8 results about "Pulmonary area" patented technology

A method of quantifying pulmonary function using a medical image includes acquiring or receiving a medical image including anatomical information for a lung region of a patient; segmenting at least one abnormal finding region in the lung region of the medical image using an artificial neural network; and predicting a quantification result related to pulmonary function based on a size of the at least one abnormal finding region.

A computer-implemented method for providing output data comprising an indication regarding the affliction of a patient with an infectious respiratory disease, the method comprises receiving magnetic resonance imaging data, the magnetic resonance imaging data acquired using a magnetic resonance imaging system, the magnetic resonance imaging data comprising a lung region of the patient; applying a trained function to the magnetic resonance imaging data to generate the output data, the trained function being based on an artificial neural network and the output data comprising the indication regarding the affliction of the patience with the infectious respiratory disease; and proving the output data.

This application relates to a method, apparatus, device, medium, and program product for quantifying CT features of idiopathic pulmonary fibrosis. The method includes: acquiring a medical image of the lung to be processed; segmenting the lung parenchyma in the medical image to obtain bilateral lung regions and total lung volume; segmenting the bilateral lung regions to obtain multiple imaging features related to idiopathic pulmonary fibrosis, and determining the volume percentage corresponding to each of the multiple imaging features related to idiopathic pulmonary fibrosis and the total lung volume; and determining the predicted functional impairment value corresponding to the volume percentage of the multiple imaging features related to idiopathic pulmonary fibrosis based on a pre-generated linear regression model, wherein the linear regression model represents the correlation between the volume percentage of the multiple imaging features related to idiopathic pulmonary fibrosis and various lung function indicators. This method can improve processing efficiency.

The present disclosure relates to a method, device and equipment for determining the phase distribution of a cardiopulmonary region signal, the method comprising: collecting a target electrical impedance tomography signal synchronized with the mechanical movement of the heart of a subject from the electrical impedance tomography signals of the chest of the subject at multiple detection times; determining an electrical impedance static image of the chest of the subject according to the target electrical impedance tomography signal; determining a heart region of interest and a lung region of interest of the subject in the electrical impedance static image respectively; determining a peak pixel point image according to the electrical impedance time variation curve corresponding to the target pixel point in the lung region of interest; calculating the sum of the pixel points in the heart region of interest and the lung region of interest, multiplying the sum of the pixel points with each peak pixel point in the peak pixel point image one by one to obtain a phase distribution map of the cardiopulmonary region signal of the subject. The voltage signal synchronized with the mechanical movement of the heart can be corresponded with the spatial position, thereby accurately reflecting the phase distribution of the blood flow signal of the cardiopulmonary region of the subject.

The present disclosure relates to a method and apparatus for segmenting pulmonary blood vessels, an electronic device, and a storage medium, and relates to the technical field of pulmonary blood vessel segmentation. The method for segmenting pulmonary blood vessels includes: obtaining an enhanced lung image and its corresponding pulmonary blood vessel image, a first non-enhanced lung image, and a preset first segmentation model; performing a registration operation on the enhanced lung image and the first non-enhanced lung image to obtain a lung registration image corresponding to the first non-enhanced lung image; performing lung region segmentation on the lung registration image to obtain a lung region image; training the preset first segmentation model based on the pulmonary blood vessel image and the lung region image; and performing pulmonary blood vessel segmentation on a second non-enhanced lung image based on the trained preset first segmentation model. Embodiments of the present disclosure can implement non-enhanced lung images of non-enhanced lung images.

The invention belongs to the technical field of biomedicine, and particularly relates to a method for evaluating the elastic contraction function of lung tissue and application thereof.The method for evaluating the elastic contraction function of the lung tissue comprises the following steps that (1) a semi-automatic segmentation module of ITK-SNAP software is adopted for lung area recognition, and an initial lung segmentation mask is generated; performing 10 voxel morphological expansion on the mask to generate an inspiration sequence mask and an expiration sequence mask; (2) outputting to obtain a shape transformation matrix; the method comprises the following steps: quantitatively analyzing lung elastic contraction characteristics of an IPF patient by using an elastic registration algorithm, and analyzing the correlation between elastic contraction parameters and lung function parameters, dyspnea degree, exercise tolerance, health-related life indexes and the correlation between pulmonary fibrosis degree and pulmonary blood vessel related parameters quantified based on HRCT (High Resolution Computed Tomography); mRI-based lung elastic contraction quantitative analysis is expected to become a novel image marker and is used for noninvasive evaluation of lung tissue elasticity of an IPF patient.

A method for lung monitoring provided by a lung monitoring system, includes: obtaining respiratory intensity of lung sound signals measured at a chest of a patient; generating lung sound tomographic images divided into lung regions by displaying a respiratory intensity at the lung regions corresponding to a position where the lung sound signals are measured; calculating a peak compliance according to a variation in a positive end-expiratory pressure (PEEP) value for each lung region of the lung sound tomographic image; and determining an optimal PEEP range of the patient based on a PEEP value corresponding to the peak compliance for each lung region.