WO2010052868A1 - 超音波画像処理方法及び装置、超音波画像処理プログラム - Google Patents
超音波画像処理方法及び装置、超音波画像処理プログラム Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
- A61B8/467—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means
- A61B8/469—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means for selection of a region of interest
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
- A61B8/5238—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for combining image data of patient, e.g. merging several images from different acquisition modes into one image
- A61B8/5246—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for combining image data of patient, e.g. merging several images from different acquisition modes into one image combining images from the same or different imaging techniques, e.g. color Doppler and B-mode
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/215—Motion-based segmentation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
- A61B8/461—Displaying means of special interest
- A61B8/463—Displaying means of special interest characterised by displaying multiple images or images and diagnostic data on one display
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/48—Diagnostic techniques
- A61B8/483—Diagnostic techniques involving the acquisition of a 3D volume of data
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5269—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts
- A61B8/5276—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts due to motion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
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- the present invention relates to an ultrasonic image processing method and apparatus and an ultrasonic image processing program capable of clearly identifying a tissue boundary when imaging a living body using ultrasonic waves.
- Patent Document 1 In an ultrasonic image processing apparatus used for medical image diagnosis, as described in Patent Document 1, for example, the elasticity coefficient distribution of a tissue is estimated based on the amount of change in a small area of a diagnostic moving image, and the hardness is calculated. A method of converting to a color map and displaying it has been disclosed. However, sharpness has deteriorated when attention is paid to the tissue boundary in order to perform elastic modulus processing. Thus, as described in Patent Document 2, there has been a technique for creating a scalar distribution image directly from a motion vector of a diagnostic moving image to improve the degree of tissue boundary discrimination.
- JP 2004-135929 A Japanese Patent Application No. 2008-079792 JP 2004-129773 A
- an x-direction component, a y-direction component, a vector length, or a vector angle with respect to a reference direction is obtained from a motion vector, and a scalar value distribution is created using the values. Therefore, all vector information is not reduced, and the vector distribution may or may not be accurately reflected by the target image.
- An object of the present invention is to provide an ultrasonic image processing method and apparatus, and an ultrasonic image processing program that can cope with different analysis methods depending on the site, diagnosis, or treatment purpose.
- the present invention has means for converting a motion vector into a scalar distribution based on a selected method.
- the motion vector distribution image is converted into a scalar distribution image based on a plurality of set regions of interest (ROI).
- ROI regions of interest
- the mixed change pattern can be decomposed into eigenvalues and extracted by taking the vector amplitude and phase into account. Boundary identification is possible.
- the sharpness of the boundary can be reduced. Noise can be removed without deterioration.
- an ultrasonic image processing method and apparatus and an ultrasonic image processing program that can cope with different analysis techniques depending on the site, diagnosis, or treatment purpose.
- FIG. 1 shows a system configuration example of the ultrasonic boundary detection method of the present invention.
- An ultrasonic probe (probe) 1 in which ultrasonic elements are arranged one-dimensionally transmits an ultrasonic beam (ultrasonic pulse) to a living body and receives an echo signal (received signal) reflected from the living body.
- a transmission signal having a delay time adjusted to the transmission focus is output by the transmission beamformer 3 and sent to the ultrasonic probe 1 through the transmission / reception changeover switch 5.
- the ultrasonic beam reflected or scattered in the living body and returned to the ultrasonic probe 1 is converted into an electric signal by the ultrasonic probe 1 and is received as a reception signal to the reception beam former 6 via the transmission / reception switch 5.
- the receiving beamformer 6 is a complex beamformer that mixes two received signals that are 90 degrees out of phase, and performs dynamic focusing to adjust the delay time according to the reception timing under the control of the control system 4. Outputs real and imaginary RF signals. This RF signal is detected by the envelope detector 7 and then converted into a video signal, which is input to the scan converter 8 and converted into image data (B-mode image data).
- image data B-mode image data
- the processing unit 10 first creates a motion vector distribution. Next, the created motion vector distribution is converted into a scalar distribution. The original image data and the corresponding motion vector distribution or scalar distribution are combined by the combining unit 12 and then displayed on the display unit 13.
- parameters for signal processing in the processing unit 10 and display image selection and setting in the synthesis unit 12 are performed. These parameters are input from the user interface 2 by an operator (diagnostic machine operator).
- an operator diagnosis machine operator
- a moving image display method for example, an original image and a vector distribution image (or scalar image) are combined into one image and displayed on a display, or two or more moving images are displayed side by side.
- FIG. 2 shows a processing example of the ultrasonic image processing method in the processing unit 11 and the synthesis unit 12 of the present invention.
- a B-mode moving image is input.
- two frames of a desired frame and a frame at a different time are extracted.
- a motion vector distribution is calculated from the two frames.
- the motion vector distribution calculation method is performed based on a block matching method as described in Patent Document 2, for example.
- Noise removal processing is performed on the calculated motion vector distribution, and the noise-removed motion vector distribution is converted to a scalar distribution.
- the scalar distribution image and the motion vector distribution image or B-mode image are combined and displayed, and the processing for one image is completed.
- FIG. 3 shows an example of motion vector distribution in the present invention.
- the region of interest ROI region of interest
- the x-direction component and the y-direction component of each vector are replaced with a real part and an imaginary part, respectively, so as to be converted into an m-by-n complex matrix.
- FIG. 4 shows a processing procedure by eigenvalue expansion.
- the two-dimensional vector distribution is converted into a complex matrix shown in Equation 1. Note that Equation 1 is described in FIG.
- eigenvalue expansion shown in Formula 2 is performed on the converted complex matrix of m rows and n columns based on numerical calculation, an eigenvalue matrix of m rows and 1 column is obtained, and a scalar value is determined from the eigenvalue matrix.
- the scalar value determination method uses the maximum absolute value or the total absolute value of eigenvalues to reflect a large change in the ROI as a scalar value.
- FIG. 5 shows an example of motion vector extraction according to the present invention in a case of a rat VX2 type tumor.
- 5A is a B-mode image
- FIG. 5B is a motion vector distribution created based on block matching from the B-mode image of FIG. 5A and the next frame image.
- FIG. 6B shows the result of calculating the eigenvalue matrix by performing eigenvalue expansion on each ROI with the ROI size 3 ⁇ 3 with respect to this motion vector distribution, and obtaining the absolute value maximum value distribution of the eigenvalue matrix.
- FIG. 6A shows the result of applying the method of converting the vector length described in Patent Document 2 into a scalar distribution in the same frame. When both are compared, the boundary of the tumor site occupying almost the upper half is detected equally, but the change in the surrounding tissue of the lower half is clearly shown in FIG. 6 (b) of the present invention. I understand that.
- the present invention can be applied to various subjects, and can be used for, for example, monitoring the position of a treatment needle tip inserted into the body.
- eigenvalue expansion was performed using a 3 ⁇ 3 square matrix as the ROI shape, but eigenvalue expansion cannot be applied when using a 3 ⁇ 5 non-square ROI, for example. Perform equivalent singular value decomposition corresponding to, and use singular values instead of eigenvalues.
- FIG. 7 shows a B-mode image
- FIG. 7B shows a motion vector distribution created based on, for example, block matching from the B-mode image of FIG. 7A and the next frame image.
- the difference in hardness between the normal tissue and the tumor was reflected in the motion vector, but in the biceps brachii muscles, the displacement of the different muscle tissues (indicated as area A, area B, and area C in the figure) Is represented by a motion vector.
- divergence (abbreviated as divergence, div) expressed by Equation 3 can be applied.
- Div can be interpreted to reflect the amount of vector divergence in the unit area.
- an knitting differential in the x direction is performed on the x component A x of the vector, and a partial differentiation in the y direction is performed on the y component A y to obtain a sum thereof.
- the measurement error in the ultrasonic image is more accurate in the y direction (beam direction) than in the x direction (azimuth direction) due to the device configuration, so a large weight (for example, 0.7) is given to the partial differential value of A y .
- a specific partial differential calculation method applies a spatial primary differential filter (for example, a Sobel filter generally used in image processing) for each ROI.
- a spatial primary differential filter for example, a Sobel filter generally used in image processing
- a y-direction Sobel filter with an ROI size of 3 ⁇ 3 is shown in Equation 4.
- Fig. 8 (a) shows the result of applying div. It can be seen that the boundary between areaA and areaB and the boundary between areaB and areaC are clearly displayed. Here, the effect on the displacement of the biceps was shown. However, since div corresponds to the amount of divergence, the degree of thermal expansion of the subject tissue is treated in HIFU (High Intensity Focused Ultrasound). This is particularly effective for monitoring control during measurement, confirmation of tissue degeneration after treatment, or for monitoring the expansion and contraction of the heart in real time using a transesophageal probe placed in the esophagus during surgery. .
- HIFU High Intensity Focused Ultrasound
- rot reflects the amount of vector rotation in a unit area.
- a specific calculation method calculates a spatial first derivative in the x direction with respect to the y component and a spatial first derivative in the y direction with respect to the x component, and obtains a difference therebetween. The value obtained is called the tensor amount.
- FIG. 8B shows the result of applying the rot process. In the display, the absolute value of the tensor amount is used. It can be seen from the figure that the characteristic can be extracted more sharply than when the boundary position is div.
- the method of scalarization is not limited to the above, and a distortion tensor (shown in Equation (6)) reflecting a change between two points, a vector inner product value or an outer product value can also be applied.
- a changeover switch is provided on the operation panel so that the diagnostic machine operator can switch between these methods according to the feature to be noticed.
- the extracted motion vector distribution includes, for example, error vectors that are likely to occur in block matching processing in a low S / N area. Therefore, in order to remove the error, smoothing is usually performed by applying a low-pass filter. However, there is a problem that the sharpness of the boundary deteriorates due to the low-pass filter processing. Therefore, in the present invention, a similarity filter that can retain boundary information while removing errors is applied.
- FIG. 9 shows an effect example of the motion vector similarity filter. Assuming ROI size 3 ⁇ 3, it is assumed with respect to interest vector I 0, four right upward vector in the same direction, the vector of the different right downward four situations. When a smoothing filter is applied as the low-pass filter, since the upper right vector and the lower right vector are the same number except for the vector of interest, the smoothed vector of interest will be in the middle right direction.
- the similarity filter described in Patent Document 3 is a method that can achieve both noise removal and edge preservation of a two-dimensional luminance image by adding to a weighted average with a greater weight as a pixel in the ROI that is closer in luminance than the pixel of interest.
- the two-dimensional vector distribution is decomposed into an x component (azimuth direction) distribution and a y component (beam direction) distribution, a similarity filter is applied to each distribution, and the x component distribution and y component after application are applied.
- a smoothed vector image is constructed from the distribution.
- the vector can be expressed as a complex number by Expression (7).
- the x component A xij of the vector I ij in the ROI corresponding to the i-th beam axis direction and the orthogonal azimuth direction is the j-th vector is associated with the real component and the y component A yij is associated with the imaginary component, it can be expressed by Equation 7.
- the processing of the similar filter calculates the load product sum of I ij with the weight W ij according to the difference from the target vector Io, and smoothes the target by smoothing by the sum of the load values. Find the vector I 0 '. By such processing, it is possible to avoid edge information deterioration of the vector of interest I 0 as shown in FIG.
- the similarity filter of the present invention can be extended to three-dimensional measurement.
- the three-dimensional measurement method uses, for example, a two-dimensional array type array transducer, and reconstructs the three-dimensional structure of the rectangular parallelepiped by acquiring multiple tomographic image data at high speed in the slice direction orthogonal to the beam direction and azimuth direction. To do. Assuming that the vector component in the slice direction of the three-dimensional data is A zijk , the vector of interest I ijk is expressed by Equation 9. In this case, the update formula using the similar filter is expressed by Equation 10 using the load W ijk of the three-dimensional distribution.
- the three-dimensional vector distribution After the three-dimensional vector distribution is determined, it is converted into a scalar distribution by a technique such as eigenvalue expansion as in the case of the two-dimensional vector distribution, and for example, divided into a plurality of two-dimensional cross sections to perform display processing.
- FIG. 10 shows a processing procedure in the ROI of the motion vector similarity filter of the present invention.
- a vector distribution is input.
- the ROI size and the half width of the load distribution are set as processing parameters.
- a one-sided distribution of Gaussian distribution shown in FIG. 11 is used as the load distribution.
- the horizontal axis represents the absolute value of the difference between the vector of interest I 0 and the other vector I ij in the ROI. The larger the difference, the smaller the load.
- the distribution shape changes according to the half width.
- convert each 2D vector to complex numbers convert each 2D vector to complex numbers.
- a new vector is calculated according to equation (8), and the process is terminated. It is possible to improve the accuracy after conversion to the scalar distribution by previously removing the noise component in the vector distribution.
- Patent Document 2 a B-mode moving image and an elastography moving image are displayed in parallel. Since the motion vector distribution information does not include errors due to non-linear factors in the elastography image, the correct behavior is shown. However, the vector distribution image alone cannot be associated with the tissue position. Therefore, it is possible to provide a diagnostic image with high visibility by superimposing a vector distribution image on a B-mode image and performing composite display.
- the processing procedure is shown in FIG. First, a B-mode original image is input. Next, the vector distribution is calculated, and then the original image and the corresponding vector distribution are combined and displayed.
- FIG. 13 shows an example of a composite image (tumor case).
- a composite image tumor case
- the magnitude and direction of change can be easily grasped.
- FIG. 12A The processing procedure is shown in FIG.
- the difference from FIG. 12A is that the vector distribution is calculated and then converted to a scalar distribution, and the original image and the scalar distribution are combined and displayed.
- the tissue boundary can be easily understood and the outline can be clearly seen, as in the colored elastomer image shown in Patent Document 1.
- the various display methods described above can be configured in such a way that the device operator selects with the changeover switch provided on the device panel.
- a default display mode that is easy to see when the power is turned on for example, a screen in which a B-mode image and a vector distribution are combined and a screen with only a scalar distribution are displayed in parallel.
- an appropriate creation processing method (rot processing, div processing, eigenvalue processing, etc.) is set in advance according to the purpose of diagnosis (tumor discrimination, treatment monitoring) and the target case.
- a changeover switch corresponding to the item is provided, and a method in which the device operator selects each item is possible.
- a scalar distribution image all three types of an ROT processing image, a DIV processing image, and an eigenvalue processing image are displayed, and four screens are displayed in parallel with a composite image (Bad and vector distribution). It becomes effective.
- the present invention is applicable not only to medical ultrasonic diagnostic / treatment devices but also to all devices that measure distortion and deviation using ultrasonic waves.
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Abstract
Description
divは単位面積におけるベクトルの発散量を反映すると解釈できる。計算の方法は,ベクトルのx成分Axに対してはx方向の編微分,y成分Ayに対してはy方向の偏微分を実施して,それらの和を求める。尚,超音波画像における測定誤差は,装置構成のためy方向(ビーム方向)がx方向(方位方向)より高精度になるので,Ayの偏微分値に大きな重み(例えば0.7),Axの偏微分値に小さな重み(例えば0.3)をかけた重み付きdivを適用するとより高精度化を図る事ができる。偏微分の具体的な算出方法は,ROI毎に空間一次微分フィルタ(例えば一般的に画像処理で用いられるソーベルフィルタ)を適用する。ROIサイズ3×3でのy方向のソーベルフィルタを数4に示す。
rotは単位面積におけるベクトルの回転量を反映する。具体的な算出方法は,y成分に対してx方向の空間一次微分,x成分に対してy方向の空間一次微分を計算し,それらの差を求める。得られた値はテンソル量と呼ばれる。図8(b)にrot処理を適用した結果を示す。表示ではテンソル量の絶対値を用いている。図より,境界位置がdivの場合よりも鮮鋭に抽出できる特性が分かる。
この場合の類似フィルタによる更新式は,三次元分布の荷重Wijkを用いて数10で表される。
Claims (15)
- 被験体に超音波を照射部により照射され、前記被験体からの超音波信号を検出部により検出した検出結果を記憶し、記憶された検出結果より検出タイミングの異なる少なくとも2フレーム画像データを作成する画像データ作成ステップと、
前記画像データを複数フレーム用い、所定の動きベクトル解析処理に基づき動きベクトル分布像を形成する動きベクトル分布像作成ステップと、
設定された複数の関心領域であるROIに基づいて、選択された方法にて前記動きベクトル分布像からスカラー分布像へ変換する変換ステップと、
を有することを特徴とする超音波画像処理方法。 - 前記変換ステップは、
前記動きベクトル分布内に着目する小領域ROIを設定し,ROI内のビーム方向成分と方位方向成分を実数成分と虚数成分に置換して複素行列に変換する第1処理ステップと、
前記複素行列を固有値展開処理して固有値行列を求める第2処理ステップと、
前記固有値行列から各固有値の絶対値の最大値か,あるいは各固有値の絶対値の総和を求めて1つのスカラー値を決定する第3処理ステップと、
前記動きベクトル分布内の複数のROIで前記第1処理ステップから第3処理ステップまでを実施してスカラー分布像を作成することを特徴とする請求項1に記載の超音波画像処理方法。 - 前記変換ステップは、
前記動きベクトル分布内に着目する小領域ROIを設定し,前記ROI内においてビーム軸に沿ったビーム方向とそれに直交する方位方向の各々に対して空間一次微分フィルタを施して微分値情報を算出する第1処理ステップと、
ROI内の前記ビーム方向微分値と前記方位方向微分値を実数成分と虚数成分に置換して複素行列に変換する第2処理ステップと、
前記複素行列を固有値展開処理して固有値を求める第3処理ステップと、
前記固有値行列から各固有値の絶対値の最大値か、あるいは各固有値の絶対値の総和か、あるいは各固有値を用いて計算される1つのスカラー値を決定する第4処理ステップと、
前記動きベクトル分布内の複数のROIで前記第1処理ステップから第4処理ステップまでを実施してスカラー分布像を作成することを特徴とする請求項1に記載の超音波画像処理方法。 - 前記変換ステップは、
前記動きベクトル分布内に着目する小領域ROIを設定し,ROI内のビーム方向成分と方位方向成分を実数成分と虚数成分に置換して複素行列に変換し,
設定された前記ROIにおいてビーム軸に沿ったビーム方向とそれに直交する方位方向の各々に対して空間一次微分フィルタを施して微分値情報を算出し,前記微分値情報に基づいてダイバージェンス演算を実施してスカラー値を決定し,
前記動きベクトル分布内の複数のROIで決定したスカラー値からスカラー分布を作成することを特徴とする請求項1に記載の超音波画像処理方法。 - 前記変換ステップは、
前記動きベクトル分布内に着目する小領域ROIを設定し,ROI内のビーム方向成分と方位方向成分を実数成分と虚数成分に置換して複素行列に変換し,
設定された前記ROIにおいてビーム方向と方位方向の各々に対して空間一次微分フィルタを施して微分値情報を算出し,前記微分値情報に基づいてローテーション演算を実施してスカラー値を決定し,
前記動きベクトル分布内の複数のROIで決定したスカラー値からスカラー分布を作成することを特徴とする請求項1に記載の超音波画像処理方法。 - 前記第3処理ステップは、
設定された前記ROIにおいてビーム方向と方位方向とに空間一次微分フィルタを施して微分値情報を算出し,前記微分値情報に基づいて歪みテンソルを求める演算を実施してスカラー値を決定することを特徴とする請求項2に記載の超音波画像処理方法。 - 前記第3処理ステップは、設定された前記ROIにおいてビーム方向かあるいは方位方向で隣接する2つのベクトル毎に内積計算を行ってスカラー値を決定し,
前記動きベクトル分布内の複数のROIで決定したスカラー値からスカラー分布を作成することを特徴とする請求項2に記載の超音波画像処理方法。 - 前記第3処理ステップは、
設定された前記ROIにおいてビーム方向かあるいは方位方向で隣接する2つのベクトル毎に外積計算を行ってテンソル値を求め,テンソル値かあるいはその絶対値からスカラー値を決定することを特徴とする請求項2に記載の超音波画像処理方法。 - 前記第3処理ステップは、設定された前記ROIにおいて,ベクトル分布をビーム方向成分スカラー分布と方位方向成分スカラー分布とに分解し,前記各方向のスカラー分布毎に類似度フィルタを適用して,適用結果の各スカラー分布から平滑化されたベクトル分布を構成し,前記平滑化されたベクトル分布に対してROI内のビーム方向成分と方位方向成分を実数成分と虚数成分に置換して複素行列に変換し、スカラー値を決定することを特徴とする請求項2に記載の超音波画像処理方法。
- 二次元アレイ状超音波探触子を使用してビーム方向及び方位方向と直交するスライス方向を追加した三次元画像情報を取得する場合には,
前記第3処理ステップは、
設定した前記ROIにおいて,ベクトル分布をビーム方向成分スカラー分布と方位方向成分スカラー分布とスライス方向成分スカラー分布とに分解し,前記各方向のスカラー分布毎に類似度フィルタを適用して,適用結果の各スカラー分布から平滑化されたベクトル分布を構成し、スカラー値を決定することを特徴とする請求項2に記載の超音波画像処理方法。 - 超音波を被検体に照射する照射部と,
前記被検体からの超音波信号を検出する検出部と,
前記検出手段の検出結果に基づいて検出タイミングの異なる少なくとも2フレームの画像データを作成する画像データ作成部と,
前記画像データを複数フレームを用い、所定の動きベクトル解析処理に基づきベクトル分布像を作成する動きベクトル分布像作成部と、
前記動きベクトル分布像をスカラー分布像へ変換する変換方法を選択する選択部と,
選択された前記変換方法に基づいて,前記動きベクトル分布像からスカラー分布像へ変換する変換部とを有することを特徴とする超音波画像処理装置。 - 前記変換部は、
前記動きベクトル分布内に着目する小領域ROIを設定し,ROI内のビーム方向成分と方位方向成分を実数成分と虚数成分に置換して複素行列に変換する第1処理手段と,
前記複素行列を固有値展開処理して固有値行列を求める第2処理手段と、
前記固有値行列から各固有値の絶対値の最大値か,あるいは各固有値の絶対値の総和を求めて1つのスカラー値を決定する第3処理手段と、
前記動きベクトル分布内の複数のROIで前記1処理手段から前記第3処理手段を実施してスカラー分布像を作成することを特徴とする請求項11記載の超音波画像処理装置。 - 前記変換部は、
前記動きベクトル分布内に着目する小領域ROIを設定し,前記ROI内においてビーム軸に沿ったビーム方向とそれに直交する方位方向の各々に対して空間一次微分フィルタを施して微分値情報を算出する第1処理と、
ROI内の前記ビーム方向微分値と前記方位方向微分値を実数成分と虚数成分に置換して複素行列に変換する第2処理と、
前記複素行列を固有値展開処理して固有値を求める第3処理と、
前記固有値行列から各固有値の絶対値の最大値か、あるいは各固有値の絶対値の総和か、あるいは各固有値を用いて計算される1つのスカラー値を決定する第4処理と、
前記動きベクトル分布内の複数のROIで前記第1処理から前記第4処理までを実施してスカラー分布像を作成することを特徴とする請求項11に記載の超音波画像処理装置。 - 前記変換部は、
前記第1処理手段で設定したROIにおいてビーム軸に沿ったビーム方向とそれに直交する方位方向の各々に対して空間一次微分フィルタを施して微分値情報を算出し,前記微分値情報に基づいてダイバージェンス演算を実施してスカラー値を決定し,
前記動きベクトル分布内の複数のROIで決定したスカラー値からスカラー分布像を作成する手段を有する請求項12に記載の超音波画像処理装置。 - コンピュータを請求項1乃至9のいずれか1項に記載の超音波画像処理方法として機能させることを特徴とする超音波画像処理プログラム。
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2011052602A1 (ja) * | 2009-10-27 | 2011-05-05 | 株式会社 日立メディコ | 超音波イメージング装置、超音波イメージング方法および超音波イメージング用プログラム |
WO2012070588A1 (ja) * | 2010-11-25 | 2012-05-31 | 株式会社日立メディコ | 超音波動画像処理方法、装置、およびプログラム |
WO2014002778A1 (ja) * | 2012-06-27 | 2014-01-03 | 日立アロカメディカル株式会社 | 超音波診断装置及び超音波表示方法 |
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Families Citing this family (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8609423B2 (en) * | 2007-05-18 | 2013-12-17 | Life Technologies Corporation | Rapid protein labeling and analysis |
US8681866B1 (en) | 2011-04-28 | 2014-03-25 | Google Inc. | Method and apparatus for encoding video by downsampling frame resolution |
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WO2014155272A1 (en) * | 2013-03-28 | 2014-10-02 | Koninklijke Philips N.V. | Real-time quality control for acquisition of 3d ultrasound images |
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US10905396B2 (en) | 2014-11-18 | 2021-02-02 | C. R. Bard, Inc. | Ultrasound imaging system having automatic image presentation |
JP6705134B2 (ja) * | 2015-08-21 | 2020-06-03 | コニカミノルタ株式会社 | 超音波画像診断装置、超音波画像処理方法及び超音波画像処理プログラム |
EP3354202B1 (en) * | 2015-09-24 | 2020-03-18 | The University Of Tokyo | Ultrasonic diagnostic system and ultrasonic diagnostic method |
CN105319725B (zh) * | 2015-10-30 | 2018-01-02 | 中国科学院遗传与发育生物学研究所 | 用于快速运动物体的超高分辨成像方法 |
EP3424433A1 (en) * | 2017-07-06 | 2019-01-09 | Koninklijke Philips N.V. | Methods and systems for processing an ultrasound image |
JP7215053B2 (ja) * | 2018-10-02 | 2023-01-31 | コニカミノルタ株式会社 | 超音波画像評価装置、超音波画像評価方法および超音波画像評価プログラム |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0810260A (ja) * | 1994-06-28 | 1996-01-16 | Fujitsu Ltd | 超音波診断装置 |
JP2004129773A (ja) | 2002-10-09 | 2004-04-30 | Hitachi Medical Corp | 超音波イメージング装置及び超音波信号処理方法 |
JP2004135929A (ja) | 2002-10-18 | 2004-05-13 | Hitachi Medical Corp | 超音波診断装置 |
JP2007330764A (ja) * | 2006-01-10 | 2007-12-27 | Toshiba Corp | 超音波診断装置及び超音波画像生成方法 |
JP2008079792A (ja) | 2006-09-27 | 2008-04-10 | Hitachi Ltd | 超音波診断装置 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP4594610B2 (ja) * | 2003-10-21 | 2010-12-08 | 株式会社東芝 | 超音波画像処理装置及び超音波診断装置 |
WO2007080895A1 (ja) * | 2006-01-10 | 2007-07-19 | Kabushiki Kaisha Toshiba | 超音波診断装置及び超音波画像生成方法 |
US20080081995A1 (en) * | 2006-10-03 | 2008-04-03 | Kang Kim | Thermal strain imaging of tissue |
-
2009
- 2009-10-29 WO PCT/JP2009/005750 patent/WO2010052868A1/ja active Application Filing
- 2009-10-29 EP EP09824569.9A patent/EP2347716B1/en not_active Not-in-force
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- 2009-10-29 US US13/128,350 patent/US9119557B2/en active Active
- 2009-10-29 CN CN200980143779XA patent/CN102202580B/zh not_active Expired - Fee Related
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0810260A (ja) * | 1994-06-28 | 1996-01-16 | Fujitsu Ltd | 超音波診断装置 |
JP2004129773A (ja) | 2002-10-09 | 2004-04-30 | Hitachi Medical Corp | 超音波イメージング装置及び超音波信号処理方法 |
JP2004135929A (ja) | 2002-10-18 | 2004-05-13 | Hitachi Medical Corp | 超音波診断装置 |
JP2007330764A (ja) * | 2006-01-10 | 2007-12-27 | Toshiba Corp | 超音波診断装置及び超音波画像生成方法 |
JP2008079792A (ja) | 2006-09-27 | 2008-04-10 | Hitachi Ltd | 超音波診断装置 |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2011052602A1 (ja) * | 2009-10-27 | 2011-05-05 | 株式会社 日立メディコ | 超音波イメージング装置、超音波イメージング方法および超音波イメージング用プログラム |
JP5587332B2 (ja) * | 2009-10-27 | 2014-09-10 | 株式会社日立メディコ | 超音波イメージング装置および超音波イメージング用プログラム |
US8867813B2 (en) | 2009-10-27 | 2014-10-21 | Hitachi Medical Corporation | Ultrasonic imaging device, ultrasonic imaging method and program for ultrasonic imaging |
WO2012070588A1 (ja) * | 2010-11-25 | 2012-05-31 | 株式会社日立メディコ | 超音波動画像処理方法、装置、およびプログラム |
JPWO2012070588A1 (ja) * | 2010-11-25 | 2014-05-19 | 株式会社日立メディコ | 超音波動画像処理方法、装置、およびプログラム |
JP5756812B2 (ja) * | 2010-11-25 | 2015-07-29 | 株式会社日立メディコ | 超音波動画像処理方法、装置、およびプログラム |
WO2014002778A1 (ja) * | 2012-06-27 | 2014-01-03 | 日立アロカメディカル株式会社 | 超音波診断装置及び超音波表示方法 |
WO2014010367A1 (ja) * | 2012-07-13 | 2014-01-16 | 日立アロカメディカル | 画像表示装置、及び組織可動性評価方法 |
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