多同位素單光子斷層影像同時擷取技術(multi-isotope simultaneous acquisition)能在檢查中得獲取具備完全對位的多種不同功能性影像而具有極大的開發潛力。並且多同位素同時取像更可使病人之各項生理參數固定，不會造成不同功能影像的診斷尚需考慮生理有所不同。然而，此技術所得到的影像品質深受能譜交疊汙染(cross-talk contamination)影響，降低量化的準確性。本實驗室過去已提出使用濾片方法且僅需開4個能窗即能提供更多資訊並能有效訓練類神經網路(ANN)做雙同位素造影的交疊修正，不過在局部熱區的量化仍有高達30%之誤差。本研究提出使用多重射束(multi-bin)的資料輸入方法以增加濾片類神經網路學習資訊和網路架構複雜度，提升能譜交疊修正法的預測準確率並拓展其應用至三同位素同時造影上，以增加多同位素同時造影的臨床可行性。
本研究使用實驗室開發之SimGATE軟體模擬西門子E-cam機台做99mTc/123I雙同位素造影，其中一個掃描機頭準直儀前放置0.1 mm金濾片，使用4個能窗取像；而三同位素造影模擬藥物為99mTc/123I/111In，金濾片厚度為0.15 mm，並以5個能窗取像。模擬所得之有/無濾片衰減的正弦圖(sinogram)影像用於ANN訓練，每個像素的預測，除了提供該像素的混合訊號外更提供相鄰位置的像素給予ANN作為輸入資料，並使其輸出該像素之99mTc、123I (及111In)經能譜交疊和散射修正後的訊號比例。經OSEM相同次數的重建後不同ANN能譜交疊修正方法一同與各別同位素影像比較，我們使用正規化均方根誤差、百分比偏移、影像像素值剖面圖、影像變異係數及對比回復等量化方法做預測影像的結果分析。
結果顯示新提出的方法在模擬假體實驗上能使99mTc/123I之預測影像與單同位素影像的正規化方均跟誤差分別達到0.053和0.0427；在影像量化中，所有預測影像局部百分比偏移皆於±6%內；99mTc/123I/111In三同位素影像訊號亦可以成功分離出來，正規化均方根誤差分別為0.073、0.055及0.034，而所有預測影像局部百分偏移可達±8%內。
本研究提出方法能使神經網路比既有方法更準確地分離多同位素影像的混合訊號，有效修正能譜交疊污染及散射的問題，並在臨床儀器限制的3~8個能窗數限制內即可達成，大幅提升臨床可用性。

Multi-isotope simultaneous acquisition (MISA) techniques of SPECT imaging have great potential because various functional images can be obtained in only one scan, and the images can be perfectly registered. However, the image quality obtained by this technique is greatly affected by crosstalk contamination, which reduces the accuracy of image quantification. Our laboratory has proposed the use of filtering methods that require only four energy windows to provide more information and more effectively training artificial neural networks (ANNs) when doing dual-isotope imaging. However, there is still up to 30% in the quantification in some local hotspots. In this study, we proposed the use of patch-type data extraction methods that considering the local information in the sinograms to increase learning information and architecture complexity of ANNs, thus improving the prediction accuracy of cross-talk correction and increasing the clinical utility of simultaneous multi-isotope imaging.
We used home-made SimGATE simulation software to create a Siemens E-cam machine for 99mTc/123I dual-isotope imaging, in which a 0.1mm gold filter was placed in front of one of the scanner head. We also simulated 99mTc/123I/111In three-isotope imaging that 0.15 mm gold filter was needed. Sinograms obtained from the simulation were used for training ANN and another sinograms obtained from other phantom were used for testing phase. The predicted sinograms were reconstructed using OSEM algorithm and the image quality was compared using the normalized root mean squared error, error map, image profile, contrast recovery coefficient, and coefficient of variation.
The results show that the NRMSEs of predicted image from the proposed method were 0.053 and 0.0427 for 99mTc and 123I respectively when applied in dual-isotope imaging. The percentage bias of the hot spot was less than ±6% for all predicted images. The signal from triple-isotope simultaneous imaging can be separated successfully. The NRMSEs of predicted 99mTc/123I/111In image is 0.073, 0.055 and 0.034, respectively. The percentage bias could be less than ±8% for all predicted images.
The proposed method successfully separate the signal from both dual isotope and three-isotope simultaneous acquisitions and effectively correct the cross-talk contamination using only four or five energy windows and generate more accurate results than the existing methods. This method greatly facilitates the clinical applications of MISA.

2008 核子醫學原理與技術: 合記圖書發行
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魏子婕 2016 濾片技術結合類神經網路進行 Tc-99m/I-123 雙同位素造影之交疊修正 清華大學生醫工程與環境科學系學位論文 1-66