Radiology Commons^™

Characterization Of A Solid-State Detector For Dosimetry In The Diagnostic Energy Range With Verification Via Monte Carlo Estimation Of Average Breast Dose In Mammography, Areej Aljabal

Theses and Dissertations

This dissertation aims to develop a simplified QA metric for estimating radiation dose in mammography. This metric, the Mid-Breast Dose (MDB) Index, maybe be proposed as an alternative dose index, consistent with average glandular dose (AGD), for routine QA. MBD was obtained using “Phantom mid-point Air Kerma (AK) measurement” for conventional mammography. The advantage of this method is that MBD can be measured directly without requiring multiple conversion factors. The accuracy of clinical measurements in computing the MBD was assessed by comparing it with the AGD estimated by the Dance formalism experimentally and Monte Carlo simulation.

MBD methodology relies on …

Modeling Of Patient-Specific Periaortic Mechanics And Pulmonary Artery Hemodynamics Based On Phase-Contrast Magnetic Resonance Imaging Sequences., Johane H. Bracamonte

Theses and Dissertations

Inverse modeling in cardiovascular medicine is a collection of methodologies that can provide non-invasive patient-specific estimations of clinical risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs.

Herein, three different phase contrast magnetic resonance imaging (MRI) modalities were implemented as input data, displacement encoding with stimulated echoes (DENSE MRI) applied, and time-resolved velocity encoding phase-contrast MRI, in 1D and 3D, applied to pulmonary artery (PA) hemodynamics.

A model to account for the effect of periaortic interactions due to static and dynamic structures …

A Deep Learning U-Net For Detecting And Segmenting Liver Tumors, Vidhya Cardozo

Theses and Dissertations

Visualization of liver tumors on simulation CT scans is challenging even with contrast-enhancement, due to the sensitivity of the contrast enhancement to the timing of the CT acquisition. Image registration to magnetic resonance imaging (MRI) can be helpful for delineation, but differences in patient position, liver shape and volume, and the lack of anatomical landmarks between the two image sets makes the task difficult. This study develops a U-Net based neural network for automated liver and tumor segmentation for purposes of radiotherapy treatment planning. Non-contrast simulation based abdominal CT axial scans of 52 patients with primary liver tumors were utilized. …

Advances In Dual-Energy Computed Tomography Imaging Of Radiological Properties, Dong Han

Theses and Dissertations

Dual-energy computed tomography (DECT) has shown great potential in the reduction of uncertainties of proton ranges and low energy photon cross section estimation used in radiation therapy planning. The work presented herein investigated three contributions for advancing DECT applications. 1) A linear and separable two-parameter DECT, the basis vector model (BVM) was used to estimate proton stopping power. Compared to other nonlinear two-parameter models in the literature, the BVM model shows a comparable accuracy achieved for typical human tissues. This model outperforms other nonlinear models in estimations of linear attenuation coefficients. This is the first study to clearly illustrate the …

Estimating The Respiratory Lung Motion Model Using Tensor Decomposition On Displacement Vector Field, Kingston Kang

Theses and Dissertations

Modern big data often emerge as tensors. Standard statistical methods are inadequate to deal with datasets of large volume, high dimensionality, and complex structure. Therefore, it is important to develop algorithms such as low-rank tensor decomposition for data compression, dimensionality reduction, and approximation.

With the advancement in technology, high-dimensional images are becoming ubiquitous in the medical field. In lung radiation therapy, the respiratory motion of the lung introduces variabilities during treatment as the tumor inside the lung is moving, which brings challenges to the precise delivery of radiation to the tumor. Several approaches to quantifying this uncertainty propose using a …

Motion-Induced Artifact Mitigation And Image Enhancement Strategies For Four-Dimensional Fan-Beam And Cone-Beam Computed Tomography, Matthew J. Riblett

Theses and Dissertations

Four dimensional imaging has become part of the standard of care for diagnosing and treating non-small cell lung cancer. In radiotherapy applications 4D fan-beam computed tomography (4D-CT) and 4D cone-beam computed tomography (4D-CBCT) are two advanced imaging modalities that afford clinical practitioners knowledge of the underlying kinematics and structural dynamics of diseased tissues and provide insight into the effects of regular organ motion and the nature of tissue deformation over time. While these imaging techniques can facilitate the use of more targeted radiotherapies, issues surrounding image quality and accuracy currently limit the utility of these images clinically.

The purpose of …

An Investigation Of Nurbs-Based Deformable Image Registration, Travis J. Jacobson

Theses and Dissertations

Deformable image registration (DIR) is an essential tool in medical image processing. It provides a means to combine image datasets, allowing for intra-subject, inter-subject, multi-modality, and multi-instance analysis, as well as motion detection and compensation. One of the most popular DIR algorithms models the displacement vector field (DVF) as B-splines, a sum of piecewise polynomials with coefficients that enable local shape control. B-splines have many advantageous properties in the context of DIR, but they often struggle to adequately model steep local gradients and discontinuities. This dissertation addresses that limitation by proposing the replacement of conventional B-splines with a generalized formulation …

Optimization Of Functional Mri Methods For Olfactory Interventional Studies At 3t, Vishwadeep Ahluwalia

Theses and Dissertations

Functional MRI technique is vital in investigating the effect of an intervention on cortical activation in normal and patient population. In many such investigations, block stimulation paradigms are still the preferred method of inducing brain activation during functional imaging sessions because of the high BOLD response, ease in implementation and subject compliance especially in patient population. However, effect of an intervention can be validly interpreted only after reproducibility of a detectable BOLD response evoked by the stimulation paradigm is first verified in the absence of the intervention. Detecting a large BOLD response that is also reproducible is a difficult task …