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Clinical Translation of Magnetic Resonance Fingerprinting for Glioblastoma Infiltration Imaging
Clinical Translation of Magnetic Resonance Fingerprinting for Glioblastoma Infiltration Imaging
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105629
- ISBN
- 9798293895434
- DDC
- 610
- 저자명
- Zhao, Walter.
- 서명/저자
- Clinical Translation of Magnetic Resonance Fingerprinting for Glioblastoma Infiltration Imaging
- 발행사항
- [Sl] : Case Western Reserve University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 163 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 주기사항
- Advisor: Ma, Dan;Li, Shuo.
- 학위논문주기
- Thesis (Ph.D.)--Case Western Reserve University, 2025.
- 초록/해제
- 요약Glioblastoma (GBM) is the most common and aggressive adult brain tumor, with a median survival time of 15 months. Medical imaging is crucial in delineating GBM tumor extent for treatment planning, with magnetic resonance imaging (MRI) being the gold standard. However, even MRI is known to underestimate the full extent of GBMs, with stereotactic biopsies revealing microscopic infiltration beyond visible enhancing tissue borders and into adjacent regions. Although recurrence overwhelmingly occurs in this edema-rich, peritumoral zone, existing MRI methods cannot discriminate GBM infiltration from non-malignant edema. As a result, there is no consensus regarding inclusion of peritumor for clinical treatment.This work details our development of magnetic resonance fingerprinting (MRF)-based imaging methods and tools for enhanced characterization and prediction of GBM peritumoral infiltration. MRF is a quantitative MRI framework for rapid multiparametric mapping of intrinsic tissue properties, with superior reproducibility and robustness over conventional, weighted MRI. MRF maps also retain the excellent soft tissue contrast common to MRI-based methods. These advantages make MRF an ideal imaging modality for accurate, reproducible, and generalizable GBM infiltration imaging.Recognizing the ubiquity of measurement error in clinical imaging studies, we frst developed methods to strengthen the sensitivity and reproducibility of quantitative MRI image analysis. We formalized a regression calibration framework to correct for imaging marker error, demonstrating its efectiveness in improving statistical power and sample size estimation in an epilepsy case study. We also devised a physics-informed discretization approach for consistent feature extraction of quantitative MRI data, which outperformed conventional discretization in a prospectively acquired multiscanner, scan-rescan dataset. Shifting to clinical application, we then established and implemented a comprehensive workfow for brain tumor imaging consisting of a clinically deployed MRF and multiparametric MRI exam protocol; automated image processing; and dissemination of analysis fndings for 1) treatment planning on the picture archiving and communication system (PACS) and 2) surgical guidance and matched histopathology sampling in the operating room. Using this workflow, we have collected data on over 100 pre-operative GBM patients and 350 patients overall, which we used to develop and validate a data-driven, self-supervised model for GBM infiltration into the peritumoral zone.
- 일반주제명
- Medicine
- 일반주제명
- Biomedical engineering
- 일반주제명
- Medical imaging
- 일반주제명
- Neurosciences
- 일반주제명
- Oncology
- 키워드
- Glioblastoma
- 키워드
- Brain tumor
- 키워드
- Cancer
- 기타저자
- Case Western Reserve University Biomedical Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aZhao, Walter.
■24510▼aClinical Translation of Magnetic Resonance Fingerprinting for Glioblastoma Infiltration Imaging
■260 ▼a[Sl]▼bCase Western Reserve University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a163 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Ma, Dan;Li, Shuo.
■5021 ▼aThesis (Ph.D.)--Case Western Reserve University, 2025.
■520 ▼aGlioblastoma (GBM) is the most common and aggressive adult brain tumor, with a median survival time of 15 months. Medical imaging is crucial in delineating GBM tumor extent for treatment planning, with magnetic resonance imaging (MRI) being the gold standard. However, even MRI is known to underestimate the full extent of GBMs, with stereotactic biopsies revealing microscopic infiltration beyond visible enhancing tissue borders and into adjacent regions. Although recurrence overwhelmingly occurs in this edema-rich, peritumoral zone, existing MRI methods cannot discriminate GBM infiltration from non-malignant edema. As a result, there is no consensus regarding inclusion of peritumor for clinical treatment.This work details our development of magnetic resonance fingerprinting (MRF)-based imaging methods and tools for enhanced characterization and prediction of GBM peritumoral infiltration. MRF is a quantitative MRI framework for rapid multiparametric mapping of intrinsic tissue properties, with superior reproducibility and robustness over conventional, weighted MRI. MRF maps also retain the excellent soft tissue contrast common to MRI-based methods. These advantages make MRF an ideal imaging modality for accurate, reproducible, and generalizable GBM infiltration imaging.Recognizing the ubiquity of measurement error in clinical imaging studies, we frst developed methods to strengthen the sensitivity and reproducibility of quantitative MRI image analysis. We formalized a regression calibration framework to correct for imaging marker error, demonstrating its efectiveness in improving statistical power and sample size estimation in an epilepsy case study. We also devised a physics-informed discretization approach for consistent feature extraction of quantitative MRI data, which outperformed conventional discretization in a prospectively acquired multiscanner, scan-rescan dataset. Shifting to clinical application, we then established and implemented a comprehensive workfow for brain tumor imaging consisting of a clinically deployed MRF and multiparametric MRI exam protocol; automated image processing; and dissemination of analysis fndings for 1) treatment planning on the picture archiving and communication system (PACS) and 2) surgical guidance and matched histopathology sampling in the operating room. Using this workflow, we have collected data on over 100 pre-operative GBM patients and 350 patients overall, which we used to develop and validate a data-driven, self-supervised model for GBM infiltration into the peritumoral zone.
■590 ▼aSchool code: 0042.
■650 4▼aMedicine
■650 4▼aBiomedical engineering
■650 4▼aMedical imaging
■650 4▼aNeurosciences
■650 4▼aOncology
■653 ▼aGlioblastoma
■653 ▼aMagnetic resonance fingerprinting
■653 ▼aBrain tumor
■653 ▼aMagnetic resonance imaging
■653 ▼aCancer
■690 ▼a0564
■690 ▼a0574
■690 ▼a0541
■690 ▼a0992
■690 ▼a0317
■71020▼aCase Western Reserve University▼bBiomedical Engineering.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0042
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360858▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
