4D Treatment Planning

4D Treatment Planning

Paul J. Keall

10

Contents

10.1 Introduction . . . 259

10.2 4D Planning Procedure . . . 260

10.3 The Need for Automation in 4D Planning . . . 261

10.3.1 Tools for Automation 1: Deformable Image Registration . . . 261

10.3.2 Tools for Automation 2: Automated Planning . 261 10.3.3 Tools for Automation 3: Dose Calculation on Multiple CT Image Sets . 261 10.3.4 Addition Tools Required for 4D Planning . . . 262

10.4 4D Conformal Radiotherapy Planning . . . 262

10.5 4D IMRT Planning . . . 262

10.6 Margins for 4D Planning . . . 264

10.7 Delivery of a 4D Treatment Plan . . . 264

10.8 Conclusion . . . 265

References . . . 265

10.1 Introduction

Four-dimensional (4D) radiotherapy can be defined as the explicit inclusion of the temporal changes in anatomy during the imaging, planning and delivery of radiotherapy [1]. High precision radiation therapy of moving targets is becoming increasingly important in this era of image-guided therapy (see Bortfeld and Chen [2] and articles therein).

The anatomy and physiology of cancerous and healthy tissues change with time, both within and between treatments. For radiotherapy patients, the addi- tional effects of radiation, potentially with concomitant chemotherapy and or | hormone therapy, can also cause anatomical changes during treatment. Though it is ac- knowledged that there are many sources of anatomical

changes, for some sites, such as the lung, liver, pan- creas, esophagus and possibly even breast, prostate and cervix, respiratory motion is a significant issue and neg- atively affects the imaging [3–15], planning [13, 16–19]

and delivery [20–29] of radiation. Due to recent tech- nological developments in both 4D imaging (refer to chapter II. 9) and 4D radiation delivery (refer to chap- ter II. 11), we are in an era in which respiratory motion can be explicitly accounted for. Reducing the deleteri- ous effects of interfraction motion is discussed in the chapters II. 7 and II. 8.

The focus of this chapter is to discuss the process of creating a 4D plan from a 4D computed tomogra- phy (CT) image set, in which the radiation beam tracks the tumor motion during 4D radiotherapy delivery. By moving the radiation beam during treatment, the inten- sity is modulated. However, for the purpose of clarity, the distinction is made between 4D planning for beams in which the dynamic multileaf collimator (DMLC) mo- tion only compensates for tumor motion (4D conformal radiotherapy) and planning for beams in which DMLC motion accounts for intensity modulation based on opti- mizing an objective function as well as compensating for tumor motion (4D IMRT). The rationale for 4D radio- therapy is to reduce geometric errors during imaging and treatment delivery, as well as to safely reduce the margins added for internal motion, which will spare healthy tissue and | or allow dose escalation.

There are methods to account for respiratory motion

in the absence of devices that account for this motion

during radiation treatment delivery. The one practiced

most commonly in clinics is the simple addition of

clinical target volume (CTV)-planning target volume

(PTV) margins that are large enough to encompass the

increased geometric uncertainties introduced by respi-

ratory motion [16]. These increased margins result in

a higher dose to normal tissue and particularly lung,

for which treatment-related toxicity is strongly corre-

lated with mean lung dose (or a similar surrogate, such

as V

) [30–35]. Thus, methods such as 4D radiother-

apy, which can potentially reduce some of the geometric

error, should result in lower treatment-related toxicity

and | or tumor dose escalation.

In this chapter, some 4D treatment-planning exam- ples are given. These plans use a 4D CT dataset acquired under an IRB-approved study at the M.D. Anderson Cancer Center [36] and the Pinnacle [3] treatment- planning system (Philips Medical Systems, Milpitas CA).

The tumor is located in the upper lobe of the right lung. The center of mass of the tumor moved approx- imately 1 cm from exhale to inhale. The gross tumor volume (GTV)-CTV margin was 8 mm based on [37], and the CTV-PTV margin was 8 mm to account for set- up error. All dose calculations shown use the collapsed cone convolution implementation of the superposition algorithm.

Throughout this chapter, it is assumed that the de- livery device in motion to account for respiration is the DMLC. However, the device in motion could equally be a robotic linear accelerator [38] or the treatment couch.

10.2 4D Planning Procedure

A generalized flowchart for 4D treatment planning is shown in Fig. 1. The first step (1) of 4D treatment plan- ning is to obtain a 4D CT scan, as described in the preceding chapter. The second step (2) is to define the anatomy for all structures of interest for dosimet- ric coverage (e.g. GTV, CTV) as well as the dosimetric avoidance | monitoring (e.g. spinal cord, lungs, heart, esophagus) on one of the 3D CT image sets constitut- ing one respiratory phase of the 4D CT. The choice of which respiratory phase to use will generally be the ex-

Fig. 1. A schematic showing the general 4D planning process for both conformal and intensity modulated radiation therapy (IMRT) delivery

hale phase, as motion is less at exhale than at inhale, and the exhale position, being a passive rather than active state, is more reproducible between respiratory cycles than the inhale position.

Once the anatomy definition is complete on one 3D CT image set, deformable image registration (explained in further detail below) can be used to create automat- ically the anatomic structures on the other respiratory phases of the 4D CT, accounting for the movement and deformation on each structure caused by the respira- tory cycle. The deformable image registration process may introduce an error that may require additional geometric margins.

Step (3) of the 4D planning process is to create a treat- ment plan on one of the 3D CT image sets. This treatment plan will be developed as the conventional conformal or IMRT plan, though the geometric CTV-PTV margins for respiratory motion may be reduced. However, as ex- plained in a separate section below, other geometric uncertainties introduced by the planning process will require additional margins. Once the plan is complete for one image set, automated planning is used to repro- duce the treatment plans on the other respiratory phases of the 4D CT, accounting for the changing anatomy by varying the multileaf collimator (MLC) positions. Be- cause the MLC is being used to account for the change in the PTV with respiration, the concept of a 4D PTV is naturally introduced.

In step (4) of Fig. 1, the dose distributions are

summed up through the deformable registration op-

erator (weighted by the fraction of time spent in each

respiratory phase), and the composite plan is displayed

for evaluation. Should this plan prove unacceptable, adjustments need to be made, and steps 3–5 are repeated.

10.3 The Need for Automation in 4D Planning

In Fig. 1, steps (1) to (6) are those typically performed for routine conformal or IMRT planning, except that a 3D CT scan is used as input rather than a 4D CT scan. Note that the extra procedures used to create and analyze the 4D plan on the ten or so CT image sets (totaling up to 1500 individual CT images) are automated. This automation consists of three tools: deformable image registration, automated planning and dose calculation on multiple CT image sets.

There are both logistical and fundamental reasons for using automated tools. The logistical reason is that performing all of the planning tasks manually on ten or more CT image sets would take an order of magnitude more human interaction time, which is clearly not fea- sible in any but the most well-staffed institutions. The fundamental reason is that to estimate the cumulative dose for the 4D plan, the dose to the moving tissues in each of the plans for each respiratory phase needs to be added. This requires an estimation of the motion for each CT voxel in each image for each respiratory phase, a process of such magnitude that automation is necessary.

10.3.1 Tools for Automation 1:

Deformable Image Registration

Deformable image registration is a tool used to map each voxel from one CT image set to the new position of that voxel in the second image set, accommodat- ing the anatomic deformation caused by respiration.

Let Ω be the coordinate space of a CT image. A time index transformation, h(x, t): Ω

→ Ω , mapping the co- ordinate space of each of the respiratory phases of the 4D CT is estimated. There are several candidate algo- rithms to determine h(x, t), including finite element methods, optical flow techniques and large deformation diffeomorphic image registration. It is unclear which algorithm(s) will prove to be the most accurate and generally applicable to the 4D radiotherapy planning problem.

Since the contoured structures are a subset of the volume on which the deformable registration transfor- mations were calculated, the contoured structures can be automatically created in other CT phases by applying the appropriate transformation, as shown in step (2) of Fig. 1. Similarly, after the treatment plans have been cre- ated, the dose distributions can also be mapped between CT phases, enabling the evaluation of the composite 4D plan, as shown in steps (3) and (4) of Fig. 1.

Using these transformations, the combined or 4D dose distribution, D

, can be given by

D4D

(x) =

i

wiDi



(x) 

(1)

where w

is the weight of the dose distribution D

for each of the constituent respiratory phase CT image sets.

The w

values correspond with the fraction of a breathing cycle spent in each respiratory phase.

10.3.2 Tools for Automation 2: Automated Planning

Rather than performing treatment planning on ten or so CT scans, scripts can be written to automate planning in order to transfer a plan generated at one CT phase to plans generated in other CT phases. For example, for 4D conformal planning, the beam parameters stipulated on the manually planned respiratory phase can be automat- ically generated on the other phases using the automatic blocking function so that the MLC conforms to the PTV for each phase and adds a margin for the penumbra.

As a note of caution for this method, in some phases the beams may pass through critical serial structures such as cord and esophagus, and, in other phases, the beams may not pass through these structures. Careful assessment of the composite plan [step (4) in Fig. 1] and making appropriate adjustments are required.

10.3.3 Tools for Automation 3:

Dose Calculation on Multiple CT Image Sets

Tied closely to automated planning is the ability to calculate dose on multiple CT scans within the same treatment plan. This task is mentioned explicitly here, because at the time of writing commercial treatment- planning systems do not offer this option. Dose cal- culation on multiple image sets is clearly important, since motion moves the anatomy and, therefore, changes the pattern of radiation interaction within the patient.

The expanded lung at inhale affects the radiation dose deposition in two competing ways. First, the radiolog- ical pathlength within the patient is reduced, causing higher primary photon fluence to be expected at the same physical depth. Second, the reduced lung density increases the range of the secondary electrons, thereby increasing the electronic disequilibrium and widening the penumbra.

An example of the importance of dose calculation

is given in Fig. 2, where the same structures (PTV,

esophagus and lungs) are calculated for the same IMRT

treatment plan, with the only variable being the CT im-

age set on which the dose was calculated. Though in this

case the DVHs for the lungs and esophagus are similar

in both cases, the PTV dose calculated on the inhale CT

Calculated on Exhale CT Calculated on Inhale CT

Fig. 2. Intensity modulated radiation therapy (IMRT) dose-volume histograms (DVHs) of the exhale planning target volume (PTV), lungs and esophagus calculated on the exhale and inhale CT of the same patient

scan shows a consistent underdose, particularly where IMRT prescriptions are often given, near D

.

10.3.4 Addition Tools Required for 4D Planning

Four-dimensional planning introduces several issues for networking, data storage and communications. Typi- cally, an order of magnitude more data are used for 4D planning, and data management tools need to developed to ensure these data are appropriately stored and com- municated to the treatment-delivery device. At the time of writing, DICOM-RT did not support 4D radiotherapy.

10.4 4D Conformal Radiotherapy Planning

As mentioned above, the flowchart in Fig. 1 is generic to both 4D conformal radiotherapy planning and 4D IMRT, the differences being in the processes of step (3) of Fig. 1. Tumor motion is predominantly along one axis (and observed to be primarily in the superior–inferior direction [39–43]). Thus, the MLC should be aligned such that the leaf motion coincides with the major axis of the tumor motion and compensates for the tumor motion. In principal, it is possible to account for motion perpendicular to the leaf motion direction. This may be easier for 4D conformal radiotherapy than 4D IMRT, unless the leaves are synchronized.

Using one of the constituent CT sets, correspond- ing with a single respiratory phase from the 4D CT, a conformal treatment plan is constructed based on the anatomy drawn in that phase. Typically, the inhale or exhale phase will be used for this step. Beam an- gles, weights, energies and modifiers should be chosen to achieve an acceptable plan in terms of PTV cover- age and critical structure doses. The plan may consist

Fig. 3a–d.Four-dimensional conformal anterior beam-view im- ages for respiratory phases:(a)inhale;(b)mid-exhale;(c)exhale;

(d)mid-inhale. The planning target volume (PTV) for each phase is conformally blocked by the dynamic multileaf collimator (DMLC) with a margin for the penumbra (0.8 cm in this case). To aid com- parison, horizontal lines are drawn at the superior edge of the PTV at inhale and the inferior PTV edge at exhale

of several stages typically used for lung cancer radio- therapy, such as anterior-posterior beams to spinal cord tolerance, followed by oblique fields. Once an acceptable plan is created for a single phase, automated planning is used to recreate the treatment plan on the other respi- ratory phase CT image sets, with the beams adapting to the changing PTV shape and position in the beam view, allowing for the appropriate penumbral margins. Exam- ples of beam-view images from 4D conformal planning are given in Fig. 3. Note that the motion of the PTV is, in this case, predominantly superior-inferior, and the alignment of the collimator is in this dimension also.

Due to changes in both the anatomy and the beam ge- ometry during each respiratory phase, the overall PTV dose in each phase will be different. However for crit- ical structures, the variation in dose is expected to be larger, as differing fractions of the beam aperture will intersect with the different critical structures. Exam- ple 4D conformal radiotherapy dose-volume histograms (DVHs) for each constituent breathing phase and for the combined dose distribution (obtained by summing the constituent dose distributions via deformable oper- ators) are given in Fig. 4 for the PTV, lungs, cord and heart (reproduced from [44]). The DVHs for the PTV at all phases and the combined (4D) DVH are all very sim- ilar (note the expanded x-axis scale). The lung DVHs are closely bunched, with the combined 4D DVH being closer to the end-inhale DVH due to the fact that DVHs typically use normalized rather than absolute volumes.

The 4D DVHs for the cord and heart appear to be near the middle of the constituent-phase DVHs. The varia- tion in cord and heart DVHs for the constituent phases is due to the change in beam aperture with respiratory phase and, hence, the fraction of the organ intersecting the beam passing through these structures, as the PTV deforms with respiratory phase.

10.5 4D IMRT Planning

There are many levels of complexity for 4D IMRT. The

simplest assumption, that the target undergoes rigid

Fig. 4a–d. Four-dimensional conformal radiotherapy dose-volume histograms (DVHs) for each breathing phase (thin solid lines) and the combined distribution (thick dashed line) for the:(a)plan-

ning target volume (PTV);(b)lungs;(c)cord;(d)heart. Note the expanded dose scale for the PTV. Reprinted from [44]

body motion without deformation, is the easiest to plan and implement; however, more sophisticated models of motion including deformation will allow even greater conformality. DVHs of the PTV, lungs and esophagus for an IMRT plan optimized on the inhale CT scan and the corresponding plan at exhale, calculated assuming rigid body PTV motion, are shown in Fig. 5. This fig- ure shows that, for this particular example, the rigid body assumption gave a uniform PTV dose; however, the dose to the lungs and esophagus was higher in the exhale phase plan in which the anatomical variations were ignored.

An example comparison of the rigid body motion as- sumption with a full replanned IMRT optimization in the exhale phase is shown in Fig. 6. This figure shows some benefit in the reoptimized plan, where the PTV dose is more homogeneous and a lower maximum esophageal dose is obtained, than with the plan calcu- lated assuming rigid body motion based on the inhale IMRT plan.

Four-dimensional segmental MLC IMRT planning is a generalization of 4D conformal planning, in that if the same, or similar, segment shapes (but different posi- tional projections) are used in the 4D IMRT plan, the 4D

SMLC IMRT is an extension of the 4D conformal plan- ning process with many apertures per beam as opposed to one. Segmental IMRT planning will be less affected by leaf velocity constraints than dynamic delivery.

Breathing will change between imaging session and delivery, thus we cannot rely on the knowledge of the patient’s breathing pattern a priori on any given day of treatment (if so, 4D radiotherapy would be feasi- ble with circa 2000 technology). Accounting for these changes can be incorporated by first improving respira- tion reproducibility with breathing training tools [45,46]

and, second, by being flexible enough during delivery to account for deviations and, ultimately, recording the de- viations from the planned treatment and reporting what was actually delivered.

For both 4D conformal planning and 4D IMRT, it

is important that tumor motion tracking is within the

mechanical capabilities of the DMLC. The mechani-

cal capabilities (maximum velocity, acceleration and

deceleration) should be known constraints within the

planning process. This DMLC does not need to track

the tumor for the entire respiratory cycle, as a beam

hold can account for short time periods during which

tumor motion exceeds the DMLC capabilities; however,

Fig. 5. Dose-volume histograms (DVHs) of the planning target volume (PTV), lungs and esophagus for an intensity modulated ra- diation therapy (IMRT) plan optimized on the inhale CT scan, and the corresponding plan at exhale calculated assuming rigid body PTV motion. The solid lines are for the IMRT plan and anatomy drawn on the exhale CT scan, and the dashed lines are for the IMRT plan and anatomy drawn on the inhale CT scan

Fig. 6. Dose-volume histograms (DVHs) of the exhale planning target volume (PTV), lungs and esophagus for an intensity mod- ulated radiation therapy (IMRT) plan optimized on the inhale CT scan, and the corresponding plan at exhale calculated assuming rigid body PTV motion. The solid lines are for the IMRT plan cal- culated assuming rigid body PTV motion from the inhale IMRT plan, and the dashed lines are for the IMRT plan optimized on the exhale CT scan

this should be satisfied for a significant fraction of the motion cycle to ensure efficient delivery.

10.6 Margins for 4D Planning

One of the reasons for performing 4D radiotherapy is to reduce the margins required for geometric uncertainties introduced by respiratory motion. However, during the act of accounting for respiratory motion, new geometric uncertainties are introduced.

First, the 4D CT used as input to the planning pro- cess is temporally discrete, typically separated into 8–15

individual respiratory phases. This discretization of the continuous temporal changes means that interpolation of the motion between these phases is necessary. The ac- curacy of this interpolation is unknown. Furthermore, the 4D CT may contain artifacts due to irregular respira- tion during acquisition. Second, the deformable image registration algorithm used will contain geometric er- rors due to limitations of either the algorithm or the artifacts in the input 4D CT data.

The correlation between the respiratory signal and the tumor motion may change with time, both between breathing cycles and between successive treatments.

This variation in correlation will translate into a tar- geting error. If the respiratory signal is external – for example, a strain gauge, spirometer or optical signal – the relationship between the respiration signal and internal motion can be determined using a 4D CT scan, and this relationship can be checked and ad- justed if necessary during the treatment course. The use of external respiration signals will not reduce the set-up error, typically 3–5 mm (1 standard deviation) for lung cancer radiotherapy [47–54], which, along with respiratory motion is a significant issue. The use of in- ternal markers for tumor tracking [7, 10, 55–57] reduces both the set-up error and the error in the actual tu- mor motion | tumor motion surrogate correlation. Thus the choice of respiratory signal or tumor motion sur- rogate will have an impact on the margins used for 4D radiotherapy.

Finally, an additional geometric error is added dur- ing radiation delivery from the finite time delay in the DMLC response due to the acquisition of the res- piratory motion, the processing of this motion, the creation of leaf-position instructions and the execu- tion of these instructions. These time delays require future prediction of the tumor position, which for res- piratory motion has proved challenging [27,58,59]. This error will decrease as the system response time de- creases and also as improved prediction algorithms are developed.

The careful analysis of each of these errors should be performed before the clinical implementation of 4D ra- diotherapy to ensure that the appropriate margins are applied and indeed that the net of the geometric uncer- tainties introduced by 4D radiotherapy are significantly less than those required for more traditional methods.

10.7 Delivery of a 4D Treatment Plan

The output of a planning process is a series of in- structions to the linear accelerator and therapy staff to ensure the correct execution of the treatment plan.

Thus, the plan needs to incorporate the constraints of the treatment device and particularly those of the DMLC;

otherwise, the plan may not be able to be delivered.

For conventional IMRT to a target assumed to be static during radiation delivery, the maximum leaf ve- locity is a constraint used in the leaf-sequencing process [60–62] if dynamic MLC delivery rather than segmen- tal MLC delivery is used. However, for 4D conformal or IMRT planning for a target moving during radia- tion delivery, the ability of the MLC to respond to the temporal position changes requires knowledge of both the velocity limitations and also the acceleration lim- itations, since the MLC needs to be able to respond to changes in target velocity. An additional beam-hold function will be required in cases where the DMLC me- chanically cannot reproduce the target motion. Initial developments of leaf sequences have been published [21, 63, 64], though further advancements in this area will be required, particularly due to the variations in respiration patterns observed on a cycle-to-cycle and day-to-day basis.

Varying breathing patterns during delivery, com- pared with those measured during the 4D CT imaging session used for treatment planning, mean that the planned and delivered doses may differ. For example, if the time spent within each breathing phase during treatment delivery, w

, differs from that during planning where fraction w

was used for the final dose calcula- tion, the combined 4D dose will change following Eq. 1.

However, using Eq. 1 with w

means that the actual dose delivered to the moving tumor and critical structures for each treatment fraction can be calculated. A problem appears if the respiration pattern limits during delivery exceed those obtained during the 4D CT session used for planning. Should such a situation occur, either the treatment should be paused until the respiration pattern returns within the limits known from planning (the pru- dent approach) or until a reasonable extrapolation of the tumor position and shape based on the respiratory signal can be made.

10.8 Conclusion

Four-dimensional treatment planning is a new process and set of tools that allows the optimal use of 4D CT data and dynamic radiation delivery. The ability to account explicitly for respiratory motion means the potential to safely reduce margins, thus allowing increased tumor dose and | or a decrease in treatment-related toxicity for sites affected by respiratory motion.

Four-dimensional radiotherapy is synergistic with adaptive radiotherapy (refer to chapters II. 7 and II. 8) in that some tools developed to account for interfrac- tion geometric variations can be applied to account for intrafraction geometric variations and vice versa. Four- dimensional planning is also appealing for Monte Carlo calculations [65], since, for a given statistical uncer- tainty, the number of particles (and hence calculation

time) required for summed 4D dose distributions (see Eq. 1) is approximately the same as that for a 3D distri- bution, meaning a speed gain of ∼ N for Monte Carlo compared with conventional algorithms, where N is the number of constituent respiratory phases in the 4D CT image set.

Four-dimensional planning is developing, along with 4D imaging and 4D radiation delivery. The tools and algorithms used for 4D radiotherapy have yet to be fully defined. Though all of these technologies are in their infancy, it is envisaged that 4D radiotherapy will become an established clinical tool in this new era of IMRT, image-guided radiotherapy and adaptive radio- therapy.

Acknowledgements.

The author wishes to acknowledge the grant support of NIH | NCI R01 CA93626. Devon Murphy carefully reviewed and significantly improved the clarity of the text. Drs. Theodore Chung, Vaughn Dill, Rohini George, Sarang Joshi, Vijay Kini, Radhe Mohan, Jeffrey Siebers, Sastry Vedam, Krishni Wijesooriya, and Jeffrey Williamson have all significantly contributed to the 4D planning project at Virginia Commonwealth University.

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