本文選題：磁共振成像 + 低噪聲��； 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：磁共振成像(MRI)技術(shù)已經(jīng)逐漸成為醫(yī)學(xué)圖像檢測(cè)領(lǐng)域中的重要手段,并且在其他領(lǐng)域也有充分應(yīng)用。磁共振圖像的好壞會(huì)對(duì)醫(yī)學(xué)臨床診斷、物體特性分析等多方面應(yīng)用造成極大的影響,致力于獲取好的磁共振圖像一直是磁共振成像探測(cè)領(lǐng)域的研究重點(diǎn)。低噪聲前置放大器作為磁共振接收機(jī)系統(tǒng)的重要組成部分直接影響著磁共振圖像的好壞。本文首先對(duì)低場磁共振系統(tǒng)中的低噪聲前置放大器的相關(guān)設(shè)計(jì)理論進(jìn)行詳細(xì)的介紹,然后利用Keysight公司的ADS仿真設(shè)計(jì)軟件完成低場磁共振系統(tǒng)中的低噪聲前置放大器的仿真設(shè)計(jì),同時(shí)根據(jù)仿真設(shè)計(jì)結(jié)果完成低噪聲前置放大器的實(shí)物制作與調(diào)試工作,最后利用Keysight公司矢量網(wǎng)絡(luò)分析儀E5061B和噪聲系數(shù)分析儀N8974A完成對(duì)放大器性能參數(shù)的測(cè)量并與同類商用磁共振系統(tǒng)中的前置放大器進(jìn)行了對(duì)比,實(shí)際測(cè)量結(jié)果顯示本文設(shè)計(jì)的低噪聲前置放大器增益達(dá)到30dB、噪聲系數(shù)約為0.5dB,達(dá)到了與商用磁共振系統(tǒng)的前置放大器性能相當(dāng)?shù)乃?甚至優(yōu)于部分商用產(chǎn)品。最后將完成的實(shí)物應(yīng)用于磁共振成像系統(tǒng)中完成水模的磁共振成像實(shí)驗(yàn),成像效果良好,進(jìn)一步驗(yàn)證了本文所設(shè)計(jì)的低噪聲前置放大器性能穩(wěn)定可靠。此外,針對(duì)多通道接收線圈給磁共振圖像帶來的非均勻性問題,本文對(duì)磁共振圖像中的非均勻場校正算法進(jìn)行了研究。首先介紹了基于濾波和非參數(shù)的兩種傳統(tǒng)的非均勻性校正算法,然后采用改進(jìn)的基于同態(tài)濾波的能量最小化方法對(duì)磁共振圖像非均勻場校正,對(duì)校正后圖像進(jìn)行了定性和定量分析,并與基于濾波和非參數(shù)的校正圖像進(jìn)行了對(duì)比。結(jié)果表明本文采用的算法能夠有效校正磁共振圖像的非均勻場,在目標(biāo)區(qū)域內(nèi)具有更高的均勻性,顯著減少背景干擾,具有更高的辨識(shí)度,并且校正結(jié)果優(yōu)于另外兩種算法。
[Abstract]:Magnetic resonance imaging (MRI) technology has gradually become an important means in the field of medical image detection, and has been fully applied in other fields. The quality of magnetic resonance images will have a great impact on the medical clinical diagnosis, object characteristics analysis and other applications. It has been the focus of research in the field of magnetic resonance imaging detection to obtain good magnetic resonance images. As an important part of magnetic resonance receiver system, low noise preamplifier directly affects the quality of magnetic resonance image. In this paper, the related design theory of low noise preamplifier in low field magnetic resonance system is introduced in detail, and then the simulation design of low noise preamplifier in low field magnetic resonance system is completed by using ADS simulation design software of Keysight company. At the same time, according to the result of simulation design, the physical fabrication and debugging of low-noise preamplifier are completed. Finally, the Keysight vector network analyzer E5061B and the noise coefficient analyzer N8974A are used to measure the performance parameters of the amplifier and compared with the preamplifier in the similar commercial magnetic resonance system. The actual measurement results show that the gain of the low noise preamplifier designed in this paper is 30 dB and the noise coefficient is about 0.5 dB, which is equivalent to the preamplifier performance of the commercial magnetic resonance system, and even better than some commercial products. Finally, the completed physical object is applied to the magnetic resonance imaging experiment of the water mode in the magnetic resonance imaging system, and the imaging effect is good, which further verifies the stability and reliability of the low noise preamplifier designed in this paper. In addition, aiming at the nonuniformity of magnetic resonance image caused by multi-channel receiving coil, the nonuniform field correction algorithm in magnetic resonance image is studied in this paper. Firstly, two traditional nonuniformity correction algorithms based on filtering and non-parameter are introduced, and then an improved energy minimization method based on homomorphic filtering is used to correct the nonuniform field of magnetic resonance images. The corrected images are qualitatively and quantitatively analyzed and compared with the filtered and nonparametric corrected images. The results show that the proposed algorithm can effectively correct the nonuniform field of the magnetic resonance image, have higher uniformity in the target region, reduce the background interference significantly, and have a higher identification degree, and the correction results are better than the other two algorithms.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN722.71;TP391.41

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本文編號(hào)：1904947