Estimating the dose variation in a volume of interest with explicit consideration of patient geometric variation

Evaluation of nonrigid registration models for interfraction dose accumulation in radiotherapy

PURPOSE

Interfraction dose accumulation is necessary to evaluate the dose distribution of an entire course of treatment by adding up multiple dose distributions of different treatment fractions. This accumulation of dose distributions is not straightforward as changes in the patient anatomy may occur during treatment. For this purpose, the accuracy of nonrigid registration methods is assessed for dose accumulation based on the calculated deformations fields.

METHODS

A phantom study using a deformable cubic silicon phantom with implanted markers and a cylindrical silicon phantom with MOSFET detectors has been performed. The phantoms were deformed and images were acquired using a cone-beam CT imager. Dose calculations were performed on these CT scans using the treatment planning system. Nonrigid CT-based registration was performed using two different methods, the Morphons and Demons. The resulting deformation field was applied on the dose distribution. For both phantoms, accuracy of the registered dose distribution was assessed. For the cylindrical phantom, also measured dose values in the deformed conditions were compared with the dose values of the registered dose distributions. Finally, interfraction dose accumulation for two treatment fractions of a patient with primary rectal cancer has been performed and evaluated using isodose lines and the dose volume histograms of the target volume and normal tissue.

RESULTS

A significant decrease in the difference in marker or MOSFET position was observed after nonrigid registration methods (p < 0.001) for both phantoms and with both methods, as well as a significant decrease in the dose estimation error (p < 0.01 for the cubic phantom and p < 0.001 for the cylindrical) with both methods. Considering the whole data set at once, the difference between estimated and measured doses was also significantly decreased using registration (p < 0.001 for both methods). The patient case showed a slightly underdosed planning target volume and an overdosed bladder volume due to anatomical deformations.

CONCLUSIONS

Dose accumulation using nonrigid registration methods is possible using repeated CT imaging. This opens possibilities for interfraction dose accumulation and adaptive radiotherapy to incorporate possible differences in dose delivered to the target volume and organs at risk due to anatomical deformations.

Synchronized dynamic dose reconstruction

Variations in target volume position between and during treatment fractions can lead to measurable differences in the dose distribution delivered to each patient. Current methods to estimate the ongoing cumulative delivered dose distribution make idealized assumptions about individual patient motion based on average motions observed in a population of patients. In the delivery of intensity modulated radiation therapy (IMRT) with a multi-leaf collimator (MLC), errors are introduced in both the implementation and delivery processes. In addition, target motion and MLC motion can lead to dosimetric errors from interplay effects. All of these effects may be of clinical importance. Here we present a method to compute delivered dose distributions for each treatment beam and fraction, which explicitly incorporates synchronized real-time patient motion data and real-time fluence and machine configuration data. This synchronized dynamic dose reconstruction method properly accounts for the two primary classes of errors that arise from delivering IMRT with an MLC: (a) Interplay errors between target volume motion and MLC motion, and (b) Implementation errors, such as dropped segments, dose over/under shoot, faulty leaf motors, tongue-and-groove effect, rounded leaf ends, and communications delays. These reconstructed dose fractions can then be combined to produce high-quality determinations of the dose distribution actually received to date, from which individualized adaptive treatment strategies can be determined.

Geometric and dosimetric evaluations of an online image-guidance strategy for 3D-CRT of prostate cancer

PURPOSE

To evaluate an online image-guidance strategy for conformal treatment of prostate cancer and to estimate margin-reduction benefits.

METHODS AND MATERIALS

Twenty-eight patients with at least 16 helical computed tomography scans were each used in this study. Two prostate soft-tissue registration methods, including sagittal rotation, were evaluated. Setup errors and rigid organ motion were corrected online; non-rigid and intrafraction motion were included in offline analysis. Various clinical target volume-planning target volume (CTV-PTV) margins were applied. Geometrical evaluations included analyses of isocenter shifts and rotations and overlap index. Dosimetric evaluations included minimum dose and equivalent uniform dose (EUD) for prostate and gEUD for rectum.

RESULTS

Average isocenter shift and rotation were (dX,dY,dZ,theta) = (0.0 +/- 0.7,-1.1 +/- 4.0,-0.1 +/- 2.5,0.7 degrees +/- 2.0 degrees ) mm. Prostate motion in anterior-posterior (AP) direction was significantly higher than superior-inferior and left-right (LR) directions. This observation was confirmed by isocenter shift in perspectives AP (1.8 +/- 1.8 mm) and RL (3.7 +/- 3.0 mm). Organ motion degrades target coverage and reduces doses to rectum. If 2% dose reduction on prostate D(99) is allowed for 90% patients, then minimum 3 mm margins are necessary with ideal image registration.

CONCLUSIONS

Significant margin reduction can be achieved through online image guidance. Certain margins are still required for nonrigid and intrafraction motion. To further reduce margin, a strategy that combines online geometric intervention and offline dose replanning is necessary.

Computed Tomography Guided Management of Interfractional Patient Variation

Interfractional patient variation occurs regularly and considerably during the radiotherapy course. Consequently, a generic but large planning target margin has to be applied when patient treatment plan design based on a single pre-treatment computed tomography scan is used to guide multifraction radiation treatment, which creates a major limiting factor for radiotherapy improvement. Planning target margins can be significantly reduced using multiple (or 4-dimensional) image feedback management in the routine treatment process. The most effective method in multiple-image feedback management of radiotherapy is the adaptive control methodology. The adaptive radiotherapy technique aims to customize each patient's treatment plan to patient-specific variation by evaluating and characterizing the systematic and random variations through image feedback and including them in adaptive planning. Adaptive radiotherapy will become a new treatment standard, in which a predesigned adaptive treatment strategy, including the schedules of imaging and replanning, will eventually replace the predesigned treatment plan in the routine clinical practice.

Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy

A greyscale-based fully automatic deformable image registration algorithm, originally known as the 'demons' algorithm, was implemented for CT image-guided radiotherapy. We accelerated the algorithm by introducing an 'active force' along with an adaptive force strength adjustment during the iterative process. These improvements led to a 40% speed improvement over the original algorithm and a high tolerance of large organ deformations. We used three methods to evaluate the accuracy of the algorithm. First, we created a set of mathematical transformations for a series of patient's CT images. This provides a 'ground truth' solution for quantitatively validating the deformable image registration algorithm. Second, we used a physically deformable pelvic phantom, which can measure deformed objects under different conditions. The results of these two tests allowed us to quantify the accuracy of the deformable registration. Validation results showed that more than 96% of the voxels were within 2 mm of their intended shifts for a prostate and a head-and-neck patient case. The mean errors and standard deviations were 0.5 mm+/-1.5 mm and 0.2 mm+/-0.6 mm, respectively. Using the deformable pelvis phantom, the result showed a tracking accuracy of better than 1.5 mm for 23 seeds implanted in a phantom prostate that was deformed by inflation of a rectal balloon. Third, physician-drawn contours outlining the tumour volumes and certain anatomical structures in the original CT images were deformed along with the CT images acquired during subsequent treatments or during a different respiratory phase for a lung cancer case. Visual inspection of the positions and shapes of these deformed contours agreed well with human judgment. Together, these results suggest that the accelerated demons algorithm has significant potential for delineating and tracking doses in targets and critical structures during CT-guided radiotherapy.

Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes

PURPOSE

Recent imaging studies have demonstrated that there can be significant changes in anatomy from day to day and over the course of radiotherapy as a result of daily positioning uncertainties and physiologic and clinical factors. There are a number of strategies to minimize such changes, reduce their impact, or correct for them. Measures to date have included improved immobilization of external and internal anatomy or adjustment of positions based on portal or ultrasound images. Perhaps the most accurate way is to use CT image-guided radiotherapy, for which the possibilities range from simple correction of setup based on daily CT images to on-line near real-time intensity modulated radiotherapy (IMRT) replanning. In addition, there are numerous intermediate possibilities. In this paper, we report the development of one such intermediate method that takes into account anatomic changes by deforming the intensity distributions of each beam based on deformations of anatomy as seen in the beam's-eye-view.

METHODS AND MATERIALS

The intensity distribution deformations are computed based on anatomy deformations discerned from the changes in the current image relative to a reference image (e.g., the pretreatment CT scan). First, a reference IMRT plan is generated based on the reference CT image. A new CT image is acquired using an in-room CT for every fraction. The anatomic structure contours are obtained for the new image. (For this article, these contours were manually drawn. When image guided IMRT methods are implemented, anatomic structure contours on subsequent images will likely be obtained with automatic or semiautomatic means. This could be achieved by, for example, first deforming the original CT image to match today's image, and then using the same deformation transformation to map original contours to today's image.) The reference intensity distributions for each beam are then deformed so that the projected geometric relationship within the beam's-eye-view between the anatomy (both target and normal tissues) extracted from the reference image and the reference intensity distribution is the same as (or as close as possible to) the corresponding relationship between anatomy derived from today's image and the newly deformed intensity distributions. To verify whether the dose distributions calculated using the deformed intensity distributions are acceptable for treatment as compared to the original intensity distributions, the deformed intensities are transformed into leaf sequences, which are then used to compute intensity and dose distributions expected to be delivered. The corresponding dose-volume histograms and dose-volume and dose-response indices are also computed. These data are compared with the corresponding data derived (a) from the original treatment plan applied to the original image, (b) from the original treatment plan applied to today's image, and (c) from a new full-fledged IMRT plan designed based on today's image.

RESULTS

Depending on the degree of anatomic changes, the use of an IMRT plan designed based on the original planning CT for the treatment of the current fraction could lead to significant differences compared to the intended dose distributions. CT-guided setup compared to the setup based on skin marks or bony landmarks may improve dose distributions somewhat. Replanning IMRT based on the current fraction's image yields the best physically deliverable plan (the "gold standard"). For the prostate and head-and-neck examples studied as proof of principle, the results of deforming intensities within each beam based on the anatomy seen in the beam's-eye-view are a good approximation of full-fledged replanning compared with other alternatives.

CONCLUSIONS

Our preliminary results encourage us to believe that deforming intensities taking into account deformation in the anatomy may be a rapid way to produce new treatment plans on-line in near real-time based on daily CT images. The methods we have developed need to be applied to a group of patients for both prostate and head-and-neck cases to confirm the validity of our approach.

Online image-guided intensity-modulated radiotherapy for prostate cancer: How much improvement can we expect? A theoretical assessment of clinical benefits and potential dose escalation by improving precision and accuracy of radiation delivery

PURPOSE

To quantify the theoretical benefit, in terms of improvement in precision and accuracy of treatment delivery and in dose increase, of using online image-guided intensity-modulated radiotherapy (IG-IMRT) performed with onboard cone-beam computed tomography (CT), in an ideal setting of no intrafraction motion/deformation, in the treatment of prostate cancer.

METHODS AND MATERIALS

Twenty-two prostate cancer patients treated with conventional radiotherapy underwent multiple serial CT scans (median 18 scans per patient) during their treatment. We assumed that these data sets were equivalent to image sets obtainable by an onboard cone-beam CT. Each patient treatment was simulated with conventional IMRT and online IG-IMRT separately. The conventional IMRT plan was generated on the basis of pretreatment CT, with a clinical target volume to planning target volume (CTV-to-PTV) margin of 1 cm, and the online IG-IMRT plan was created before each treatment fraction on the basis of the CT scan of the day, without CTV-to-PTV margin. The inverse planning process was similar for both conventional IMRT and online IG-IMRT. Treatment dose for each organ of interest was quantified, including patient daily setup error and internal organ motion/deformation. We used generalized equivalent uniform dose (EUD) to compare the two approaches. The generalized EUD (percentage) of each organ of interest was scaled relative to the prescription dose at treatment isocenter for evaluation and comparison. On the basis of bladder wall and rectal wall EUD, a dose-escalation coefficient was calculated, representing the potential increment of the treatment dose achievable with online IG-IMRT as compared with conventional IMRT.

RESULTS

With respect to radiosensitive tumor, the average EUD for the target (prostate plus seminal vesicles) was 96.8% for conventional IMRT and 98.9% for online IG-IMRT, with standard deviations (SDs) of 5.6% and 0.7%, respectively (p < 0.0001). The average EUDs of bladder wall and rectal wall for conventional IMRT vs. online IG-IMRT were 70.1% vs. 47.3%, and 79.4% vs. 72.2%, respectively. On average, a target dose increase of 13% (SD = 9.7%) can be achieved with online IG-IMRT based on rectal wall EUDs and 53.3% (SD = 15.3%) based on bladder wall EUDs. However, the variation (SD = 9.7%) is fairly large among patients; 27% of patients had only minimal benefit (<5% of dose increment) from online IG-IMRT, and 32% had significant benefit (>15%-41% of dose increment).

CONCLUSIONS

The ideal maximum dose increment achievable with online IG-IMRT is, on average, 13% with respect to the dose-limiting organ of rectum. However, there is a large interpatient variation, ranging <5%-41%. The results can be applied to calibrate other practical online image-guided techniques for prostate cancer radiotherapy, when intratreatment organ motion/deformation and machine delivery accuracy are considered.

Monte Carlo simulations with time-dependent geometries to investigate effects of organ motion with high temporal resolution

PURPOSE

To calculate the dose in time-dependent geometry, the results of three-dimensional calculations are usually performed separately and combined. This approach becomes cumbersome when high temporal resolution is required, if the geometry is complex, or if interplay effects between different, independently moving systems are to be studied. The purpose of this project was the implementation of continuous (four-dimensional [4D]) Monte Carlo simulation to study the irradiation of tumors under respiratory motion.

METHODS AND MATERIALS

In taking advantage of object-oriented programming, we implemented 4D Monte Carlo dose calculation. Local dose depositions in the patient are calculated while beam configuration and organ positions are changed continuously. Deformable image registration is used to describe the CT voxel displacement over time.

RESULTS

The 4D Monte Carlo technique is applied to a lung cancer case planned for proton therapy. We show that the effect of motion on the dose distribution can be simulated effectively based on statistical motion parameterizations acting on the geometry or based on patient-specific 4D CT information.

CONCLUSION

We present a novel method able to calculate dose with underlying time-dependent geometry. The technique allows 4D dose calculation in arbitrary time scales in a single simulation even for double-dynamic systems (e.g., time-dependent beam delivery under organ motion).

Mutual information based CT registration of the lung at exhale and inhale breathing states using thin-plate splines

The advent of dynamic radiotherapy modeling and treatment techniques requires an infrastructure to weigh the merits of various interventions (breath holding, gating, tracking). The creation of treatment planning models that account for motion and deformation can allow the relative worth of such techniques to be evaluated. In order to develop a treatment planning model of a moving and deforming organ such as the lung, registration tools that account for deformation are required. We tested the accuracy of a mutual information based image registration tool using thin-plate splines driven by the selection of control points and iterative alignment according to a simplex algorithm. Eleven patients each had sequential CT scans at breath-held normal inhale and exhale states. The exhale right lung was segmented from CT and served as the reference model. For each patient, thirty control points were used to align the inhale CT right lung to the exhale CT right lung. Alignment accuracy (the standard deviation of the difference in the actual and predicted inhale position) was determined from locations of vascular and bronchial bifurcations, and found to be 1.7, 3.1, and 3.6 mm about the RL, AP, and IS directions. The alignment accuracy was significantly different from the amount of measured movement during breathing only in the AP and IS directions. The accuracy of alignment including thin-plate splines was more accurate than using affine transformations and the same iteration and scoring methodology. This technique shows promise for the future development of dynamic models of the lung for use in four-dimensional (4-D) treatment planning.

Evaluation of the influence of breathing on the movement and modeling of lung tumors

PURPOSE

Respiration causes movement and potential shape change in lung tumors that are not fully appreciated using conventional free-breathing CT models for radiotherapy planning. Although target expansion has the potential to ensure proper tumor coverage in the face of motion on a free-breathing CT scan, large variations in how individual patients' tumors move may make such expansions difficult to uniformly define. In addition, excessive expansion may result in the unnecessary inclusion of normal lung in the treated volume. This study was designed to evaluate the influence of breathing movement on tumors and to assess the validity of the free-breathing CT scan for target delineation in the lung.

METHODS AND MATERIALS

Data from 16 consecutive lung cancer patients who underwent treatment planning CT scans at inhale and exhale and during free breathing on a fast helical CT scanner were analyzed. Gross tumor volumes (GTV) were defined on each scan. A composite GTV was created by combining the inhale and exhale GTVs (COMP). Two methods of expansion were used to compare COMP to the free-breathing GTV (FREE). First the free-breathing data set was expanded uniformly by 1 cm (FREE + 1). Next, a nonuniform expansion was generated in all 6 directions to ensure complete coverage of COMP with the minimal subtended volume (FREE + EXP). The amount of excess normal lung treated with these 2 expansions was compared. The volume of the COMP missed using the 1-cm expansion was determined.

RESULTS

There was a significant amount of excess normal lung tissue treated with the uniform 1-cm (FREE + 1) expansion, as well as with the nonuniform (FREE + EXP) expansion. In addition, there were also cases where this technique led to marginal miss of the tumor, including one case where 47% of the overall tumor was missed with this 1-cm (FREE + 1) expansion. An attempt to create a systematic model for expansion was not successful. Although the mean expansions in the anterior-posterior, superior-inferior, and right-left directions were reasonable (0.9, 1.0, and 0.8 cm, respectively), the large intrapatient variations (sigma 0.6 cm anterior-posterior, 0.7 cm superior-inferior, and 0.5 cm right-left) suggest difficulty in assigning a simple rule for population target expansion. Some extension of FREE outside of the borders of COMP was observed, suggesting the need for evaluation of reproducibility over multiple breathing states.

CONCLUSIONS

Traditional methods of expanding the GTV to CTV by 1 cm are less than ideal. This method tends to include more normal lung than necessary and may lead to marginal miss. Interpatient tumor movement variations further prohibit defining a simple rule for nonuniform expansion that would minimize the volume of normal lung in the target. Although the development of target volumes by combining information from breath-hold CT scans at inhale and exhale states shows some promise in minimizing excess lung irradiated while maintaining adequate tumor coverage, further tests of breathing reproducibility need to be performed to provide a confident baseline for defining target expansions by this technique.

Dose correlation for thoracic motion in radiation therapy of breast cancer

This work investigates the dose correlation for deformed objects due to thoracic motion for radiotherapy treatment of breast cancer. An analytical model has been developed to reconstruct patient anatomy based on the assumption that the body will expand or compress proportionally during respiration. The patient geometry at any phase during a breathing pattern is reconstructed using the CT data taken at the inspiration and expiration phases and the breathing level which can be related to the measured chest wall motion. A correlation between the voxels in the inspiration (or expiration) geometry and the voxels in the reconstructed geometry at any phase of the breathing pattern is established so that the dose can be accumulated during a treatment. The method has been implemented for treatment planning dose calculation by interfacing with a Monte Carlo code. The patient geometry files for different phases of the breathing pattern are generated and the three-dimensional dose data are obtained from the Monte Carlo simulations. The final dose distribution is reconstructed from the dose data at different breathing phases based on patient's breathing pattern associated with chest wall movements.

Evaluation of mechanical precision and alignment uncertainties for an integrated CT/LINAC system

A new integrated CT/LINAC combination, in which the CT scanner is inside the radiation therapy treatment room and the same patient couch is used for CT scanning and treatment (after a 180-degree couch rotation), should allow for accurate correction of interfractional setup errors. The purpose of this study was to evaluate the sources of uncertainties, and to measure the overall precision of this system. The following sources of uncertainty were identified: (1) the patient couch position on the LINAC side after a rotation, (2) the patient couch position on the CT side after a rotation, (3) the patient couch position as indicated by its digital readout, (4) the difference in couch sag between the CT and LINAC positions, (5) the precision of the CT coordinates, (6) the identification of fiducial markers from CT images, (7) the alignment of contours with structures in the CT images, and (8) the alignment with setup lasers. The largest single uncertainties (one standard deviation or 1 SD) were found in couch position on the CT side after a rotation (0.5 mm in the RL direction) and the alignment of contours with the CT images (0.4 mm in the SI direction). All other sources of uncertainty are less than 0.3 mm (1 SD). The overall precision of two setup protocols was investigated in a controlled phantom study. A protocol that relies heavily on the mechanical integrity of the system, and assumes a fixed relationship between the LINAC isocenter and the CT images, gave a predicted precision (1 SD) of 0.6, 0.7, and 0.6 mm in the SI, RL and AP directions, respectively. The second protocol reduces reliance on the mechanical precision of the total system, particularly the patient couch, by using radio-opaque fiducial markers to transfer the isocenter information from the LINAC side to the CT images. This protocol gave a slightly improved predicted precision of 0.5, 0.4, and 0.4 mm in the SI, RL and AP directions, respectively. The distribution of phantom position after CT-based correction confirmed these results. Knowledge of the individual sources of uncertainty will allow alternative setup protocols to be evaluated in the future without the need for significant additional measurements.