J. Wolthaus

Frameless Stereotactic Body Radiotherapy for Lung Cancer Using Four-Dimensional Cone Beam CT Guidance

PURPOSE To quantify the localization accuracy and intrafraction stability of lung cancer patients treated with frameless, four-dimensional (4D) cone beam computed tomography (CBCT)-guided stereotactic body radiotherapy (SBRT) and to calculate and validate planning target volume (PTV) margins to account for the residual geometric uncertainties. MATERIALS AND METHODS Sixty-five patients with small peripheral lung tumors were treated with SBRT without a body frame to 54 Gy in three fractions. For each fraction, three 4D-CBCT scans were acquired: before treatment to measure and correct the time-weighted mean tumor position, after correction to validate the correction applied, and after treatment to estimate the intrafraction stability. Patient-specific PTV margins were computed and subsequently validated using Monte Carlo error simulations. RESULTS Systematic tumor localization inaccuracies (1 SD) were 0.8, 0.8, and 0.9 mm for the left-right, craniocaudal, and anteroposterior direction, respectively. Random localization inaccuracies were 1.1, 1.1, and 1.4 mm. Baseline variations were 1.8, 2.9, and 3.0 mm (systematic) and 1.1, 1.5, and 2.0 mm (random), indicating the importance of image guidance. Intrafraction stability of the target was 1.2, 1.2, and 1.8 mm (systematic) and 1.3, 1.5, and 1.8 mm (random). Monte Carlo error simulations showed that patient-specific PTV margins (5.8-10.5 mm) were adequate for 94% of the evaluated cases (2-28 mm peak-to-peak breathing amplitude). CONCLUSIONS Frameless SBRT can be safely administered using 4D-CBCT guidance. Even with considerable breathing motion, the PTV margins can safely be kept small, allowing patients with larger tumors to benefit from the advantages of SBRT. In case bony anatomy would be used as a surrogate for tumor position, considerably larger PTV margins would be required.

COMPARISON OF DIFFERENT STRATEGIES TO USE FOUR-DIMENSIONAL COMPUTED TOMOGRAPHY IN TREATMENT PLANNING FOR LUNG CANCER PATIENTS

PURPOSE To discuss planning target volumes (PTVs) based on internal target volume (PTVITV), exhale-gated radiotherapy (PTVGating), and a new proposed midposition (PTVMidP; time-weighted mean tumor position) and compare them with the conventional free-breathing CT scan PTV (PTVConv). METHODS AND MATERIALS Respiratory motion induces systematic and random geometric uncertainties. Their contribution to the clinical target volume (CTV)-to-PTV margins differs for each PTV approach. The uncertainty margins were calculated using a dose-probability-based margin recipe (based on patient statistics). Tumor motion in four-dimensional CT scans was determined using a local rigid registration of the tumor. Geometric uncertainties for interfractional setup errors and tumor baseline variation were included. For PTVGating, the residual motion within a 30% gating (time) window was determined. The concepts were evaluated in terms of required CTV-to-PTV margin and PTV volume for 45 patients. RESULTS Over the patient group, the PTVITV was on average larger (+6%) and the PTVGating and PTVMidP smaller (-10%) than the PTVConv using an off-line (bony anatomy) setup correction protocol. With an on-line (soft tissue) protocol the differences in PTV compared with PTVConv were +33%, -4%, and 0, respectively. CONCLUSIONS The internal target volume method resulted in a significantly larger PTV than conventional CT scanning. The exhale-gated and mid-position approaches were comparable in terms of PTV. However, mid-position (or mid-ventilation) is easier to use in the clinic because it only affects the planning part of treatment and not the delivery.

Fusion of respiration-correlated PET and CT scans: correlated lung tumour motion in anatomical and functional scans

Lower lobe lung tumours in particular can move up to 2 cm in the cranio-caudal direction during the respiration cycle. This breathing motion causes image artefacts in conventional free-breathing computed tomography (CT) and positron emission tomography (PET) scanning, rendering delineation of structures for radiotherapy inaccurate. The purpose of this study was to develop a method for four-dimensional (4D) respiration-correlated (RC) acquisition of both CT and PET scans and to develop a framework to fuse these modalities. The breathing signal was acquired using a thermometer in the breathing airflow of the patient. Using this breathing signal, the acquired CT and PET data were grouped to the corresponding respiratory phases, thereby obtaining 4D CT and PET scans. Tumour motion curves were assessed in both image modalities. From these tumour motion curves, the deviation with respect to the mean tumour position was calculated for each phase. The absolute position of the centre of the tumour, relative to the bony anatomy, in the RCCT and gated PET scans was determined. This 4D acquisition and 4D fusion methodology was performed for five patients with lower lobe tumours. The peak-to-peak amplitude range in this sample group was 1-2 cm. The 3D tumour motion curve differed less than 1 mm between PET and CT for all phases. The mean difference in amplitude was less than 1 mm. The position of the centre of the tumour (relative to the bony anatomy) in the RCCT and gated PET scan was similar (difference <1 mm) when no atelectasis was present. Based on these results, we conclude that the method described in this study allows for accurate quantification of tumour motion in CT and PET scans and yields accurate respiration-correlated 4D anatomical and functional information on the tumour region.

On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion

Respiratory motion causes artifacts in cone-beam (CB) CT images acquired on slow rotating scanners integrated with linear accelerators. Respiration-correlated CBCT has been proposed to correct for the respiratory motion but only a subset of the CB projections is used to reconstruct each frame of the 4D CBCT image and, therefore, adequate image quality requires long acquisition times. In this article, the authors develop an on-the-fly solution to estimate and compensate for the respiratory motion in the reconstruction of a 3D CBCT image from all the CB projections. An a priori motion model of the patient respiratory cycle is estimated from the 4D planning CT. During the acquisition, the model is correlated with the respiration using a respiratory signal extracted from the CB projections. The estimated motion is next compensated for in an optimized reconstruction algorithm. The motion compensated for is forced to be null on average over the acquisition time to ensure that the compensation results in a CBCT image which describes the mean position of each organ, even if the a priori motion model is inaccurate. Results were assessed on simulated, phantom, and patient data. In all experiments, blur was visually reduced by motion-compensated CBCT. Simulations showed robustness to inaccuracies of the motion model observed on patient data such as amplitude variations, phase shifts, and setup errors, thus proving the efficiency of the compensation using an a priori motion model. Noise and view-aliasing artifacts were lower on motion-compensated CBCT images with 1 min scan than on respiration-correlated CBCT images with 4 min scan. Finally, on-the-fly motion estimation and motion-compensated reconstruction were within the acquisition time of the CB projections and the CBCT image available a few seconds after the end of the acquisition. In conclusion, the authors developed and implemented a method for correcting the respiratory motion during the treatment fractions which can replace respiration-correlated CBCT for improving image quality while decreasing acquisition time.

On-the-Fly Motion-Compensated Cone-Beam CT Using an a Priori Motion Model

Respiratory motion causes artifacts in slow-rotating cone-beam (CB) computed tomography (CT) images acquired for example for image guidance of radiotherapy. Respiration-correlated CBCT has been proposed to correct for the respiratory motion, but the use of a subset of the CB projections to reconstruct each frame of the 4D CBCT image limits their quality, thus requiring a longer acquisition time. Another solution is motion-compensated CBCT which consists of reconstructing a single 3D CBCT image at a reference position from all the CB projections by using an estimate of the respiratory motion in the reconstruction algorithm. In this paper, we propose a method for motion-compensated CBCT which allows to reconstruct the image on-the-fly, i.e. concurrent with acquisition. Before the CB acquisition, a model of the patient motion over the respiratory cycle is estimated from the planning 4D CT. The respiratory motion is then computed on-the-fly from this model using a respiratory signal extracted from the CB projections and incorporated into the motion-compensated CBCT reconstruction algorithm. The proposed method is evaluated on 26 CBCT scans of 3 patients acquired with two protocols used for static and respiration-correlated CBCT respectively. Our results show that this method provides CBCT images within a few seconds after the end of the acquisition where most of the motion artifacts have been removed.

MO‐E‐M100J‐04: Planning Target Volume Determination

Treatment planning should be based on a high fidelity representation of the patients anatomy, as it will be at time of treatment. The imaging setup (couch, immobilization, alignment) should therefore be representative for the treatment situation. When basing a treatment plan on a single scan (which is current clinical practice), the optimal imaging strategy is therefore to create an anatomical model that is as close as possible to the “average” anatomy. For random organ motion, plan optimization can therefore be achieved by averaging the geometry of multiple scans (i.e., adaptive radiotherapy). For periodic motion it has been proposed to use a scan with the organs as close as possible to their time‐weighted average position — by selecting a single scan out of 4DCT. Using these methods, the systematic errors due to treatment preparation are minimized and the dose distributions will be very close to a full 4D planning (using all scans out of a 4DCT scan). Using image guidance some other error sources can be eliminated. However, even with image guidance there are residual errors: such as uncertainty in GTV and CTV delineation, in image registration, and in setup correction and uncertainty due to intra‐fraction motion. Currently, simple margin recipes are used to estimate the correct CTV‐PTV margin such that the net effect of all these uncertainties does not compromise the goal of the treatment: eradicate the tumor while sparing normal tissues. One should be aware, however, that the most simple margin recipes are based on many assumptions: such as Gaussian distributions, penumbra width in water, systematic error SD > random error SD, and plans with a more or less uniform dose distribution. In case of image guidance for lungtumors all of these assumptions break down, and simple recipes typically will overestimate the required margin. Based on a more detailed analysis, it appears that the margin that can be used for hypofractionated lungradiotherapy can be very small (1 cm or less), even for large respiratory amplitudes (2 cm). Educational Objectives: 1. Understand the need for a representative planning CT, i.e., minimize systematic errors. 2. Understand the difference between systematic and random errors, and the effect that respiratory motion has on the delivery of radiotherapy. 3. Understand the need for margins, even in case of image‐guided.radiotherapy. 4. Understand the derivation of margins for image‐guidedradiotherapy of lungcancer.

SU‐DD‐A2‐04: A Simple Method to Reconstruct a Representative Mid‐Ventilation CT Scan From 4D Respiration Correlated CT Scans for Radiotherapy Treatment Planning of Lung Cancer Patients

Purpose: Four‐dimensional (4D) imaging techniques can be used to obtain (respiration) artifact‐free CTimages of the thorax. However, its use in radiotherapy is limited since clinical treatment planning systems are currently not able to use the full 4D data. The purpose of this study was to reconstruct a representative single 3D CT scan from the 4D data set (with tumor closest to the mean position) for use in radiotherapy planning of lungtumors to enable reduction of treatment error margins. Method and Materials: After acquisition of the 4D CT scan (10 frames), the tumor is manually segmented (roughly) in the first frame and automatically (gray‐value) registered to the tumor in the subsequent frames. This gives the motion of the tumor during the respiratory cycle in 3D. Subsequently, from the cranio‐caudal (CC) tumor motion curve, the mean tumor position and its corresponding mid‐ventilation (MV) time‐percentage are calculated. The CT scan for planning is reconstructed at this time‐percentage. As indication of the merit of this concept, its effect on margins from CTV to PTV and on the PTV volume was calculated covering respiratory motion, respiratory baseline variation and setup errors (systematic and random). Results: Based on 13 patients, the worst tumor position accuracy (with respect to the mean tumor position) in the mid‐ventilation CT scan occurred in the anterior‐posterior direction: −0.7±0.8 mm (due to hysteresis). For these patients, the errors in conventional free‐breathing CT were estimated to be 0±3.4 mm (CC) and 0±1.4 mm (AP). The mid‐ventilation concept resulted in margin reduction up to 45% and a PTV volume reduction up to 35%. Conclusion: The mid‐ventilation concept, based on tumor motion, is a simple method to obtain an artifact‐free CT scan with smaller systematic errors compared to conventional CT scans. Significant reduction of the PTV volume can be achieved.

WE‐E‐J‐6C‐05: Variability of Four Dimensional CT Patient Models

Purpose: Several studies address the use of 4D CT to increase the geometrical accuracy of radiotherapy for lung cancer patients. A single 4D CT scan, however, gives a ‘snapshot’ movie loop of the patients respiratory motion. In this study, we investigated the variability of respiratory motion over the course of radiotherapy using repeated 4D cone beam CT (4DCBCT) scans. Method and Materials: 4DCBCT scans of 10 lung cancer patients were acquired on the linac just prior to irradiation for 7–13 fractions. The diaphragm motion, extracted automatically from the projection images, was analyzed in terms of period and phase-histogram (relative time spend in [exhale, exhale-to-inhale, inhale and inhale-to-exhale]). Tumor motion was determined by registering a region of interest from the planning CT to each phase of the 4DCBCT. Results: The average breathing period ranged from 2.1 s to 5.6 s for different patients, with an inter- and intra-fraction variation of 0.8 s SD. The average phase histogram was [0.3 0.23 0.2 0.27] with little variation between fractions and patients, i.e., patients generally spend more time at exhale than inhale. The mean (over all patients) intra-fraction variation (SD) of the average tumor position relative to the bony anatomy was 1.6 mm LR, 2.7 mm SI and 2.3 mm AP, reflecting baseline breathing variation. The peak-to-peak tumor motion ranged from 0.4 to 2.0 cm, with an inter-fraction variability of 16% (1SD). Conclusion: Substantial variation in respiratory frequency, mean tumor position and peak-to-peak amplitude was found. These uncertainties can be taken into account by adapting the CTV-to-PTV margin accordingly. 4D-patient models should be regularly updated, e.g., by acquiring 4DCBCT prior to treatment, for safe implementation of precision 4D radiotherapy techniques like gating and tracking. Conflict of Interest: This research was sponsored by Elekta Oncology Systems Ltd.