Synchronized moving aperture radiation therapy (SMART) has been proposed to account for tumor motions during radiotherapy in prior work. The basic idea of SMART is to synchronize the moving radiation beam aperture formed by a dynamic multileaf collimator (DMLC) with the tumor motion induced by respiration. In this paper, a two-dimensional (2D) superimposing leaf sequencing method is presented for SMART. A leaf sequence optimization strategy was generated to assure the SMART delivery under realistic delivery conditions. The study of delivery performance using the Varian LINAC and the Millennium DMLC showed that clinical factors such as collimator angle, dose rate, initial phase and machine tolerance affect the delivery accuracy and efficiency. An in-house leaf sequencing software was developed to implement the 2D superimposing leaf sequencing method and optimize the motion-corrected leaf sequence under realistic clinical conditions. The analysis of dynamic log (Dynalog) files showed that optimization of the leaf sequence for various clinical factors can avoid beam hold-offs which break the synchronization of SMART and fail the SMART dose delivery. Through comparison between the simulated delivered fluence map and the planed fluence map...
Respiration-induced tumor motion during intensity modulated radiotherapy (IMRT) of non-small cell lung cancer (NSCLC) could cause substantial differences between planned and delivered doses. However, it has been shown that for conventionally fractionated IMRT motion effects average out over the course of many treatments, but this might not be true for hypofractionated IMRT (IMHFRT). Numerical simulations were performed for 9 NSCLC patients (11 tumors) to evaluate this problem. Dose distributions to the Clinical Target Volume (CTV) and Internal Target Volume (ITV) were retrospectively calculated using the previously calculated leaf motion files but with the addition of typical periodic motion (i.e., amplitude 0.36–1.26 cm, 3–8 sec period). A typical IMHFRT prescription of 20 Gy×3 fractions was assumed. For the largest amplitude (1.26 cm), the average±standard deviation of the ratio of simulated to planned mean dose, minimum dose, D95 and V95 were 0.98±0.01, 0.88±0.09, 0.94±0.05 and 0.94±0.07 for the CTV, and 0.99±0.01, 0.99±0.03, 0.98±0.02 and 1.00±0.01 for the ITV. There was minimal dependence on period or initial phase. For typical tumor geometries and respiratory amplitudes changes in target coverage are minimal but can be significant for larger amplitudes...
In this study, four dimensional computed tomography (4DCT) scanning was performed during free breathing on a 16-slice Positron emission tomography PET /computed tomography (CT) for abdomen and thoracic patients. Images were sorted into 10 phases based on the temporal correlation between surface motion and data acquisition with an Advantage Workstation. Gross tumor volume gross tumor volume (GTV) s were manually contoured on all 10 phases of the 4DCT scan. GTVs in the multiple CT phases were called GTV4D. GTV4D plus an isotropic margin of 1.0 cm was called CTV4D. Two sets of planning target volume (PTV) 4D (PTV4D) were derived from the CTV4D, i.e. PTV4D2cm = CTV4D plus 1 cm setup margin (SM) and 1 cm internal margin (IM) and PTV4D1.5cm = CTV4D plus 1 cm SM and 0.5cm IM. PTV3D was derived from a CTV3D of the helical CT scan plus conventional margins of 2 cm. PTVgated was generated only selecting three CT phases, with a total margin of 1.5 cm. All four volumes were compared. To quantify the extent of the motion, we selected the two phases where the tumor exhibited the greatest range of motion. We also studied the effect of different PTV volumes on dose to the surrounding critical structures. Volume of CTV4D was greater than that of CTV3D. We found...
Purpose: As a counterpart of 4DCT in the treatment planning stage of radiotherapy treatment, 4D cone beam computed tomography (4DCBCT) method has been proposed to verify tumor motion trajectories before radiation therapy treatment delivery. Besides 4DCBCT acquisition using slower gantry rotation speed or multiple rotations, a new method using the prior image constrained compressed sensing (PICCS) image reconstruction method and the standard 1-min data acquisition were proposed. In this paper, the PICCS-4DCBCT method was combined with deformable registration to validate its capability in motion trajectory extraction using physical phantom data, simulated human subject data from 4DCT and in vivo human subject data.
In radiation therapy many motion management and alignment techniques rely on the accuracy of an internal fiducial acting as a surrogate for target motion within the lung. Although fiducials are routinely used as surrogates for tumor motion, the extent to which varying spatial locations in the lung move similarly to other locations has yet to be quantitatively analyzed. In an attempt to analyze the motion correlation throughout the lung, ten primary lung cancer patients underwent IRB-approved 4DCT scans in the supine position. Deformable registration produced motion vectors for each voxel between exhalation and inhalation. Modeling was performed for each vector and all surrounding vectors within the lung in order to determine the mean 3D Euclidean distance necessary for an implanted fiducial to correlate with surrounding tissue motion to within 3 mm (left lower: 1.7 cm, left upper: 2.1 cm, right lower 1.6 cm, and right upper 2.9 cm). No general implantation rule of where to position a fiducial with respect to the tumor was found as the motion is highly patient and lobe specific. Correlation maps are presented showcasing spatial anisotropy of the motion of tissue surrounding the tumor.
The purpose of this study was to validate the dose prescription defined to the gross tumor volume (GTV) 3D and 4D dose distributions of stereotactic radiotherapy for lung cancer. Treatment plans for 94 patients were generated based on computed tomography (CT) under free breathing. A uniform margin of 8 mm was added to the internal target volume (ITV) to generate the planning target volume (PTV). A leaf margin of 2 mm was added to the PTV. The prescription dose was defined such that 99% of the GTV should receive 100% of the dose using the Monte Carlo calculation (iPlan RT DoseTM) for 6-MV photon beams. The 3D dose distribution was determined using CT under free breathing. The 4D dose distribution plan was recalculated to investigate the effect of tumor motion using the same monitor units as those used for the 3D dose distribution plan. D99 (99% of the GTV) in the 4D plan was defined as the average D99 in each of the four breathing phases (0%, 25%, 50% and 75%). The dose difference between maximum and minimum at D99 of the GTV in 4D calculations was 0.6 ± 1.0% (range 0.2–4.6%). The average D99 of the GTV from 4D calculations in most patients was almost 100% (99.8 ± 1.0%). No significant difference was found in dose to the GTV between 3D and 4D dose calculations (P = 0.67). This study supports the clinical acceptability of treatment planning based on the dose prescription defined to the GTV.
Digital phantoms continue to play a significant role in modeling and characterizing medical imaging. The currently available XCAT phantom incorporates both the flexibility of mathematical phantoms and the realistic nature of voxelized phantoms. This phantom generates images based on a regular breathing pattern and can include arbitrary lung tumor trajectories. In this work, we present an algorithm that modifies the current XCAT phantom to generate 4D imaging data based on irregular breathing. First, a parameter is added to the existing XCAT phantom to include any arbitrary tumor motion. This modification introduces the desired tumor motion but, comes at the cost of decoupled diaphragm, chest wall and lung motion. To remedy this problem diaphragm and chest wall motion is first modified based on initial tumor location and then input to the XCAT phantom. This generates a phantom with synchronized respiratory motion. Mapping of tumor motion trajectories to diaphragm and chest wall motion is done by adaptively calculating a scale factor based on tumor to lung contour distance. The distance is calculated by projecting the initial tumor location to lung edge contours characterized by quadratic polynomials. Data from 10 patients were used to evaluate the accuracy between actual independent tumor location and the location obtained from the modified XCAT phantom. The rmse and standard deviations for 10 patients in x...
Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc.) provide a noninvasive, non-ionizing, low-cost, and practical approach to obtain respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions...
To achieve a better therapeutic effect and suppress side effects for lung cancer treatments, latency involved in current radiotherapy devices is aimed to be compensated for improving accuracy of continuous (not gating) irradiation to a respiratory moving tumor. A novel prediction method of lung tumor motion is developed for compensating the latency. An essential core of the method is to extract information valuable for the prediction, that is, the periodic nature inherent in respiratory motion. A seasonal autoregressive model useful to represent periodic motion has been extended to take into account the fluctuation of periodic nature in respiratory motion. The extended model estimates the fluctuation by using a correlation-based analysis for
adaptation. The prediction performance of the proposed method was evaluated by using data sets of actual tumor motion and compared with those of the state-of-the-art methods. The proposed method demonstrated a high performance within submillimeter accuracy. That is, the average error of 1.0 s ahead predictions was 0.931 ± 0.055 mm. The accuracy achieved by the proposed method was the best among those by the others. The results suggest that the method can compensate the latency with sufficient accuracy for clinical use and contribute to improve the irradiation accuracy to the moving tumor.
Cycle-to-cycle variations in respiratory motion can cause significant geometric and dosimetric errors in the administration of lung cancer radiation therapy. A common limitation of the current strategies for motion management is that they assume a constant, reproducible respiratory cycle. In this work, we investigate the feasibility of using rapid MRI for providing long-term imaging of the thorax in order to better capture cycle-to-cycle variations. Two nonsmall-cell lung cancer patients were imaged (free-breathing, no extrinsic contrast, and 1.5 T scanner). A balanced steady-state-free-precession (b-SSFP) sequence was used to acquire cine-2D and cine-3D (4D) images. In the case of Patient 1 (right midlobe lesion, ~40 mm diameter), tumor motion was well correlated with diaphragmatic motion. In the case of Patient 2, (left upper-lobe lesion, ~60 mm diameter), tumor motion was poorly correlated with diaphragmatic motion. Furthermore, the motion of the tumor centroid was poorly correlated with the motion of individual points on the tumor boundary, indicating significant rotation and/or deformation. These studies indicate that image quality and acquisition speed of cine-2D MRI were adequate for motion monitoring. However, significant improvements are required to achieve comparable speeds for truly 4D MRI. Despite several challenges...
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon...
We have designed a simulation framework for motion studies in radiation therapy by integrating the anthropomorphic NCAT phantom into a 4D Monte Carlo dose calculation engine based on DPM. Representing an artifact-free environment, the system can be used to identify class solutions as a function of geometric and dosimetric parameters. A pilot dynamic conformal study for three lesions (~ 2.0 cm) in the right lung was performed (70 Gy prescription dose). Tumor motion changed as a function of tumor location, according to the anthropomorphic deformable motion model. Conformal plans were simulated with 0 to 2 cm margin for the aperture, with additional 0.5 cm for beam penumbra. The dosimetric effects of intensity modulated radiotherapy (IMRT) vs. conformal treatments were compared in a static case. Results show that the Monte Carlo simulation framework can model tumor tracking in deformable anatomy with high accuracy, providing absolute doses for IMRT and conformal radiation therapy. A target underdosage of up to 3.67 Gy (lower lung) was highlighted in the composite dose distribution mapped at exhale. Such effects depend on tumor location and treatment margin and are affected by lung deformation and ribcage motion. In summary, the complexity in the irradiation of moving targets has been reduced to a controlled simulation environment...
A spring-dashpot system based on the Voigt model was developed to model the
correlation between abdominal respiratory motion and tumor motion during lung
radiotherapy. The model was applied to clinical data comprising 52 treatment
beams from 10 patients, treated on the Mitsubishi Real-Time Radiation Therapy
system, Sapporo, Japan. In Stage 1, model parameters were optimized for
individual patients and beams to determine reference values and to investigate
how well the model can describe the data. In Stage 2, for each patient the
optimal parameters determined for a single beam were applied to data from other
beams to investigate whether a beam-specific set of model parameters is
sufficient to model tumor motion over a course of treatment.
In Stage 1 the baseline root mean square (RMS) residual error for all
individually-optimized beam data was 0.90 plus or minus 0.40 mm. In Stage 2,
patient-specific model parameters based on a single beam were found to model
the tumor position closely, even for irregular beam data, with a mean increase
with respect to Stage 1 values in RMS error of 0.37 mm. On average the obtained
model output for the tumor position was 95% of the time within an absolute
bound of 2.0 mm and 2.6 mm in Stage 1 and 2...
Using fiducial markers on patient's body surface to predict the tumor
location is a widely used approach in lung cancer radiotherapy. The purpose of
this work is to propose an algorithm that automatically identifies a sparse set
of locations on the patient's surface with the optimal prediction power for the
tumor motion. The sparse selection of markers on the external surface and the
assumed linear relationship between the marker motion and the internal tumor
motion are represented by a prediction matrix. Such a matrix is determined by
solving an optimization problem, where the objective function contains a
sparsity term that penalizes the number of markers chosen on the patient's
surface. The performance of our algorithm has been tested on realistic clinical
data of four lung cancer patients. Thoracic 4DCT scans with 10 phases are used
for the study. On a reference phase, a grid of points are casted on the
patient's surface (except for patient's back) and propagated to other phases
via deformable image registration of the corresponding CT images. Tumor
locations at each phase are also manually delineated. We use 9 out of 10 phases
of the 4DCT images to identify a small group of surface markers that are most
correlated with the motion of the tumor...
Lung tumor motion due to respiration poses a challenge in the application of
modern three-dimensional conformal radiotherapy. Direct tracking of the lung
tumor during radiation therapy is very difficult without implanted fiducial
markers. Indirect tracking relies on the correlation of the tumor's motion and
the surrogate's motion. The present paper presents an analysis of the
correlation between the tumor motion and the diaphragm motion in order to
evaluate the potential use of diaphragm as a surrogate for tumor motion. We
have analyzed the correlation between diaphragm motion and superior-inferior
lung tumor motion in 32 fluoroscopic image sequences from 10 lung cancer
patients. A simple linear model and a more complex linear model that accounts
for phase delays between the two motions have been used. Results show that the
diaphragm is a good surrogate for tumor motion prediction for most patients,
resulting in an average correlation factor of 0.94 and 0.98 with each model
respectively. The model that accounts for delays leads to an average
localization prediction error of 0.8mm and an error at the 95% confidence level
of 2.1mm. However, for one patient studied, the correlation is much weaker
compared to other patients. This indicates that...
To date, image localization of mobile tumors prior to radiation delivery has primarily been confined to 2D and 3D technologies, such as fluoroscopy and 3D cone-beam CT (3D-CBCT). Due to the limited information from these images, larger volumes of healthy tissue are often irradiated in order to ensure the radiation field encompasses the entirety of the target motion. Since the overarching goal of radiation therapy is to deliver maximum dose to cancerous cells and simultaneously minimize the radiation delivered to healthy surrounding tissues, it would be ideal to use 4D imaging to obtain time-resolved volume images of the tumor motion during respiration.
4D-CBCT imaging has been previously investigated, but has not yet seen large clinical translation due to the obstacles of long acquisition time and large image radiation dose. Furthermore, 4D-CBCT currently requires the use of external surrogates to correlate the patient's respiration with the image acquisition process. This correlation has been under question by a multitude of studies demonstrating the uncertainties that exist between the surrogate and the actual motion of the internal anatomy. Errors in the correlation process may result in image artifacts, which could potentially lead to reconstructions with inaccurate target volumes...
Imaging respiratory induced tumor motion in the radiation therapy treatment room could eliminate the necessity for large motion encompassing margins that result in excessive irradiation of healthy tissues. Currently available image guidance technologies are ill-suited for this task. Two-dimensional fluoroscopic images are acquired with sufficient speed to image respiratory motion. However, volume information is not present, and soft tissue structures are often not visible because a large volume is projected onto a single plane. Currently available volumetric imaging modalities are not acquired with sufficient speed to capture full motion trajectory information. Four-dimensional cone-beam computed tomography (4D CBCT) using a gantry mounted 2D flat panel imaging device has been proposed but has been limited by high doses, long scan times and severe under-sampling artifacts. The focus of the work completed in this thesis was to find ways to improve 4D imaging using a gantry mounted 2D kV imaging system. Specifically, the goals were to investigate methods for minimizing imaging dose and scan time while achieving consistent, controllable, high quality 4D images.
First, we introduced four-dimensional digital tomosynthesis (4D DTS) and characterized its potential for 3D motion analysis using a motion phantom. The motion phantom was programmed to exhibit motion profiles with various known amplitudes in all three dimensions and scanned using a 2D kV imaging system mounted on a linear accelerator. Two arcs of projection data centered about the anterior-posterior and lateral axes were used to reconstruct phase resolved DTS coronal and sagittal images. Respiratory signals were obtained by analyzing projection data...
Purpose: Ensuring that tumor motion is within the radiation field for high-dose and high-precision radiosurgery in areas greatly influenced by respiratory motion. Therefore tracking the target or gating the radiation beam by using real-time imaging and surrogate motion monitoring methods are employed. However, these methods cannot be used to depict the effect of respiratory motion on tumor deviation. Therefore, an investigation of parameters for method predicting the tumor motion induced by respiratory motion multiple steps ahead of real time is performed. Currently, algorithms exist to make predictions about future real-time events, however these methods are tedious or unable to predict far enough in advance.
Methods and Materials: The algorithm takes data collected from the Varian RPM$ System, which is a one-dimensional (1D) surrogate signal of amplitude versus time. After the 1D surrogate signal is obtained, the algorithm determines on average what an approximate respiratory cycle is over the entire signal using a rising edge function. The signal is further dividing it into three components: (a) training component is the core portion of the data set which is further divided into subcomponents of length equal to the input component; (b) input component serves as the parameter searched for throughout the training component...
Probabilistic planning is an evolving approach for tumor motion management in which reproducibility of probability distribution function (PDF) of tumor motion is critical yet unclear. Aim of the first study is to evaluate the reproducibility of tumor motion PDF in stereotactic body radiation therapy (SBRT) using cine megavoltage (MV) images. External surrogate is used clinically in 4DCT imaging and radiation treatments for respiratory motion monitoring. However, studies have shown questionable correlation between external surrogate motion and internal tumor motion. Thus, Aim of the second study is to evaluate the correlation of external surrogate motion and internal tumor motion from a statistical point of view.
20 lung cancer patients who underwent SBRT treatment using 3D conformal technique were included in our study. During simulation, 4DCT scan assisted with RPM system was done. Cine MV images acquired during treatments were collected to extract tumor motion trajectories. For each patient, tumor motion PDFn was generated using 3 "usable" beams for each fraction. Patients without at least 3 "usable" beams were excluded. PDFn reproducibility (Rn) was calculated using the Dice Coefficient between PDFn to a "ground-truth" PDF (PDFg). The mean of Rn (Rm) was calculated for each patient and correlated to mean tumor motion rang (Am). Change of Rm during the course of SBRT treatments was also evaluated.
Thirteen patients were kept for further analysis. The tumor motion PDF during the treatments can be determined using cine MV images. The reproducibility of lung tumor motion PDF decreased exponentially as the tumor motion range increased and also decreased slightly throughout the course of treatments.