V. R. Kini

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.

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.

On the use of EPID-based implanted marker tracking for 4D radiotherapy.

Four-dimensional (4D) radiotherapy delivery to dynamically moving tumors requires a real-time signal of the tumor position as a function of time so that the radiation beam can continuously track the tumor during the respiration cycle. The aim of this study was to develop and evaluate an electronic portal imaging device (EPID)-based marker-tracking system that can be used for real-time tumor targeting, or 4D radiotherapy. Three gold cylinders, 3 mm in length and 1 mm in diameter, were implanted in a dynamic lung phantom. The phantom range of motion was 4 cm with a 3-s breathing period. EPID image acquisition parameters were modified, allowing image acquisition in 0.1 s. Images of the stationary and moving phantom were acquired. Software was developed to segment automatically the marker positions from the EPID images. Images acquired in 0.1 s displayed higher noise and a lower signal-noise ratio than those obtained using regular (> 1 s) acquisition settings. However, the markers were still clearly visible on the 0.1-s images. The motion of the phantom blurred the images of the markers and further reduced the signal-noise ratio, though they could still be successfully segmented from the images in 10-30 ms of computation time. The positions of gold markers placed in the lung phantom were detected successfully, even for phantom velocities substantially higher than those observed for typical lung tumors. This study shows that using EPID-based marker tracking for 4D radiotherapy is feasible, however, changes in linear accelerator technology and EPID-based image acquisition as well as patient studies are required before this method can be implemented clinically.

Is the diaphragm motion probability density function normally distributed

During radiotherapy treatment planning, the margins given to the clinical target volume to form the planning target volume accounts for internal motion and set-up error. Most margin formulas assume that the underlying distributions are independent and normal. Clinical data suggests that the set-up error probability density function (pdf) can be considered to have an approximately normal distribution. However, there is evidence that internal motion does not have a normal distribution. Thus, in general, a convolution of the two pdfs should be performed to determine the total geometric error. The goals of this article were to (1) determine if the internal motion pdf due to respiration can be characterized using a normal distribution, and (2) if not, determine if the total geometric uncertainty for combining internal motion and set-up error can be characterized by a normal distribution. Sixty fluoroscopy diaphragm motion data sets were obtained using three breathing training types: free breathing, audio instruction, and visual feedback. Diaphragm motion was used as a surrogate for liver and lung cancer motion. The data were analyzed with normality tests in the following groups: (1) single motion measurements, (2) combined motion measurements for each patient, and (3) combined motion measurements for all patients. Following this analysis, the diaphragm motion pdfs were convolved with a set-up error pdf, and the standard deviation of the set-up error pdf at which the total geometric error pdf became normal was determined. At set-up error standard deviation values of at least 0.27 and 0.1 cm for free breathing, 0.57 and 0.42 cm for audio instruction, and 0.55 and 0 cm for visual feedback, for single motion measurements and combined motion measurements for each patient, respectively, total geometric error pdfs became approximately normal. When the motion measurements for all the patients were combined, diaphragm motion pdfs were approximately normal for all feedback types. Therefore, for treatment planning purposes in the absence of individual patient measurements, the diaphragm motion pdf can be considered an approximately normal distribution. However, care should be taken when determining a margin based on individual patients measurements as the total geometric error will, in general, not be normally distributed.