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Advanced Image Reconstruction and Sampling Pattern Optimization in Silent MRI
Advanced Image Reconstruction and Sampling Pattern Optimization in Silent MRI
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153012
- ISBN
- 9798384045021
- DDC
- 621.3
- 저자명
- Xiang, Haowei.
- 서명/저자
- Advanced Image Reconstruction and Sampling Pattern Optimization in Silent MRI
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 92 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Fessler, Jeffrey A.;Noll, Douglas C.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약Silent magnetic resonance imaging (mri) is a technology that allows for mri scans to be conducted with less acoustic noise than traditional mri technologies. This technology is important for a few reasons: First, the loud noises generated by traditional mri machines can be uncomfortable for some patients, particularly those with anxiety disorders, dementia, or sensory sensitivities. Second, silent mri can be useful in auditory and speaking studies. Third, the noise generated by traditional mri machines can interfere with speech communication, making it difficult for healthcare providers to communicate with patients during the scan.Model-based image reconstruction (mbir) is a technique in mri that uses mathematical models and mri physics to suppress image noise, reduce acquisition time, and improve image quality, especially in dynamic and quantitative mri. In this study, we first combined the silent mri and mbir and developed reconstruction method for both static and dynamic mri to reduce image noise and artifacts, improve image quality and resolution, and boost functional/quantitative analysis. Secondly, we optimized sampling trajectory to improve the k-space coverage and reduce image artifacts in the reconstruction. Lastly, we proposed methods of designing shaped rf pulse, and developed variable flip angle schemes to create more uniform longitudinal magnetization.Through model-based image reconstruction, we found that signal modeling using two-system matrices resulted in reduced signal artifact from overlapping echoes and improved SNR of close to 1.4 relative to reconstruction with a single system matrix. The development of joint reconstruction methods, which estimate multiple echoes simultaneously, played a crucial role in improving the temporal signal-to-noise ratio and reducing noise artifacts. The k-space trajectory optimization can improve image quality and reduce undersampling artifact by sampling more efficiently. Additionally, the optimization of RF pulse designs facilitated better magnetization and more uniform signal excitation across the imaging volume, maximizing total magnetization and achieving more uniform excitation profiles. These findings suggest that by carefully designing reconstruction algorithms, sampling patterns, and excitation modules, the image quality of silent mri can be improved for broader use in both research and clinical settings.
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer engineering
- 일반주제명
- Medical imaging
- 키워드
- Looping star
- 기타저자
- University of Michigan Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153012
■006m o d
■007cr#unu||||||||
■020 ▼a9798384045021
■035 ▼a(MiAaPQ)AAI31631464
■035 ▼a(MiAaPQ)umichrackham005666
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aXiang, Haowei.
■24510▼aAdvanced Image Reconstruction and Sampling Pattern Optimization in Silent MRI
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a92 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Fessler, Jeffrey A.;Noll, Douglas C.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aSilent magnetic resonance imaging (mri) is a technology that allows for mri scans to be conducted with less acoustic noise than traditional mri technologies. This technology is important for a few reasons: First, the loud noises generated by traditional mri machines can be uncomfortable for some patients, particularly those with anxiety disorders, dementia, or sensory sensitivities. Second, silent mri can be useful in auditory and speaking studies. Third, the noise generated by traditional mri machines can interfere with speech communication, making it difficult for healthcare providers to communicate with patients during the scan.Model-based image reconstruction (mbir) is a technique in mri that uses mathematical models and mri physics to suppress image noise, reduce acquisition time, and improve image quality, especially in dynamic and quantitative mri. In this study, we first combined the silent mri and mbir and developed reconstruction method for both static and dynamic mri to reduce image noise and artifacts, improve image quality and resolution, and boost functional/quantitative analysis. Secondly, we optimized sampling trajectory to improve the k-space coverage and reduce image artifacts in the reconstruction. Lastly, we proposed methods of designing shaped rf pulse, and developed variable flip angle schemes to create more uniform longitudinal magnetization.Through model-based image reconstruction, we found that signal modeling using two-system matrices resulted in reduced signal artifact from overlapping echoes and improved SNR of close to 1.4 relative to reconstruction with a single system matrix. The development of joint reconstruction methods, which estimate multiple echoes simultaneously, played a crucial role in improving the temporal signal-to-noise ratio and reducing noise artifacts. The k-space trajectory optimization can improve image quality and reduce undersampling artifact by sampling more efficiently. Additionally, the optimization of RF pulse designs facilitated better magnetization and more uniform signal excitation across the imaging volume, maximizing total magnetization and achieving more uniform excitation profiles. These findings suggest that by carefully designing reconstruction algorithms, sampling patterns, and excitation modules, the image quality of silent mri can be improved for broader use in both research and clinical settings.
■590 ▼aSchool code: 0127.
■650 4▼aElectrical engineering
■650 4▼aComputer engineering
■650 4▼aMedical imaging
■653 ▼aImage reconstruction
■653 ▼aLooping star
■653 ▼aFunctional magnetic resonance imaging
■653 ▼aTrajectory optimization
■653 ▼aSilent magnetic resonance imaging
■690 ▼a0544
■690 ▼a0574
■690 ▼a0464
■71020▼aUniversity of Michigan▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164515▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
