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Purpose: To design a methodology based on numerical phantom for reconstruction of dose delivered to moving lung tumors., Methods: MatlabTM 7.6 was used to generate a 4D numerical lung phantom (NLP). Customer parameter files were used as input to this NLP, which consists of multiple ellipsoids representing body, lung, cord and tumor. In this study, we studied the impact of varying breathing pattern on a left lower lobe tumor, where the tumor motion was simulated on the daily breathing pattern of the patient acquired using real time positioning management (RPMTM) system from Varian Medical Systems. Based on the daily breathing pattern, the original RPM signal and the original tumor trajectory, 5 sets of motion trajectories were simulated. This was then used to build 10 different phases of the numerical phantom. Average Intensity Projection (AIP) was then generated from the different phases. The actual delivered dose on the 5 AIP sets were compared to the intended dose on the original planning AIP image set., Results: The mean target coverage (TC) recomputed on the 5 AIP sets was approximately 18% lower than the TC for the planning AIP image set. The mean homogeneity index (HI) recomputed on the 5 sets, was approximately 5 times higher than HI for the planning AIP image set. The lung NTDmean dose was approximately 9.5 Gy3 and did not differ much., Conclusions: The presented numerical simulation framework may assist in monitoring the changes in dose accumulation due to changes in the patient's breathing on a daily basis. This can also be used for validation of new motion tracking algorithms and its impact of dose coverage., (© 2012 American Association of Physicists in Medicine.)

Purpose: To demonstrate that Recurrence quantification analysis (RQA) can be used as a quantitative decision making tool to classify patients breathing pattern and select treatment strategy for maneuvering the tumor motion : (a) MIP based treatment (b) 4D treatment using non-linear prediction only (c) 4D treatment non-linear control prediction and breathing control., Method and Materials: In our previous work we established that breathing patterns can be described as a 5-6 dimensional nonlinear, stationary and deterministic system that exhibits sensitive dependence on initial conditions. Recurrence plots enable one to investigate an m-dimensional state space trajectory through a two-dimensional representation of its recurrences where the value of a specific pixel is 1 if the distance between the two corresponding trajectory points is less than a threshold value ε. Important measures calculated are: Recurrence Rate (RR), %Determinism, Divergence, Shannon Entropy, LMean, and Renyi entropy (K2). Time Resolved RQA: By implementing a sliding window design, each of the above calculated parameters is computed multiple times. Alignment of those parameters with the time series reveals details not obvious in the 1 -dimensional time series data. The breathing pattern for seven randomly chosen volunteers were recorded using the RPM system for 15 minutes. Non-linear prediction was performed and the normalized root mean square error (NRMSE) was calculated for each of the volunteer data., Results: The threshold value ε was chosen such that the Recurrence Rate was equal to 1%. There is a strong correlation of NRMSE with Entropy, Determinism and LMean. Time resolved RR locates strong Unstable Periodic Orbits(UPOs), i.e. patterns of uninterrupted equally spaced diagonal lines., Conclusions: RQAs could prove to be a very powerful tool for design of personalized treatment regimen. Entropy, Determinism in conjunction with strong UPOs can be used to determine if patients are suitable candidates for prediction and chaos control., (© 2012 American Association of Physicists in Medicine.)

Purpose: Prediction methods for breathing patterns, which are crucial to deal with system latency in treatments of moving lung tumors using state-space methodologies based on non-linear dynamics are contrasted to linear predictive methods., Method and Materials: In our previous work we established that breathing patterns can be described as a 5-6 dimensional nonlinear, stationary and deterministic system that exhibits sensitive dependence on initial conditions. In this work, nonlinear prediction methods are used to predict the short-term evolution of the respiratory system for 3 patients. Single step and N-point multi step prediction are performed for sampling rates of 5Hz, 10Hz, and 30Hz. We compare the employed nonlinear prediction methods with respect to prediction accuracy to Infinite Impulse Response (IIR) prediction filters. The simplest form of local prediction is finding similar segments of scalar time series data in a higher dimensional embedding space. Hence, we predict the future value x(t)of N-time steps ahead by simply finding the average of nearest neighbor points to the point x(t) in the past and using them to estimate x(t+N), yielding a local average model (LAM). Local linear models (LLM) which are linear autoregressive models that hold only for a region around the target point formed by the nearest neighbor points is combined with a set of linear regularization techniques to solve ill-posed regression problems are also implemented., Results: For all sampling frequencies, both single step and N-point multi step prediction results obtained using LAM and LLM with regularization methods are better than IIR prediction filters for the selected sample patients., Conclusions: The use of non-linear prediction methods for predicting the breathing pattern of lung cancer patients may lead to improved, robust and accurate long-term prediction to account for system latencies., (© 2012 American Association of Physicists in Medicine.)

Purpose: Helical tomotherapy is a complex delivery technique, integrating CT image guidance and intensity modulated radiotherapy in a single system. The integration of the CT detector ring on the gantry not only allows patient position verification but is also often used to perform various QA procedures. This convenience lacks the rigor of a machine-independent QA process., Methods: In this article, a Si strip detector, known as the Dose Magnifying Glass (DMG), was used to perform machine-independent QA measurements of the multileaf collimator alignment, leaf open time threshold, and leaf fluence output factor (LFOF)., Results: The DMG measurements showed good agreements with EDR2 film for the MLC alignment test while the CT detector agrees well with DMG measurements for leaf open time threshold and LFOF measurements. The leaf open time threshold was found to be approximately 20 ms. The LFOF measured with the DMG agreed within error with the CT detector measured LFOF., Conclusions: The DMG with its 0.2 mm spatial resolution coupled to TERA ASIC allowed real-time high temporal resolution measurements of the tomotherapy leaf movement. In conclusion, DMG was shown to be a suitable tool for machine-independent QA of a tomotherapy unit.

Cone-beam computed tomography (CBCT) using an "on-board" x-ray imaging device integrated into a radiation therapy system has recently been made available for patient positioning, target localization, and adaptive treatment planning. One of the challenges for gantry mounted image-guided radiation therapy (IGRT) systems is the slow acquisition of projections for cone-beam CT (CBCT), which makes them sensitive to any patient motion during the scans. Aiming at motion artifact reduction, four-dimensional CBCT (4D CBCT) techniques have been introduced, where a surrogate for the target's motion profile is utilized to sort the cone-beam data by respiratory phase. However, due to the limited gantry rotation speed and limited readout speed of the on-board imager, fewer than 100 projections are available for the image reconstruction at each respiratory phase. Thus, severe undersampling streaking artifacts plague 4D CBCT images. In this paper, the authors propose a simple scheme to significantly reduce the streaking artifacts. In this method, a prior image is first reconstructed using all available projections without gating, in which static structures are well reconstructed while moving objects are blurred. The undersampling streaking artifacts from static structures are estimated from this prior image volume and then can be removed from the phase images using gated reconstruction. The proposed method was validated using numerical simulations, experimental phantom data, and patient data. The fidelity of stationary and moving objects is maintained, while large gains in streak artifact reduction are observed. Using this technique one can reconstruct 4D CBCT datasets using no more projections than are acquired in a 60 s scan. At the same time, a temporal gating window as narrow as 100 ms was utilized. Compared to the conventional 4D CBCT reconstruction, streaking artifacts were reduced by 60% to 70%.