Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking

Four-dimensional radiotherapy planning for DMLC-based respiratorymotion tracking

Paul J. Kealla!

Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298

Sarang JoshiDepartment of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina 27514

S. Sastry Vedam, Jeffrey V. Siebers, and Vijaykumar R. KiniDepartment of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298

Radhe MohanDepartment of Radiation Physics, The University of Texas M. D. Anderson Cancer Center,Houston, Texas 77030

sReceived 9 July 2004; revised 2 February 2005; accepted for publication 3 February 2005;published 16 March 2005d

Four-dimensionals4Dd radiotherapy is the explicit inclusion of the temporal changes in anatomyduring the imaging, planning, and delivery of radiotherapy. Temporal anatomic changes can occurfor many reasons, though the focus of the current investigation is respiration motion for lungtumors. The aim of this study was to develop 4D radiotherapy treatment-planning methodology forDMLC-based respiratory motion tracking. A 4D computed tomographysCTd scan consisting of aseries of eight 3D CT image sets acquired at different respiratory phases was used for treatmentplanning. Deformable image registration was performed to map each CT set from the peak-inhalerespiration phase to the CT image sets corresponding to subsequent respiration phases. Deformableregistration allows the contours defined on the peak-inhale CT to be automatically transferred to theother respiratory phase CT image sets. Treatment planning was simultaneously performed on eachof the eight 3D image sets via automated scripts in which the MLC-defined beam aperture conformsto the PTVswhich in this case equaled the GTV due to CT scan length limitationsd plus a penum-bral margin at each respiratory phase. The dose distribution from each respiratory phase CT imageset was mapped back to the peak-inhale CT image set for analysis. The treatment intent of 4Dplanning is that the radiation beam defined by the DMLC tracks the respiration-induced targetmotion based on a feedback loop including the respiration signal to a real-time MLC controller.Deformation with respiration was observed for the lung tumor and normal tissues. This deformationwas verified by examining the mapping of high contrast objects, such as the lungs and cord,between image sets. For the test case, dosimetric reductions for the cord, heart, and lungs werefound for 4D planning compared with 3D planning. 4D radiotherapy planning for DMLC-basedrespiratory motion tracking is feasible and may offer tumor dose escalation and/or a reduction intreatment-related complications. However, 4D planning requires new planning tools, such as de-formable registration and automated treatment planning on multiple CT image sets. ©2005American Association of Physicists in Medicine. fDOI: 10.1118/1.1879152g

Key words: 4D radiotherapy, deformable image registration, treatment planning

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I. INTRODUCTION

The advent of four-dimensionals4Dd thoracic CT scans thcreate separate CT images at discrete phases of the retory cycle1–9 introduces the issue of how best to useinformationstypically over 1000 slices for a full 4D CT scof the thoraxd for radiation therapy planning.

The use of multiple sequential CT data sets to impradiotherapy’s ability to account for interfraction motiotermed “adaptive” radiotherapy, has been discussed by Yetal.,10–14University of Wisconsin reports,15–19and others.20,21

In this approach, positioning and dosimetric informa
from CT scans acquired during treatment is used to vary
942 Med. Phys. 32 „4…, April 2005 0094-2405/2005/32 „4…

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patient positioning and/or the treatment beams for suquent treatments. Such strategies allow improved targeterage and/or normal tissue dose reduction.

4D radiotherapy planning to account for respiratorytion applies concepts of adaptive radiotherapy to a 4Ddata set, given that the radiation treatment beam will tthe respiration-induced anatomical changes during treatIn 4D radiotherapy planning, different treatment plansgenerated for different respiratory phases with the constrof the treatment device’s ability to deliver these plans.definition is consistent with that of the consensus of theTRO 2003 “Time—the 4th dimension in radiotherapy” pathat 4D radiotherapy is the “explicit inclusion of the tem
ral changes of anatomy during the imaging, planning and
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943 Keall et al. : 4D radiotherapy planning 943

delivery of radiotherapy.”22 Note that the definition of 4Dradiotherapy planning defined here differs from thatcussed by Shiratoet al.;23 of the breath-hold and frebreathing CT scans acquired in their study, only oneused for treatment planning. Though the tumor tracking oimplanted fluoroscopic marker via x-ray is real-time,treatment delivery itself is gated.24–31

The aim of this research was to develop 4D radiotheplanning methodology to account for respiratory motioning DMLC-based tumor tracking and provide a proof-principle example of 4D radiotherapy planning.

II. METHODS AND MATERIALS

A. 4D CT image

The 4D CT patient image acquired and used for this sis described in detail in the publication by Vedamet al.2 Thepatient was a 50-year-old male with stage IIB non-smalllung cancer located in the right hilum. The 4D CT consisof eight discrete respiratory phases: peak inhale, earlyhale, mid exhale, late exhale, peak exhale, early inhaleinhale, and late inhale. Due to both tube heat and reconstion time limitations, only a 10.5 cm region of the patienanatomy was scanned, covering the gross tumor vosGTVd. On the peak-inhale CT image, the following anatowas defined: GTV, lungs, heart, and the spinal cord.PINNACLE treatment-planning systemsPhilips Medical Systems, Milpitas, CAd was used. The choice of using the peinhale CT image for the reference is somewhat arbithowever it is advisable to use either peak inhale or pexhale as these phases have the fewest motion artifac4D CT. Because the scan only covered a 10.5 cm rangensure that the planning target volumesPTVd did not extendoutside the CT volume, the PTV was assumed to equaclinical target volumesCTVd, which was assumed toequal to the GTV. Though this assumption is unrealisticclinical radiotherapy, to achieve the aims of this study,assumption was deemed acceptable.

B. Deformable image registration

Large deformation diffeomorphic image registration32–37

was used to map with a one-to-one correspondence all pin the CT image from one respiratory phasespeak inhaledwith corresponding points on the other respiratory phaaccommodating for the anatomic deformation caused bypiration. LetV be the coordinate space of the peak-inhaleimage. A time index transformation,hsx,td :Vt→V, map-ping the coordinate space of each of the respiratory phasthe 4D CT is estimated. The transformation is constrainebe one to one while accommodating large deformationexpressing it as a solution to a viscous-fluid, ordinary diential equation:

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Medical Physics, Vol. 32, No. 4, April 2005

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stant within the respiratory cycle and tissue deformationbe tracked by directly correlating the CT images at diffephases of respiration using a minimum mean squaredmetric: Fshd=eVuIAshsx,tdd− IBsxdu2 dx, the difference between the intensities in the transformed imageIA and imageIB. Should the approximation of constant tissue density pnot to be valid, adding a termfsIAd to IAshsx,tdd in Fshdwould improve the algorithm.fsIAd should be inverse Jacbian of the transformationh as the Jacobian measureslocal volume change, and density is inversely proportionvolume. The inclusion of this term significantly increasescomplexity of the minimization problem, and is the subof ongoing research. Note also that as the anatomy incin the CT data sets obtained differ near the boundaries,interpolation of the deformation fields near the edges odata sets is performed. The accuracy of this process hato be sufficiently quantified.

The transformationh is estimated in a manner that mimizes the metricDshd while at the same time constrainithe transformation to satisfy the laws of continuum mecics, derived from viscous-fluid modeling using the NavStokes equations on the velocity fields. Having computetransformationh that maps the peak-inhale CT onto eacthe respiratory phases of the 4D CT, the segmentatioeach of the respiratory phases is accomplished by appthe transformation to the contourssi.e., physician-drawn contoursd of the peak-inhale CT. The transformationh thusquantifies the organ motion and the fine-featured organ schanges that occur during the breathing cycle. Verificatiodeformation registration on contours was performed bysual inspection of those structures with sharp anatomcontrast boundaries, i.e., the external skin contour, the land the spinal cord. These contours represent structuremove little with respirationse.g., the cord in supine positiodand structures that move significantly with respirationse.g.,the lungsd. Quantification of the difference between manudefined anatomical points and those automatically dmined using the deformable image registration wasformed. Four anatomic points were chosen: a bifurcationleft lower lobe vessel, the lateral aspect of a right lowerbronchiole just proximal to a bifurcation, the anterior-maspect of the vertebral body at the level of the carina, anright nipple. The first two points represent structures inlung likely to move with respiration, the third point iscontrol, and the fourth point represents chest wall moThese points were manually contoured on each respirphase CT set. Taking the points defined on theinspiration CT image set, the points were automaticallytermined on subsequent respiratory phase CT image seing the transformations from the deformable imregistrations.

In a separate study using this algorithm,38 the accuracy othe deformation process was quantified by comparingmanual segmentation for inhale and exhale CT scans olung cancer patients. The results yielded a median err0.7 mm and a 95% confidence error of 3.1 mm, with
-errors largest in the superior-inferior direction. This is ex-

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Scripts were written to allow information associated wone CT image set, such as anatomic contours and dostributions, to be transferred to other respiratory phases o4D CT via the deformation field. These scripts consisprograms that extract data from the treatment-planningtem, such as contours or dose distributions associatedone CT image set, operate on this data via the transfotions described in the following, and input the transformcontours/dose distributions back into the treatment-plansystem. The interactions with the treatment-planning syused the PinnComm scripting language, which is availwith PINNACLE. Thus, the contours defined on the peinhale CT image were transferred to the other respiraphase of the 4D CT by passing through the deformableage registration operator, as shown in Fig. 1. Due to limtions of the contour storage structure of the treatmplanning system for 4D planning applications, thex and ysin-sliced points were deformed correctly, however, an aagez deformationsthe average of the individualz deforma-tion of constituent in-slice contour pointsd was used. Thilimitation will be eliminated in future versionsPINNACLE.39

After treatment planning was performed for each resptory phasessee section below for specific planning detad,the dose distribution for each CT phase was transforback to the peak-inhale CT image set for evaluation ofentire treatment plan accounting for all respiratory phawhere the combined or 4D dose distribution,D4D, is givenby

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for each of thehA, . . . ,Hj CT phases. For this study, eqweighting was used for each phase, as approximatelynumbers of images were assigned for each phase.2 However,in general thewi values will not be equal. Further inform

FIG. 1. A schematic showing how contours defined on one phase, icase inhale, are automatically transferred to the exhale image throudeformation operator.

tion on the use of deformable image registration for dose


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accumulation can be found in Refs. 33 and 40–52.

C. Treatment planning

A six-field coplanar nonopposed conformal plan wasveloped on the peak-inhale phase, with the beam angledominantly in the anterior directionsangles 20°, 140°, 180220°, 300°, and 340°d. 6 MV energy was used for all beamNo wedges were used for the fields. The multileaf collimwas aligned in the superior–inferior direction, whichbeen noted to be the predominant direction of motionlung tumors.53–57 A 0.8 cm margin was added outsidePTV to account for penumbral effects, and the multileaflimator was used to define the field boundary. The bweights were manually selected to give a reasonably unPTV dose with acceptable critical structure doses.

Automated scripts were written using the PinnCoscripting language and external programs to transfertreatment plan for the peak-inhale CT for each of the orespiratory phases. The autoblocking utility of the treatmplanning system allowed conformality of the field shapthe PTV in each respiratory phase, as shown in Fig. 2 forphases of the posterior beam. To create the final com4D plan, the dose for each constituent respiratory phasesummed via deformable image registration. For thisproach the beams weights used for the manually plaphase were the same for each automatically planned retory phase. It would be possiblesthough not investigatehered to optimize the beam weights for the 4D plan whmay further improve the plan quality.

To create the 3D plan, the PTV for each of the respiraphases was superimposed, i.e., the 3D PTV is the unieach 4D PTV phase. The treatment plan for the peak-inphase was the same as that used for the 4D plan, excethe fields conformed to the 3D PTV as shown in Fig. 2.3D vs 4D comparison performed here is a “best-case” cparison for 3D in that no extra margins were added tocount for the artifacts during imaging,1,2,27,58–64and additional internal margins were required to ensure that thewas adequately dosed during delivery in which respiramotion occurred. It should also be noted that a clinicaltreatment plan should have additional geometric marginaccount for the errors in the deformable image registraprocess and also the ability of the DMLC to track the movtarget.65–67 The 3D and 4D treatment plans are compare

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FIG. 2. The beam view of the posterior beam, with the aperture se0.8 cm margin surroundingsad the 3D planning target volumesPTVd, sbd thepeak-inhale 4D PTV, andscd the peak-exhale 4D PTV. Note the obviodeformation of the PTV at the different respiration phases.

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III. RESULTS

A. Deformable image registration

The comparison of the deformable image registrationgorithm results compared with points manually definedeach respiratory phase are shown in Fig. 3. The differebetween the manual and automatically defined pointswithin one CT slice thicknesss0.4 cmd for all points studiedIt is interesting to note that the displacements predictedeformable registration showed smaller variations fphase to phase than the manually defined points.

The 4D PTVs in axial, coronal, and sagittal views at fphases of the respiratory cycle are shown in Fig. 4.volumes encompassed by the skin, heart, PTV, and luneach respiratory phase are given in Fig. 5. Note thanormal tissue values shown in Fig. 5 are limited to theume within the CT scans10.5 cm in lengthd, which did notencompass the entire thorax. The PTV volumes are fconstant apart from the early inspiration phase value thapproximately 7% smaller than the other volumes. It isclear if this low value is an artifact of the 4D CT itself, tdeformable image registration algorithm, or simply duevariations introduced by the 4 mm CT slice thickness.lung volumes change as expected with respiration. Theume encompassed by the skins stays nearly constantslessthan 35 cc change with respirationd for the CT scan lengththough we would expect for a full thoracic CT scan thatvolume would change by the volume of air inhaledsaccounting for pressure differencesd. The heart changes by less th5% from the mean volume, and is also influenced by carmotion. The cord changes by less than 0.5 cc from the mvolume.

The centroid of the 4D PTV for each respiratory phasshown in Fig. 6, giving evidence of cyclic motion asserved in the fluoroscopic implanted marker study of S

53

FIG. 3. Manualsclosed symbolsd and automaticallysopen symbolsd deter-mined displacements of four anatomical points from the same point dein the end inspiration respiratory phase CT image set. Lt lung v=bifurcation in a left lower lobe vessel. Rt bronchiole=the lateral aspea right lower lobe bronchiole just proximal to a bifurcation. Ant verte=the anterior-most aspect of the vertebral body at the level of the carinnipple=the right nipple. The error bars of 0.4 cmscorresponding to one Cslice thicknessd is shown on the left lung vessel curve. Each anatomicais offset 0.5 cm for clarity.

penwooldeet al. Note that the minimum and maximum


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superior–inferior positions of the PTV centroid do not cocide with the peak inhale and peak exhale positions as dmined from the respiratory signal. Thus there is evidenca phase shift between the internal motion and the respirsignal. This phase shift itself does not degrade the 4Dhowever, variations in the phase shift, or more genevariations in the correlation between the internal motionrespiratory signal, would negatively impact the accuracthe 4D delivery.

B. 4D treatment plan

The dose distribution for each CT phase and thedistribution transformed back to the peak-inhale CT imset for evaluation of the entire treatment plan compiledall respiratory phasessthe 4D pland is shown in Fig. 7. Thdose distributions in each phase are fairly similar due torelatively s,10%d small change in overall volume of tPTV for each respiratory phase, and the adaptation otreatment plan to these positional and volumetric Pchanges.

Dose–volume histogramssDVHsd for each constituenbreathing phase and for the combined dose distribsfound by summing the constituent dose distributions viaformable operatorsd are given in Fig. 8 for the PTV, lungcord, and heart. The DVHs for the PTV at all phases ancombineds4Dd DVH are all very similarsnote the expandex-axis scaled. The lung DVHs are closely bunched, withcombined 4D DVH closer to the end-inhale DVH due tofact that DVHs typically use normalized rather than absovolumes, and that the entire lung was not included in thescan. The 4D DVHs for the cord and heart appear to bethe middle of the constituent-phase DVHs. The variatiocord and heart DVHs for the constituent phases is due tchange in beam aperture with respiratory phase and, hthe fraction of the organ intersecting the beam pasthrough these structures, as the PTV deforms with restory phase. Note that the 4D cord DVH seems to excee3D DVHs around 10 Gy. This is possible due to the dsummation to obtain the 4D dose, in that the 4D DVH

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FIG. 4. The PTV contoured on the computed tomographysCTd anatomy inaxial view sad–sdd, coronal viewsed–shd, and sagittal viewsid–sld. The fourrespiration phases shown are peak inhalesad, sed, andsid; mid exhalesbd, sfd,and sjd; peak exhalescd, sgd, andskd; and mid inhalesdd, shd, andsld. Notethat the anatomy was explicitly contoured on the peak-inhale imagesthe PTVs on the other breathing phases were automatically created bathe deformable registration transformations.

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ing a simple example of a two voxel structure with dose0 and 1, then the minimum dose in any voxel will be 0,the minimum dose in the summed/averaged voxel cagreater than 0. For example, phase A has doses 0 aphase B has doses of 1 and 0, then the 4D dose would band 0.5 in the two voxels, respectively.

C. 3D vs 4D

A comparison of the 3D PTV and peak-inhale PTVgiven in Fig. 9. As expected, the 3D PTV is larger thanpeak-inhale-phase PTV. DVHs for the 3D and 4D treatmplans are seen Fig. 10. The isodose distributions appearrelatively similar for the central axis slice shown. Howethe DVHs show dosimetric reductions for the lungs, heand cord for the 4D plan. Though the comparisons here

FIG. 5. The volumes of the entire computed tomographysCTd anatomy involume sGTVdg, scd heart,sdd cord, andsed skin. Note that the entire volimitations.

somewhat modest gains for 4D vs 3D planning, as expande


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upon in Sec. IV, the current comparisons do not includetifacts during conventional CT imaging, which are a knosource of error.

IV. DISCUSSION

A 4D treatment-planning process for DMLC-based reratory motion tracking has been developed and appliedexisting 4D CT image. Dosimetric advantages were fofor the 4D treatment plan compared with the 3D plannontarget structures. Though no general conclusions cdrawn from the single proof-of-principle example case donstrated here, clearly, if the respiratory motion is explicaccounted for during the imaging, planning, and deliverradiotherapy, some dosimetric improvements are expeApplying the 4D planning method to a larger patient co

h of the eight respiratory phases:sad PTV, sbd lungs fminus the gross tumos of organssbd–sed were not included in the 4D CT scan due to acquis

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therapy, both compared with conventional 3D treatmentsalso respiratory gated and breath-hold treatments.

Without automated tools, an order-of-magnitude increin workload for 4D treatment planning over 3D planningrequired due to the order-of-magnitude increase in inputand tasks such as contouring multiple data sets, cretreatment plans on these data sets, and analyzing thedistribution for each of these images. Such an increaneither desirable nor feasible. The tools that can reduce

FIG. 6. The position of the centroids of the 4D planning target volsPTVd for each of the eight respiratory phases.

FIG. 8. Dose–volume histograms for each breathing phasesthin solid line
volume sPTVd, sbd lungs,scd cord, andsdd heart.

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workload to essentially that of a 3D treatment plan areformable image registration and automated planning.formable image registration tools for radiotherapy purpare relatively new and untested. As with any process,metric uncertainties in the registration method will neebe quantified and taken into account during the planningcess. Development and testing of these algorithms ontiple 4D CT sets is required for these tools, which willcome an integral part of treatment-planning software afield moves toward image-guided adaptive and 4D ratherapy. Indeed, combining dose distributions without tthat map corresponding anatomical points in differentsets is problematic.

FIG. 7. The process of combining the constituent dose distributionseach breathing phase onto the composite dose distribution to creacombined or 4D plan. The 20, 60, 66, and 70 Gy isodose lines are sNote the similarity in dose distribution for each respiratory phase.

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Planning for 4D radiotherapy must account for the gmetric uncertainties not accounted for by the respiratracking. Such uncertainties include setup error and intertion motion as well as the 4D-specific uncertainties sucartifacts in 4D CT scans,9,68 deformable image registratioaccuracy,38 variations in respiratory signal and interanatomy motion,69–72 motion prediction errors,73–75 and aquantification of the DMLC positional and temporal acracy as it is being operated in a mode that it was not initdesigned for. Obviously, for 4D radiotherapy to be a viaoption the sum of these geometric uncertainties shoulsignificantly less than the sum of the geometric uncertaifor 3D radiotherapy.

There are both similarities and differences betweenradiotherapy that accounts for respiratory motion and ative radiotherapy that accounts for interfractdisplacements.10–21 Both 4D and adaptive radiotherapy pon multiple CT images, require dose calculation on multCT images, and require deformable registration and metto obtain integral dose distributions and DVHs. However,

FIG. 9. The 4D PTV for the peak-inhale phasesdark shadingd, and the 3DPTV created by combining the PTVs from all respiratory phasesslight shad-ingd. The axialsad, coronalsbd, and sagittalscd views are shown. Note thtotal volumes for the peak-inhale PTV is 197 cm3, and the 3D PTV237 cm3.

radiotherapy is delivering radiation to a moving target and,


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thus, is affected by beam-delivery system constraints, suMLC velocity and acceleration limitations, that do not affadaptive radiotherapy. For IMRT optimization, 4D radtherapy allows optimization on several data sets simneously and within the leaf motion constraints, wheadaptive radiotherapy is restricted to optimization on cudata sets with knowledge of previous data sets. Withsimilarities and commons tools required for 4D and adapradiotherapy, these techniques are complementary rathecontradictory. The 4D planning method described in thisticle is sensitive to variations in the respiratory surroused and the target anatomy motion. Adaptive radiothestrategies, which for 4D radiotherapy would involve requiring the 4D image of the patient and the respirationnal, and hence reacquiring the correlation, would reducesensitivity sand also provide valuable information as tomagnitude of this effectd.

Using 4D radiotherapy to explicitly account for tchange in target shape with respiration by varying the Mpositions logically leads to the concept of the 4D PTV, whthe PTV for a given respiratory phase is fixed in spacethe ICRU definition;76,77 however, the PTV changes wtime. The 4D PTV concept is analogous in adaptive ratherapy, where the PTV changes with time due to intertional changes in the GTV position and shape.

Apart from applying the 4D radiotherapy plannmethod to a significant number of patient cases, thelogical step is to develop a 4D IMRT plannimethodology,65–67 though due to beam-delivery system cstraints, such as leaf acceleration and velocity limitationsleaf sequencing for a 4D IMRT plan is nontrivial. The deery aspects of 4D treatment plans66,67,73,74,78–81including respiration prediction to account for the system responsehave not been addressed here and are the subject of oninvestigations.

Reproducibility in respiratory patterns during the imagand delivery of 4D radiotherapy is an important issue reqing further investigation. Irreproducible respiration pattemay preclude some patients as candidates for 4D rtherapy, whilst breathing training techniques82 may be required for the majority of lung cancer patients in ordemaintain breathing regularity over a protracted courstherapy. Since extracranial stereotactic radiotherapysESRdtechniques83–90have a much shorter duration of therapy,cohort of patients may be most suitable for initial clintesting of 4D radiotherapy. This reduction in overall trement time means that the respiration reproducibility reqments are only for a week or so as opposed to six orweeks of standard therapy. Another advantage of app4D radiotherapy to ESR patients is that the lesions arecally smaller than those treated with conventional therthus, the ratio of respiratory-induced motion to the tuvolume is higher and the fractional improvement withradiotherapy over standard delivery will be higher. Awith the condensed course of therapy for ESR patients,cally more resources per fraction are utilized, and, thustentially resource-intensive strategies, such as 4D r
therapy, can be employed.

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Though not explicitly part of this manuscript, the delivof 4D radiotherapy using DMLC-based respiratory motracking should be mentioned here. Based on respirphysiology literature,91–97and our institutional study collecing 330 respiratory motion traces from lung canpatients,98 cycle-to-cycle and day-to-day variations in resration occur. Thus a feedback loopssee for example Fig. 8Ref. 99d acquiring the patient’s respiratory signal and feedthis information to a four-dimensional controller which comunicates in real-time with the MLC will be required. Vaing breathing patterns during delivery, compared with thmeasured during the 4D CT imaging session used for tment planning, mean that the planned and delivered dmay differ. For example, if the time spent within each breing phase during treatment delivery,wd, differs from thaduring planning where fractionwi was used for the final docalculation, the combined 4D dose will change followings1d. However, using Eq.s1d with wd means that the actudose delivered to the moving tumor and critical structureeach treatment fraction can be calculated. A problem apif the respiration pattern limits during delivery exceed thobtained during the 4D CT session used for planning. Shsuch a situation occur, either the treatment should be pauntil the respiration pattern returns within the limits knofrom planningsthe prudent approachd or until a reasonabextrapolation of the tumor position and shape based orespiratory signal can be made.

V. CONCLUSION

4D radiotherapy planning for DMLC-based respiratmotion tracking has been shown to be feasible and maytumor dose escalation and/or a reduction in treatment-recomplications. In principle, 4D planning requires little mhuman interaction time than a 3D plan, however, new pning tools such as deformable registration and autom

FIG. 10. Dose–volume histogram comparison of the 3Dssolid linesd treat-ment plan and the 4Dsdashed linesd plan for the PTV, lungs, cord, and hea

treatment planning on multiple CT image sets are required.


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ACKNOWLEDGMENTS

The authors wish to acknowledge the support of NIH/R01 CA93626. Devon Murphy carefully reviewed andnificantly improved the clarity of this manuscript. Dr. Adrew Lauve assisted with contour delineation. Rohini Geassisted with collection of background material. Dr. SWebb and Catherine Coolens from the Royal Marsdenpital provided useful feedback.

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Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking

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