Improving Arterial Spin Labeling in Clinical Application With Deep Learning
Improving Arterial Spin Labeling in Clinical Application With Deep Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153037
- ISBN
- 9798346740179
- DDC
- 610
- 저자명
- Shou, Qinyang.
- 서명/저자
- Improving Arterial Spin Labeling in Clinical Application With Deep Learning
- 발행사항
- [Sl] : University of Southern California, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 122 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
- 주기사항
- Advisor: Wang, Danny.
- 학위논문주기
- Thesis (Ph.D.)--University of Southern California, 2024.
- 초록/해제
- 요약Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique that can measure human cerebral blood flow (CBF) non-invasively. However, clinical application of this technique remains challenging due to the intrinsic low signal-to-noise ratio (SNR) and long scan time. Also, heterogeneity of ASL imaging protocols across vendor platforms make quantification not reliable. Traditional methods of denoising usually assumes an image models and/or noise characteristics, which may not well represent the real data. Deep Learning (DL)-based models can learn the underlying patterns purely from real data. Recent developments of DL in image processing and image generation provide powerful tools to improve clinical applications of medical imaging, such as improving image quality, reducing time for image acquisition, etc. However, while a handful studies have demonstrated the feasibility of DL applications on ASL, there remain large gaps in the reliable application of DL methods for improving the clinical use of ASL on multiple vendor platforms with different imaging protocols (e.g., single-delay and multi-delay). The purpose of this work is to adapt, optimize and apply some of the latest DL techniques, including Transformer and diffusion model to improving the clinical translation of ASL by improving the image quality and/or reduce scan time, and generating the missing modality to enable CBF quantification to improve standardization cross vendors for the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.There are three specific aims in this study. In the first aim, a flexible Transformer-based DL denoising scheme will be developed and evaluated for 3D ASL to improve SNR and/or reduce scan time for both single-delay and multi-delay ASL data. Our hypothesis is that with the completion of aim 1, we will be able to improve the image quality for ASL acquired from multiple vendors with the trained model without introducing bias in quantification of cerebral blood flow (CBF) and/or arterial transit time (ATT). In the second aim, the proposed DL framework and the trained model will be adapted to a high-resolution pediatric multi-delay ASL dataset for perfusion imaging of pediatric choroid plexus. Since there are no reference images for this cutting edge application, some self-supervised learning techniques will be explored. We will compare the performance of the proposed deep learning method with state-of-the-art conventional denoising method like total generalized variation (TGV). Our hypothesis is that with completion of aim 2, the proposed deep learning method will show better performance than the traditional method, both improving image quality and the test-retest reliability for pediatric choroid plexus perfusion imaging.In the third aim, generative diffusion model will be applied to generating the M0 from the control image for Siemens 3D pulsed ASL (PASL) scans in the ADNI-3 dataset, where M0 is not acquired. The generated M0 can be used to quantify CBF for analysis of CBF variations among subjects with normal cognition, mild cognitive impairment (MCI) and AD. This can help resolve the heterogeneity of different type of ASL scans to standardize quantification of CBF in the AD dataset. Our hypothesis is that with the completion of aim 3, Siemens 3D PASL scans in the ADNI-3 dataset can be used to quantify CBF with the acquired control images and the generated M0. Analysis of CBF data from different MR vendors will reveal similar characteristic of deficits in quantitative CBF in MCI and AD subjects compared to normal, which can show better differentiation between AD and normal people compared to using non-standardized perfusion images.In conclusion, with the completion of the three specific aims, we will show that latest DL methods such as Transformers and diffusion models have the potential to improve ASL in clinical applications by enhancing the image quality and better standardization.
- 일반주제명
- Biomedical engineering
- 키워드
- Deep learning
- 기타저자
- University of Southern California Biomedical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798346740179
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■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aShou, Qinyang.
■24510▼aImproving Arterial Spin Labeling in Clinical Application With Deep Learning
■260 ▼a[Sl]▼bUniversity of Southern California▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a122 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-05, Section: B.
■500 ▼aAdvisor: Wang, Danny.
■5021 ▼aThesis (Ph.D.)--University of Southern California, 2024.
■520 ▼aArterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique that can measure human cerebral blood flow (CBF) non-invasively. However, clinical application of this technique remains challenging due to the intrinsic low signal-to-noise ratio (SNR) and long scan time. Also, heterogeneity of ASL imaging protocols across vendor platforms make quantification not reliable. Traditional methods of denoising usually assumes an image models and/or noise characteristics, which may not well represent the real data. Deep Learning (DL)-based models can learn the underlying patterns purely from real data. Recent developments of DL in image processing and image generation provide powerful tools to improve clinical applications of medical imaging, such as improving image quality, reducing time for image acquisition, etc. However, while a handful studies have demonstrated the feasibility of DL applications on ASL, there remain large gaps in the reliable application of DL methods for improving the clinical use of ASL on multiple vendor platforms with different imaging protocols (e.g., single-delay and multi-delay). The purpose of this work is to adapt, optimize and apply some of the latest DL techniques, including Transformer and diffusion model to improving the clinical translation of ASL by improving the image quality and/or reduce scan time, and generating the missing modality to enable CBF quantification to improve standardization cross vendors for the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.There are three specific aims in this study. In the first aim, a flexible Transformer-based DL denoising scheme will be developed and evaluated for 3D ASL to improve SNR and/or reduce scan time for both single-delay and multi-delay ASL data. Our hypothesis is that with the completion of aim 1, we will be able to improve the image quality for ASL acquired from multiple vendors with the trained model without introducing bias in quantification of cerebral blood flow (CBF) and/or arterial transit time (ATT). In the second aim, the proposed DL framework and the trained model will be adapted to a high-resolution pediatric multi-delay ASL dataset for perfusion imaging of pediatric choroid plexus. Since there are no reference images for this cutting edge application, some self-supervised learning techniques will be explored. We will compare the performance of the proposed deep learning method with state-of-the-art conventional denoising method like total generalized variation (TGV). Our hypothesis is that with completion of aim 2, the proposed deep learning method will show better performance than the traditional method, both improving image quality and the test-retest reliability for pediatric choroid plexus perfusion imaging.In the third aim, generative diffusion model will be applied to generating the M0 from the control image for Siemens 3D pulsed ASL (PASL) scans in the ADNI-3 dataset, where M0 is not acquired. The generated M0 can be used to quantify CBF for analysis of CBF variations among subjects with normal cognition, mild cognitive impairment (MCI) and AD. This can help resolve the heterogeneity of different type of ASL scans to standardize quantification of CBF in the AD dataset. Our hypothesis is that with the completion of aim 3, Siemens 3D PASL scans in the ADNI-3 dataset can be used to quantify CBF with the acquired control images and the generated M0. Analysis of CBF data from different MR vendors will reveal similar characteristic of deficits in quantitative CBF in MCI and AD subjects compared to normal, which can show better differentiation between AD and normal people compared to using non-standardized perfusion images.In conclusion, with the completion of the three specific aims, we will show that latest DL methods such as Transformers and diffusion models have the potential to improve ASL in clinical applications by enhancing the image quality and better standardization.
■590 ▼aSchool code: 0208.
■650 4▼aBiomedical engineering
■653 ▼aArterial spin labeling
■653 ▼aClinical application
■653 ▼aDeep learning
■690 ▼a0541
■690 ▼a0800
■71020▼aUniversity of Southern California▼bBiomedical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-05B.
■790 ▼a0208
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164729▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
