Matching Items (5)
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- All Subjects: Diagnostic imaging
- Creators: Kodibagkar, Vikram
- Creators: Pavlicek, William
Description
Ionizing radiation used in the patient diagnosis or therapy has negative effects on the patient body in short term and long term depending on the amount of exposure. More than 700,000 examinations are everyday performed on Interventional Radiology modalities [1], however; there is no patient-centric information available to the patient or the Quality Assurance for the amount of organ dose received. In this study, we are exploring the methodologies to systematically reduce the absorbed radiation dose in the Fluoroscopically Guided Interventional Radiology procedures. In the first part of this study, we developed a mathematical model which determines a set of geometry settings for the equipment and a level for the energy during a patient exam. The goal is to minimize the amount of absorbed dose in the critical organs while maintaining image quality required for the diagnosis. The model is a large-scale mixed integer program. We performed polyhedral analysis and derived several sets of strong inequalities to improve the computational speed and quality of the solution. Results present the amount of absorbed dose in the critical organ can be reduced up to 99% for a specific set of angles. In the second part, we apply an approximate gradient method to simultaneously optimize angle and table location while minimizing dose in the critical organs with respect to the image quality. In each iteration, we solve a sub-problem as a MIP to determine the radiation field size and corresponding X-ray tube energy. In the computational experiments, results show further reduction (up to 80%) of the absorbed dose in compare with previous method. Last, there are uncertainties in the medical procedures resulting imprecision of the absorbed dose. We propose a robust formulation to hedge from the worst case absorbed dose while ensuring feasibility. In this part, we investigate a robust approach for the organ motions within a radiology procedure. We minimize the absorbed dose for the critical organs across all input data scenarios which are corresponding to the positioning and size of the organs. The computational results indicate up to 26% increase in the absorbed dose calculated for the robust approach which ensures the feasibility across scenarios.
ContributorsKhodadadegan, Yasaman (Author) / Zhang, Muhong (Thesis advisor) / Pavlicek, William (Thesis advisor) / Fowler, John (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays and eddy currents can cause spatial and temporal distortions in the encoding gradient waveforms. This causes sampling discrepancies between the actual and the ideal k-space trajectory. Reconstruction assuming an ideal trajectory can result in shading and blurring artifacts in spiral images. Current methods to estimate such hardware errors require many modifications to the pulse sequence, phantom measurements or specialized hardware. This work presents a new method to estimate time-varying system delays for spiral-based trajectories. It requires a minor modification of a conventional stack-of-spirals sequence and analyzes data collected on three orthogonal cylinders. The method is fast, robust to off-resonance effects, requires no phantom measurements or specialized hardware and estimate variable system delays for the three gradient channels over the data-sampling period. The initial results are presented for acquired phantom and in-vivo data, which show a substantial reduction in the artifacts and improvement in the image quality.
ContributorsBhavsar, Payal (Author) / Pipe, James G (Thesis advisor) / Frakes, David (Committee member) / Kodibagkar, Vikram (Committee member) / Arizona State University (Publisher)
Created2013
Description
Computed tomography (CT) is one of the essential imaging modalities for medical diagnosis. Since its introduction in 1972, CT technology has been improved dramatically, especially in terms of its acquisition speed. However, the main principle of CT which consists in acquiring only density information has not changed at all until recently. Different materials may have the same CT number, which may lead to uncertainty or misdiagnosis. Dual-energy CT (DECT) was reintroduced recently to solve this problem by using the additional spectral information of X-ray attenuation and aims for accurate density measurement and material differentiation. However, the spectral information lies in the difference between two low and high energy images or measurements, so that it is difficult to acquire the accurate spectral information due to amplification of high pixel noise in the resulting difference image. In this work, a new model and an image enhancement technique for DECT are proposed, based on the fact that the attenuation of a high density material decreases more rapidly as X-ray energy increases. This fact has been previously ignored in most of DECT image enhancement techniques. The proposed technique consists of offset correction, spectral error correction, and adaptive noise suppression. It reduced noise, improved contrast effectively and showed better material differentiation in real patient images as well as phantom studies.
ContributorsPark, Kyung Kook (Author) / Metin, Akay (Thesis advisor) / Pavlicek, William (Committee member) / Akay, Yasemin (Committee member) / Towe, Bruce (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2011
Description
Dynamic susceptibility contrast MRI (DSC-MRI) is a powerful tool used to quantitatively measure parameters related to blood flow and volume in the brain. The technique is known as a “bolus-tracking” method and relies upon very fast scanning to accurately measure the flow of contrast agent into and out of a region of interest. The need for high temporal resolution to measure contrast agent dynamics limits the spatial coverage of perfusion parameter maps which limits the utility of DSC-perfusion studies in pathologies involving the entire brain. Typical clinical DSC-perfusion studies are capable of acquiring 10-15 slices, generally centered on a known lesion or pathology.
The methods developed in this work improve the spatial coverage of whole-brain DSC-MRI by combining a highly efficient 3D spiral k-space trajectory with Generalized Autocalibrating Partial Parallel Acquisition (GRAPPA) parallel imaging without increasing temporal resolution. The proposed method is capable of acquiring 30 slices with a temporal resolution of under 1 second, covering the entire cerebrum with isotropic spatial resolution of 3 mm. Additionally, the acquisition method allows for correction of T1-enhancing leakage effects by virtue of collecting two echoes, which confound DSC perfusion measurements. The proposed DSC-perfusion method results in high quality perfusion parameter maps across a larger volume than is currently available with current clinical standards, improving diagnostic utility of perfusion MRI methods, which ultimately improves patient care.
The methods developed in this work improve the spatial coverage of whole-brain DSC-MRI by combining a highly efficient 3D spiral k-space trajectory with Generalized Autocalibrating Partial Parallel Acquisition (GRAPPA) parallel imaging without increasing temporal resolution. The proposed method is capable of acquiring 30 slices with a temporal resolution of under 1 second, covering the entire cerebrum with isotropic spatial resolution of 3 mm. Additionally, the acquisition method allows for correction of T1-enhancing leakage effects by virtue of collecting two echoes, which confound DSC perfusion measurements. The proposed DSC-perfusion method results in high quality perfusion parameter maps across a larger volume than is currently available with current clinical standards, improving diagnostic utility of perfusion MRI methods, which ultimately improves patient care.
ContributorsTurley, Dallas C (Author) / Pipe, James G (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Frakes, David (Committee member) / Sadleir, Rosalind (Committee member) / Schmainda, Kathleen (Committee member) / Arizona State University (Publisher)
Created2017
Description
Compressed sensing magnetic resonance spectroscopic imaging (MRSI) is a noninvasive and in vivo potential diagnostic technique for cancer imaging. This technique undersamples the distribution of specific cancer biomarkers within an MR image as well as changes in the temporal dimension and subsequently reconstructs the missing data. This technique has been shown to retain a high level of fidelity even with an acceleration factor of 5. Currently there exist several different scanner types that each have their separate analytical methods in MATLAB. A graphical user interface (GUI) was created to facilitate a single computing platform for these different scanner types in order to improve the ease and efficiency with which researchers and clinicians interact with this technique. A GUI was successfully created for both prospective and retrospective MRSI data analysis. This GUI retained the original high fidelity of the reconstruction technique and gave the user the ability to load data, load reference images, display intensity maps, display spectra mosaics, generate a mask, display the mask, display kspace and save the corresponding spectra, reconstruction, and mask files. Parallelization of the reconstruction algorithm was explored but implementation was ultimately unsuccessful. Future work could consist of integrating this parallelization method, adding intensity overlay functionality and improving aesthetics.
ContributorsLammers, Luke Michael (Author) / Kodibagkar, Vikram (Thesis director) / Hu, Harry (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05