Faculty Bibliography

PURPOSE/OBJECTIVE:To investigate the dosimetric difference due to the different point A definitions in cervical cancer low-dose-rate (LDR) intracavitary brachytherapy. MATERIAL AND METHODS/METHODS:Twenty CT-based LDR brachytherapy plans of 11 cervical patients were retrospectively reviewed. Two plans with point As following the modified Manchester system which defines point A being 2 cm superior to the cervical os along the tandem and 2 cm lateral (Aos), and the American Brachytherapy Society (ABS) guideline definition in which the point A is 2 cm superior to the vaginal fornices instead of os (Aovoid) were generated. Using the same source strength, two plans prescribed the same dose to Aos and Aovoid. Dosimetric differences between plans including point A dose rate, treatment volume encompassed by the prescription isodose line (TV), and dose rate of 2 cc of the rectum and bladder to the prescription dose were measured. RESULTS:On average Aovoid was 8.9 mm superior to Aos along the tandem direction with a standard deviation of 5.4 mm. With the same source strength and arrangement, Aos dose rate was 19% higher than Aovoid dose rate. The average TV(Aovoid) was 118.0 cc, which was 30% more than the average TV(Aos) of 93.0 cc. D2cc/D(Aprescribe) increased from 51% to 60% for rectum, and increased from 89% and 106% for bladder, if the prescription point changed from Aos to Aovoid. CONCLUSIONS:Different point A definitions lead to significant dose differences. Careful consideration should be given when changing practice from one point A definition to another, to ensure dosimetric and clinical equivalency from the previous clinical experiences.

PURPOSE/OBJECTIVE:In the presence of random uncertainties, delivered radiation treatment doses in patient likely exhibit a statistical distribution. The expected dose and variance of this distribution are unknown and are most likely not equal to the planned value since the current treatment planning systems cannot exactly model and simulate treatment machine. Relevant clinical questions are 1) how to quantitatively estimate the expected delivered dose and extrapolate the expected dose to the treatment dose over a treatment course and 2) how to evaluate the treatment dose relative to the corresponding planned dose. This study is to present a systematic approach to address these questions and to apply this approach to patient-specific IMRT (PSIMRT) plan verifications. METHODS:The expected delivered dose in patient and variance are quantitatively estimated using Student T distribution and Chi Distribution, respectively, based on pre-treatment QA measurements. Relationships between the expected dose and the delivered dose over a treatment course and between the expected dose and the planned dose are quantified with mathematical formalisms. The requirement and evaluation of the pre-treatment QA measurement results are also quantitatively related to the desired treatment accuracy and to the to-be-delivered treatment course itself. The developed methodology was applied to PSIMRT plan verification procedures for both QA result evaluation and treatment quality estimation. RESULTS:Statistically, the pre-treatment QA measurement process was dictated not only by the corresponding plan but also by the delivered dose deviation, number of measurements, treatment fractionation, potential uncertainties during patient treatment, and desired treatment accuracy tolerance. For the PSIMRT QA procedures, in theory, more than one measurement had to be performed to evaluate whether the to-be-delivered treatment course would meet the desired dose coverage and treatment tolerance. CONCLUSION/CONCLUSIONS:By acknowledging and considering the statistical nature of multi-fractional delivery of radiation treatment, we have established a quantitative methodology to evaluate the PSIMRT QA results. Both the statistical parameters associated with the QA measurement procedure and treatment course need to be taken into account to evaluate the QA outcome and to determine whether the plan is acceptable and whether additional measures should be taken to reduce treatment uncertainties. The result from a single QA measurement without the appropriate statistical analysis can be misleading. When the required number of measurements is comparable to the planned number of fractions and the variance is unacceptably high, action must be taken to either modify the plan or adjust the beam delivery system.

Clinical objective IMRT QA is a new approach to conduct patient specific IMRT QA by evaluating the QA result based on metrics like the structure dose volume histograms (DVH) on the planning CT image. Different from traditional 2D planar dose based IMRT QA in which all the measurements and evaluations were based on a phantom, clinical objective IMRT QA reveals the direct clinical impact of any measured variation based on the predicted delivered dose to the patient target volumes and critical organs. In this study, we proposed a method to implement the clinical objective IMRT QA procedure by using generally available dosimetry equipment in a radiation oncology clinic. The proposed procedure was tested and validated on five IMRT plans by using a solid water phantom, which served as a test patient, along with total 30 ion chamber measurements. The result showed that the proposed QA procedure successfully achieved the goal of clinical objective IMRT QA. Based on the ion chamber measurements, the QA procedure predicted that the average variations between the planned and the delivered doses were within 1.9%. This method may hold potentials to help radiation oncology clinics to move from the traditional IMRT QA to the clinical objective IMRT QA.

PURPOSE/OBJECTIVE:The management of thoracic malignancies with radiation therapy is complicated by continuous target motion. In this study, a real time motion analysis approach is proposed to improve the accuracy of patient setup. METHODS:For 11 lung cancer patients a long training fluoroscopy was acquired before the first treatment, and multiple short testing fluoroscopies were acquired weekly at the pretreatment patient setup of image guided radiotherapy (IGRT). The data analysis consisted of three steps: first a 4D target motion model was constructed from 4DCT and projected to the training fluoroscopy through deformable registration. Then the manifold learning method was used to construct a 2D subspace based on the target motion (kinetic) and location (static) information in the training fluoroscopy. Thereafter the respiratory phase in the testing fluoroscopy was determined by finding its location in the subspace. Finally, the phase determined testing fluoroscopy was registered to the corresponding 4DCT to derive the pretreatment patient position adjustment for the IGRT. The method was tested on clinical image sets and numerical phantoms. RESULTS:The registration successfully reconstructed the 4D motion model with over 98% volume similarity in 4DCT, and over 95% area similarity in the training fluoroscopy. The machine learning method derived the phase values in over 98% and 93% test images of the phantom and patient images, respectively, with less than 3% phase error. The setup approach achieved an average accumulated setup error less than 1.7 mm in the cranial-caudal direction and less than 1 mm in the transverse plane. All results were validated against the ground truth of manual delineations by an experienced radiation oncologist. The expected total time for the pretreatment setup analysis was less than 10 s. CONCLUSIONS:By combining the registration and machine learning, the proposed approach has the potential to improve the accuracy of pretreatment setup for patients with thoracic malignancy.

The purpose of this study was to evaluate the visibility and artifact created by gold, carbon, and polymer fiducial markers in a simple phantom across computed tomography (CT), kilovoltage (kV), and megavoltage (MV) linear accelerator imaging and MV tomotherapy imaging. Three types of fiducial markers (gold, carbon, and polymer) were investigated for their visibility and artifacts in images acquired with various modalities and with different imaging parameters (kV, mAs, slice thickness). The imaging modalities include kV CT, 2D linac-based kilovoltage and megavoltage X-ray imaging systems, kV cone-beam CT, and normal and fine tomotherapy imaging. The images were acquired on a phantom constructed using Superflab bolus in which markers of each type were inserted into the center layer. The visibility and artifacts produced by each marker were assessed qualitatively and quantitatively. All tested markers could be identified clearly on the acquired CT and linac-based kV images; gold markers demonstrated the highest contrast. On the CT images, gold markers produced a significant artifact, while no artifacts were observed with polymer markers. Only gold markers were visible when using linac-based MV and tomotherapy imaging. For linac-based kV images, the contrast increased with kV and mAs values for all the markers, with the gold being the most pronounced. On CT images, the contrast increased with kV for the gold markers, while decreasing for the polymer and carbon marker. With the bolus phantom used, we found that when kV imaging-based treatment verification equipment is available, polymer and carbon markers may be the preferred choice for target localization and patient treatment positioning verification due to less image artifacts. If MV imaging will be the sole modality for positioning verification, it may be necessary to use gold markers despite the artifacts they create on the simulation CT images.

PURPOSE/OBJECTIVE:To determine, whether the presence of gold fiducial markers would improve the inter- and intraphysician accuracy in the delineation of the surgical cavity compared with a matched group of patients who did not receive gold fiducial markers in the setting of accelerated partial-breast irradiation (APBI). METHODS AND MATERIALS/METHODS:Planning CT images of 22 lumpectomy cavities were reviewed in a cohort of 22 patients; 11 patients received four to six gold fiducial markers placed at the time of surgery. Three physicians categorized the seroma cavity according to cavity visualization score criteria and delineated each of the 22 seroma cavities and the clinical target volume. Distance between centers of mass, percentage overlap, and average surface distance for all patients were assessed. RESULTS:The mean seroma volume was 36.9 cm(3) and 34.2 cm(3) for fiducial patients and non-fiducial patients, respectively (p = ns). Fiducial markers improved the mean cavity visualization score, to 3.6 ± 1.0 from 2.5 ± 1.3 (p < 0.05). The mean distance between centers of mass, average surface distance, and percentage overlap for the seroma and clinical target volume were significantly improved in the fiducial marker patients as compared with the non-fiducial marker patients (p < 0.001). CONCLUSIONS:The placement of gold fiducial markers placed at the time of lumpectomy improves interphysician identification and delineation of the seroma cavity and clinical target volume. This has implications in radiotherapy treatment planning for accelerated partial-breast irradiation and for boost after whole-breast irradiation.

PURPOSE/OBJECTIVE:In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation-and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. METHODS:The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. RESULTS:The ACRASM segmentation algorithm was compared to the original active shape mode (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to 6.54 mm for ASM. The volume overlap ratio ranged from 79% to 91% for ACRASM and from 44% to 80% for ASM. These data demonstrated that the segmentation results of ACRASM were in better agreement with the corresponding benchmarks than those of ASM. The developed registration algorithm was quantitatively evaluated by comparing the registered target volumes from the pCT to the benchmarks on the CBCT. The mean distance and the root mean square error ranged from 0.38 to 2.2 mm and from 0.45 to 2.36 mm, respectively, between the CBCT images and the registered pCT. The mean overlap ratio of the prostate volumes ranged from 85.2% to 95% after registration. The average time of the ACRASM-based segmentation was under 1 min. The average time of the global transformation was from 2 to 4 min on two 3D volumes and the average time of the local transformation was from 20 to 34 s on two deformable superquadrics mesh models. CONCLUSIONS:A novel and fast segmentation and deformable registration method was developed to capture the transformation between the planning and treatment images for external beam radiotherapy of prostate cancers. This method increases the computational efficiency and may provide foundation to achieve real time adaptive radiotherapy.

Tagged magnetic resonance imaging (tagged MRI or tMRI) provides a means of directly and noninvasively displaying the internal motion of the myocardium. Reconstruction of the motion field is needed to quantify important clinical information, e.g., the myocardial strain, and detect regional heart functional loss. In this paper, we present a three-step method for this task. First, we use a Gabor filter bank to detect and locate tag intersections in the image frames, based on local phase analysis. Next, we use an improved version of the robust point matching (RPM) method to sparsely track the motion of the myocardium, by establishing a transformation function and a one-to-one correspondence between grid tag intersections in different image frames. In particular, the RPM helps to minimize the impact on the motion tracking result of 1) through-plane motion and 2) relatively large deformation and/or relatively small tag spacing. In the final step, a meshless deformable model is initialized using the transformation function computed by RPM. The model refines the motion tracking and generates a dense displacement map, by deforming under the influence of image information, and is constrained by the displacement magnitude to retain its geometric structure. The 2D displacement maps in short and long axis image planes can be combined to drive a 3D deformable model, using the moving least square method, constrained by the minimization of the residual error at tag intersections. The method has been tested on a numerical phantom, as well as on in vivo heart data from normal volunteers and heart disease patients. The experimental results show that the new method has a good performance on both synthetic and real data. Furthermore, the method has been used in an initial clinical study to assess the differences in myocardial strain distributions between heart disease (left ventricular hypertrophy) patients and the normal control group. The final results show that the proposed method is capable of separating patients from healthy individuals. In addition, the method detects and makes possible quantification of local abnormalities in the myocardium strain distribution, which is critical for quantitative analysis of patients' clinical conditions. This motion tracking approach can improve the throughput and reliability of quantitative strain analysis of heart disease patients, and has the potential for further clinical applications

PURPOSE/OBJECTIVE:High-speed nonrigid registration between the planning CT and the treatment CBCT data is critical for real time image guided radiotherapy (IGRT) to improve the dose distribution and to reduce the toxicity to adjacent organs. The authors propose a new fully automatic 3D registration framework that integrates object-based global and seed constraints with the grayscale-based "demons" algorithm. METHODS:Clinical objects were segmented on the planning CT images and were utilized as meshless deformable models during the nonrigid registration process. The meshless models reinforced a global constraint in addition to the grayscale difference between CT and CBCT in order to maintain the shape and the volume of geometrically complex 3D objects during the registration. To expedite the registration process, the framework was stratified into hierarchies, and the authors used a frequency domain formulation to diffuse the displacement between the reference and the target in each hierarchy. Also during the registration of pelvis images, they replaced the air region inside the rectum with estimated pixel values from the surrounding rectal wall and introduced an additional seed constraint to robustly track and match the seeds implanted into the prostate. The proposed registration framework and algorithm were evaluated on 15 real prostate cancer patients. For each patient, prostate gland, seminal vesicle, bladder, and rectum were first segmented by a radiation oncologist on planning CT images for radiotherapy planning purpose. The same radiation oncologist also manually delineated the tumor volumes and critical anatomical structures in the corresponding CBCT images acquired at treatment. These delineated structures on the CBCT were only used as the ground truth for the quantitative validation, while structures on the planning CT were used both as the input to the registration method and the ground truth in validation. By registering the planning CT to the CBCT, a displacement map was generated. Segmented volumes in the CT images deformed using the displacement field were compared against the manual segmentations in the CBCT images to quantitatively measure the convergence of the shape and the volume. Other image features were also used to evaluate the overall performance of the registration. RESULTS:The algorithm was able to complete the segmentation and registration process within 1 min, and the superimposed clinical objects achieved a volumetric similarity measure of over 90% between the reference and the registered data. Validation results also showed that the proposed registration could accurately trace the deformation inside the target volume with average errors of less than 1 mm. The method had a solid performance in registering the simulated images with up to 20 Hounsfield unit white noise added. Also, the side by side comparison with the original demons algorithm demonstrated its improved registration performance over the local pixel-based registration approaches. CONCLUSIONS:Given the strength and efficiency of the algorithm, the proposed method has significant clinical potential to accelerate and to improve the CBCT delineation and targets tracking in online IGRT applications.