45 results about "Computed tomography angiography" patented technology

A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.

A contrast enhanced dynamic study of a subject is performed with a CT system. A series of undersampled image data sets are acquired during the study with successive data sets acquired at interleaved projection angles. More fully sampled image data sets are formed by transforming the x-ray attenuation projection data to k-space and then sharing peripheral k-space data between undersampled k-space data sets. Artifacts due to undersampling are thus reduced without significantly affecting the time resolution of a series of reconstructed images.

The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.

A system and method for automatically segmenting bone regions from Computed Tomography Angiography (CTA) volume data is disclosed. A locally operated bone detector distinguishes between bone regions and contrast agent filled vessels. A filtering operator removes small noise from the detected bone regions. A dilator expands the filtered detected bone regions to adjacent trabecular bones. A processor applies the dilated detected bone regions to the CTA volume data. A display shows the applied CTA volume data.

The embodiment of the invention discloses a device and a method for detecting a blood vessel deformation area. The device comprises a classifier constructing unit, an area extracting unit, a morphological characteristic extracting unit and a detecting unit, wherein the classifier constructing unit is used for establishing a classifier according to a data classification model by taking morphological characteristics reflecting a border shape and/or an area shape in a blood vessel area as a classification basis; a characteristic space in the classifier is used for marking the presence or absence of deformation of the blood vessel area; the area extracting unit is used for extracting a blood vessel area to be detected from a blood vessel image of a computed tomography angiography (CTA); the morphological characteristic extracting unit is used for extracting morphological characteristics same as the morphological characteristics in the classifier in the blood vessel area to be detected; and the detecting unit is used for identifying the characteristic space corresponding to the extracted morphological characteristics in the classifier, and acquiring a deformation detection result of the blood vessel area to be detected according to the corresponding characteristic space. The device and the method can improve the sensitivity and the accuracy of the detection result.

The invention discloses a vascular stenosis detection method and device. The method comprises the steps of acquiring a computed tomography angiography (CTA) image for a blood vessel to be detected; straightening and imaging the CTA image at a plurality of set angles to obtain a plurality of straightened images at a plurality of set angles; carrying out width measurement on the straightened imagesone by one, and outputting a width curve corresponding to the straightened image with the set angle; and performing plaque positioning screening on the width curve one by one, and determining a narrowarea of the blood vessel to be detected according to a plaque positioning screening result. By implementing the method and the equipment provided by the invention, the accuracy of vascular stenosis detection can be improved.

A method and apparatus for machine learning based detection of vessel orientation tensors of a target vessel from a medical image is disclosed. For each of a plurality of voxels in a medical image, such as a computed tomography angiography (CTA), features are extracted from sampling patches oriented to each of a plurality of discrete orientations in the medical image. A classification score is calculated for each of the plurality of discrete orientations at each voxel based on the features extracted from the sampling patches oriented to each of the plurality of discrete orientations using a trained vessel orientation tensor classifier. A vessel orientation tensor at each voxel is calculated based on the classification scores of the plurality of discrete orientations at that voxel.

Thoracic aortic aneurysm is a common and lethal disease that requires regular imaging surveillance to determine timing of surgical repair and prevent major complications such as rupture. Current cross-sectional imaging surveillance techniques, largely based on computed tomography angiography (CTA) or magnetic resonance angiography (MRA), are focused on measurement of maximal aortic diameter, although this approach is limited to fixed anatomic positions and is prone to significant measurement error. The present techniques demonstrate novel approaches (generally termed herein “Vascular Deformation Mapping (VDM)”) for assessing changes in aortic dimensions. The present techniques quantify three-dimensional changes in the anatomic dimensions of a vessel through a process that involves non-rigid co-registration of serial imaging data and quantification of vascular deformation on a 3D surface model using some derivation of the spatial deformations resulting from the optimized spatial transform.

The invention belongs to the medical image processing technical field and provides a method for extracting cardiovascular vessels from CTA (computed tomography angiography) images, a device for extracting cardiovascular vessels from CTA images, an apparatus for extracting cardiovascular vessels from CTA images and a storage medium. The method includes the following steps that: etching operation and expansion operation are sequentially performed on image data by using preset structural elements, so that a structural template is obtained; layer-by-layer transformation is performed on the sectional images of the structural template, so that a first ascending aorta structure in the structural template can be obtained, aorta center coordinates and an aorta radius are obtained from the last sectional image layer of the structural template; and a binary sphere structure is built according to the aorta center coordinates and the aorta radius, and the first ascending aorta structure is combined with the structural template and the binary sphere structure, so that a second ascending aorta structure can be obtained through combination. With the method, device, apparatus and storage medium of the invention adopted, ascending aortas and the shapes of aortic sinuses at the roots of the ascending aortas can be extracted, and therefore, the structures and shapes of the aortas can be accurately visualized, and the solving level and capability of medical image research on clinical problems can be improved.

The invention relates to medical image processing, and aims to provide a method for simultaneously segmenting liver and a blood vessel in a CTA (computed tomography angiography) image. The method comprises the following steps of (1) performing model initialization; (2) optimizing a new variational energy model, and simultaneously segmenting the liver and the blood vessel; (3) performing post-processing to obtain a liver contour and a blood vessel contour respectively. According to the method for simultaneously segmenting the liver and the blood vessel, the adverse impact of weak boundaries, noise and the like can be effectively overcome; particularly, due to the introduction of gray level distribution information of local neighborhoods, the liver can be well distinguished from adjacent organs or soft tissues, and the phenomenon of over-segmentation can be effectively prevented.

The invention provides a rapid algebraic reconstruction technique (ART) applied to computed tomography (CT) imaging, belonging to the technical field of biomedical imaging, nondestructive test, etc. Based on traditional ART, the rapid ART of the invention randomly selects partial hyperplane, and performs further acceleration adjustment operation on the projection solution vector obtained through the traditional ART on the partial hyperplane, thereby allowing the solution vector after adjustment and update to be optimal on the connection line between an original solution vector and the hyperplane solution vector of last iteration. According to the rapid ART, a different hyperplane projection sequence can be employed for each iteration. A random projection sequence of each iteration allows higher efficiency under various conditions. Compared with the traditional ART, the rapid ART substantially increases an algorithm convergence speed, and can be applied to rapid CT image reconstruction under incomplete projection conditions.

An embodiment of the invention discloses head and neck CTA (computed tomography angiography) image layering method and device. The method includes: acquiring a head and neck CTA image with a scanning table image removed; determining a head and neck partition of the head and neck CTA image; acquiring a head CTA image according to the head and neck partition; determining a first partition of the head CTA image, and determining a second partition of the head CTA image. Before vessel separation, the head and neck CTA image are layered in advance according to the structural features of head and neck, and accordingly efficiency of separating vessels in the head and neck CTA image is improved.

A method of modeling a structure of a coronary artery of a subject may include: forming a learning-based shape model of the structure of the artery, based on positions of landmarks acquired from three-dimensional images; receiving a target image; and/or modeling the artery structure included in the target image, using the model. An apparatus for modeling a structure of a coronary artery may include: a memory configured to store a learning-based shape model of the artery, the learning-based shape model being formed based on positions of a plurality of landmarks acquired from three-dimensional images, the plurality of the landmarks corresponding to the artery; a communication circuit configured to receive a target image; and/or a processing circuit configured to model the artery structure included in the target image, using the model.

A method and system for automatically detecting systemic arteries in arbitrary field-of-view computed tomography angiography (CTA) includes fully-automatically analyzing a medical image represented by a digital image representation.

The invention discloses a pulmonary embolism detection system based on a convolutional neural network. The system comprises: a candidate region extraction network, which is a full convolutional network using automatic encoding and decoding with jump connection, performing candidate region extraction on a computed tomography angiography image to be detected, and obtaining a plurality of false positive candidate regions with different sizes; a 3D affine transformation network, used for generating cubes with aligned blood vessels and fixed sizes from a plurality of false positive candidate areaswith different sizes and taking out three orthogonal layers of the cubes; and a false positive prediction screening network, used for inputting the three orthogonal layers into a 2D classification network containing two full connection layers to carry out false positive prediction screening. The method can solve the problem of error accumulation. 3D image features with higher discriminability canbe automatically extracted, the influence of the volume effect is reduced, and the method does not depend on the experience of researchers. The accuracy is improved while the recall rate is ensured.

The invention discloses a high-pressure injection system used for computed tomography angiography, relates to the technical field of therapeutic equipment, and is mainly for solving the problem that scanned images include many has more artifacts which affect the accuracy of the diagnosis. The high-pressure injection system includes a first injection syringe, a second injection syringe and a mounting frame body used for mounting the first injection syringe and the second injection syringe, the heads of the first injection syringe and the second injection syringe are connected to the heart through the same catheter, the first injection syringe and the second injection syringe are fixed at one end of the mounting frame body, and the mounting frame body is provided with a power assembly for driving the first injection syringe and the second injection syringe. Compared with single-syringe contrast agent injection, the simultaneous injection of the dual injection syringes of the invention can minimize artifacts with appropriate contrast attenuation level while performing CT scanning so as to obtain high-quality images.

The invention discloses a coronary artery projection image generation method and device and a computer readable medium. One embodiment of the method comprises the following steps: acquiring a coronaryartery computed tomography angiography (CTA) sequence image; carrying out segmentation processing on the coronary artery CTA sequence image to generate two connected region images; identifying the two connected region images to generate a left coronary artery three-dimensional segmentation image and a right coronary artery three-dimensional segmentation image; and performing image projection processing on the left coronary artery three-dimensional segmented image and the right coronary artery three-dimensional segmented image to generate a left coronary artery tree two-dimensional image and aright coronary artery tree two-dimensional image. According to the invention, the left coronary artery CTA sequence image can be processed by the artificial intelligence model to generate the left coronary artery three-dimensional segmentation image and the right coronary artery three-dimensional segmentation image; therefore, maximum density projection is generated for the left coronary artery tree and the right coronary artery tree, interference of other tissues or blood vessel branches on coronary arteries is eliminated, and doctors can observe related problems of disease diagnosis such asblood vessel bifurcation, calcification or stenosis.