Ranjini Tolakanahalli

Hippocampal-Sparing Whole-Brain Radiotherapy: A “How-To” Technique Using Helical Tomotherapy and Linear Accelerator–Based Intensity-Modulated Radiotherapy

PURPOSE Sparing the hippocampus during cranial irradiation poses important technical challenges with respect to contouring and treatment planning. Herein we report our preliminary experience with whole-brain radiotherapy using hippocampal sparing for patients with brain metastases. METHODS AND MATERIALS Five anonymous patients previously treated with whole-brain radiotherapy with hippocampal sparing were reviewed. The hippocampus was contoured, and hippocampal avoidance regions were created using a 5-mm volumetric expansion around the hippocampus. Helical tomotherapy and linear accelerator (LINAC)-based intensity-modulated radiotherapy (IMRT) treatment plans were generated for a prescription dose of 30 Gy in 10 fractions. RESULTS On average, the hippocampal avoidance volume was 3.3 cm(3), occupying 2.1% of the whole-brain planned target volume. Helical tomotherapy spared the hippocampus, with a median dose of 5.5 Gy and maximum dose of 12.8 Gy. LINAC-based IMRT spared the hippocampus, with a median dose of 7.8 Gy and maximum dose of 15.3 Gy. On a per-fraction basis, mean dose to the hippocampus (normalized to 2-Gy fractions) was reduced by 87% to 0.49 Gy(2) using helical tomotherapy and by 81% to 0.73 Gy(2) using LINAC-based IMRT. Target coverage and homogeneity was acceptable with both IMRT modalities, with differences largely attributed to more rapid dose fall-off with helical tomotherapy. CONCLUSION Modern IMRT techniques allow for sparing of the hippocampus with acceptable target coverage and homogeneity. Based on compelling preclinical evidence, a Phase II cooperative group trial has been developed to test the postulated neurocognitive benefit.

The effect and stability of MVCT images on adaptive TomoTherapy

Use of helical TomoTherapy‐based MVCT imaging for adaptive planning is becoming increasingly popular. Treatment planning and dose calculations based on MVCT require an image value to electron density calibration to remain stable over the course of treatment time. In this work, we have studied the dosimetric impact on TomoTherapy treatment plans due to variation in image value to density table (IVDT) curve as a function of target degradation. We also have investigated the reproducibility and stability of the TomoTherapy MVCT image quality over time. Multiple scans of the TomoTherapy “Cheese” phantom were performed over a period of five months. Over this period, a difference of 4.7% in the HU values was observed in high‐density regions while there was no significant variation in the image values for the low densities of the IVDT curve. Changes in the IVDT curves before and after target replacement were measured. Two clinical treatment sites, pelvis and prostate, were selected to study the dosimetric impact of this variation. Dose was recalculated on the MVCTs with the planned fluence using IVDT curves acquired before and after target change. For the cases studied, target replacement resulted in an overall difference of less than 5%, which can be significant for hypo‐fractionated cases. Hence, it is recommended to measure the IVDT curves on a monthly basis and after any major repairs/replacements. PACS numbers: 87.55.Qr, 87.56.bd, 87.57.C, 87.57.Q

Prior image constrained scatter correction in cone-beam computed tomography image-guided radiation therapy

X-ray scatter is a significant problem in cone-beam computed tomography when thicker objects and larger cone angles are used, as scattered radiation can lead to reduced contrast and CT number inaccuracy. Advances have been made in x-ray computed tomography (CT) by incorporating a high quality prior image into the image reconstruction process. In this paper, we extend this idea to correct scatter-induced shading artifacts in cone-beam CT image-guided radiation therapy. Specifically, this paper presents a new scatter correction algorithm which uses a prior image with low scatter artifacts to reduce shading artifacts in cone-beam CT images acquired under conditions of high scatter. The proposed correction algorithm begins with an empirical hypothesis that the target image can be written as a weighted summation of a series of basis images that are generated by raising the raw cone-beam projection data to different powers, and then, reconstructing using the standard filtered backprojection algorithm. The weight for each basis image is calculated by minimizing the difference between the target image and the prior image. The performance of the scatter correction algorithm is qualitatively and quantitatively evaluated through phantom studies using a Varian 2100 EX System with an on-board imager. Results show that the proposed scatter correction algorithm using a prior image with low scatter artifacts can substantially mitigate scatter-induced shading artifacts in both full-fan and half-fan modes.

Independent quality assurance of a helical tomotherapy machine using the dose magnifying glass

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.

SU‐E‐J‐146: Time Series Prediction of Lung Cancer Patients’ Breathing Pattern Based on Nonlinear Dynamics

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.

Exact fan-beam reconstruction via ramp-filtered backprojection and local compensation

A novel exact fan-beam image reconstruction formula is presented and validated using both mathematical phantom data and clinical data. This algorithm takes the form of the standard ramp filtered backprojection (FBP) algorithm plus local compensation terms. An equal weighting scheme is utilized in this algorithm in order to properly account for redundantly measured projection data. The algorithm has the desirable property of maintaining a mathematically exact result for: the full scan mode (2π), the short scan mode (π+ full fan angle), and the super-short scan mode (less than (π + full fan angle)). Another desirable feature of this algorithm is that it is derivative-free. The derivative-free nature of this algorithm distinguishes it from other exact fan-beam FBP algorithms.

Reduction of metal artifacts using subtracted CT projection data

Metal artifacts in many cases significantly limit non-invasive imaging in evaluation of cerebrovascular patients who have undergone prior aneurysm clipping or coiling. The data inconsistency due to the presence of metal produces streaks in the reconstructed CT slices, which then manifest themselves in the coronal and sagittal reprojections most often used to display CT angiographic data. In DSA, no CT reconstructions are performed and the presence of metal only produces a reduction in SNR behind the metal unless misregistration produces artifacts. In this paper, we have begun to investigate a new method to obtain DSA-like images by using a CT scanner. In this approach, sinogram data is obtained from the multi-slice scanner using the same scan parameters before and after contrast injection. These sets of data are registered, subtracted and rebinned to generate radiography-like images. This new method to form DSA-like images from a CT scanner is called Digital Subtraction Topography (DST). Importantly, CT image reconstruction procedure is not performed to obtain DST images. In principle, the disturbing metal artifacts in the CT images do not appear in the DST images. A number of topographic images representing each of the gantry angles are obtained. These images give clinical information at all angles with AP and RL resolution equivalent to that in the CT slices. Resolution in the SI direction is determined by the CT slice thickness, which can be sub millimeter. The conventional CT image reconstruction can also be applied to DST datasets to generate CT DSA images. In the absence of misregistration, the metal artifacts in the reconstructed CT DSA images could be reduced.

SU‐E‐T‐662: Does Optimizing the Placement of Machine Isocenter Affect the Overall Optimized Plan Obtained Using Tomotherapy Treatment Planning System ? A Dosimetric and Analysis Study

PURPOSE To study the effect of isocenter placement with respect to DICOM center of patient CT images, on IMRT optimization using TomoTherapy Hi-ART(TM) Methods: The unique feature of TomoTherapy Hi-ART(TM) machine is the presence of flattening-filter-free(FFF) beam, off-axis IMRT optimization capability and rotational delivery. To cover all possibilities, we choose seven different treatment sites (3 Head and Neck(HN), 1 Hippocampal avoidance, 1 SBRT and 2 brain cases) previously planned and treated on Tomotherapy for this study. For fair comparison planning parameters, used previously for clinical patient treatment were used and left unchanged throughout the study for each case. Four plans were generated for each patient in which the center of patient CT datasets were shifted incrementally from +5 cm to -10 cm with respect to isocenter in the AP direction. Treatment time as a function of planning target volume (PTV) distances from isocenter was studied. Homogeneity index (HI), Target coverage (TC) for PTVs and the mean and max doses of organs at risk (OARs) as a function of the respective distances from the isocenter was also studied. RESULTS Up to 13% reduction in treatment time (∼100 sec) for HN cases was obtained with PTV coverage by changing the distance of PTV from isocenter by 1.5 cm. Particularly, for SBRT, 3% reduction (80 sec) in treatment time was obtained with identical PTV coverage while decreasing max cord dose by 2 Gy. CONCLUSION Optimization of patient position can thus be accomplished in rotational IMRT delivery by decreasing the distance of PTV from the isocenter while maximizing the distance of OARs from the isocenter thus minimizing treatment time and maximizing organ sparing without compromising tumor coverage. This work can be extended for doing fair comparison of plan quality achieved using various emerging technologies based on FFF modes of delivery.

SU-E-J-150: To Design a Methodology Based on Numerical Phantom for Reconstruction of Dose Delivered to Moving Lung Tumors

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 patients breathing on a daily basis. This can also be used for validation of new motion tracking algorithms and its impact of dose coverage.

SU‐E‐J‐144: Recurrence Quantification Analysis of Lung Cancer Patients' Breathing Pattern

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.