Matching Items (3)
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- All Subjects: Compressed Sensing
- All Subjects: Electric coils
- Creators: Kodibagkar, Vikram
Description
Magnetic Resonance Imaging (MRI) is an efficient non-invasive imaging tool widely used in medical field to produce high quality images. The MRI signal is detected with specifically developed radio frequency (RF) systems or "coils". There are several key parameters to evaluate the performance of RF coils: signal-to-noise ratio (SNR), homogeneity, quality factor (Q factor), sensitivity, etc. The choice of coil size and configuration depends on the object to be imaged. While surface coils have better sensitivity, volume coils are often employed to image a larger region of interest (ROI) as they display better spatial homogeneity. For the cell labeling and imaging studies using the newly developed siloxane based nanoemulsions as 1H MR reporter probes, the first step is to determine the sensitivity of signal detection under controlled conditions in vitro. In this thesis, a novel designed 7 Tesla RF volume coil was designed and tested for detection of small quantities of siloxane probe as well as for imaging of labeled tumor spheroid. The procedure contains PCB circuit design, RF probe design, test and subsequent modification. In this report, both theory and design methodology will be discussed.
ContributorsWang, Haiqing (Author) / Kodibagkar, Vikram (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Sadleir, Rosalind (Committee member) / Arizona State University (Publisher)
Created2014
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
Description
Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique that offers a unique ability to provide the spatial distribution of relevant biochemical compounds (metabolites). The ‘spectrum’ of information provided by MRSI is used as biomarkers for the differential diagnosis of several diseases such as cancer or neurological disorders. Treatment responsive brain tumors can appear similar to non-responsive tumors on conventional anatomical MR images, earlier in the therapy, leading to a poor prognosis for many patients. Biomarkers such as lactate are particularly of interest in the oncological studies of solid tumors to determine their energy metabolism, blood flow, and hypoxia. Despite the capability of nearly all clinical MRI scanners to perform MRSI only limited integration of MRSI into routine clinical studies has occurred to date. The major challenges affecting its true potential are the inherently long acquisition time, low signal-to-noise (SNR) of the signals, overlapping of spectral lines, or the presence of artifacts. The goal of this dissertation work is to facilitate MRSI in routine clinical studies without affecting the current patient throughput.
In this work, the Compressed Sensing (CS) strategy was used to accelerate conventional Point RESolved Spectroscopy (PRESS) MRSI by sampling well below the Shannon-Nyquist limit. Two undersampling strategies, namely the pseudo-random variable density and a novel a priori method was developed and implemented on a clinical scanner. Prospectively undersampled MRSI data was acquired from patients with various brain-related concerns. Spatial-spectral post-processing and CS reconstruction pipeline was developed for multi-channel undersampled data. The fidelity of the CS-MRSI method was determined by comparing the CS reconstructed data to the fully sampled data. Statistical results showed that the a priori approach maintained high spectral fidelity compared to the fully sampled reference for an 80% reduction in scan time. Next, an improvement to the CS-MRSI reconstruction was achieved by incorporating coil sensitivity maps as support in the iterative process. Further, a CS-MRSI-based fast lactate spectroscopic imaging method was developed and implemented to achieve complete water and fat suppression for accurate spatial localization and quantification of lactate in tumors. In vitro phantoms were developed, and the sequence was tested to determine the efficacy of CS-MRSI for low SNR signals, the efficacy of the CS acceleration was determined with statistical analysis.
ContributorsBikkamane Jayadev, Nutandev (Author) / Kodibagkar, Vikram (Thesis advisor) / Chang, John (Committee member) / Robison, Ryan (Committee member) / Smith, Barbara (Committee member) / Sohn, Sung-Min (Committee member) / Arizona State University (Publisher)
Created2021