8 results about "Volumetric imaging" patented technology

Volume-of-interest (“VOI”) imaging with computed tomography (“CT”) (e.g., VOI CT in a helical or axial acquisition geometry) is used to produce higher resolution, lower noise images with less radiation dose than is required for equivalent resolution and noise characteristics using standard data acquisitions. Truncation artifacts introduced by the VOI imaging are removed or otherwise reduced using a deep learning model that is trained to remove truncation artifacts from VOI images.

The application relates to the technical field of ultrasonic three-dimensional imaging, and provides a three-dimensional full-focusing imaging method based on a row-column addressing ultrasonic probe, wherein according to the spatial position of an array element of the row-column addressing ultrasonic probe, the imaging area range and the imaging pixel size, the propagation flight time of each pixel point in a three-dimensional space to each array element is calculated; a first group of three-dimensional full-matrix data is acquired by row-by-row excitation and emission of row array elements and by receiving echo signals by column array elements; a second group of three-dimensional full-matrix data is obtained by column-by-column excitation and emission of column array elements and by receiving echo signals by row array elements; and the first group of three-dimensional full-matrix data and the second group of three-dimensional full-matrix data are processed by a three-dimensional full-focusing algorithm based on the total propagation flight time of each pixel point to each array element, so that a three-dimensional volume image of a measured structure is obtained. Under the premise of reducing the system channel number and the hardware complexity, high-resolution and high-quality three-dimensional volume imaging is realized, and the method has good engineering realizability and popularization potential.

This invention relates to the field of biomedical engineering technology and discloses a method and device for time-calibrated meridian detection based on multimodal sensing. The method includes the following steps: acquiring optical volumetric imaging and skin surface temperature signals of the subject, calculating the systematic rhythm shift index, and determining the time-anchored acupoint nodes to generate a composite AC constant current source signal; initiating time-division isolation driving timing, sequentially injecting the constant current source signal individually into the target acupoint, time-anchored acupoint, and non-meridian reference node, while simultaneously acquiring differential voltage signals; extracting the complex impedance modulus of each node through orthogonal demodulation and calculating the frequency domain polarizability; using the polarizability of the non-meridian reference node and the time-anchored acupoint node as static and physiological upper limit benchmarks, respectively, performing differential and normalization operations to output a normalized meridian index. This invention eliminates baseline drift caused by the endogenous diurnal rhythm in the human body, blocks electrical ground crosstalk in multi-channel measurements, and improves the objectivity and data stability of meridian feature detection.

A method for a radiation treatment system includes: a treatment-delivering X-ray source configured to rotate about an isocenter of the radiation treatment system and direct treatment X-rays to a target volume; an imaging X-ray source configured to rotate about the isocenter of the radiation treatment system and direct imaging X-rays to a target region that includes the target volume; and a processor. The processor is configured to perform the steps of: displaying a first set of multiple first user input elements, wherein each first user input element corresponds to a different anatomical region; in response to receiving a first user input via a specific first user input element included in the first set, displaying a second set of multiple second user input elements, wherein each second user input element corresponds to a different imaging protocol for an anatomical region that is associated with the specific first user input element in the first set; and in response to receiving a second user input via a specific second user input element included in the second set, acquiring at least one X-ray image of the target region via the imaging X-ray source and an imaging protocol that is associated with the specific second user input element.

Provided herein are methods, systems, and computer program products for dynamic volumetric imaging of an imageable volume including a sample, e.g., a biological sample, with an imaging instrument. A first z-stack of images of a sample is captured by moving an objective of the imaging instrument relative to the sample. Each image of the first z-stack of images has a z- index within the z-stack and a first field of view. A density metric of the first z-stack of images is determined. The density metric corresponds to a point density of one or more target molecule in the first field of view. The density metric is compared with a threshold. A second z-stack is captured, based on the comparison of the metric with the threshold, by moving the objective of the imaging instrument relative to the sample. The second z-stack comprises at least one additional image of the sample within the first field of view, and is interleaved with the first z-stack.

Perform 2D / 3D alignment with high precision. [Solution] The medical image processing apparatus according to the embodiment includes a processing circuit. The processing circuit generates a first map representing the estimated location of a medical device within an anatomical region based on a 2D (two-dimensional) image acquired by a medical imaging device, which represents the medical device within an anatomical region of a patient or other subject. The processing circuit generates a second map representing the expected location of the medical device within an anatomical region based on volumetric imaging data representing the anatomical region and a plurality of alignment parameters that define the pose of the medical imaging device. The processing circuit compares the first map and the second map with each other. The processing circuit updates the plurality of alignment parameters based on the comparison between the first map and the second map.

This invention relates to the technical field of non-destructive testing of welded metal structures, and provides a three-dimensional imaging method for welded metal structures based on row-column addressed ultrasonic phased arrays. The method includes: performing ultrasonic phased array testing on the welded metal structure to acquire full matrix capture data; constructing a three-dimensional imaging Cartesian coordinate system for the welded structure; calculating the three-dimensional propagation paths of ultrasonic waves from the array's row and column sources to each imaging point in three-dimensional space according to spatial geometric relationships, and calculating the corresponding propagation times based on the propagation paths; performing denoising, delay summation, and vector coherence factor weighting on the full matrix capture data to obtain the final three-dimensional volumetric imaging result; and reconstructing and visualizing the data to obtain the three-dimensional imaging result of internal defects in the welded metal structure. Accurate modeling of the ultrasonic propagation path within the three-dimensional imaging framework allows for full utilization of the multi-path information from the row and column sources, improving the imaging accuracy and spatial positioning precision of internal defects in the welded structure.

Systems and methods are provided for automated image analysis of volumetric image data depicting blood vessels. Volumetric imaging data, such as computed tomography angiography data, are processed by an image segmenter implemented as a convolutional neural network to generate a lumen mask identifying vessel lumen within the volumetric image data. In implementations, the image segmenter predicts binary masks on multiple two-dimensional slices extracted from the volumetric image data along different axes and / or angles, and the predicted masks are combined by voting, averaging, and / or nonlinear filtering to form a three-dimensional lumen segmentation. A three-dimensional segmented vessel model is formed from the lumen segmentation, a narrowing region is detected, and dimensional parameters of the narrowing region are computed, including at least stenosis length, stenosis thickness, and minimum lumen cross-sectional area. The dimensional parameters are output in a machine-readable format. Optionally, computational flow parameters and classification outputs may be generated.