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Does the γ dose distribution comparison technique default to the distance to agreement test in clinical dose distributions

PURPOSE To determine the validity of the assumption that the γ dose distribution comparison tool defaults to the distance to agreement test under conditions of clinically relevant steep dose gradients and γ test criteria. METHODS The assumption was tested by computing the angle θ between the dose axis and γ vector for clinical treatment plans. θ was a function of the evaluated dose distribution dose gradient and the ratio (α) of the dose difference to distance to agreement (DTA) criteria. Dose distributions from prostate, head and neck, and lung clinical treatment plans were examined: 50 treatment plans were selected for each of the prostate and head neck sites and 27 treatment plans were selected for lung. Dose-gradient histograms were prepared for each of the treatment plans using α = 1%/mm (e.g., 3%, 3 mm dose difference and DTA criteria, respectively). To determine how frequently different values of α were used in publications, papers that referenced the original γ paper were examined to identify the dose difference and DTA criteria used in those publications. In order to compare θ calculated using α = 1%/mm to θ for other values of α, the relationship between θ and α was determined. RESULTS For most of the targets and critical structures, the maximum value of θ approached 90°, so the assumption that the γ tool defaulted to the DTA test in steep dose gradients was correct. Most of the published papers using the γ tool employed the 3%, 3 mm dose difference and DTA criteria, respectively. Most of the other evaluations used criteria such that α ≥ 1%/mm, so the conclusions relating to the examined dose distributions applied. There were a few papers employing very small values of α (including one where α = 0.17%/mm), breaking the assumption that the γ dose comparison tool defaulted to the DTA tool in steep dose gradients. CONCLUSIONS Most published cases utilized values of α ≥ 1%/mm, and for those the implicit assumption that the γ dose comparison tool defaults to the DTA test in steep dose gradient regions was true. There were a few cases for which α was small enough to potentially invalidate this assumption. Care should be taken by investigators when selecting the γ test criteria to assure that the γ test will appropriately default to the DTA test in the steepest dose gradients being evaluated.

Technical Note: Simulation of 4DCT tumor motion measurement errors.

PURPOSE To determine if and by how much the commercial 4DCT protocols under- and overestimate tumor breathing motion. METHODS 1D simulations were conducted that modeled a 16-slice CT scanner and tumors moving proportionally to breathing amplitude. External breathing surrogate traces of at least 5-min duration for 50 patients were used. Breathing trace amplitudes were converted to motion by relating the nominal tumor motion to the 90th percentile breathing amplitude, reflecting motion defined by the more recent 5DCT approach. Based on clinical low-pitch helical CT acquisition, the CT detector moved according to its velocity while the tumor moved according to the breathing trace. When the CT scanner overlapped the tumor, the overlapping slices were identified as having imaged the tumor. This process was repeated starting at successive 0.1 s time bin in the breathing trace until there was insufficient breathing trace to complete the simulation. The tumor size was subtracted from the distance between the most superior and inferior tumor positions to determine the measured tumor motion for that specific simulation. The effect of the scanning parameter variation was evaluated using two commercial 4DCT protocols with different pitch values. Because clinical 4DCT scan sessions would yield a single tumor motion displacement measurement for each patient, errors in the tumor motion measurement were considered systematic. The mean of largest 5% and smallest 5% of the measured motions was selected to identify over- and underdetermined motion amplitudes, respectively. The process was repeated for tumor motions of 1-4 cm in 1 cm increments and for tumor sizes of 1-4 cm in 1 cm increments. RESULTS In the examined patient cohort, simulation using pitch of 0.06 showed that 30% of the patients exhibited a 5% chance of mean breathing amplitude overestimations of 47%, while 30% showed a 5% chance of mean breathing amplitude underestimations of 36%; with a separate simulation using pitch of 0.1 showing, respectively, 37% overestimation and 61% underestimation. CONCLUSIONS The simulation indicates that commercial low-pitch helical 4DCT processes potentially yield large tumor motion measurement errors, both over- and underestimating the tumor motion.

A Method for Assessing Ground-Truth Accuracy of the 5DCT Technique

PURPOSE To develop a technique that assesses the accuracy of the breathing phase-specific volume image generation process by patient-specific breathing motion model using the original free-breathing computed tomographic (CT) scans as ground truths. METHODS Sixteen lung cancer patients underwent a previously published protocol in which 25 free-breathing fast helical CT scans were acquired with a simultaneous breathing surrogate. A patient-specific motion model was constructed based on the tissue displacements determined by a state-of-the-art deformable image registration. The first image was arbitrarily selected as the reference image. The motion model was used, along with the free-breathing phase information of the original 25 image datasets, to generate a set of deformation vector fields that mapped the reference image to the 24 nonreference images. The high-pitch helically acquired original scans served as ground truths because they captured the instantaneous tissue positions during free breathing. Image similarity between the simulated and the original scans was assessed using deformable registration that evaluated the pointwise discordance throughout the lungs. RESULTS Qualitative comparisons using image overlays showed excellent agreement between the simulated images and the original images. Even large 2-cm diaphragm displacements were very well modeled, as was sliding motion across the lung-chest wall boundary. The mean error across the patient cohort was 1.15 ± 0.37 mm, and the mean 95th percentile error was 2.47 ± 0.78 mm. CONCLUSION The proposed ground truth-based technique provided voxel-by-voxel accuracy analysis that could identify organ-specific or tumor-specific motion modeling errors for treatment planning. Despite a large variety of breathing patterns and lung deformations during the free-breathing scanning session, the 5-dimensionl CT technique was able to accurately reproduce the original helical CT scans, suggesting its applicability to a wide range of patients.

Is there an ideal set of prospective scan acquisition phases for fast-helical based 4D-CT?

The article aims to determine if a prospective acquisition algorithm can be used to find the ideal set of free-breathing phases for fast-helical model-based 4D-CT. A retrospective five-patient dataset that consisted of 25 repeated free breathing CT scans per patient was used. The sum of the square root amplitude difference between all the breathing phases was defined as an objective function to determine the optimality of sets of breathing phases. The objective function was intended to determine if a specific set of breathing phases would yield a motion model that could accurately predict the motion in all 25 CT scans. Voxel specific motion models were calculated using all combinations of N scans from 25 breathing trajectories, (3  ⩽  N  ⩽  25), and the minimum number of scans required to absolutely characterize the motion model was analyzed. This analysis suggests that the number of scans could potentially be reduced to as few as five scans. When the objective function was large, the resulting motion model provided an excellent approximation to the motion model created using all 25 scans.

SU‐E‐J‐73: Effect of 4D‐CT Image Artifacts On the 3D Lung Registration Accuracy: A Parametric Study Using a GPU‐Accelerated Multi‐Resolution Multi‐Level Optical Flow

PURPOSE To perform a parametric study of the effect of registration parameters on 4D-CT image registration accuracy. METHODS AND MATERIALS A GPU based 4D-CT image registration that registers the 4D lung anatomy using a multi-level multi-contrast optical flow was used for this study. A set of 14 4D-CT datasets was employed for this study. The multi-level lung anatomy was segmented into the surface contour, blood vessels and parenchyma regions using OsiriX. The registration started at the lowest resolution of a 3D volume. Within each resolution level, the volumes were registered using optical flow. The motion field was first computed for surface contour pairs in the lowest resolution. At this stage, all the voxels except those on the surface contour (the lowest level of anatomical representation) were not included. GPU based Thin-Plate Splines was applied to the motion field so that voxels surrounding the surface contour had an initial displacement motion, which was closer to the actual value. The motion field was iteratively updated until the highest (original) resolution of the volume was processed. RESULTS The GPU implementation provided a speed-up of >50x as compared to the CPU implementation. The registration accuracy varied non-linearly with the kernel size. For both kernel size and smoothness factor, a non-linear correlation was observed towards the registration accuracy with an optimal value being 5 cu.mm and 200, respectively. The accuracy improved with the number of resolution and contrast levels with 4 and 3, respectively, providing optimal registration accuracy. Finally, usage of the first 3 anatomical level representations provided the optimum registration accuracy as opposed to 4 or more levels. CONCLUSION A parametric 4D image registration analyses showed its relationship to the registration accuracy to be non-linear. A patient breathing and CT-scanner specific study will quantitatively relate the registration errors on treatment planning and delivery.

TH‐CD‐202‐06: A Method for Characterizing and Validating Dynamic Lung Density Change During Quiet Respiration

PURPOSE To obtain a functional relationship that calibrates the lung tissue density change under free breathing conditions through correlating Jacobian values to the Hounsfield units. METHODS Free-breathing lung computed tomography images were acquired using a fast helical CT protocol, where 25 scans were acquired per patient. Using a state-of-the-art deformable registration algorithm, a set of the deformation vector fields (DVF) was generated to provide spatial mapping from the reference image geometry to the other free-breathing scans. These DVFs were used to generate Jacobian maps, which estimate voxelwise volume change. Subsequently, the set of 25 corresponding Jacobian and voxel intensity in Hounsfield units (HU) were collected and linear regression was performed based on the mass conservation relationship to correlate the volume change to density change. Based on the resulting fitting coefficients, the tissues were classified into parenchymal (Type I), vascular (Type II), and soft tissue (Type III) types. These coefficients modeled the voxelwise density variation during quiet breathing. The accuracy of the proposed method was assessed using mean absolute difference in HU between the CT scan intensities and the model predicted values. In addition, validation experiments employing a leave-five-out method were performed to evaluate the model accuracy. RESULTS The computed mean model errors were 23.30±9.54 HU, 29.31±10.67 HU, and 35.56±20.56 HU, respectively, for regions I, II, and III, respectively. The cross validation experiments averaged over 100 trials had mean errors of 30.02 ± 1.67 HU over the entire lung. These mean values were comparable with the estimated CT image background noise. CONCLUSION The reported validation experiment statistics confirmed the lung density modeling during free breathing. The proposed technique was general and could be applied to a wide range of problem scenarios where accurate dynamic lung density information is needed. This work was supported in part by NIH R01 CA0096679.

SU-F-J-135: Tumor Displacement-Based Binning for Respiratory-Gated Time-Independent 5DCT Treatment Planning

PURPOSE A published 5DCT breathing motion model enables image reconstruction at any user-selected breathing phase, defined by the model as a specific amplitude (v) and rate (f). Generation of reconstructed phase-specific CT scans will be required for time-independent radiation dose distribution simulations. This work answers the question: how many amplitude and rate bins are required to describe the tumor motion with a specific spatial resolution? METHODS 19 lung-cancer patients with 21 tumors were scanned using a free-breathing 5DCT protocol, employing an abdominally positioned pneumatic-bellows breathing surrogate and yielding voxel-specific motion model parameters α and β corresponding to motion as a function of amplitude and rate, respectively. Tumor GTVs were contoured on the first (reference) of 25 successive free-breathing fast helical CT image sets. The tumor displacements were binned into widths of 1mm to 5mm in 1mm steps and the total required number of bins recorded. The simulation evaluated the number of bins needed to encompass 100% of the breathing-amplitude and between the 5th and 95th percentile amplitudes to exclude breathing outliers. RESULTS The mean respiration-induced tumor motion was 9.90mm ± 7.86mm with a maximum of 25mm. The number of bins required was a strong function of the spatial resolution and varied widely between patients. For example, for 2mm bins, between 1-13 amplitude bins and 1-9 rate bins were required to encompass 100% of the breathing amplitude, while 1-6 amplitude bins and 1-3 rate bins were required to encompass 90% of the breathing amplitude. CONCLUSION The strong relationship between number of bins and spatial resolution as well as the large variation between patients implies that time-independent radiation dose distribution simulations should be conducted using patient-specific data and that the breathing conditions will have to be carefully considered. This work will lead to the assessment of the dosimetric impact of binning resolution. This study is supported by Siemens Healthcare.

SU‐D‐202‐06: Prospective Free‐Breathing CT Scan Selection for 5DCT

PURPOSE 5DCT employs 25 fast helical scans and breathing surrogate monitoring to sample the respiratory cycle. Deformable image registration is used to fit a correspondence model between tissue motion and breathing amplitude and rate. The number of scans was chosen to ensure a high probability that tissues were imaged at sufficiently distinct breathing phases for accurate modeling of the entire breathing cycle. This work describes a method to prospectively select scan start times and reduce the protocols number of scans from 25 to 6. METHODS Breathing traces from 7 patients imaged with 5DCT were used to simulate acquisition of 6 scans. Breathing phase was estimated using only observations from previous time points. Cross-correlation between the representative breath and the most recent half period was continuously computed. If phase and cross-correlation criteria were met, scans were triggered with a 2 second delay before acquisition. Blind acquisition, 6 scans separated by a fixed delay, was modeled at staggered start times. The spread of prospectively and blindly sampled breathing waveforms was characterized using a previously published objective function. RESULTS Prospectively selected scans ranked on average in the 84th percentile of objective function values obtained by blind acquisition at staggered start times for 7 patient breathing traces. CONCLUSION A method to prospectively determine scan start times for 5DCT was developed and tested by simulating acquisition on patient breathing traces. The method is computationally inexpensive enough for real-time implementation and would result in an imaging dose of less than one quarter of the current 5DCT protocol.

TH‐CD‐303‐04: A Method for Assessing Ground‐Truth Accuracy of a Motion Model Based 4DCT Technique

Purpose: To develop a technique that validates a breathing motion model and its reconstructed phase-specific image generation process using the original free-breathing images as ground truths. Methods: 16 lung cancer patients underwent the published protocol where 25 free-breathing fast helical CT scans were acquired with a simultaneous breathing surrogate. The first image was arbitrarily selected as the reference image. For constructing patient-specific lung motion model, state-of-the-art deformable image registration was employed to determine lung tissue displacement. The motion model was used, along with the free-breathing phase information of the original 25 image datasets, to generate a set of deformation vector fields (DVF) that mapped the reference image to the 24 non-reference images. The set of original images was simulated by applying the inverted model DVF to the reference image. To test the robustness of model simulation over the entire lung region, the model simulated image was deformably registered to the original scan. The resulting deformation vector magnitude evaluated the point-wise discordance in the lung region. Results: Qualitative comparison of image overlay showed excellent agreement between the simulated and the original images. The mean error across the patient cohort was 1.15±0.37 mm, while the mean 95th percentile error was 2.47±0.78 mm. Conclusion: Despite a large variety of breathing patterns and lung deformations, the proposed technique can accurately reproduce the original free-breathing helical CT scans, suggesting its applicability to a wide range of patients. The proposed ground truth based analysis is unique in CT-based breathing motion modeling for radiation therapy and will provide uncertainty estimations in the model-based 4DCT breathing motion estimate of tumors and normal organs. This work was supported in part by NIH R01 CA096679

SU-C-BRA-05: Fast Generation of Respiratory Gated CT Images at User Selected Breathing Phases On a Graphics Processing Unit

Purpose: The previously published 5DCT respiratory gated image acquisition and analysis technique enables generation of images at any user selected breathing phase. This work describes acceleration of the image generation process using a graphics processing unit (GPU) and its application to internal target volume (ITV) definition and the creation of simulated cine scans. Methods: 25 fast helical, free breathing CT scans of 7 lung cancer patients were acquired using a low dose protocol with simultaneous breathing surrogate monitoring. For each patient, the first scan was deformably registered to the following 24. Deformation vectors were used to determine voxel-specific parameters of a motion model. A single, low noise reference image in the geometry of the first scan was created using image averaging. The motion model was used to predict the deformation from the reference image to selected breathing phases. Internal target volumes were generated by deforming a single contour of the gross tumor volume (GTV) to the most common breathing phases accounting for 90% of observed respiration. Simulated cines were created by generating volumetric images at 0.25 second intervals along the measured breathing trace and taking slices at desired positions. Computations were performed on an NVIDIA Tesla K40. Results: Calculation of motion model parameters took approximately 3 seconds per dataset. Image generation took approximately 0.25 seconds total for a 450 x 450 x 300 image with isotropic 1 mm3 resolution. Conclusion: GPU acceleration enabled rapid generation of breathing gated CT images using the 5DCT technique and facilitated use of a novel method for defining customized lung tumor ITVs that account for a specified percentage of observed respiration, and the creation of simulated cine images in a clinically acceptable time frame. Investigation of the differences between ITVs generated using the technique described here and ITVs defined on conventional 4DCT datasets is ongoing.