Faculty Bibliography

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.

Image Guided Radiation Therapy (IGRT) improves radiation therapy for prostate cancer by facilitating precise radiation dose coverage of the object of interest, and minimizing dose to adjacent normal organs. In an effort to optimize IGRT, we developed a fast segmentation-registration-segmentation framework to accurately and efficiently delineate the clinically critical objects in Cone Beam CT images obtained during radiation treatment. The proposed framework started with deformable models automatically segmenting the prostate, bladder, and rectum in planning CT images. All models were built around seed points and involved in the CT image under the influence of image features using the level set formulation. The deformable models were then converted into meshless point sets and underwent a 3D non rigid registration from the planning CT to the treatment CBCT. The motion of deformable models during the registration was constrained by the global shape prior on the target surface during the deformation. The meshless formulation provided a convenient interface between deformable models and the image feature based registration method. The final registered deformable models in the CBCT domain were further refined using the interaction between objects and other available image features. The segmentation results for 15 data sets has been included in the validation study, compared with manual segmentations by a radiation oncologist. The automatic segmentation results achieved a satisfactory convergence with manual segmentations and met the speed requirement for on line IGRT.

The purposes of this study were to develop a semiautomated cardiac contour segmentation method for use with cine displacement-encoded MRI and evaluate its accuracy against manual segmentation. This segmentation model was designed with two distinct phases: preparation and evolution. During the model preparation phase, after manual image cropping and then image intensity standardization, the myocardium is separated from the background based on the difference in their intensity distributions, and the endo- and epi-cardial contours are initialized automatically as zeros of an underlying level set function. During the model evolution phase, the model deformation is driven by the minimization of an energy function consisting of five terms: model intensity, edge attraction, shape prior, contours interaction, and contour smoothness. The energy function is minimized iteratively by adaptively weighting the five terms in the energy function using an annealing algorithm. The validation experiments were performed on a pool of cine data sets of five volunteers. The difference between the semiautomated segmentation and manual segmentation was sufficiently small as to be considered clinically irrelevant. This relatively accurate semiautomated segmentation method can be used to significantly increase the throughput of strain analysis of cine displacement-encoded MR images for clinical applications