磁共振成像二維相位解纏方法研究
發(fā)布時(shí)間：2018-07-25 15:52
【摘要】：磁共振成像得到的信號(hào)是復(fù)數(shù)形式,包含幅度和相位。常規(guī)診斷中,往往用到的是磁共振的幅度圖像。然而,其相位也包含了大量信息,例如自旋原子核的移動(dòng)速度、磁場(chǎng)的不均勻度和磁化率變化等。因此磁共振相位可以用來估計(jì)主磁場(chǎng)均勻性和獲取臨床相關(guān)的生理參數(shù)。 但從復(fù)數(shù)信號(hào)中提取真實(shí)相位時(shí),相位值卻會(huì)被限制在(-π,π)弧度區(qū)間內(nèi),位于該區(qū)間外的真實(shí)相位被纏繞到這一區(qū)間內(nèi)。此現(xiàn)象稱為相位纏繞,得到的相位稱為纏繞相位。從纏繞相位恢復(fù)真實(shí)相位的過程就叫做相位解纏。噪聲、欠采樣和物體不連續(xù)的存在使相位解纏變得困難。 本文提出了三種新的二維相位解纏方法。模擬數(shù)據(jù)和實(shí)際磁共振相位數(shù)據(jù)被用來評(píng)估這些方法的表現(xiàn)。 第一種方法是基于離散粒子群優(yōu)化算法的枝切線法。這種方法先將整幅圖像的殘差分成幾組；在每組內(nèi)使用離散粒子群優(yōu)化算法對(duì)正負(fù)極性殘差進(jìn)行配對(duì)；用枝切線連接每組內(nèi)配好對(duì)的正負(fù)極性殘差；最后繞過這些枝切線進(jìn)行相位解纏。 與最新的基于人工智能的枝切法對(duì)比,這種方法能在較短時(shí)間內(nèi)得到合理的枝切線連接,且通過對(duì)殘差分組進(jìn)一步降低了計(jì)算時(shí)間。 第二種相位解纏方法是基于直接求解法的加權(quán)最小Lp范數(shù)法。它將整個(gè)相位圖像的解纏相位梯度與纏繞相位梯度之間差值的加權(quán)Lp范數(shù)作為優(yōu)化目標(biāo)函數(shù)；將這個(gè)目標(biāo)函數(shù)轉(zhuǎn)化成一個(gè)方程組,其系數(shù)矩陣采用稀疏結(jié)構(gòu)儲(chǔ)存和表達(dá)；最后使用直接求解法求解方程組。由于方程組的系數(shù)矩陣與解纏相位有關(guān),因此采取迭代方式得到最終的解纏結(jié)果。 與一些常用方法相比,這種方法能有效減少計(jì)算時(shí)間且解纏結(jié)果更準(zhǔn)確。 第三種方法是基于掩碼的區(qū)域增長(zhǎng)法。這種方法采用一種新的掩碼提取方式將殘差合理地連接起來作為掩碼中的零點(diǎn)；將掩碼與相位導(dǎo)數(shù)方差結(jié)合構(gòu)成最終的質(zhì)量圖,這樣連接殘差經(jīng)過的點(diǎn)均被當(dāng)成零質(zhì)量(也就是質(zhì)量最差)的點(diǎn),會(huì)被滯留到最后才被相位解纏；接著根據(jù)質(zhì)量圖將整幅圖像分成多個(gè)區(qū)域,在每個(gè)區(qū)域內(nèi)單獨(dú)進(jìn)行相位解纏,其中質(zhì)量最差的那個(gè)區(qū)域從多個(gè)方向進(jìn)行相位加權(quán)平均；最后將多個(gè)區(qū)域融合在一起。 與最新的區(qū)域增長(zhǎng)相位解纏方法(PHUN)相比,這種方法運(yùn)算速度快并能夠限制誤差的傳播。
[Abstract]:The signal obtained by magnetic resonance imaging is complex and contains amplitude and phase. In conventional diagnosis, the amplitude image of magnetic resonance is often used. However, its phase also contains a lot of information, such as the moving speed of spin nuclei, the variation of magnetic field inhomogeneity and magnetic susceptibility, etc. Therefore, the magnetic resonance phase can be used to estimate the homogeneity of the main magnetic field and to obtain clinical physiological parameters. However, when the real phase is extracted from the complex signal, the phase value will be limited to (- 蟺, 蟺) radians, and the real phase located outside the complex signal will be entangled in this region. This phenomenon is called phase winding, and the resulting phase is called winding phase. The process of recovering the real phase from the winding phase is called phase unwrapping. The presence of noise, undersampling and discontinuity makes phase unwrapping difficult. In this paper, three new two-dimensional phase unwrapping methods are proposed. Analog data and actual magnetic resonance phase data are used to evaluate the performance of these methods. The first method is branch tangent method based on discrete particle swarm optimization. In this method, the residuals of the whole image are divided into several groups, the discrete particle swarm optimization algorithm is used to match the positive and negative pole residuals in each group, the branch tangent is used to connect the positive and negative pole residuals in each group. Finally, phase unwrapping is carried out by bypassing the tangent of these branches. Compared with the new branch cutting method based on artificial intelligence, this method can get reasonable branch tangent connection in a short time, and further reduce the computing time by grouping the residual error. The second phase unwrapping method is the weighted least LP norm method based on the direct solution method. The weighted LP norm of the difference between the unwrapped phase gradient and the winding phase gradient of the whole phase image is taken as the optimization objective function, the objective function is transformed into a set of equations, and the coefficient matrix is stored and expressed by sparse structure. Finally, the direct solution method is used to solve the equations. Because the coefficient matrix of the equations is related to the unwrapping phase, the final unwrapping results are obtained by iterative method. Compared with some commonly used methods, this method can effectively reduce the calculation time and the unwrapping result is more accurate. The third method is the region growth method based on mask. In this method, a new mask extraction method is used to reasonably connect the residuals as the zeros in the mask, and the mask and the variance of the phase derivative are combined to form the final quality map. In this way, the points through which the link residuals are passed are treated as zero mass (that is, the worst quality points), and they are detained until the last phase unwrapping; then, according to the quality map, the whole image is divided into multiple regions, and the phase unwrapping is carried out separately in each region. The region with the lowest quality is weighted from multiple directions, and the multiple regions are fused together. Compared with the new region growth phase unwrapping method (PHUN), this method is faster and can limit the propagation of errors.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：R310
本文編號(hào)：2144306
[Abstract]:The signal obtained by magnetic resonance imaging is complex and contains amplitude and phase. In conventional diagnosis, the amplitude image of magnetic resonance is often used. However, its phase also contains a lot of information, such as the moving speed of spin nuclei, the variation of magnetic field inhomogeneity and magnetic susceptibility, etc. Therefore, the magnetic resonance phase can be used to estimate the homogeneity of the main magnetic field and to obtain clinical physiological parameters. However, when the real phase is extracted from the complex signal, the phase value will be limited to (- 蟺, 蟺) radians, and the real phase located outside the complex signal will be entangled in this region. This phenomenon is called phase winding, and the resulting phase is called winding phase. The process of recovering the real phase from the winding phase is called phase unwrapping. The presence of noise, undersampling and discontinuity makes phase unwrapping difficult. In this paper, three new two-dimensional phase unwrapping methods are proposed. Analog data and actual magnetic resonance phase data are used to evaluate the performance of these methods. The first method is branch tangent method based on discrete particle swarm optimization. In this method, the residuals of the whole image are divided into several groups, the discrete particle swarm optimization algorithm is used to match the positive and negative pole residuals in each group, the branch tangent is used to connect the positive and negative pole residuals in each group. Finally, phase unwrapping is carried out by bypassing the tangent of these branches. Compared with the new branch cutting method based on artificial intelligence, this method can get reasonable branch tangent connection in a short time, and further reduce the computing time by grouping the residual error. The second phase unwrapping method is the weighted least LP norm method based on the direct solution method. The weighted LP norm of the difference between the unwrapped phase gradient and the winding phase gradient of the whole phase image is taken as the optimization objective function, the objective function is transformed into a set of equations, and the coefficient matrix is stored and expressed by sparse structure. Finally, the direct solution method is used to solve the equations. Because the coefficient matrix of the equations is related to the unwrapping phase, the final unwrapping results are obtained by iterative method. Compared with some commonly used methods, this method can effectively reduce the calculation time and the unwrapping result is more accurate. The third method is the region growth method based on mask. In this method, a new mask extraction method is used to reasonably connect the residuals as the zeros in the mask, and the mask and the variance of the phase derivative are combined to form the final quality map. In this way, the points through which the link residuals are passed are treated as zero mass (that is, the worst quality points), and they are detained until the last phase unwrapping; then, according to the quality map, the whole image is divided into multiple regions, and the phase unwrapping is carried out separately in each region. The region with the lowest quality is weighted from multiple directions, and the multiple regions are fused together. Compared with the new region growth phase unwrapping method (PHUN), this method is faster and can limit the propagation of errors.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：R310
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 楊維,李歧強(qiáng);粒子群優(yōu)化算法綜述[J];中國(guó)工程科學(xué);2004年05期
2 劉亞濤;俎棟林;包尚聯(lián);;水、脂分離磁共振成像Dixon方法[J];中國(guó)醫(yī)學(xué)物理學(xué)雜志;2012年06期
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