S. S. Vedam

Acquiring a four-dimensional computed tomography dataset using an external respiratory signal

Four-dimensional (4D) methods strive to achieve highly conformal radiotherapy, particularly for lung and breast tumours, in the presence of respiratory-induced motion of tumours and normal tissues. Four-dimensional radiotherapy accounts for respiratory motion during imaging, planning and radiation delivery, and requires a 4D CT image in which the internal anatomy motion as a function of the respiratory cycle can be quantified. The aims of our research were (a) to develop a method to acquire 4D CT images from a spiral CT scan using an external respiratory signal and (b) to examine the potential utility of 4D CT imaging. A commercially available respiratory motion monitoring system provided an external tracking signal of the patients breathing. Simultaneous recording of a TTL X-Ray ON signal from the CT scanner indicated the start time of CT image acquisition, thus facilitating time stamping of all subsequent images. An over-sampled spiral CT scan was acquired using a pitch of 0.5 and scanner rotation time of 1.5 s. Each image from such a scan was sorted into an image bin that corresponded with the phase of the respiratory cycle in which the image was acquired. The complete set of such image bins accumulated over a respiratory cycle constitutes a 4D CT dataset. Four-dimensional CT datasets of a mechanical oscillator phantom and a patient undergoing lung radiotherapy were acquired. Motion artefacts were significantly reduced in the images in the 4D CT dataset compared to the three-dimensional (3D) images, for which respiratory motion was not accounted. Accounting for respiratory motion using 4D CT imaging is feasible and yields images with less distortion than 3D images. 4D images also contain respiratory motion information not available in a 3D CT image.

Motion adaptive x-ray therapy: a feasibility study.

Intrafraction motion caused by breathing requires increased treatment margins for chest and abdominal radiotherapy and may lead to motion artefacts in dose distributions during intensity modulated radiotherapy (IMRT). Technologies such as gated radiotherapy may significantly increase the treatment time, while breath-hold techniques may be poorly tolerated by pulmonarily compromised patients. A solution that allows reduced margins and dose distribution artefacts, without compromising delivery time, is to synchronously follow the target motion by adapting the x-ray beam using a dynamic multileaf collimator (MLC), i.e. motion adaptive x-ray therapy, or MAX-T for short. Though the target is moving with time, in the MAX-T beam view the target is static. The MAX-T method superimposes the target motion due to respiration onto the beam originally planned for delivery. Thus during beam delivery the beam is dynamically changing position with respect to the isocentre using a dynamic MLC, the leaf positions of which are dependent upon the target position. Synchronization of the MLC motion and target motion occurs using respiration gated radiotherapy equipment. The concept and feasibility of MAX-T and the capability of the treatment machine to deliver such a treatment were investigated by performing measurements for uniform and IMRT fields using a mechanical sinusoidal oscillator to simulate target motion. Target dose measurements obtained using MAX-T for a moving target were found to be equivalent to those delivered to a static target by a static beam.

Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker

The aim of this work was to quantify the ability to predict intrafraction diaphragm motion from an external respiration signal during a course of radiotherapy. The data obtained included diaphragm motion traces from 63 fluoroscopic lung procedures for 5 patients, acquired simultaneously with respiratory motion signals (an infrared camera-based system was used to track abdominal wall motion). During these sessions, the patients were asked to breathe either (i) without instruction, (ii) with audio prompting, or (iii) using visual feedback. A statistical general linear model was formulated to describe the relationship between the respiration signal and diaphragm motion over all sessions and for all breathing training types. The model parameters derived from the first session for each patient were then used to predict the diaphragm motion for subsequent sessions based on the respiration signal. Quantification of the difference between the predicted and actual motion during each session determined our ability to predict diaphragm motion during a course of radiotherapy. This measure of diaphragm motion was also used to estimate clinical target volume (CTV) to planning target volume (PTV) margins for conventional, gated, and proposed four-dimensional (4D) radiotherapy. Results from statistical analysis indicated a strong linear relationship between the respiration signal and diaphragm motion (p<0.001) over all sessions, irrespective of session number (p=0.98) and breathing training type (p=0.19). Using model parameters obtained from the first session, diaphragm motion was predicted in subsequent sessions to within 0.1 cm (1 sigma) for gated and 4D radiotherapy. Assuming a 0.4 cm setup error, superior-inferior CTV-PTV margins of 1.1 cm for conventional radiotherapy could be reduced to 0.8 cm for gated and 4D radiotherapy. The diaphragm motion is strongly correlated with the respiration signal obtained from the abdominal wall. This correlation can be used to predict diaphragm motion, based on the respiration signal, to within 0.1 cm (1 sigma) over a course of radiotherapy.

Determining parameters for respiration-gated radiotherapy

Respiration-gated radiotherapy for tumor sites affected by respiratory motion will potentially improve radiotherapy outcomes by allowing reduced treatment margins leading to decreased complication rates and/or increased tumor control. Furthermore, for intensity-modulated radiotherapy (IMRT), respiratory gating will minimize the hot and cold spot artifacts in dose distributions that may occur as a result of interplay between respiratory motion and leaf motion. Most implementations of respiration gating rely on the real time knowledge of the relative position of the internal anatomy being treated with respect to that of an external marker. A method to determine the amplitude of motion and account for any difference in phase between the internal tumor motion and external marker motion has been developed. Treating patients using gating requires several clinical decisions, such as whether to gate during inhale or exhale, whether to use phase or amplitude tracking of the respiratory signal, and by how much the intrafraction tumor motion can be decreased at the cost of increased delivery time. These parameters may change from patient to patient. A method has been developed to provide the data necessary to make decisions as to the CTV to PTV margins to apply to a gated treatment plan.

Patient training in respiratory-gated radiotherapy

Respiratory gating is used to counter the effects of organ motion during radiotherapy for chest tumors. The effects of variations in patient breathing patterns during a single treatment and from day to day are unknown. We evaluated the feasibility of using patient training tools and their effect on the breathing cycle regularity and reproducibility during respiratory-gated radiotherapy. To monitor respiratory patterns, we used a component of a commercially available respiratory-gated radiotherapy system (Real Time Position Management (RPM) System, Varian Oncology Systems, Palo Alto, CA 94304). This passive marker video tracking system consists of reflective markers placed on the patients chest or abdomen, which are detected by a wall-mounted video camera. Software installed on a PC interfaced to this camera detects the marker motion digitally and records it. The marker position as a function of time serves as the motion signal that may be used to trigger imaging or treatment. The training tools used were audio prompting and visual feedback, with free breathing as a control. The audio prompting method used instructions to breathe in or breathe out at periodic intervals deduced from patients own breathing patterns. In the visual feedback method, patients were shown a real-time trace of their abdominal wall motion due to breathing. Using this, they were asked to maintain a constant amplitude of motion. Motion traces of the abdominal wall were recorded for each patient for various maneuvers. Free breathing showed a variable amplitude and frequency. Audio prompting resulted in a reproducible frequency; however, the variability and the magnitude of amplitude increased. Visual feedback gave a better control over the amplitude but showed minor variations in frequency. We concluded that training improves the reproducibility of amplitude and frequency of patient breathing cycles. This may increase the accuracy of respiratory-gated radiation therapy.

Predicting respiratory motion for four-dimensional radiotherapy

Adapting radiation delivery to respiratory motion is made possible through corrective action based on real-time feedback of target position during respiration. The advantage of this approach lies with its ability to allow tighter margins around the target while simultaneously following its motion. A significant hurdle to the successful implementation of real-time target-tracking-based radiation delivery is the existence of a finite time delay between the acquisition of target position and the mechanical response of the system to the change in position. Target motion during the time delay leads to a resultant lag in the systems response to a change in tumor position. Predicting target position in advance is one approach to ensure accurate delivery. The aim of this manuscript is to estimate the predictive ability of sinusoidal and adaptive filter-based prediction algorithms on multiple sessions of patient respiratory patterns. Respiratory motion information was obtained from recordings of diaphragm motion for five patients over 60 sessions. A prediction algorithm that employed both prediction models-the sinusoidal model and the adaptive filter model-was developed to estimate prediction accuracy over all the sessions. For each session, prediction error was computed for several time instants (response time) in the future (0-1.8 seconds at 0.2-second intervals), based on position data collected over several signal-history lengths (1-7 seconds at 1-second intervals). Based on patient data included in this study, the following observations are made. Qualitative comparison of predicted and actual position indicated a progressive increase in prediction error with an increase in response time. A signal-history length of 5 seconds was found to be the optimal signal history length for prediction using the sinusoidal model for all breathing training modalities. In terms of overall error in predicting respiratory motion, the adaptive filter model performed better than the sinusoidal model. With the adaptive filter, average prediction errors of less than 0.2 cm (1sigma) are possible for response times less than 0.4 seconds. In comparing prediction error with system latency error (no prediction), the adaptive filter model exhibited lesser prediction errors as compared to the sinusoidal model, especially for longer response time values (>0.4 seconds). At smaller response time values (<0.4 seconds), improvements in prediction error reduction are required for both predictive models in order to maximize gains in position accuracy due to prediction. Respiratory motion patterns are inherently complex in nature. While linear prediction-based prediction models perform satisfactorily for shorter response times, their prediction accuracy significantly deteriorates for longer response times. Successful implementation of real-time target-tracking-based radiotherapy requires response times less than 0.4 seconds or improved prediction algorithms.

Quantifying the effect of intrafraction motion during breast IMRT planning and dose delivery

Respiratory motion during intensity modulated radiation therapy (IMRT) causes two types of problems. First, the clinical target volume (CTV) to planning target volume (PTV) margin needed to account for respiratory motion means that the lung and heart dose is higher than would occur in the absence of such motion. Second, because respiratory motion is not synchronized with multileaf collimator (MLC) motion, the delivered dose is not the same as the planned dose. The aims of this work were to evaluate these problems to determine (a) the effects of respiratory motion and setup error during breast IMRT treatment planning, (b) the effects of the interplay between respiratory motion and multileaf collimator (MLC) motion during breast IMRT delivery, and (c) the potential benefits of breast IMRT using breath-hold, respiratory gated, and 4D techniques. Seven early stage breast cancer patient data sets were planned for IMRT delivered with a dynamic MLC (DMLC). For each patient case, eight IMRT plans with varying respiratory motion magnitudes and setup errors (and hence CTV to PTV margins) were created. The effects of respiratory motion and setup error on the treatment plan were determined by comparing the eight dose distributions. For each fraction of these plans, the effect of the interplay between respiratory motion and MLC motion during IMRT delivery was simulated by superimposing the respiratory trace on the planned DMLC leaf motion, facilitating comparisons between the planned and expected dose distributions. When considering respiratory motion in the CTV-PTV expansion during breast IMRT planning, our results show that PTV dose heterogeneity increases with respiratory motion. Lung and heart doses also increase with respiratory motion. Due to the interplay between respiratory motion and MLC motion during IMRT delivery, the planned and expected dose distributions differ. This difference increases with respiratory motion. The expected dose varies from fraction to fraction. However, for the seven patients studied and respiratory trace used, for no breathing, shallow breathing, and normal breathing, there were no statistically significant differences between the planned and expected dose distributions. Thus, for breast IMRT, intrafraction motion degrades treatment plans predominantly by the necessary addition of a larger CTV to PTV margin than would be required in the absence of such motion. This motion can be limited by breath-hold, respiratory gated, or 4D techniques.

Potential radiotherapy improvements with respiratory gating.

Gating is a relatively new and potentially useful therapeutic addition to external beam radiotherapy applied to regions affected by intra-fraction motion. The impact was of gating on treatment margins, image artifacts, and volume and positional accuracy was investigated by CT imaging of sinusoidally moving spheres. The motion of the spheres simulates target motion. During the CT imaging of dynamically moving spheres, gating reproduced the static volume to within 1%, whereas errors of over 20% were observed where gating was not used. Using a theoretical analysis of margins, gating alone or in combination with an electronic portal imaging device may allow a 2–11 mm reduction in the CTV to PTV margin, and thus less healthy tissue need be irradiated. Gating may allow a reduction of treatment margins, an improvement in image quality, and an improvement in positional and volumetric accuracy of the gross tumor volume.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery.

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6∕18MV), treatment delivery (3D∕4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

Respiratory regularity gated 4D CT acquisition: concepts and proof of principle.

Four-dimensional CT images are generally sorted through a post-acquisition procedure correlating images with a time-synchronized external respiration signal. The patient’s ability to maintain reproducible respiration is the limiting factor during 4D CT, where artifacts occur in approximately 85% of scans with current technology. To reduce these artifacts and their subsequent effects during radiotherapy planning, a method for improved 4D CT image acquisition that relies on gating 4D CT acquisition based on the real time monitoring of the respiration signal has been proposed. The respiration signal and CT data acquisition are linked, such that data from irregular breathing cycles, which cause artifacts, are not acquired by gating CT acquisition by the respiratory signal. A proof-of-principle application of the respiratory regularity gated 4D CT method using patient respiratory signals demonstrates the potential of this method to reduce artifacts currently found in 4D CT scans. Numerical simulations indicate a potential reduction in motion within a respiratory phase bin by 20–40% depending on tolerances chosen. Additional advantages of the proposed method are dose reduction by eliminating unnecessary oversampling and obviating the need for post-processing to create the 4D CT data set.