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Record W7052634684

Simulation, Design, and Implementation of Approaches to RF-Encoded (TRASE) MRI

2022· dissertation· en· W7052634684 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsRadiofrequency coilElectromagnetic coilMagnetic resonance imagingImage resolutionSolenoidRadio frequencyReal-time MRIEncoding (memory)Noise (video)
DOInot available

Abstract

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Magnetic Resonance Imaging (MRI) has become a powerful medical imaging tool in the last four-five decades. Technological advancements in MRI have largely focused on increasing the field strength - to achieve higher signal to noise ratio (SNR) - but there is a strong motivation behind developing low-field MRIs. Low-field MRIs are cheaper, smaller, lighter and lower-power devices in comparison to their standard clinical counterparts. Therefore, low-field MRIs are good candidates for developing portable MRIs for point-of-care (PoC) applications. One prominent need for such portable and low-field MRIs is in space missions, for studying and monitoring astronaut health. TRansmit Array Spatial Encoding (TRASE) is a novel MRI technique in which the spatial encoding is based on phase gradients of the transmit radio frequency (RF) magnetic field (B1). The TRASE technique is useful for building low-field and low-weight MRI systems, and is therefore suitable for space MRI applications.\n\nThe objective of the three studies described in this thesis was to further advance the TRASE technique, using both simulation and experimental approaches in 1-dimensional (1D), 1.5D, and 2D TRASE, for applications in space MRI prototypes. Prior to this work, TRASE had been successfully used to obtain clinically relevant 2D images of the wrist, using a Helmholtz-Maxwell coil array design. However, the image resolution was restricted due to coil efficiency and complexity, and further work was underway to develop a new RF coil design for TRASE. A Twisted Solenoid (TS) coil design for TRASE applications was being developed by the team at the University of Alberta (UofA). Even so, many aspects of the TRASE MRI technique remained unstudied but were crucial to the development of portable MRIs using TRASE.\n\nThe first of the three studies presented here focused on a (simulation-based) 1D TRASE pulse sequence optimization scheme. This study investigated the performance of a set of variants of a 1D TRASE sequence under conditions of |B1| errors. Results showed that, using optimum transmit pulse phases, high quality image encoding is achievable over ~90% of the Nyquist field-of-view (FOV) for a practically realizable variation in B1 amplitude of 11%. This improved significantly upon the performance of a previously-reported sequence which generated ~75% usable FOV within the Nyquist FOV. The results from this work can also be expanded for use in 2D TRASE imaging since a 2D TRASE pulse sequence uses a series of 1D TRASE echo trains.\n\nPrior to the second study presented here, the first MRI images from a prototype Space MRI in our lab, the "Owl" MRI, had already been obtained using 1D TRASE encoding in combination with natural slice selection. For this, 1D TRASE encoding was performed at multiple B0 frequencies (to achieve natural slice selection based on the in-built B0 gradient), and the subsequent 1D TRASE images were stacked to generate a 2D image (referred to as 1.5D TRASE imaging). With the newly developed TS coil design, work on the Owl MRI presented in this second study here was based on two objectives: 1) Obtain 1.5D TRASE images from the Owl MRI using a new transmit coil array composed of a TS coil and a Saddle coil pair (replacing the original Cube coil set), and 2) Use the Owl MRI as a developmental prototype for the Merlin MRI (another Space MRI prototype that was being developed for zero-G flight tests). In this second study, one of the first 1.5D TRASE images from the Owl MRI using the second generation Owl transmit coils is presented.\n\nMotivating the third study, a two-coil TS transmit array was being implemented for 1D TRASE by the UofA team. This work was followed by an attempt to perform 2D TRASE imaging using the TS coil array design. However, it became clear that imaging was negatively impacted due to inductive coupling among the concentrically placed B1 field coils. To understand the coupling issues, the third study presented here investigated the effects of coupled B1 fields on 2D TRASE imaging. Since TRASE relies on the use of multiple RF fields (B1 fields with different phase gradients) for k-space traversal, a TRASE pulse sequence requires RF pulses that are produced by switching between the transmit coils (B1 fields). However, interactions among the transmit RF coils can cause un-driven coils to produce unwanted B1 fields that impair the spatial encoding. The purpose of the third study was to investigate the effects of B1 field coupling using Bloch equation-based simulations and to determine the acceptable level of B1 field interactions for 2D TRASE imaging. The simulations show that 2D TRASE MRI (using a three-coil setup) displays ideal performance for pairwise coupling constant lower than k = 0.01 while having acceptable performance up to k = 0.1. This translates into S12 decoupling requirements in the range of ~(-50 dB to -30 dB) required for successful 2D TRASE MRI in this study. This result is of crucial importance for designers of practical TRASE transmit array systems.\n\nThe studies described in this thesis were all part of the the work undertaken at the Space MRI Lab, Usask, to develop MRI technology for space use. A novel research initiative such as building MRIs for space is usually a dynamic endeavour. As progress is made, new challenges are discovered which steer the path towards the final objective. The research work presented in this thesis is not different. It represents an array of studies that were conducive to the overall process of developing the technology for space MRIs using TRASE.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.189
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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