Thoracic target volume delineation using various maximum-intensity projection computed tomography image sets for stereotactic body radiation therapy

The motion of lung tumors during respiration makes the accurate delivery of radiation therapy to the thorax difficult because it increases the uncertainty of target position. The adoption of four-dimensional computed tomography (4D-CT) has allowed us to determine how a tumor moves with respiration for each individual patient. Using information acquired during a 4D-CT scan, we can define the target, visualize motion, and calculate dose during the planning phase of the radiotherapy process. One image data set that can be created from the 4D-CT acquisition is the maximum-intensity projection (MIP). The MIP can be used as a starting point to define the volume that encompasses the motion envelope of the moving gross target volume (GTV). Because of the close relationship that exists between the MIP and the final target volume, we investigated four MIP data sets created with different methodologies (3 using various 4D-CT sorting implementations, and one using all available cine CT images) to compare target delineation. It has been observed that changing the 4D-CT sorting method will lead to the selection of a different collection of images; however, the clinical implications of changing the constituent images on the resultant MIP data set are not clear. There has not been a comprehensive study that compares target delineation based on different 4D-CT sorting methodologies in a patient population. We selected a collection of patients who had previously undergone thoracic 4D-CT scans at our institution, and who had lung tumors that moved at least 1 cm. We then generated the four MIP data sets and automatically contoured the target volumes. In doing so, we identified cases in which the MIP generated from a 4D-CT sorting process under-represented the motion envelope of the target volume by more than 10% than when measured on the MIP generated from all of the cine CT images. The 4D-CT methods suffered from duplicate image selection and might not choose maximum extent images. Based on our results, we suggest utilization of a MIP generated from the full cine CT data set to ensure a representative inclusive tumor extent, and to avoid geometric miss.

Evaluation of deformable image registration for improved 4D CT-derived ventilation for image guided radiotherapy

Recent treatment planning studies have demonstrated the use of physiologic images in radiation therapy treatment planning to identify regions for functional avoidance. This image-guided radiotherapy (IGRT) strategy may reduce the injury and/or functional loss following thoracic radiotherapy. 4D computed tomography (CT), developed for radiotherapy treatment planning, is a relatively new imaging technique that allows the acquisition of a time-varying sequence of 3D CT images of the patient's lungs through the respiratory cycle. Guerrero et al. developed a method to calculate ventilation imaging from 4D CT, which is potentially better suited and more broadly available for IGRT than the current standard imaging methods. The key to extracting function information from 4D CT is the construction of a volumetric deformation field that accurately tracks the motion of the patient's lungs during the respiratory cycle. The spatial accuracy of the displacement field directly impacts the ventilation images; higher spatial registration accuracy will result in less ventilation image artifacts and physiologic inaccuracies. Presently, a consistent methodology for spatial accuracy evaluation of the DIR transformation is lacking. Evaluation of the 4D CT-derived ventilation images will be performed to assess correlation with global measurements of lung ventilation, as well as regional correlation of the distribution of ventilation with the current clinical standard SPECT. This requires a novel framework for both the detailed assessment of an image registration algorithm's performance characteristics as well as quality assurance for spatial accuracy assessment in routine application. Finally, we hypothesize that hypo-ventilated regions, identified on 4D CT ventilation images, will correlate with hypo-perfused regions in lung cancer patients who have obstructive lesions. A prospective imaging trial of patients with locally advanced non-small-cell lung cancer will allow this hypothesis to be tested. These advances are intended to contribute to the validation and clinical implementation of CT-based ventilation imaging in prospective clinical trials, in which the impact of this imaging method on patient outcomes may be tested.

Reduction of setup uncertainties and tumor motion in the radiation therapy of lung tumors

Because the goal of radiation therapy is to deliver a lethal dose to the tumor, accurate information on the location of the tumor needs to be known. Margins are placed around the tumor to account for variations in the daily position of the tumor. If tumor motion and patient setup uncertainties can be reduced, margins that account for such uncertainties in tumor location in can be reduced allowing dose escalation, which in turn could potentially improve survival rates. ^ In the first part of this study, we monitor the location of fiducials implanted in the periphery of lung tumors to determine the extent of non-gated and gated fiducial motion, and to quantify patient setup uncertainties. In the second part we determine where the tumor is when different methods of image-guided patient setup and respiratory gating are employed. In the final part we develop, validate, and implement a technique in which patient setup uncertainties are reduced by aligning patients based upon fiducial locations in projection images. ^ Results from the first part indicate that respiratory gating reduces fiducial motion relative to motion during normal respiration and setup uncertainties when the patients were aligned each day using externally placed skin marks are large. The results from the second part indicate that current margins that account for setup uncertainty and tumor motion result in less than 2% of the tumor outside of the planning target volume (PTV) when the patient is aligned using skin marks. In addition, we found that if respiratory gating is going to be used, it is most effective if used in conjunction with image-guided patient setup. From the third part, we successfully developed, validated, and implemented on a patient a technique for aligning a moving target prior to treatment to reduce the uncertainties in tumor location. ^ In conclusion, setup uncertainties and tumor motion are a significant problem when treating tumors located within the thoracic region. Image-guided patient setup in conjunction with treatment delivery using respiratory gating reduces these uncertainties in tumor locations. In doing so, margins around the tumor used to generate the PTV can be reduced, which may allow for dose escalation to the tumor. ^

Design and Optimization of Four-dimensional Cone-beam Computed Tomography in Image-guided Radiation Therapy

The influence of respiratory motion on patient anatomy poses a challenge to accurate radiation therapy, especially in lung cancer treatment. Modern radiation therapy planning uses models of tumor respiratory motion to account for target motion in targeting. The tumor motion model can be verified on a per-treatment session basis with four-dimensional cone-beam computed tomography (4D-CBCT), which acquires an image set of the dynamic target throughout the respiratory cycle during the therapy session. 4D-CBCT is undersampled if the scan time is too short. However, short scan time is desirable in clinical practice to reduce patient setup time. This dissertation presents the design and optimization of 4D-CBCT to reduce the impact of undersampling artifacts with short scan times. This work measures the impact of undersampling artifacts on the accuracy of target motion measurement under different sampling conditions and for various object sizes and motions. The results provide a minimum scan time such that the target tracking error is less than a specified tolerance. This work also presents new image reconstruction algorithms for reducing undersampling artifacts in undersampled datasets by taking advantage of the assumption that the relevant motion of interest is contained within a volume-of-interest (VOI). It is shown that the VOI-based reconstruction provides more accurate image intensity than standard reconstruction. The VOI-based reconstruction produced 43% fewer least-squares error inside the VOI and 84% fewer error throughout the image in a study designed to simulate target motion. The VOI-based reconstruction approach can reduce acquisition time and improve image quality in 4D-CBCT.