基于正則化方法的磁共振圖像去噪與重建技術(shù)的研究
發(fā)布時(shí)間：2018-11-01 12:24
【摘要】：磁共振成像(Magnetic Resonance Imaging,MRI)技術(shù)因其無電離輻射,診斷信息豐富等優(yōu)勢在醫(yī)療領(lǐng)域發(fā)揮著重要的作用。然而,磁共振儀器直接獲取到的圖像信號往往會受到噪聲、采集方式等因素的影響而產(chǎn)生退化現(xiàn)象。從獲取的退化信號中恢復(fù)原始信號可通過正則化求逆來進(jìn)行。正則化方法所構(gòu)建的數(shù)學(xué)模型由確保數(shù)據(jù)一致性的誤差項(xiàng)和正則化約束項(xiàng)組成,其中,如何構(gòu)造先驗(yàn)約束項(xiàng)是影響圖像復(fù)原質(zhì)量的重要因素。本課題對基于正則化方法的磁共振圖像去噪和重建模型進(jìn)行了研究,深入分析和探討了其建立到求解的過程。首先,在磁共振圖像的去噪方面,本文以達(dá)到較好的去噪效果并盡可能地降低去噪過程的計(jì)算復(fù)雜度為核心目標(biāo),研究了對全變分(Total Variation,TV)做出改進(jìn)的幾種高階方法,通過具體實(shí)驗(yàn)探討了它們各自的優(yōu)勢,并針對傳統(tǒng)高階全變分(Higher Degree Total Variation,HDTV)求解方法復(fù)雜度較高求解耗時(shí)長的缺點(diǎn)提出了一種快速最大值-最小值(Fast Majorization-Minimization,FMM)算法,通過引入輔助變量進(jìn)行交替求解,使計(jì)算效率提高了5-7倍,實(shí)現(xiàn)了質(zhì)量與效率兼顧的高階全變分正則化去噪這一目的。其次,本文對基于壓縮感知理論(Compressed Sensing,CS)的正則化磁共振圖像重建方法進(jìn)行了研究。在對經(jīng)典正則化模型的研究基礎(chǔ)上,本文提出了一種混合正則化方法來實(shí)現(xiàn)對圖像的自適應(yīng)稀疏促進(jìn),在高倍數(shù)欠采樣的情況下實(shí)現(xiàn)更高質(zhì)量的磁共振圖像重建。在二維圖像的模擬實(shí)驗(yàn)中,混合正則化方法能夠達(dá)到優(yōu)于現(xiàn)有對比方法的重建質(zhì)量。此外,本文將其進(jìn)行了理論擴(kuò)展,用于三維圖像的重建中,通過實(shí)驗(yàn)對比發(fā)現(xiàn)混和正則化能夠重建出效果更佳的三維磁共振圖像。最后,在動態(tài)磁共振成像方面,由于動態(tài)圖像數(shù)據(jù)冗余度較大,可采用CS-MRI技術(shù)對其進(jìn)行欠采樣重建,從而實(shí)現(xiàn)高效率的圖像獲取。本文延用了對靜態(tài)圖像所提出的混合正則化重建方法,將其與時(shí)間域上的低秩方法相結(jié)合,針對動態(tài)磁共振成像提出了低秩-混合全變分(Low Rank-Combined HDTV,LR-CHDTV)正則化方法,實(shí)現(xiàn)了在高成像加速倍數(shù)下重建出高質(zhì)量的動態(tài)磁共振圖像這一目標(biāo)。
[Abstract]:Magnetic resonance imaging (Magnetic Resonance Imaging,MRI) technology plays an important role in medical field because of its advantages of non-ionizing radiation and abundant diagnostic information. However, the image signals directly obtained by magnetic resonance instruments are often affected by noise, acquisition methods and other factors, resulting in degradation phenomenon. The restoration of the original signal from the acquired degenerate signal can be done by regularizing the original signal. The mathematical model constructed by the regularization method consists of error terms and regularization constraints to ensure the consistency of data. Among them, how to construct a priori constraint term is an important factor affecting the quality of image restoration. In this paper, the model of Mr image denoising and reconstruction based on regularization method is studied, and the process of establishing and solving the model is analyzed and discussed. Firstly, in the aspect of denoising of magnetic resonance image, aiming at achieving better denoising effect and reducing the computational complexity of denoising process as far as possible, several high-order methods to improve total variation (Total Variation,TV) are studied. Their respective advantages are discussed through specific experiments, and a fast Max-Minimum (Fast Majorization-Minimization,FMM) algorithm is proposed to solve the problem that the complexity of the traditional high-order total variational (Higher Degree Total Variation,HDTV (Higher Degree Total Variation,HDTV) method is high and time-consuming. By introducing auxiliary variables to solve the problem alternately, the computational efficiency is improved 5-7 times, and the goal of high order total variational regularization denoising is realized, which takes both quality and efficiency into account. Secondly, the regularized Mr image reconstruction method based on compressed perception theory (Compressed Sensing,CS) is studied in this paper. Based on the study of the classical regularization model, a hybrid regularization method is proposed to achieve the adaptive sparse enhancement of the image and the reconstruction of the magnetic resonance image with higher quality under the condition of high multiple under-sampling. In the simulation experiment of 2D images, the hybrid regularization method can achieve better reconstruction quality than the existing contrast methods. In addition, the theory is extended to reconstruct 3D images. The results of experiments show that mixing regularization can reconstruct 3D MRI images with better results. Finally, in the aspect of dynamic magnetic resonance imaging, because of the large redundancy of dynamic image data, CS-MRI technology can be used for under-sampling and reconstruction to achieve high efficiency image acquisition. In this paper, the hybrid regularization method for static images is extended and combined with the low rank method in time domain. For dynamic magnetic resonance imaging, a low rank mixed total variational (Low Rank-Combined HDTV,LR-CHDTV) regularization method is proposed. The goal of reconstruction of high quality dynamic magnetic resonance images with high imaging acceleration is achieved.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2303917
[Abstract]:Magnetic resonance imaging (Magnetic Resonance Imaging,MRI) technology plays an important role in medical field because of its advantages of non-ionizing radiation and abundant diagnostic information. However, the image signals directly obtained by magnetic resonance instruments are often affected by noise, acquisition methods and other factors, resulting in degradation phenomenon. The restoration of the original signal from the acquired degenerate signal can be done by regularizing the original signal. The mathematical model constructed by the regularization method consists of error terms and regularization constraints to ensure the consistency of data. Among them, how to construct a priori constraint term is an important factor affecting the quality of image restoration. In this paper, the model of Mr image denoising and reconstruction based on regularization method is studied, and the process of establishing and solving the model is analyzed and discussed. Firstly, in the aspect of denoising of magnetic resonance image, aiming at achieving better denoising effect and reducing the computational complexity of denoising process as far as possible, several high-order methods to improve total variation (Total Variation,TV) are studied. Their respective advantages are discussed through specific experiments, and a fast Max-Minimum (Fast Majorization-Minimization,FMM) algorithm is proposed to solve the problem that the complexity of the traditional high-order total variational (Higher Degree Total Variation,HDTV (Higher Degree Total Variation,HDTV) method is high and time-consuming. By introducing auxiliary variables to solve the problem alternately, the computational efficiency is improved 5-7 times, and the goal of high order total variational regularization denoising is realized, which takes both quality and efficiency into account. Secondly, the regularized Mr image reconstruction method based on compressed perception theory (Compressed Sensing,CS) is studied in this paper. Based on the study of the classical regularization model, a hybrid regularization method is proposed to achieve the adaptive sparse enhancement of the image and the reconstruction of the magnetic resonance image with higher quality under the condition of high multiple under-sampling. In the simulation experiment of 2D images, the hybrid regularization method can achieve better reconstruction quality than the existing contrast methods. In addition, the theory is extended to reconstruct 3D images. The results of experiments show that mixing regularization can reconstruct 3D MRI images with better results. Finally, in the aspect of dynamic magnetic resonance imaging, because of the large redundancy of dynamic image data, CS-MRI technology can be used for under-sampling and reconstruction to achieve high efficiency image acquisition. In this paper, the hybrid regularization method for static images is extended and combined with the low rank method in time domain. For dynamic magnetic resonance imaging, a low rank mixed total variational (Low Rank-Combined HDTV,LR-CHDTV) regularization method is proposed. The goal of reconstruction of high quality dynamic magnetic resonance images with high imaging acceleration is achieved.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前5條
1 馬杰;王曉云;張志偉;劉雅莉;;一種基于全變分正則化低秩稀疏分解的動態(tài)MRI重建方法[J];光電子·激光;2016年01期
2 蔣明峰;劉淵;徐文龍;馮杰;汪亞明;;基于全變分?jǐn)U展方法的壓縮感知磁共振成像算法研究[J];電子與信息學(xué)報(bào);2015年11期
3 王水花;張煜東;;壓縮感知磁共振成像技術(shù)綜述[J];中國醫(yī)學(xué)物理學(xué)雜志;2015年02期
4 李青;楊曉梅;李紅;;基于壓縮感知的自適應(yīng)正則化磁共振圖像重構(gòu)[J];計(jì)算機(jī)應(yīng)用;2012年02期
5 翁卓;謝國喜;劉新;熊承義;鄭海榮;邱本勝;;基于k空間加速采集的磁共振成像技術(shù)[J];中國生物醫(yī)學(xué)工程學(xué)報(bào);2010年05期
相關(guān)碩士學(xué)位論文 前1條
1 余麗紅;基于全變分的磁共振圖像去噪算法的研究[D];南方醫(yī)科大學(xué);2009年
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