Image-guided adaptive gating of lung cancer radiotherapy: a computer simulation study

Technical Note: Combination of multiple EPID imager layers improves image quality and tracking performance of low contrast-to-noise objects

PURPOSE

We hypothesized that combining multiple amorphous silicon flat panel layers increases photon detection efficiency in an electronic portal imaging device (EPID), improving image quality and tracking accuracy of low-contrast targets during radiotherapy.

METHODS

The prototype imager evaluated in this study contained four individually programmable layers each with a copper converter layer, Gd₂ O₂ S scintillator, and active-matrix flat panel imager (AMFPI). The imager was placed on a Varian TrueBeam linac and a Las Vegas phantom programmed with sinusoidal motion (peak-to-peak amplitude = 20 mm, period = 3.5 s) was imaged at a frame rate of 10 Hz with one to four layers activated. Number of visible circles and CNR of least visible circle (depth = 0.5 mm, diameter = 7 mm) was computed to assess the image quality of single and multiple layers. A previously validated tracking algorithm was employed for auto-tracking. Tracking error was defined as the difference between the programmed and tracked positions of the circle. Pearson correlation coefficient (R) of CNR and tracking errors was computed.

RESULTS

Motion-induced blurring significantly reduced circle visibility. During four cycles of phantom motion, the number of visible circles varied from 11-23, 13-24, 15-25, and 16-26 for one-, two-, three-, and four-layer imagers, respectively. Compared with using only a single layer, combining two, three, and four layers increased the median CNR by factors of 1.19, 1.42, and 1.71, respectively and reduced the average tracking error from 3.32 mm to 1.67 mm to 1.47 mm, and 0.74 mm, respectively. Significant correlations (P~10^-9 ) were found between the tracking error and CNR.

CONCLUSION

Combination of four conventional EPID layers significantly improves the EPID image quality and tracking accuracy for a poorly visible object which is moving with a frequency and amplitude similar to respiratory motion.

Inversed-Planned Respiratory Phase Gating in Lung Conformal Radiation Therapy

PURPOSE

To assess whether the optimal gating window for each beam during lung radiation therapy with respiratory gating will be dependent on a variety of patient-specific factors, such as tumor size and location and the extent of relative tumor and organ motion.

METHODS AND MATERIALS

To create optimal gating treatment plans, we started from an optimized clinical plan, created a plan per respiratory phase using the same beam arrangements, and used an inverse planning optimization approach to determine the optimal gating window for each beam and optimal beam weights (ie, monitor units). Two pieces of information were used for optimization: (1) the state of the anatomy at each phase, extracted from 4-dimensional computed tomography scans; and (2) the time spent in each state, estimated from a 2-minute monitoring of the patient's breathing motion. We retrospectively studied 15 lung cancer patients clinically treated by hypofractionated conformal radiation therapy, for whom 45 to 60 Gy was administered over 3 to 15 fractions using 7 to 13 beams. Mean gross tumor volume and respiratory-induced tumor motion were 82.5 cm³ and 1.0 cm, respectively.

RESULTS

Although patients spent most of their respiratory cycle in end-exhalation (EE), our optimal gating plans used EE for only 34% of the beams. Using optimal gating, maximum and mean doses to the esophagus, heart, and spinal cord were reduced by an average of 15% to 26%, and the beam-on times were reduced by an average of 23% compared with equivalent single-phase EE gated plans (P<.034, paired 2-tailed t test).

CONCLUSIONS

We introduce a personalized respiratory-gating technique in which inverse planning optimization is used to determine patient- and beam-specific gating phases toward enhancing dosimetric quality of radiation therapy treatment plans.

Imaging and dosimetric errors in 4D PET/CT-guided radiotherapy from patient-specific respiratory patterns: a dynamic motion phantom end-to-end study

Effective positron emission tomography / computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [(18)F]FDG. The lung lesion insert was driven by six different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses, and 2%-2 mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10-20%, treatment planning errors were 5-10%, and treatment delivery errors were 5-30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5-10% in PET/CT imaging, <5% in treatment planning, and <2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT planning, and RT delivery under a dose painting paradigm is feasible within an integrated respiratory motion phantom workflow. For a limited set of cases, the magnitude of errors was comparable during PET/CT imaging and treatment delivery without motion compensation. Errors were moderately mitigated during PET/CT imaging and significantly mitigated during RT delivery with motion compensation. This dynamic motion phantom end-to-end workflow provides a method for quality assurance of 4D PET/CT-guided radiotherapy, including evaluation of respiratory motion compensation methods during imaging and treatment delivery.

Suitability of markerless EPID tracking for tumor position verification in gated radiotherapy

PURPOSE

To maximize the benefits of respiratory gated radiotherapy (RGRT) of lung tumors real-time verification of the tumor position is required. This work investigates the feasibility of markerless tracking of lung tumors during beam-on time in electronic portal imaging device (EPID) images of the MV therapeutic beam.

METHODS

EPID movies were acquired at ∼2 fps for seven lung cancer patients with tumor peak-to-peak motion ranges between 7.8 and 17.9 mm (mean: 13.7 mm) undergoing stereotactic body radiotherapy. The external breathing motion of the abdomen was synchronously measured. Both datasets were retrospectively analyzed in PortalTrack, an in-house developed tracking software. The authors define a three-step procedure to run the simulations: (1) gating window definition, (2) gated-beam delivery simulation, and (3) tumor tracking. First, an amplitude threshold level was set on the external signal, defining the onset of beam-on/-off signals. This information was then mapped onto a sequence of EPID images to generate stamps of beam-on/-hold periods throughout the EPID movies in PortalTrack, by obscuring the frames corresponding to beam-off times. Last, tumor motion in the superior-inferior direction was determined on portal images by the tracking algorithm during beam-on time. The residual motion inside the gating window as well as target coverage (TC) and the marginal target displacement (MTD) were used as measures to quantify tumor position variability.

RESULTS

Tumor position monitoring and estimation from beam's-eye-view images during RGRT was possible in 67% of the analyzed beams. For a reference gating window of 5 mm, deviations ranging from 2% to 86% (35% on average) were recorded between the reference and measured residual motion. TC (range: 62%-93%; mean: 77%) losses were correlated with false positives incidence rates resulting mostly from intra-/inter-beam baseline drifts, as well as sudden cycle-to-cycle fluctuations in exhale positions. Both phenomena can lead to considerable deviations (with MTD values up to a maximum of 7.8 mm) from the intended tumor position, and in turn may result in a marginal miss. The difference between tumor traces determined within the gating window against ground truth trajectory maps was 1.1 ± 0.7 mm on average (range: 0.4-2.3 mm).

CONCLUSIONS

In this retrospective analysis of motion data, it is demonstrated that the system is capable of determining tumor positions in the plane perpendicular to the beam direction without the aid of fiducial markers, and may hence be suitable as an online verification tool in RGRT. It may be possible to use the tracking information to enable on-the-fly corrections to intra-/inter-beam variations by adapting the gating window by means of a robotic couch.

Tumor control probability reduction in gated radiotherapy of non-small cell lung cancers: a feasibility study

We studied the feasibility of evaluating tumor control probability (TCP) reductions for tumor motion beyond planned gated radiotherapy margins. Tumor motion was determined from cone‐beam CT projections acquired for patient setup, intrafraction respiratory traces, and 4D CTs for five non‐small cell lung cancer (NSCLC) patients treated with gated radiotherapy. Tumors were subdivided into 1 mm sections whose positions and doses were determined for each beam‐on time point. (The dose calculation model was verified with motion phantom measurements.) The calculated dose distributions were used to generate the treatment TCPs for each patient. The plan TCPs were calculated from the treatment planning dose distributions. The treatment TCPs were compared to the plan TCPs for various models and parameters. Calculated doses matched phantom measurements within 0.3% for up to 3 cm of motion. TCP reductions for excess motion greater than 5 mm ranged from 1.7% to 11.9%, depending on model parameters, and were as high as 48.6% for model parameters that simulated an individual patient. Repeating the worst case motion for all fractions increased TCP reductions by a factor of 2 to 3, while hypofractionation decreased these reductions by as much as a factor of 3. Treatment motion exceeding gating margins by more than 5 mm can lead to considerable TCP reductions. Appropriate margins for excess motion are recommended, unless applying daily tumor motion verification and adjusting the gating window.

PACS numbers: 87.55.dk, 87.57.Q‐

The effectiveness of combined gating and re-scanning for treating mobile targets with proton spot scanning. An experimental and simulation-based investigation

Organ motion is one of the major obstacles in radiotherapy and charged particle therapy. Even more so, the theoretical advantages of dose distributions in scanned ion beam therapy may be lost due to the interplay between organ motion and beam scanning. Several techniques for dealing with this problem have been devised. In re-scanning, the target volume is scanned several times to average out the motion effects. In gating and breath-hold, dose is only delivered if the tumour is in a narrow window of position. Experiments have been performed to verify if gating and re-scanning are effective means of motion mitigation. Dose distributions were acquired in a lateral plane of a homogeneous phantom. For a spherical target volume and regular motion gating was sufficient. However, for realistic, irregular motion or a patient target volume, gating did not reduce the interplay effect to an acceptable level. Combining gating with re-scanning recovered the dose distributions. The simplest re-scanning approach, where a treatment plan is duplicated several times and applied in sequence, was not efficient. Simulations of different combinations of gating window sizes and re-scanning schemes revealed that reducing the gating window is the most efficient approach. However, very small gating windows are not robust for irregular motion.

2D/4D marker-free tumor tracking using 4D CBCT as the reference image

Tumor motion caused by respiration is an important issue in image-guided radiotherapy. A 2D/4D matching method between 4D volumes derived from cone beam computed tomography (CBCT) and 2D fluoroscopic images was implemented to track the tumor motion without the use of implanted markers. In this method, firstly, 3DCBCT and phase-rebinned 4DCBCT are reconstructed from cone beam acquisition. Secondly, 4DCBCT volumes and a streak-free 3DCBCT volume are combined to improve the image quality of the digitally reconstructed radiographs (DRRs). Finally, the 2D/4D matching problem is converted into a 2D/2D matching between incoming projections and DRR images from each phase of the 4DCBCT. The diaphragm is used as a target surrogate for matching instead of using the tumor position directly. This relies on the assumption that if a patient has the same breathing phase and diaphragm position as the reference 4DCBCT, then the tumor position is the same. From the matching results, the phase information, diaphragm position and tumor position at the time of each incoming projection acquisition can be derived. The accuracy of this method was verified using 16 candidate datasets, representing lung and liver applications and one-minute and two-minute acquisitions. The criteria for the eligibility of datasets were described: 11 eligible datasets were selected to verify the accuracy of diaphragm tracking, and one eligible dataset was chosen to verify the accuracy of tumor tracking. The diaphragm matching accuracy was 1.88 ± 1.35 mm in the isocenter plane and the 2D tumor tracking accuracy was 2.13 ± 1.26 mm in the isocenter plane. These features make this method feasible for real-time marker-free tumor motion tracking purposes.

Objected constrained registration and manifold learning: a new patient setup approach in image guided radiation therapy of thoracic cancer

PURPOSE

The management of thoracic malignancies with radiation therapy is complicated by continuous target motion. In this study, a real time motion analysis approach is proposed to improve the accuracy of patient setup.

METHODS

For 11 lung cancer patients a long training fluoroscopy was acquired before the first treatment, and multiple short testing fluoroscopies were acquired weekly at the pretreatment patient setup of image guided radiotherapy (IGRT). The data analysis consisted of three steps: first a 4D target motion model was constructed from 4DCT and projected to the training fluoroscopy through deformable registration. Then the manifold learning method was used to construct a 2D subspace based on the target motion (kinetic) and location (static) information in the training fluoroscopy. Thereafter the respiratory phase in the testing fluoroscopy was determined by finding its location in the subspace. Finally, the phase determined testing fluoroscopy was registered to the corresponding 4DCT to derive the pretreatment patient position adjustment for the IGRT. The method was tested on clinical image sets and numerical phantoms.

RESULTS

The registration successfully reconstructed the 4D motion model with over 98% volume similarity in 4DCT, and over 95% area similarity in the training fluoroscopy. The machine learning method derived the phase values in over 98% and 93% test images of the phantom and patient images, respectively, with less than 3% phase error. The setup approach achieved an average accumulated setup error less than 1.7 mm in the cranial-caudal direction and less than 1 mm in the transverse plane. All results were validated against the ground truth of manual delineations by an experienced radiation oncologist. The expected total time for the pretreatment setup analysis was less than 10 s.

CONCLUSIONS

By combining the registration and machine learning, the proposed approach has the potential to improve the accuracy of pretreatment setup for patients with thoracic malignancy.

External respiratory motion analysis and statistics for patients and volunteers

We analyzed a large patient and volunteer study of external respiratory motion in order to develop a population database of respiratory information. We analyzed 120 lung, liver, and abdominal patients and 25 volunteers without lung disease to determine the extent of motion using the Varian Real‐Time Position Management system. The volunteer respiratory motion was measured for both abdominal and thoracic placement of the RPM box. Evaluation of a subset of 55 patients demonstrates inter‐ and intrafraction variation over treatment. We also calculated baseline drift and duty cycle for patients and volunteers. The mean peak‐to‐peak amplitude (SD) for the patients was 1.0 (0.5) cm, and for the volunteers it was abdomen 0.8 (0.3) cm and thoracic 0.2 (0.2) cm. The mean period (SD) was 3.6 (1.0) s, 4.2 (1.1) s, and 4.1 (0.8) s, and the mean end exhale position (SD) was 60% (6), 58% (7), and 56% (7) for patient, volunteer abdomen, and volunteer thoracic, respectively. Baseline drift was greater than 0.5 cm for 40% of patients. We found statistically significant differences between the patient and volunteer groups. Peak‐to‐peak amplitude was significantly larger for patients than the volunteer abdominal measurement and the volunteer abdominal measurement is significantly larger than the volunteer thoracic measurement. The patient group also exhibited significantly larger baseline drift than the volunteer group. We also found that peak‐to‐peak amplitude was the most variable parameter for both intra‐ and interfraction motion. This database compilation can be used as a resource for expected motion when using external surrogates in radiotherapy applications.

PACS number: 87.19.Wx, 87.55.Km