Browsing by Subject "Tomosynthesis"
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Item Open Access Computer Aided Detection of Masses in Breast Tomosynthesis Imaging Using Information Theory Principles(2008-09-18) Singh, SwateeBreast cancer screening is currently performed by mammography, which is limited by overlying anatomy and dense breast tissue. Computer aided detection (CADe) systems can serve as a double reader to improve radiologist performance. Tomosynthesis is a limited-angle cone-beam x-ray imaging modality that is currently being investigated to overcome mammography's limitations. CADe systems will play a crucial role to enhance workflow and performance for breast tomosynthesis.
The purpose of this work was to develop unique CADe algorithms for breast tomosynthesis reconstructed volumes. Unlike traditional CADe algorithms which rely on segmentation followed by feature extraction, selection and merging, this dissertation instead adopts information theory principles which are more robust. Information theory relies entirely on the statistical properties of an image and makes no assumptions about underlying distributions and is thus advantageous for smaller datasets such those currently used for all tomosynthesis CADe studies.
The proposed algorithm has two 2 stages (1) initial candidate generation of suspicious locations (2) false positive reduction. Images were accrued from 250 human subjects. In the first stage, initial suspicious locations were first isolated in the 25 projection images per subject acquired by the tomosynthesis system. Only these suspicious locations were reconstructed to yield 3D Volumes of Interest (VOI). For the second stage of the algorithm false positive reduction was then done in three ways: (1) using only the central slice of the VOI containing the largest cross-section of the mass, (2) using the entire volume, and (3) making decisions on a per slice basis and then combining those decisions using either a linear discriminant or decision fusion. A 92% sensitivity was achieved by all three approaches with 4.4 FPs / volume for approach 1, 3.9 for the second approach and 2.5 for the slice-by-slice based algorithm using decision fusion.
We have therefore developed a novel CADe algorithm for breast tomosynthesis. The techniques uses an information theory approach to achieve very high sensitivity for cancer detection while effectively minimizing false positives.
Item Open Access Correlation Imaging for Improved Cancer Detection(2008-11-10) Chawla, AmarpreetWe present a new x-ray imaging technique, Correlation Imaging (CI), for improved breast and lung cancer detection. In CI, multiple low-dose radiographic images are acquired along a limited angular arc. Information from unreconstructed angular projections is directly combined to reduce the effect of overlying anatomy - the largest bottleneck in diagnosing cancer with projection imaging. In addition, CI avoids reconstruction artifacts that otherwise limit the performance of tomosynthesis. This work involved assessing the feasibility of the CI technique, its optimization, and its implementation for breast and chest imaging.
First a theoretical model was developed to determine the diagnostic information content of projection images using a mathematical observer. The model was benchmarked for a specific application in assessing the impact of reduced dose in mammography. Using this model, a multi-factorial task-based framework was developed to optimize the image acquisition of CI using existing low-dose clinical data. The framework was further validated using a CADe processor. Performance of CI was evaluated on mastectomy specimens at clinically relevant doses and further compared to tomosynthesis. Finally, leveraging on the expected improvement in breast imaging, a new hardware capable of CI acquisition for chest imaging was designed, prototyped, evaluated, and experimentally validated.
The theoretical model successfully predicted diagnostic performance on mammographic backgrounds, indicating a possible reduction in mammography dose by as much as 50% without adversely affecting lesion detection. Application of this model on low-dose clinical data showed that peak CI performance may be obtained with 15-17 projections. CAD results confirmed similar trends. Mastectomy specimen results at higher dose revealed that the performance of optimized breast CI may exceed that of mammography and tomosynthesis by 18% and 8%, respectively. Furthermore, for both CI and tomosynthesis, highest dose setting and maximum angular span with an angular separation of 2.75o was found to be optimum, indicating a threshold in the number of projections per angular span for optimum performance.
Finally, for the CI chest imaging system, the positional errors were found to be within 1% and motion blur to have minimal impact on the system MTF. The clinical images had excellent diagnostic quality for potentially improved lung cancer detection. The system was found to be robust and scalable to enable advanced applications for chest radiography, including novel tomosynthesis trajectories and stereoscopic imaging.
Item Open Access CT Radiation Dosimetry Study using Monte Carlo Simulation and Computational Anthropomorphic Phantoms(2012) Zhang, YakunThere are three main x-ray based modalities for imaging the thorax: radiography, tomosynthesis, and computed tomography (CT). CT perhaps provides the highest level of feature resolution but at notably higher radiation dose, which has increased the concern among radiation protection professionals. Being able to accurately assess the radiation dose patients receive during CT procedures is a crucial step in the management of CT dose. To identify the best imaging modality for patients, the American College of Radiology published the guiding principle of "The right exam, for the right reason, at the right time". To implement this principle in making an appropriate choice between standard chest projection imaging, tomosynthesis, and CT, the organ and effective dose for each modality should be accurately known. This thesis work attempted to explain the effect on dose results when choosing different types of computational phantoms used in CT dosimetry; this work also compared radiation dose across three main x-ray based modalities on one common platform for different body shape adults.
The first part of this thesis compared organ doses, effective doses, and risk indices from 13 representative adult CT protocols using four types of reference phantoms (XCAT, ICRP 110, ImPACT, and CT-Expo). Despite closely-matched organ mass, total body weight, and height, large differences in organ dose exist due to variation in organ location, spatial distribution, and dose approximation method. Dose differences for fully irradiated radiosensitive organs were much smaller than those for partially irradiated organs. Weighted dosimetry quantities including effective dose, male risk indices, k factors, and male q factors agreed well across phantoms. The female risk indices and q factors varied considerably across phantoms.
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 Quantitative Breast Tomosynthesis Imaging: From Phantoms to Patients(2011) Shafer, Christina MaeBreast cancer is currently the most common non-skin cancer and the second leading cause of cancer-related death in women here in the United States. X-ray mammography is currently the standard clinical imaging modality for breast cancer screening and diagnosis due to its high sensitivity and resolution at a low patient dose. With the advancement of breast imaging from analog to digital, quantitative measurements rather than qualitative assessments can be made from these images. One such measurement, mammographic breast density (i.e. the percentage of the entire breast volume that is taken up by dense glandular tissue), has been shown to be a biomarker well correlated with cancer risk. However, a digital mammogram still suffers from its projective nature. The resulting overlap of normal breast tissue can obscure lesions, limit quantitative measurement accuracy, and present false alarms leading to unnecessary recall studies. To address this key limitation, several 3D imaging techniques have been developed such as breast magnetic resonance imaging (MRI), dedicated breast computed tomography (CT), and digital breast tomosynthesis (tomo). Perhaps the most recently developed modality is tomo, which is a limited-angle cone-beam CT of the breast compressed in the same geometry as mammography. Because tomo retains all the aforementioned advantages of mammography but adds depth information and can be built based on an existing digital mammography device, measuring breast density and extracting other quantitative features from tomo images was a major focus of this study.
Before attempting to measure breast density and other features from reconstructed tomo image volumes, the quantitative potential of this imaging modality was assessed. First, we explored a slice-by-slice technique that measures tissue density using only the information from a single slice from the reconstructed tomosynthesis volume with geometrically simple tissue-equivalent phantoms. Once this task has been satisfactorily performed, we studied a probabilistic approach toward quantitation of the entire 3D volume. Some work has been done previously in the realm of 2D hidden Markov random fields (HMRFs) to categorize mammograms according to their Wolf pattern, detect mammographic lesions, and segment satellite and mixed media (text/photograph) images. For this project, a 3D hidden Markov model (HMM) method was developed and applied to tomo images under the simplified assumption that the possible tissue type of each tomo voxel is either adipose (fatty) or glandular (dense). Because adipose and glandular tissue is easily distinguished in MR images, patient breast MRIs were used to train, validate, and finally to assess the accuracy of our HMM segmentation algorithm when applied to tomo images by comparing the volumetric breast density to the MRI breast density for the same patient. Because they are so often studied conjunctively, several image texture features were calculated and compared between MRI and tomo as well.
Another aim of our study was to investigate whether changes in macroscopic 3D imaging features (texture and density) can accurately predict the chemoprevention response that was measured with Random Periareolar Fine Needle Aspiration (RPFNA) cytology for a uniquely young high-risk cohort of women. This aim to investigate the potential of combining multi-modality macroscopic 3D imaging information with a cytological measure of risk and then investigating how response to tamoxifen and other chemoprevention treatment affects each of these risk biomarkers in young, high-risk women is completely novel in the fields of medical physics and biomedical engineering.
Item Embargo Read like a Radiologist: Cancer Detection using Multi-view Correspondence in Digital Breast Tomosynthesis(2023) Ren, YinhaoBreast cancer is the second leading cause of cancer death among women, with approximately 43,780 related deaths annually. Effective screening programs are essential in reducing mortality rates by providing early diagnosis and treatment. Historically, mammography has been the most reliable screening method, significantly dropping the per capita mortality rate since its widespread adoption in the 1980s. However, the increasing workload of approximately 40 million mammography procedures conducted annually in the US poses a significant challenge to the healthcare system. This leads to reports of high rates of burnout among breast radiologists, which can decrease reading accuracy and patient care quality. Therefore, there is a need to improve the existing breast cancer screening workflow to address this challenge.
CAD algorithms have been developed to reduce radiologists' workload and address burnout. However, existing single-view CAD systems often have limited cancer detection performance as well as clinical impact. To overcome this limitation, we collaborated with iCAD (Nashua, NA) to develop a novel Computer-Aided-Detection (CAD) framework for digital breast tomosynthesis (DBT) that mimics the multi-view mammography reading practice used by breast radiologists. As of May 2023, the algorithm is under the initial submission for FDA 510(k) approval. This dissertation introduces a multi-view DBT lesion detection framework consisting of four chapters. Chapter 1 highlights the challenges of breast cancer screening and the necessity for an enhanced DBT CAD algorithm. Chapter 2 presents the single-view detection pipeline, while chapter 3 proposes the ipsilateral refinement concept that improves cancer lesion detection performance. Chapter 4 outlines the temporal matching concept that enhances system-level performance by integrating lesion temporal growth information. Chapter 5 showcases a few additional studies that supported the development of the multi-view lesion detection algorithm.
Our design uses cascaded task-specific models for each of our proposed modules, enabling intermediate reasoning of the multi-view reading steps. This approach allows the radiologist to inspect the output generated by the CAD system and verify the reasoning behind the system's decision, providing an additional layer of validation for complex high-stakes decisions.
Item Open Access Three-dimensional computer generated breast phantom based on empirical data(MEDICAL IMAGING 2008: PHYSICS OF MEDICAL IMAGING, PTS 1-3, 2008) Li, CM; Segars, WP; Lo, JY; Veress, AI; Boone, JM; III, DJT