Jason Pukala

A virtual phantom library for the quantification of deformable image registration uncertainties in patients with cancers of the head and neck.

PURPOSEnDeformable image registration (DIR) is being used increasingly in various clinical applications. However, the underlying uncertainties of DIR are not well-understood and a comprehensive methodology has not been developed for assessing a range of interfraction anatomic changes during head and neck cancer radiotherapy. This study describes the development of a library of clinically relevant virtual phantoms for the purpose of aiding clinicians in the QA of DIR software. These phantoms will also be available to the community for the independent study and comparison of other DIR algorithms and processes.nnnMETHODSnEach phantom was derived from a pair of kVCT volumetric image sets. The first images were acquired of head and neck cancer patients prior to the start-of-treatment and the second were acquired near the end-of-treatment. A research algorithm was used to autosegment and deform the start-of-treatment (SOT) images according to a biomechanical model. This algorithm allowed the user to adjust the head position, mandible position, and weight loss in the neck region of the SOT images to resemble the end-of-treatment (EOT) images. A human-guided thin-plate splines algorithm was then used to iteratively apply further deformations to the images with the objective of matching the EOT anatomy as closely as possible. The deformations from each algorithm were combined into a single deformation vector field (DVF) and a simulated end-of-treatment (SEOT) image dataset was generated from that DVF. Artificial noise was added to the SEOT images and these images, along with the original SOT images, created a virtual phantom where the underlying ground-truth DVF is known. Images from ten patients were deformed in this fashion to create ten clinically relevant virtual phantoms. The virtual phantoms were evaluated to identify unrealistic DVFs using the normalized cross correlation (NCC) and the determinant of the Jacobian matrix. A commercial deformation algorithm was applied to the virtual phantoms to show how they may be used to generate estimates of DIR uncertainty.nnnRESULTSnThe NCC showed that the simulated phantom images had greater similarity to the actual EOT images than the images from which they were derived, supporting the clinical relevance of the synthetic deformation maps. Calculation of the Jacobian of the ground-truth DVFs resulted in only positive values. As an example, mean error statistics are presented for all phantoms for the brainstem, cord, mandible, left parotid, and right parotid.nnnCONCLUSIONSnIt is essential that DIR algorithms be evaluated using a range of possible clinical scenarios for each treatment site. This work introduces a library of virtual phantoms intended to resemble real cases for interfraction head and neck DIR that may be used to estimate and compare the uncertainty of any DIR algorithm.

A GPU based high‐resolution multilevel biomechanical head and neck model for validating deformable image registration

PURPOSEnValidating the usage of deformable image registration (dir) for daily patient positioning is critical for adaptive radiotherapy (RT) applications pertaining to head and neck (HN) radiotherapy. The authors present a methodology for generating biomechanically realistic ground-truth data for validating dir algorithms for HN anatomy by (a) developing a high-resolution deformable biomechanical HN model from a planning CT, (b) simulating deformations for a range of interfraction posture changes and physiological regression, and (c) generating subsequent CT images representing the deformed anatomy.nnnMETHODSnThe biomechanical model was developed using HN kVCT datasets and the corresponding structure contours. The voxels inside a given 3D contour boundary were clustered using a graphics processing unit (GPU) based algorithm that accounted for inconsistencies and gaps in the boundary to form a volumetric structure. While the bony anatomy was modeled as rigid body, the muscle and soft tissue structures were modeled as mass-spring-damper models with elastic material properties that corresponded to the underlying contoured anatomies. Within a given muscle structure, the voxels were classified using a uniform grid and a normalized mass was assigned to each voxel based on its Hounsfield number. The soft tissue deformation for a given skeletal actuation was performed using an implicit Euler integration with each iteration split into two substeps: one for the muscle structures and the other for the remaining soft tissues. Posture changes were simulated by articulating the skeletal structure and enabling the soft structures to deform accordingly. Physiological changes representing tumor regression were simulated by reducing the target volume and enabling the surrounding soft structures to deform accordingly. Finally, the authors also discuss a new approach to generate kVCT images representing the deformed anatomy that accounts for gaps and antialiasing artifacts that may be caused by the biomechanical deformation process. Accuracy and stability of the model response were validated using ground-truth simulations representing soft tissue behavior under local and global deformations. Numerical accuracy of the HN deformations was analyzed by applying nonrigid skeletal transformations acquired from interfraction kVCT images to the models skeletal structures and comparing the subsequent soft tissue deformations of the model with the clinical anatomy.nnnRESULTSnThe GPU based framework enabled the model deformation to be performed at 60 frames/s, facilitating simulations of posture changes and physiological regressions at interactive speeds. The soft tissue response was accurate with a R(2) value of >0.98 when compared to ground-truth global and local force deformation analysis. The deformation of the HN anatomy by the model agreed with the clinically observed deformations with an average correlation coefficient of 0.956. For a clinically relevant range of posture and physiological changes, the model deformations stabilized with an uncertainty of less than 0.01 mm.nnnCONCLUSIONSnDocumenting dose delivery for HN radiotherapy is essential accounting for posture and physiological changes. The biomechanical model discussed in this paper was able to deform in real-time, allowing interactive simulations and visualization of such changes. The model would allow patient specific validations of the dir method and has the potential to be a significant aid in adaptive radiotherapy techniques.

The effect of temporal HU variations on the uncertainty of dose recalculations performed on MVCT images

Over the course of radiation therapy, a patients anatomy may change substantially. The relatively recent addition of frequent in-room imaging to assist with patient localization has provided a database of images that may be used to recalculate dose distributions for adaptive radiotherapy purposes. The TomoTherapy Hi-Art II unit (Accuray Inc., Sunnyvale, CA, USA) uses a helical scanning geometry and a megavoltage (MV) beam to acquire volumetric patient images. This study evaluated the uncertainty of dose calculations performed on megavoltage CT (MVCT) images as a function of temporal Hounsfield Unit (HU) variations observed in the imaging system over three years on two machines. A baseline error between dose calculations performed on kVCT and MVCT images was established using a series of phantoms. This baseline error ranged from -1.4% to 0.6%. Materials of differing densities were imaged and MVCT numbers were measured periodically. The MVCT number of solid water varied from 5 to 103 HU and consistently increased prior to target replacement. Finally, the dosimetric uncertainty of the temporal HU variation was assessed using MVCT images of typical head and neck, lung and prostate cancer patients. Worst-case MVCT recalculation errors could approach 5%, 7% and 10% for the head and neck, lung and prostate images, respectively. However, if a tolerance of ±30 HU were maintained for the MVCT number of solid water, dosimetric errors were limited to ±2.5%, ±3% and ±4%, respectively.

The Mobius AIRO mobile CT for image‐guided proton therapy: Characterization & commissioning

Purpose The purpose of this study was to characterize the Mobius AIRO Mobile CT System for localization and image‐guided proton therapy. This is the first known application of the AIRO for proton therapy. Methods Five CT images of a Catphan®504 phantom were acquired on the AIRO Mobile CT System, Varian EDGE radiosurgery system cone beam CT (CBCT), Philips Brilliance Big Bore 16 slice CT simulator, and Siemens SOMATOM Definition AS 20 slice CT simulator. DoseLAB software v.6.6 was utilized for image quality analysis. Modulation transfer function, scaling discrepancy, geometric distortion, spatial resolution, overall uniformity, minimum uniformity, contrast, high CNR, and maximum HU deviation were acquired. Low CNR was acquired manually using the CTP515 module. Localization accuracy and CT Dose Index were measured and compared to reported values on each imaging device. For treatment delivery systems (Edge and Mevion), the localization accuracy of the 3D imaging systems were compared to 2D imaging systems on each system. Results The AIRO spatial resolution was 0.21 lp mm−1 compared with 0.40 lp mm−1 for the Philips CT Simulator, 0.37 lp mm−1 for the Edge CBCT, and 0.35 lp mm−1 for the Siemens CT Simulator. AIRO/Siemens and AIRO/Philips differences exceeded 100% for scaling discrepancy (191.2% and 145.8%). The AIRO exhibited higher dose (>27 mGy) than the Philips CT Simulator. Localization accuracy (based on the MIMI phantom) was 0.6° and 0.5 mm. Localization accuracy (based on Stereophan) demonstrated maximum AIRO‐kV/kV shift differences of 0.1 mm in the x‐direction, 0.1 mm in the y‐direction, and 0.2 mm in the z‐direction. Conclusions The localization accuracy of AIRO was determined to be within 0.6° and 0.5 mm despite its slightly lower image quality overall compared to other CT imaging systems at our institution. Based on our study, the Mobile AIRO CT system can be utilized accurately and reliably for image‐guided proton therapy.

SU-E-T-529: The Dosimetric Uncertainty of Helical MVCT Images for Use in Adaptive Radiotherapy

Purpose: To determine the dosimetric uncertainty of MVCT images obtained with a helical TomoTherapy unit for use in adaptive radiotherapy evaluations. Methods: A water, head, and thorax phantom were used to quantify the baseline uncertainty in dose recomputations. Each phantom was planned and then re‐imaged using helical MVCT. Dose was recalculated on the MVCT images and compared to the plan DVHs for each target. Next, the variation of the MVCT images over three years on two machines was assessed. MVCT images of the TomoTherapy CT density calibration phantom were analyzed to find the variation in the solid water CT number over this time. To quantify the dosimetric changes resulting from this temporal variability, dosimetric endpoints were compared versus solid water CT number changes for an oropharynx patient.Results: The D95, D50, and D05 dosimetric endpoints were compared for each phantom in the baseline evaluation. D50 deviated by −0.4%, −1.3%, and 0.7% from the planned dose for the water, head, and thorax phantoms, respectively. The solid water CT number varied, over time, from a maximum of 108 HU to a minimum of −3 HU. This translated into a total dosimetric variation in the D50 of the oropharynx patients PTV of 2.9% (−1.7% to +1.2%). The parotids varied less than the target with a total D50 variation of 1.5% and 1.2% for the right and left parotids, respectively Conclusions: This work suggests that the observed temporal variation of the MVCT number alone does not translate into dosimetric discrepancies greater than 3%. Maintaining the solid water MVCT number within 30 HU of its calibrated value should be sufficient to achieve dose recalculation results within 2.5% of the expected values, including the baseline error. This study was funded in part by a grant from TomoTherapy, Inc.