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

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。

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.