Browsing by Author "Lo, Joseph YuanChieh"
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Item Open Access Adaptive Filtering for Breast Computed Tomography: An Improvement on Current Segmentation Methods for Creating Virtual Breast Phantoms(2015) Erickson, DavidComputerized breast phantoms have been popular for low-cost alternatives to collecting clinical data by combing them with highly realistic simulation tools. Image segmentation of three-dimensional breast computed tomography (bCT) data is one method to create such phantoms, but requires multiple image processing steps to accurately classify the tissues within the breast. One key step in our segmentation routine is the use of a bilateral filter to smooth homogeneous regions, preserve edges and thin structures, and reduce the sensitivity of the voxel classification to noise corruption. In previous work, the well-known process of bilateral filtering was completed on the entire bCT volume with the primary goal of reducing the noise in the entire volume. In order to improve on this method, knowledge of the varying bCT noise in each slice was used to adaptively increase or decrease the filtering effect as a function of distance to the chest wall. Not only does this adaptive bilateral filter yield thinner structures in the segmentation result but is adaptive on a case-by-case basis, allowing for easy implementation with future virtual phantom generations.
Item Open Access Developing a Quality Index for Dose-Volume Histograms Based on Physician Preference(2015) Price, Alex TylerThe purpose of this study was to create a DVH quality index that can be used as a comparison tool between two separate plans and as a clinical workflow tool to improve plan quality resulting in better patient outcome. To create this DVH quality index, physician preference was used as the basis of the dose-volume relationship quantification rather than biological models since physicians are the ones who drive plan quality within in a clinic. An intra-patient observership study was created to gather the qualitative and quantitative from radiation oncologists who ranked a set of plans of varying plan quality from a specific patient. The qualitative data gave rise to the formation of the algorithm to produce a DVH quality index while the quantitative data drove the weighting factors within the algorithm. The intra-patient study validated the algorithms ability to determine the best DVH among separate plans from a specific patient. An inter-patient study was then introduced to validate the DVH quality index across the spectrum of scores given by the algorithm by comparing the algorithm's rank list with the oncologists' rank lists. These studies used spearman rank correlation tests to compare the rank lists between the algorithm and the oncologists. The perfect index that the algorithm can calculate is 10. Subsequently any penalization that occurs within the DVH will be subtracted away from the score of 10 with no bottom limit. For the intra-patient study, the mean correlation coefficient of our group's algorithm with the oncologists is 0.726 and the mean correlation coefficient of the oncologists with each other oncologist is 0.564. In the inter-patient study, the correlations proved to be stronger where the mean correlation coefficient of the algorithm with the oncologists is 0.822 and the mean correlation coefficient of the oncologists with each other is 0.699. Since our mean correlation coefficients with the oncologists for the intra-patient and the inter-patient study is higher than the mean correlation coefficient of the oncologists with each other, we can state that we represent a general oncologist within the hospital system when ranking DVHs.
Item Open Access Evaluation of Quantitative Potential of Breast Tomosynthesis Using a Voxelized Anthropomorphic Breast Phantom(2010) Mehtaji, Deep SunilPurpose: To assess the quantitative potential of breast tomosynthesis by estimating the percent density of voxelized anthropomorphic breast phantoms.
Methods and Materials:A Siemens breast tomosynthesis system was modeled using Monte Carlo methods and a voxelized anthropomorphic breast phantom. The images generated by the simulation were reconstructed using Siemens filtered back-projection software. The non-uniform background due to scatter, heel effect, and limited angular sampling was estimated by simulating and subtracting images of a uniform 100% fatty breast phantom. To estimate the density of each slice, the total number of fatty and glandular voxels was calculated both before and after applying a thresholding algorithm to classify voxels as fat vs. glandular. Finally, the estimated density of the reconstructed slice was compared to the known percent density of the corresponding slice from the voxelized phantom. This percent density estimation comparison was done for a 35%- and a 60%-dense 5cm breast phantom.
Results: Without thresholding, overall density estimation errors for the central eleven slices were 4.97% and 2.55% for the 35% and 60% dense phantoms, respectively. After thresholding to classify voxels as fat vs. glandular, errors for central eleven were 7.99% and 6.26%, respectively. Voxel to voxel matching of the phantom vs. reconstructed slice demonstrated 75.69% and 75.25% respectively of voxels were correctly classified.
Conclusion: The errors in slice density estimation were <8% for both the phantoms thus implying that quantification of breast density using tomosynthesis is possible. However, limitations of the acquisition and reconstruction process continue to pose challenges in density estimation leading to potential voxel to voxel errors that warrant further investigation.
Item Open Access High-resolution, anthropomorphic, computational breast phantom: fusion of rule-based structures with patient-based anatomy(2017) Chen, XinyuanWhile patient-based breast phantoms are realistic, they are limited by low resolution due to the image acquisition and segmentation process. The purpose of this study is to restore the high frequency components for the patient-based phantoms by adding power law noise (PLN) and breast structures generated based on mathematical models. First, 3D radial symmetric PLN with β=3 was added at the boundary between adipose and glandular tissue to connect broken tissue and create a high frequency contour of the glandular tissue. Next, selected high-frequency features from the FDA rule-based computational phantom (Cooper’s ligaments, ductal network, and blood vessels) were fused into the phantom. The effects of enhancement in this study were demonstrated by 2D mammography projections and digital breast tomosynthesis (DBT) reconstruction volumes. The addition of PLN and rule-based models leads to a continuous decrease in β. The new β is 2.76, which is similar to what typically found for reconstructed DBT volumes. The new combined breast phantoms retain the realism from segmentation and gain higher resolution after restoration.
Item Open Access Inter-Instituion Application of Knowledge-Based IMRT Treatment Planning(2012) Good, DavidIntensity Modulated Radiation Therapy (IMRT) has allowed a large degree of healthy tissue sparing while delivering therapeutic dose to tumors. However, the treatment planning process for IMRT is iterative and time consuming and the resultant plan quality is dependent on the skill and experience of the planner.
Following the work of Chanyavanich, a knowledge-based approach to IMRT treatment planning was used to generate high quality IMRT plans for patients from another hospital, using previously treated Duke plans as a reference library. An image-similarity metric was used to identify the patient from our database with the most similar anatomy to each new patient. Parameters from the Duke plan were then modified and applied to the new patient, resulting in quality dose distributions.
In conclusion, the treatment planning time was reduced to approximately ten minutes for all cases, and the resultant plans were frequently of higher quality than the original, manually produced plans. The quality of the Duke treatment plans was preserved as the plans were adapted to new patient anatomy.
Item Open Access Knowledge-Based Radiation Therapy Database Optimization on Head and Neck Cancer(2015) Lee, Gen JooIntensity modulated radiation therapy (IMRT) is commonly used to treat head and neck cancer but relies on the experience and skill of the treatment planner. Previous knowledge based radiation therapy (KBRT) used a database of 105 patient cases from Duke University Medical Center to derive the constraints and fluence map to be used as input into Eclipse. Because IMRT relies heavily on the treatment planner, a re-optimized database was created to further improve the current database and its results.
Each of the 105 patient cases was re-optimized to further lower the dose to organs at risk while keeping the planning target volume (PTV) homogeneity. Out of 105 patients, 41 had noticeable improvements and 64 had minimal or no difference. The previous versions of KBRT were used to find the matching patient based on geometry and to derive constraints that would be inputted into Eclipse for optimization. Two methods of KBRT were tested. The first method used a dose warping algorithm to compute constraints and the second method used constraints from the matching patient.
The results from the old database and re-optimized database that used the dose warping algorithm produced dose volume histograms with little to no differences. The results using constraints from matching patient showed improvements in ipsilateral parotid, larynx, oral cavity, and brainstem after re-optimization. Comparing method one and method two, there were no significant benefits of re-optimizing as the dose warping algorithm was able to produce similar results. The dose warping algorithm was significantly worse for contralateral parotid but significantly better for brainstem.
Item Open Access Multi-Case Knowledge-Based IMRT Treatment Planning in Head and Neck Cancer(2014) Grzetic, ShelbyPurpose: HNC IMRT treatment planning is a challenging process that relies heavily on the planner's experience. Previously, we used the single, best match from a library of manually planned cases to semi-automatically generate IMRT plans for a new patient. The current multi-case Knowledge Based Radiation Therapy (MC-KBRT) study utilized different matching cases for each of six individual organs-at-risk (OARs), then combined those six cases to create the new treatment plan.
Methods: From a database of 103 patient plans created by experienced planners, MC-KBRT plans were created for 40 (17 unilateral and 23 bilateral) HNC "query" patients. For each case, 2D beam's-eye-view images were used to find similar geometric "match" patients separately for each of 6 OARs. Dose distributions for each OAR from the 6 matching cases were combined and then warped to suit the query case's geometry. The dose-volume constraints were used to create the new query treatment plan without the need for human decision-making throughout the IMRT optimization. The optimized MC-KBRT plans were compared against the clinically approved plans and Version 1 (original KBRT) using the dose metrics: mean, median, and maximum (brainstem and cord+5mm) doses.
Results: Compared to Version 1, MC-KBRT had no significant reduction of the dose to any of the OARs in either unilateral/bilateral cases. Compared to the manually-planned unilateral cases, there was significant reduction of the oral cavity mean/median dose (>2Gy) at the expense of the contralateral parotid. Compared to the manually-planned bilateral cases, reduction of dose was significant in the ipsilateral parotid, larynx, and oral cavity (>3Gy mean/median) while maintaining PTV coverage.
Conclusion: MC-KBRT planning in head and neck cancer generates IMRT plans with equivalent dose sparing to manually created plans. MC-KBRT using multiple case matches does not show significant dose reduction compared to using a single match case with dose warping.
Item Open Access Optimization of Hidden Markov Model for Density Estimation in Breast Imaging(2012) Hon, SylviaBreast cancer is the second leading cause of cancer-related death in women in the United States; it is fundamentally important to detect risks as accurately and as early as possible for prevention. As breast density often correlates with risk factors, 3D Hidden Markov Model method is one quantitative approach to measuring density in breast tomosynthesis images. The purpose of developing this method was to overcome the poor depth resolution in tomosynthesis images and to get a more accurate density estimation of a woman's breast through 3D imaging.
Previous study showed that 3D Hidden Markov Model was able to accurately measure breast density for tomosynthesis images that matched closely with the defined ground truth. However qualitatively looking at the segmentation, there were noise and voxels segmented as glandular or adipose that did not reflect the breast anatomy. In addition, the model training process was very time consuming and required many patient cases. To address these limitations, this research was conducted to optimize three of the key model parameters, namely the intensity rescaling of the images, the spatial relation of the neighbors to the current voxel, and the number of training cases. The HMM model was trained using 100 breast MRI volume then validated on 12 breast tomosynthesis volumes.
The HMM model was simplified by cutting the number of grayscale intensity levels in half, while sampling distance between neighboring voxels was doubled and tripled. In spite of these changes, however, there was no significant change to the breast density estimation of the tomo images. We identified new challenges in matching grayscale histogram intensities between breast MRI and the two breast tomosynthesis prototype models, and we gained understanding of how the neighbor's distance brought in different segmentation characteristics.