DC 欄位 | 值 | 語言 |
dc.contributor.author | 陳中明 | zh_TW |
dc.creator | 陳中明 | - |
dc.date | 1999-07-31 | zh_TW |
dc.date.accessioned | 2006-07-26T02:54:42Z | - |
dc.date.accessioned | 2018-06-29T00:46:48Z | - |
dc.date.available | 2006-07-26T02:54:42Z | - |
dc.date.available | 2018-06-29T00:46:48Z | - |
dc.date.issued | 1999-07-31 | - |
dc.identifier | 882213E002016 | en |
dc.identifier.uri | http://ntur.lib.ntu.edu.tw//handle/246246/22318 | - |
dc.description.abstract | 正子斷層掃描(PET)是一種提供被能
釋放出正子之放射性同位素所標記的化學
物於人體中之分佈的影像方法。和提供解剖
學資料的CT 與MRI 所不同的是, PET 透
露了人體中活體之生理與代謝之功能性的
訊息。臨床上,在形狀上起變化以前的早期
診斷可以藉由研究 PET 影像中的生理或代
謝的病變而達成。因此,PET 已成為現代診
斷中最重要的影像工具之一。於 PET 中,
新陳代謝的強度是由置於人體外部的偵測
器所間接觀測到的。而用間接的觀測值來重
建實際的影像,這是一種典型的統計逆向問
題。由於這種問題解的不良性,所以,沒
有正則化的PET 影像將會有雜訊及邊界的
假象。這是PET 的能力限制,並不能藉由
改良儀器設計來解決。所以為了要有較好的
重建影像,我們需要去考慮專家的見解或其
它的斷層掃描系統,例如:X-ray CT, MRI
等掃描器,所提供的相關資訊。
相關的邊界資訊可以提供有用的訊
息。但是因為解剖學上的人體器官構造與實
際的新陳代謝情形並不盡相同,所以,邊界
資訊可能是不完全的或是不正確的。因此交
互參照是重要而明智的。我們考慮有偶發事
件及衰減情形的PET,研究交互參照式的最
大概似估計重建法,並以修改後的EM演算
法來處理。特別是,我們將研究快速的影像
重建演算法,包含著連接式及平行式處理的
步驟。在本計畫中,我們將使用IBM SP2
及工作站網路作為平行演算法的發展平
台。而本計畫的目標是應用相關但不完全的
邊界資訊,使用一部或多部的電腦來找到快
速、有效、且可行的方法,以重建PET 的
影像。這可用來改進PET 的重建影像,並
且可以用來整合其它不同的斷層掃描系統
以形成完整的專家系統。 | zh_TW |
dc.description.abstract | Positron Emission Tomography (PET) is
an imaging modality giving distribution of
positron-emitting isotope-labeled chemicals in
the human body. Unlike X-ray CT and
MRI, which provide anatomical data, PET
reveals functional information on in vivo
physiology and metabolism of the human
body. Clinically, early detection of a disease
before morphologically distinguishable may
be achieved through PET by studying
physiological or metabolic disorders. Hence,
PET has become one of the most important
imaging tools in modern diagnosis. The
intensity of metabolic activity is indirectly
observed through the scintillation detectors
outside a human body. The reconstruction
from indirect observations to a target image is
a typical problem in statistical inverse
problem. Due to the inherent ill-posedness
of statistical inverse problems, the
reconstructed images of positron emission
tomography (PET) without regularization will
have noise and edge artifacts. This is the
limit of PET, which can not be resolved from
the improvement of instrumental designs. In
order to have better reconstructed images, it is
necessary to borrow the strength from the
related information from expertise or other
tomography systems, such as X-ray CT scan,
MRI, and so forth.
The correlated boundary information
may offer the useful information in reducing
the noise and edge artifacts. However, the
boundary information may be incomplete or
incorrect since the anatomy boundaries are
different from the functional ones. Thus,
cross-reference is important to make use the
boundary information wisely. In this project,
we will study the cross-reference
reconstruction methods for the maximum
likelihood estimate with the adapted EMalgorithm for PET in the presence of
accidental coincidence (AC) events and
attenuation. In particular, fast reconstruction
algorithms for both sequential and parallel
approaches will be investigated, which is very
important for the practical use of the proposed
PET reconstruction algorithms. In this
project, we will use a cluster of computers as
the platform of the parallel reconstruction
algorithms. The aim is to find the fast,
efficient and reliable approaches that can
reconstruct the PET images with the related
but incomplete boundary information with
single or multiple computers. The proposed
approaches will not only improve the quality
of the reconstructed PET images but also
establish a bridge to an expert system for
various tomography systems. | en |
dc.format | application/pdf | en |
dc.format.extent | 47290 bytes | en |
dc.format.mimetype | application/pdf | en |
dc.language | zh-TW | - |
dc.language.iso | zh_TW | zh_TW |
dc.publisher | 臺北市:國立臺灣大學醫學工程學研究所 | zh_TW |
dc.rights | 國立臺灣大學醫學工程學研究所 | zh_TW |
dc.subject | 正子斷層掃描(PET) | zh_TW |
dc.subject | 統計逆向問題 | zh_TW |
dc.subject | 最大概似估計量 | zh_TW |
dc.subject | EM 演算法 | zh_TW |
dc.subject | 正則化 | zh_TW |
dc.subject | 平行演算法 | zh_TW |
dc.subject | positron emission tomography
(PET) | en |
dc.subject | statistical inverse problems | en |
dc.subject | maximum
likelihood estimator | en |
dc.subject | EM algorithm | en |
dc.subject | regularization | en |
dc.subject | parallel algorithms | en |
dc.subject.classification | [SDGs]SDG3 | - |
dc.title | 正子斷層掃描之快速交互參照式最大概似估計影像重建演算法 | zh_TW |
dc.title | Accelerated Cross-Reference Maximum Likelihood Estimates for PET Image Reconstruction | en |
dc.type | report | en |
dc.identifier.uri.fulltext | http://ntur.lib.ntu.edu.tw/bitstream/246246/22318/1/882213E002016.pdf | - |
dc.coverage | 計畫年度:88;起迄日期:1998-08-01/1999-07-31 | zh_TW |
item.fulltext | with fulltext | - |
item.openairetype | report | - |
item.languageiso639-1 | zh_TW | - |
item.openairecristype | http://purl.org/coar/resource_type/c_93fc | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Biomedical Engineering | - |
crisitem.author.orcid | 0000-0002-0023-5817 | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | College of Engineering | - |
顯示於: | 醫學工程學研究所
|