淺表軟組織超聲信號處理與皮下脂肪厚度測量研究
發(fā)布時間：2018-08-11 16:19
【摘要】：超聲波技術近年來迅速發(fā)展并廣泛應用于醫(yī)學診斷、治療和工業(yè)檢測領域。與生物組織作用后的超聲波，是反映生物組織性質的一個信息載體。在生物醫(yī)學超聲基礎研究中，探索超聲波信息載體與生物組織間的聯(lián)系，尋找超聲波中體現(xiàn)組織的一些結構特征參量，一直都是人們研究熱點課題之一。實際中的超聲診斷技術，例如超聲診斷儀，探頭從體表接收到的體內超聲信號是淺表軟組織（皮膚、脂肪和肌肉等）、體內組織和超聲系統(tǒng)相互作用的結果，這就為體內組織結構特征識別帶來困難；另一方面，超聲診斷儀獲得的信號大都是經過了檢波處理，是過濾了的信號，丟失了很多組織性質和結構特征信息。因此，，對于淺表軟組織的超聲檢測信號處理，不管從理論還是實際運用來講均應屬于研究的熱點。 針對淺表軟組織結構的超聲檢測研究并未深入，使得淺表界面結構識別、定位還不準確情況，本文提出了“皮膚-淺層脂肪-淺筋膜-深層脂肪-深筋膜-肌肉”的淺表分層結構，利用信號的卷積模型分析淺表界面信號特征。研究課題的目的有兩方面，一方面，本文對淺表軟組織分層結構的超聲檢測課題，利用信號處理技術探討界面的信號特征提取和識別；另一方面，研究課題嘗試使用超聲波檢測技術研制脂肪厚度測量儀，測量人體局部皮下脂肪厚度，從而隨時檢驗運動和節(jié)食效果，更期望為超聲吸脂提供一種監(jiān)測手段。 本課題完成的主要工作、成果和創(chuàng)新點總結有以下幾點： 對淺表（皮膚、脂肪、筋膜和肌肉）的生物組織特性及結構分布特征進行分析，結合淺表超聲檢測的射頻回波信號，找出各反射回波代表的淺層組織界面。利用脈沖反射法測量界面之間的距離，即各組織層厚度，將測量結果與B超診斷儀、直尺作對比，從而確定組織界面回波，為進一步界面信號特征提取做準備。 雖然軟組織間的聲阻抗相差不大，但淺表軟組織有其特殊的結構特征，特別是淺筋膜和深筋膜處的界面薄而且界面數(shù)等因個體不同各異，造成超聲回波在這兩處信號特征明顯，因此我們可以通過這兩處的回波信號特征識別來測定人體脂肪厚度。 為了提取筋膜界面信號特征，本文從理論上利用信號卷積模型對界面信號相互疊加效應進行解釋。在此基礎上，通過Matlab軟件對信號卷積模型進行模擬驗證，并將卷積模型應用于離體豬淺表組織界面信號的特征識別。實驗結果證實了振蕩波數(shù)作為信號特征識別參量的可行性。 超聲射頻信號攜帶了淺表軟組織的大量結構特征信息，但由于探頭發(fā)射波形有一定的持續(xù)時間以及受檢測系統(tǒng)的影響，而在實際淺組織中筋膜界面又相距很近，在界面處反射波就會不同程度的相互疊加，造成界面波形的不易識別。本文應用小波變換具有恒Q（品質因數(shù)）性質、對信號的時寬和帶寬的局部分析能力，對淺表信號在不同頻率范圍內進行多分辨分析，實現(xiàn)了筋膜界面回波的時域信號重構。從重構界面信號能更清晰看出應用振蕩波數(shù)作為界面特征量的可行性，證實卷積模型解釋信號疊加的正確性。 本文中的信號處理方法是第一次應用于淺表軟組織結構的超聲檢測課題，研究工作還未見相關文獻報道。實驗卷積模型及其對振蕩波數(shù)的解釋可用于人體軟組織間界面或其它相似界面結構特征的識別。離體豬肉脂肪厚度實驗證明儀器能較好的測量較厚且組織結構均勻的脂肪厚度，而對于人體腹部實驗，由于呼吸運動，超聲回波信號伴隨著噪音干擾，這使得淺表軟組織的回波信號難以檢測。如何消除測量干擾，提高信號的穩(wěn)定性，是測量儀器設計無論從軟件還是硬件都是進一步深入研究的課題。
[Abstract]:Ultrasound technology has developed rapidly in recent years and is widely used in medical diagnosis, treatment and industrial detection. Ultrasound after interaction with biological tissue is an information carrier reflecting the nature of biological tissue. Some structural parameters of tissues have always been one of the hotspots of research. In practice, ultrasonic diagnostic techniques, such as ultrasonic diagnostic apparatus, the ultrasonic signals received by the probe from the body surface are the results of superficial soft tissues (skin, fat, muscle, etc.), the interaction between the tissues in the body and the ultrasonic system, which is the structure of the body. On the other hand, the signals obtained by ultrasonic diagnostic instruments are mostly filtered signals, which lose a lot of information about the nature and structure of tissues.
In view of the fact that the ultrasonic detection of superficial soft tissue structure is not thoroughly studied, which makes the identification and localization of superficial interface structure inaccurate, the superficial layer structure of skin-superficial fat-superficial fascia-deep fat-deep fascia-muscle is proposed in this paper. The convolution model is used to analyze the signal characteristics of superficial interface. There are two aspects. On the one hand, the ultrasonic detection of superficial soft tissue layered structure, using signal processing technology to explore the interface signal feature extraction and recognition; on the other hand, the research topic attempts to use ultrasonic detection technology to develop a fat thickness measurement instrument, measuring the thickness of human local subcutaneous fat, so as to detect movement at any time. And dieting results are more likely to provide a monitoring tool for ultrasound liposuction.
The main tasks, achievements and innovations of this project are summarized as follows:
The biological tissue characteristics and structure distribution characteristics of superficial tissues (skin, fat, fascia and muscle) were analyzed, and the superficial tissue interfaces represented by the reflected echoes were found out by combining with the radio frequency echo signals detected by superficial ultrasound. By contrast, the echo of the tissue interface is determined, so as to prepare for further feature extraction of interface signals.
Although there is little difference in acoustic impedance between soft tissues, superficial soft tissues have their special structural characteristics, especially the thin interface between superficial fascia and deep fascia, and the number of interfaces varies from individual to individual, resulting in the obvious characteristics of ultrasonic echoes in these two places. Therefore, we can identify the characteristics of echo signals at these two places to determine the human body. Fat thickness.
In order to extract the signal characteristics of the fascial interface, the signal convolution model is used to explain the superposition effect of the interface signals in theory. Based on this, the convolution model is simulated and verified by Matlab software, and the convolution model is applied to identify the characteristics of the interface signal of the pig superficial tissue in vitro. The wave number is the feasibility of signal recognition.
Ultrasound radio frequency signal carries a lot of structural information of superficial soft tissue, but the waveform emitted by the probe has a certain duration and is affected by the detection system. In the actual superficial tissue, the fascial interface is very close, and the reflected waves will overlap at the interface to different degrees, which makes the interface waveform difficult to identify. The wavelet transform has the property of constant Q (quality factor) and the ability of local analysis of the time-width and bandwidth of the signal. The superficial signal is analyzed by multi-resolution in different frequency range. The time-domain signal reconstruction of the fascial interface echo is realized. It is proved that convolution model can explain the correctness of signal superposition.
Experimental convolution model and its interpretation of oscillatory wavenumber can be used to identify the structural features of human soft tissue interfaces or other similar interfaces. In vitro pork fat thickness tester It is difficult to detect the echo signal of superficial soft tissue because of the noise disturbance accompanied by breathing movement in abdominal experiment. How to eliminate the measurement interference and improve the stability of the signal is the design of the measuring instrument whether from software or hardware. These are all topics to be further studied.
【學位授予單位】：重慶醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R310
本文編號：2177537
[Abstract]:Ultrasound technology has developed rapidly in recent years and is widely used in medical diagnosis, treatment and industrial detection. Ultrasound after interaction with biological tissue is an information carrier reflecting the nature of biological tissue. Some structural parameters of tissues have always been one of the hotspots of research. In practice, ultrasonic diagnostic techniques, such as ultrasonic diagnostic apparatus, the ultrasonic signals received by the probe from the body surface are the results of superficial soft tissues (skin, fat, muscle, etc.), the interaction between the tissues in the body and the ultrasonic system, which is the structure of the body. On the other hand, the signals obtained by ultrasonic diagnostic instruments are mostly filtered signals, which lose a lot of information about the nature and structure of tissues.
In view of the fact that the ultrasonic detection of superficial soft tissue structure is not thoroughly studied, which makes the identification and localization of superficial interface structure inaccurate, the superficial layer structure of skin-superficial fat-superficial fascia-deep fat-deep fascia-muscle is proposed in this paper. The convolution model is used to analyze the signal characteristics of superficial interface. There are two aspects. On the one hand, the ultrasonic detection of superficial soft tissue layered structure, using signal processing technology to explore the interface signal feature extraction and recognition; on the other hand, the research topic attempts to use ultrasonic detection technology to develop a fat thickness measurement instrument, measuring the thickness of human local subcutaneous fat, so as to detect movement at any time. And dieting results are more likely to provide a monitoring tool for ultrasound liposuction.
The main tasks, achievements and innovations of this project are summarized as follows:
The biological tissue characteristics and structure distribution characteristics of superficial tissues (skin, fat, fascia and muscle) were analyzed, and the superficial tissue interfaces represented by the reflected echoes were found out by combining with the radio frequency echo signals detected by superficial ultrasound. By contrast, the echo of the tissue interface is determined, so as to prepare for further feature extraction of interface signals.
Although there is little difference in acoustic impedance between soft tissues, superficial soft tissues have their special structural characteristics, especially the thin interface between superficial fascia and deep fascia, and the number of interfaces varies from individual to individual, resulting in the obvious characteristics of ultrasonic echoes in these two places. Therefore, we can identify the characteristics of echo signals at these two places to determine the human body. Fat thickness.
In order to extract the signal characteristics of the fascial interface, the signal convolution model is used to explain the superposition effect of the interface signals in theory. Based on this, the convolution model is simulated and verified by Matlab software, and the convolution model is applied to identify the characteristics of the interface signal of the pig superficial tissue in vitro. The wave number is the feasibility of signal recognition.
Ultrasound radio frequency signal carries a lot of structural information of superficial soft tissue, but the waveform emitted by the probe has a certain duration and is affected by the detection system. In the actual superficial tissue, the fascial interface is very close, and the reflected waves will overlap at the interface to different degrees, which makes the interface waveform difficult to identify. The wavelet transform has the property of constant Q (quality factor) and the ability of local analysis of the time-width and bandwidth of the signal. The superficial signal is analyzed by multi-resolution in different frequency range. The time-domain signal reconstruction of the fascial interface echo is realized. It is proved that convolution model can explain the correctness of signal superposition.
Experimental convolution model and its interpretation of oscillatory wavenumber can be used to identify the structural features of human soft tissue interfaces or other similar interfaces. In vitro pork fat thickness tester It is difficult to detect the echo signal of superficial soft tissue because of the noise disturbance accompanied by breathing movement in abdominal experiment. How to eliminate the measurement interference and improve the stability of the signal is the design of the measuring instrument whether from software or hardware. These are all topics to be further studied.
【學位授予單位】：重慶醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R310
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