本文選題：能譜CT + 光子計數(shù)探測器�。� 參考：《清華大學》2016年博士論文

【摘要】：CT是現(xiàn)代醫(yī)學必不可少的檢查手段,在許多疾病的診斷中都起著不可替代的作用。隨著光子計數(shù)探測器技術的發(fā)展,光子計數(shù)能譜CT得到了越來越多的關注。它可以提供傳統(tǒng)CT所沒有的物質(zhì)分辨能力,對造影成像和軟組織成像具有重要意義;同時它比雙能CT噪聲更低,可以降低患者所受的劑量。本文以降低能譜CT重建圖像噪聲為目標,從重建算法和探測器系統(tǒng)優(yōu)化兩方面對能譜CT進行了研究。在重建算法方面,針對投影數(shù)據(jù)信息冗余的特點,本文提出了一種基于加權子集的投影分解算法。它將能區(qū)拆分成不同的組合,對每一個組合使用多項式進行物質(zhì)分解。然后利用標定數(shù)據(jù)計算權重系數(shù)對它們進行加權組合,從而極小化分解噪聲。模擬和實驗結果表明所提出的方法接近可能達到的最小分解噪聲。物質(zhì)分解圖像重建過程中,我們利用物質(zhì)分解圖像與衰減圖像的相似性,提出了加權非局部TV極小化的優(yōu)化重建算法。通過計算衰減圖像像素之間的相似性作為權重,構造加權非局部TV的目標函數(shù)并使用ASD-POCS算法求解,并對算法主要參數(shù)的影響進行了討論并制訂了自適應選擇策略。模擬和實驗結果表明所提出的算法相比于一些已有方法邊緣保持性能更好。探測器系統(tǒng)優(yōu)化的重點是閾值優(yōu)化。我們首先對探測器的能量響應函數(shù)進行了建模,以求解閾值優(yōu)化所需的等效能譜。本文提出了一種混合蒙特卡洛模型,使用GEANT4模擬X射線光子進入探測器的過程,然后使用簡化數(shù)值模型描述后續(xù)的統(tǒng)計漲落和電荷共享。模型中加入了探測器的抗電荷共享邏輯。進一步通過測量金屬熒光譜擬合出模型的關鍵參數(shù),并使用光機能譜對能量響應函數(shù)模型的有效性進行了驗證。我們進一步提出了投影概率密度函數(shù)加權的CRLB作為探測器閾值優(yōu)化目標函數(shù)。結合探測器的能量響應函數(shù),使用差分進化算法對探測器閾值進行優(yōu)化以極小化加權CRLB,從而實現(xiàn)對重建圖像噪聲水平的極小化。模擬和實驗結果驗證了算法的有效性和對參數(shù)選擇的魯棒性。基于這個方法,進一步研究了探測器主要參數(shù)對重建噪聲和最優(yōu)閾值的影響規(guī)律。
[Abstract]:Ct is an indispensable examination method in modern medicine and plays an irreplaceable role in the diagnosis of many diseases. With the development of photon counting detector technology, photon counting spectrum CT has been paid more and more attention. It can provide material resolution that traditional CT does not have, and has important significance for imaging and soft tissue imaging. At the same time, it has lower noise than dual-energy CT and can reduce the dose of patients. The aim of this paper is to reduce the noise of the reconstructed image with the aim of reducing the noise of the reconstructed image. The spectral CT is studied from two aspects: the reconstruction algorithm and the system optimization of the detector. In the aspect of reconstruction algorithm, a projection decomposition algorithm based on weighted subset is proposed in this paper, according to the characteristics of information redundancy of projection data. It splits the energy region into different combinations and decomposes each of them using polynomials. Then the weighted coefficients are calculated by the calibration data to minimize the decomposition noise. The simulation and experimental results show that the proposed method is close to the minimum decomposition noise that can be achieved. In the process of material decomposition image reconstruction, we propose a weighted nonlocal TV minimization algorithm based on the similarity between the material decomposition image and the attenuated image. By calculating the similarity between attenuated image pixels as weights, the objective function of weighted non-local TV is constructed and solved by ASD-POCS algorithm. The influence of the main parameters of the algorithm is discussed and an adaptive selection strategy is worked out. Simulation and experimental results show that the proposed algorithm has better edge retention performance than some existing methods. The focus of detector system optimization is threshold optimization. The energy response function of the detector is first modeled to solve the equivalent energy spectrum required for threshold optimization. In this paper, a hybrid Monte Carlo model is proposed, in which GEANT4 is used to simulate the process of X-ray photons entering the detector, and then a simplified numerical model is used to describe the subsequent statistical fluctuations and charge sharing. The anti-charge sharing logic of the detector is added to the model. Furthermore, the key parameters of the model are fitted by measuring the metal fluorescence spectrum, and the validity of the energy response function model is verified by optical spectroscopy. Furthermore, we propose CRLB weighted by projection probability density function as the objective function of detector threshold optimization. Combined with the energy response function of the detector, the threshold of the detector is optimized by using the differential evolution algorithm to minimize the weighted CRLBs, so that the noise level of the reconstructed image can be minimized. Simulation and experimental results show that the algorithm is effective and robust to parameter selection. Based on this method, the influence of the main parameters of the detector on the reconstruction noise and the optimal threshold is further studied.
【學位授予單位】：清華大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41;R814.42

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