使用單光子斷層掃描 (SPECT)進行Tc-99m/I-123雙同位素造影，可以在同時間觀察兩種示蹤劑於體內分布的情形，且兩張影像於空間上能完全對位。然而，要分開這兩種同位素並不容易，因為Tc-99m與I-123的能量非常接近，會有嚴重的交疊污染，使影像出現假影。使用類神經網路方法進行交疊修正具有很高的正確性，但需開很多個能窗 (>8)，於臨床上不易執行。本研究利用濾片提供光子衰減差異的資訊，結合類神經網路方法進行雙同位素造影的交疊修正。此方法僅需開四個能窗即可執行。
本方法使用0.2 mm的金濾片作為分開Tc-99m與I-123光子之工具。濾片裝置於準直儀前方，每個角度都必須收集無、有濾片之投影資訊。我們使用的類神經網路架構總共分為三層：第一層為輸入層，共有8個輸入值，為四個能窗無、有濾片之投影數據；第二層為隱藏層，包含10個節點；第三層為輸出層，有4個輸出節點，為Tc-99m與I-123光子在無、有濾片之掃描中真實事件的分率。根據輸出值，即可獲得交疊與散射修正後的Tc-99m與I-123之投影。我們使用Gate/MPHG蒙地卡羅程式模擬three-rod假體與NCAT假體的雙同位素造影，以驗證本研究方法。
根據影像量化結果，我們的方法能使Tc-99m/I-123雙同位素影像回復至接近無交疊、散射污染的單同位素造影影像。和其它散射修正法比較，我們提出的方法遠優於不對稱能窗法，且近似於26能窗類神經網路法的結果。使用濾片結合類神經網路，能有效處理雙同位素造影中的交疊污染，且僅需4個能窗即可執行。

Simultaneous Tc-99m/I-123 dual-isotope SPECT imaging allows assessment of two physiological functions under identical conditions, without any image registration. However, the separation of these radionuclides is difficult, because their energy is close. Images can be severely distorted due to crosstalk. Use of an artificial neural network (ANN) has been previously shown to be an effective tool in compensating crosstalk and scatter. Conventional ANN techniques require a large number of energy windows (>8); however, such a capability is not available in most clinical SPECT systems. In this study, we proposed a filter method to improve the ANN techniques with only 4 energy windows.
In this work, we chose 0.2 mm gold filter to separate Tc-99m and I-123, based on the attenuation difference of the gamma of each isotope. The filter is placed over the collimator. We designed an ANN with 8 input, 10 nodes in the hidden layer, and four nodes in the output layer. The inputs were count ratios in four energy windows from scans with and w/o filter. The outputs layer provided the ratio of estimated primary to total photons for Tc-99m and I-123, w/ and w/o filter. The outputs of the ANN are then combined to form two primary data with crosstalk and scatter corrected each for the Tc-99m and I-123. A GATE/MPHG Monte Carlo code is used for the DISA SPECT simulation. In this work, a three-rod phantom and a NCAT phantom were used to validate the proposed method.
The results showed that quantitative recovery from dual isotope image was comparable to that from single-isotope imaging. Compared to images using other crosstalk compensation methods, our results were much better than that of asymmetric window method and closed to the results obtained from conventional ANN technique. Using the filter method incorporated with ANN, crosstalk was successfully compensated and only 4 energy window were needed.

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