Katie Lamar

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-169

May 12, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-169.pdf

Magnetic Resonance Imaging (MRI) is a noninvasive imaging modality that can provide high spatial resolution images of human anatomy in addition to rich dynamic and quantitative information without the need for ionizing radiation. Unfortunately, this high spatial resolution comes at the cost of very low temporal resolution, leaving adequate time for motion to corrupt one's images. Motion correction and prevention remains a main research focus in the MR community. In this work, we explore a motion tracking method that uses microwave frequencies, known as the Beat Pilot Tone (BPT), to detect a wide range of motion without the need for additional on-subject hardware and or changes to the MRI sequence. The BPT, unlike other RF tracking methods, has sensitivity and accuracy comparable to conventional motion tracking methods (e.g. respiratory bellows, electrocardiogram (ECG), photoplethysmogram (PPG)).

In this work, we focus on demonstrating the effectiveness of the BPT at resolving motion in cardiac and abdominal images both retrospectively and prospectively. We show that the BPT provides highly sensitive and accurate motion estimates, resulting in retrospectively-corrected images comparable to the images corrected using the motion estimates provided by ECG and respiratory bellows. We also implemented a prospective free-breathing BPT navigated 3D SPGR application in Vista.ai's RTHawk MR Research Platform. We show that with minimal real-time processing techniques, we are able to attain images comparable to images provided by GE's respiratory bellows navigated 3D SPGR LAVA ASPIR sequence.

Advisors: Michael Lustig

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BibTeX citation:

@mastersthesis{Lamar:EECS-2023-169,
    Author= {Lamar, Katie},
    Editor= {Lustig, Michael and Anand, Suma},
    Title= {Respiratory and Cardiac Motion Correction Using the Beat Pilot Tone},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-169.html},
    Number= {UCB/EECS-2023-169},
    Abstract= {Magnetic Resonance Imaging (MRI) is a noninvasive imaging modality that can provide high spatial resolution images of human anatomy in addition to rich dynamic and quantitative information without the need for ionizing radiation. Unfortunately, this high spatial resolution comes at the cost of very low temporal resolution, leaving adequate time for motion to corrupt one's images. Motion correction and prevention remains a main research focus in the MR community. In this work, we explore a motion tracking method that uses microwave frequencies, known as the Beat Pilot Tone (BPT), to detect a wide range of motion without the need for additional on-subject hardware and or changes to the MRI sequence. The BPT, unlike other RF tracking methods, has sensitivity and accuracy comparable to conventional motion tracking methods (e.g. respiratory bellows, electrocardiogram (ECG), photoplethysmogram (PPG)).

In this work, we focus on demonstrating the effectiveness of the BPT at resolving motion in cardiac and abdominal images both retrospectively and prospectively. We show that the BPT provides highly sensitive and accurate motion estimates, resulting in retrospectively-corrected images comparable to the images corrected using the motion estimates provided by ECG and respiratory bellows. We also implemented a prospective free-breathing BPT navigated 3D SPGR application in Vista.ai's RTHawk MR Research Platform. We show that with minimal real-time processing techniques, we are able to attain images comparable to images provided by GE's respiratory bellows navigated 3D SPGR LAVA ASPIR sequence.},
}

EndNote citation:

%0 Thesis
%A Lamar, Katie 
%E Lustig, Michael 
%E Anand, Suma 
%T Respiratory and Cardiac Motion Correction Using the Beat Pilot Tone
%I EECS Department, University of California, Berkeley
%D 2023
%8 May 12
%@ UCB/EECS-2023-169
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-169.html
%F Lamar:EECS-2023-169