. E. Ahunbay and X. A. Li: Gradient maintenance: A new algorithm for online replanning
algorithm requires only the accurate delineation of the targets and does not require the delineation of OARs on the image of the day, dramatically reducing replanning time and human effort. Since only the target volume needs to be delineated, this algorithm would be especially beneficial for tumor sites with a large number of OARs and a small target volume (e.g., abdomen and pelvis). Another advantage is that compared to the conventional optimization, the GM optimization’s objective function (OF) would be less affected by variations in the daily anatomy (since it is based only on the dose gradients, not the dose–volume criteria of OARs). Therefore, the GM algorithm’s OF would need less tweaking and trial and error by the planner which could enhance the ability to automate the replanning process. The purpose of this work is to present the idea of the GM algorithm by testing if the daily plans generated by the GM algorithm without using OAR contours would be comparable to the daily plans that are optimized using the full sets of OAR contours. 2. MATERIALS AND METHODS 2.A. Implementation
The main idea of the GM algorithm is to reproduce the original dose gradients, as achieved in the original plan, during the replanning process on daily images. The algorithm starts to capture the dose gradients from the dose distribution in the original plan for each of the OARs in the vicinity of the target and then proceeds with a replanning optimization process aiming to maintain the originally achieved dose gradients on the anatomy of the day based on the daily image. The dose gradient in each OAR is represented by the dose distributions in a series of partial concentric rings (PCRs) (shells in 3D) that are generated automatically. Described below are the key steps of the GM algorithm. 2.A.1. Planning images
2.A.1.a. Generation of original plan. Generation of the original plan is done by using a regular, conventional planning technique, e.g., IMRT optimization, to achieve the desired dose–volume objectives for both targets and OARs based on the full sets of contours on the planning images. The obtained original plan can be the best generated plan possible since the planner is not under the time pressure as in the online replanning process. 2.A.1.b. Construction of PCRs. A number of concentric rings for a target are generated by expanding the target [e.g., clinical target volume (CTV)] outward by variable or equal thicknesses (e.g., 5 mm in present testing). Then, for each OAR(i) (i = 1,..., number of relevant OARs), and each concentric ring( j), j = 1: number of concentric rings, PCR(i, j) is formed by the intersection of the ring( j) with the projection of the OAR(i) [e.g., PCR(i,1): from 0 to 5 mm, PCR(i,2): from 5 to 10 mm, and so on toward OAR(i)]. The projection of OAR(i) on the concentric ring( j) is calculated by finding the points on the ring surface where the surface normal Medical Physics, Vol. 42, No. 6, June 2015
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vectors intersect with the OAR(i); however, the projections may further be modified based on the user’s discretion. The modification may be manual since automation is not critical for this offline part of the process. In this study, we uniformly expanded the projections by 5 mm in every direction to account for the daily variations of the position of the OARs or the size relative to the target volume. For the PCRs toward the rectum, we included the whole posterior surface of the prostate CTV. Figure 1 shows an example of the PCRs generated for the rectum and bladder for a prostate cancer case. The portions of the concentric rings covering the whole target which are not included in any of the PCR ( j = 1: number of OARs) structures (the regions that are not toward any particular OAR) are denoted as PCR-rest( j). The dose–volume properties of these PCR-rest are also extracted in the original plan (Sec. 2.A.1.c below) and used in the OF for the daily optimization (Sec. 2.A.2.c below). 2.A.1.c. Extraction of dose gradients. From the original plan DVHs of each PCR, a set of dose–volume-parameters, including mean dose, and/or equivalent uniform dose (EUD), are extracted. As an example, Fig. 2 shows the DVHs for (a) the planning target volume (PTV) and OARs and (b) a series of PCRs that are reconstructed from the dose distribution of the original plan for a prostate cancer case. The dose gradients are captured by the PCRs (and their DVH parameters) via two mechanisms: (1) interPCR variation: the dose drop-off from one PCR to the next is captured by gradual drop in dose values from central PCRs to peripheral ones and (2) intraPCR variation: the dose drop within the volume of the PCR is reflected in the shape of the DVH of the PCR. Therefore, by capturing the DVH parameters of the concentric rings and forcing the same parameters on the daily image, the same dose gradients will be maintained. Also, it should be noted that the PCR thickness does not need to be modified based on dose gradient steepness, since it would still be preserved within the DVH of a thick PCR. 2.A.1.d. Construction of objective functions. Construction of objective functions for online replanning is done by combining the dose–volume constraints of the target (the same as those used to generate the original plan) and PCRs. The dose–volume constraints that were extracted from the original plan DVH in step 2.A.1.d are used in the OF for the PCRs. A software tool was developed to carry out steps 2.A.1.c and 2.A.1.d. 2.A.2. Image of the day
2.A.2.a. Delineation of the target. Target structure is the only volume of interest that needs delineation (manually and/or automatic) on the daily image set. We typically use autosegmentation based on deformable image registration (DIR) with manual editing if necessary afterward. 2.A.2.b. Generation of PCRs on the daily image. This is accomplished by two automated steps: (i) determining the deformation field between the original and newly delineated targets, and (ii) populating the PCRs from the planning image to the daily images using the deformation field. A “contour based” DIR is run to generate the deformation field where
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F. 1. Examples of PCRs in the (a) transverse and (b) sagittal views for a prostate cancer case.
only the contoured shapes are used by the DIR with all the image intensity information removed from the image sets. All the volumes of interest other than the target volumes are removed and not included in the DIR process. The target volumes are masked with uniform intensity (1000 HU), and
the outside of the target volume is masked with 500 HU. The values of 500 and 1000 HU are completely arbitrary and any other two distinct numbers could be used. The only goal of this process is to find correspondence between the surface points of two already delineated target volumes; therefore,
F. 2. Dose–volume histograms and the EUD dose–volume constraints of (a) the target and OARs for the original plan of a prostate cancer case, and (b) the sample PCRs of the rectum and bladder. Medical Physics, Vol. 42, No. 6, June 2015
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F. 3. Demonstration of deformation field between surface points of the CTV (prostate + seminal vesicles) on plan CT [(a) and (b)] and daily CT [(c) and (d)]. Matching points by the calculated deformation field have the same color on both surfaces, which shows that the contour based deformable registration was adequately accurate in matching the points on the planning surface with corresponding regions on the daily target surface. This deformation field allows the surface region toward the rectum defined on the planning target surface (e) to be transferred to its matching surface region on the daily target volume (f).
the DIR process is very simple and robust. A regular image intensity based DIR on the other hand has to solve the difficult problem of finding correspondence between image intensities of two image sets; therefore, it is typically confounded by erroneous results. The accuracy of this process is demonstrated in Fig. 3, in a case where there is a large variation between the planning and daily CTV (prostate + seminal vesicles) shapes. The figure demonstrates that the surface points on the planning target volume [Figs. 3(a) and 3(b)] are accurately matched to the corresponding ones in the daily target volume [Figs. 3(c) and 3(d)] by the contour-based deformable image registration. Medical Physics, Vol. 42, No. 6, June 2015
The corresponding points on both surfaces are assigned the same colors. It can be observed that approximately same anatomical locations on the plan CT [Figs. 3(a) and 3(b)] and daily CTs [Figs. 3(c) and 3(d)] are matched with each other. This deformation field is used to transfer the projection area for each OAR [for example, rectum shown in figure] from the plan CT (e) to daily CT (f). Multiple concentric rings are generated from these projection surfaces to be used for GM optimization. 2.A.2.c. Reoptimization. Reoptimization is done by using the objective functions determined in Sec. 2.A.1.d. To reach the objective function (dose gradients) faster, the original plan
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E. E. Ahunbay and X. A. Li: Gradient maintenance: A new algorithm for online replanning
(Sec. 2.A.1.a) may be used as the starting point during the reoptimization process (also called a “warm start”). 2.B. Validation
In this proof-of-principle study, the effectiveness of the GM algorithm was demonstrated and tested on five prostate and five pancreas patients for 35 and 25 fractions total for prostate and pancreas cases, respectively. For the one prostate cancer case, we used 14 fractions, and for another prostate case, we used six fractions, while for the rest of the cases, we used five randomly picked fractions. All daily CTs were acquired during the routine IGRT using a CT-on-rails (CTVision, Siemens). The original plans were designed to deliver 75.6 Gy in 42 fractions for the prostate cases and 50.4 Gy in 28 fractions for the pancreas case based on appropriate dose–volume constraints for both target and OARs. A CTV–PTV margin of 3 mm was used on both the original and the daily targets, to generate the PTV3mm target volume which was used both as the target volume during optimization and for evaluating the daily dose distributions. The 3 mm margin was used to account for all the uncertainties other than the interfractional errors (intrafractional uncertainties, delineation uncertainties, mechanical uncertainties, etc.). The daily dosimetric evaluations of tumor coverage were performed using the PTV3mm volume instead of the CTV because uncertainties that require the 3 mm PTV margin will still be present but unseen on the daily image and will still have effect on the actual dose that CTV receives. The adequate coverage of CTV can only be ascertained with the adequate coverage of the PTV3mm in the daily plan. The PCRs on the planning CTs and their dose–volume parameters from the original plans were obtained following the method described above (Secs. 2.A.1.b and 2.A.1.c). Plans generated for each daily CT set: 1. The repositioning plan (to mimic regular IGRT): The original plan is applied to the daily CT via rigid body alignment (translation only) based on the best visual alignment of the original GTV with the daily GTV (as a replication of the clinical IGRT process). This is achieved by copying the GTV from the original plan to the daily CT and manually/visually seeking an alignment that maximizes the overlap between the planning and daily GTVs. The original plan is copied to the daily plan based on this alignment, and the doses are recalculated on the daily CT without any changes to the original plan parameters (MLC shapes, beam angles, monitor units, etc.). Note that the actual clinical practice of IGRT alignment may vary based on physician preference; however, for our analysis, we used alignment based on maximizing GTV overlap for objectivity and simplicity purposes. There could always be better alignment that prioritizes a particular OAR sparing, but any deviation from our alignment would result in worsening of target coverage and likely increase of other OAR doses. 2. The optimization plan: A new optimization plan using the whole set of CTV and OAR contours on the daily Medical Physics, Vol. 42, No. 6, June 2015
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CT and the same dose–volume constraints as used for the original plan. PTV3mm is used for the target structure (CTV + 3 mm margin). 3. The GM plan: A plan optimized using the dose–volume constraints of the CTV and PCRs based on the daily CT. Neither contours nor dose–volume constraints of the OARs were used. PTV3mm is used for the target structure (CTV + 3 mm margin). 4. The segment aperture morphing (SAM) plan: Segment aperture morphing algorithm5 as implemented by Prowess (Panther, CA) as realART online planning tool as applied to the original plan. SAM adjusts the segment shapes via taking into account the variation of the target shape from the beam’s eye view. SAM only uses the target contour and is a fast online replanning tool, therefore, was included in this study for comparison purposes. All plans including the original plans based on the planning CT and the three daily plans based on the daily CT were generated using an in-house tool in conjunction with a planning system (Panther, Prowess, Inc.). The plan qualities including dose coverage of target and sparing of OARs for the three daily plans were compared using the full set of contours on daily images. Since GM method aims at preserving the original dose gradients toward each OAR, large variations in the OARs’ position relative to the target may potentially cause GM method to deliver suboptimal results. To address this issue, we investigated the effect of variation in the (a) relative OAR distance to target and (b) the relative change in the OAR projection area on the target surface. For (a), we calculated the minimum and average distances of all voxels in the OARs to target surface for each daily CT. In the superior–inferior direction, we only considered the region of the OARs that is in the same CT slices with the target volumes. For (b), we calculated two different surface regions on the daily target volumes. (1) S-daily: the projection area of daily OARs on the surface of the daily target; (2) S-deformed: the projection of the original plan OAR area on the planning target, deformed to the daily target volume/surface. Note that the S-deformed is the surface that is used to generate the PCRs for the GM method on the daily image. The S-daily is the ideal surface of the daily target that faces a particular daily OAR volume. This S-daily is not available during the online process since the daily contours are not generated online. The fraction of S-daily that is covered by the S-deformed is an indication of what portion of the daily OAR will be covered by the daily PCRs that are generated from S-deformed. We calculated the coverage amount both with and without the 5 mm surface expansion which was applied to account for this type of uncertainty. We plotted the OAR dose ratios of GM method to the optimization and repositioning plans as a function of the minimum and average OAR distance change (a) and coverage of S-daily by the
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E. E. Ahunbay and X. A. Li: Gradient maintenance: A new algorithm for online replanning
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T I. Average dosimetric parameters for the four scenarios (reposition, optimization, GM, and SAM) for the prostate cases are presented. The p-values of paired two-tailed Wilcoxon signed-rank tests for the comparison amongst the GM method (scenario 1) and reposition (scenario 3), and optimization (scenario 2) and SAM (scenario 4) are also presented. Results from prostate cases Reposition: 1
Optimization: 2
GM: 3
SAM: 4
p: 1–3
p: 2–3
p: 3–4
Bladder
Mean (Gy) V 70 Gy (%) V 45 Gy (%)
26.4 ± 11.4 3.9 ± 4.7 12.6 ± 13.4
25.4 ± 12.0 2.6 ± 3.9 10.7 ± 13.0
23.0 ± 9.9 2.1 ± 2.9 9.4 ± 10.3
26.7 ± 12.0 3.7 ± 5.1 12.2 ± 14.1
