本文選題：磁共振成像 + 部分k空間　； 參考：《上海交通大學》2012年碩士論文

【摘要】：磁共振成像(Magnetic Resonance Imaging, MRI)是一種新型的，非侵入的成像方式。在臨床應用中，由于磁共振具有很好的軟組織對比度、成像參數(shù)多、三維空間的分辨能力高、可任意方向斷層、不會產(chǎn)生致電離輻射的內在特點以及易為患者接受等優(yōu)點[1][2]，而越來越受歡迎，得到了廣泛的應用。 當代磁共振成像應用對空間分辨率(Spatial Resolution)，瞬時清晰度(Temporal Resolution)，信噪比(Signal-to-Noise Ratio, SNR)及成像速度有相當高的要求[3][4]。然而常規(guī)的磁共振成像時間過長，同時被驗者身體中的生理性運動會產(chǎn)生影像的模糊和對比度的失真，無法應用于運動器官以及神經(jīng)系統(tǒng)等的研究。有效提高成像速度已成為磁共振領域一項非常重要的研究，在縮短檢查時間的同時提高成像質量和改善病人被檢查時的舒適性。 事實上，成像速度一直以來就是磁共振進一步廣泛應用的瓶頸問題。為了提高成像速度，除了改善磁共振設備硬件性能之外，目前最常用的方法就是部分k空間數(shù)據(jù)重建。其優(yōu)勢在于不需要改變現(xiàn)有硬件設備結構和掃描方式，是一種低成本的快速成像方法。 整體變分(Total Variation, TV)方法最初被提出用于圖像去噪，目前已發(fā)展成為磁共振重建成像領域一種比較常用的方法。它通過對圖像模型變分最小的處理，目的就是在移除圖像中噪聲及不需要的小尺度細節(jié)的同時，保護那些突變的不連續(xù)區(qū)域。然而TV法是基于圖像是由連續(xù)片段組成的假定，在一些實際情況中效果往往不太理想。針對這種情況，近幾年發(fā)展推廣出了一種廣義全局變量(Total GeneralizedVariation, TGV)方法，用于解決這類問題。 本文對這兩種方法做了大量實驗，其中TV方法同時應用了牛頓法和共軛梯度兩種不同的求解方法。在對組成結構比較復雜的圖像（例如T2大腦圖）進行重建時，TGV方法要優(yōu)于TV方法，尤其在高度欠采樣的情況下；而對于圖像結構相對簡單的圖像（比如Shepp-Logan模型），TGV方法沒有明顯的優(yōu)勢，甚至效果更不理想，，而且所需時間更長。
[Abstract]:Magnetic Resonance imaging (MRI) is a new, non-invasive imaging method.In clinical application, magnetic resonance imaging has good contrast of soft tissue, many imaging parameters, high resolution of three-dimensional space, and can be used in any direction.The inherent characteristics of non-ionizing radiation and the advantages of being easily accepted by patients [1] [2] have become more and more popular and have been widely used.The application of modern magnetic resonance imaging has quite high requirements for spatial resolution spatial resolution, instantaneous resolution and temporal resolution, signal-to-noise ratio (SNR) and imaging speed [3] [4].However, the conventional magnetic resonance imaging (MRI) is too long, and the physiologic motion in the body of the subject produces blurred image and distortion of contrast, so it can not be applied to the study of motor organs and nervous system and so on.Improving imaging speed effectively has become a very important research in the field of magnetic resonance. It can shorten the inspection time and improve the quality of imaging and the comfort of patients when they are examined.In fact, imaging speed has always been a bottleneck in the further application of MRI.In order to improve the imaging speed, in addition to improving the hardware performance of magnetic resonance devices, the most commonly used method is partial k spatial data reconstruction.Its advantage is that it does not need to change the existing hardware structure and scanning mode, it is a low-cost fast imaging method.The global variational Total variation (TV) method was originally proposed for image denoising and has been developed into a common method in the field of magnetic resonance imaging (MRR).By processing the minimum variation of the image model, the aim is to protect the discontinuous region of the mutation while removing the noise and the unnecessary small-scale details of the image.However, the TV method is based on the assumption that the image is composed of continuous segments.In view of this situation, a generalized global variable Total Generalized variation (TGV) method has been developed in recent years to solve this kind of problems.In this paper, a large number of experiments have been done on these two methods, among which Newton method and conjugate gradient method are used in TV method.The TGV method is superior to the TV method in the reconstruction of complex images (such as T2 brain map), especially in the case of highly under-sampling.However, for images with relatively simple image structure (such as Shepp-Logan model / TGV method), there is no obvious advantage, even less effective, and the time required is longer.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R318.0

【參考文獻】

相關期刊論文 前2條

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2 駱建華，呂維雪;模糊多準則圖象重建技術[J];計算機學報;1996年08期

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