11 results about "Patient dose" patented technology

The invention relates to the technical field of nasopharyngeal carcinoma radiotherapy, in particular to a nasopharyngeal carcinoma radiotherapy dose automatic prediction method based on deep learning, and the method comprises the following steps: S1, analyzing obtained original data; s2, writing a program to pre-process all the original data in batches; s3, calculating a corresponding dose volume histogram according to the anatomical contour and the patient dose distribution; and S4, inputting the preprocessed data into a designed deep learning model for dose distribution training and prediction, and comparing with an existing dose prediction method to verify the innovativeness and feasibility of the scheme. Dose prediction is automatically carried out through the deep learning model, manual rule design is not needed, high-quality dose distribution can be rapidly generated, batch preprocessing is carried out on all original data, dose distribution training and prediction are carried out by inputting the data into the designed deep learning model, the calculation performance is improved, and the method is suitable for large-scale popularization and application. And the training and reasoning efficiency is improved.

The invention discloses a radiotherapy robustness optimization method and system, and the method comprises the steps: obtaining the clinical diagnosis and treatment information and anatomical feature information of a patient, and outputting unbiased scene dose three-dimensional distribution based on a dose prediction model; setting the maximum placement deviation of the patient, and obtaining deviation scene dose three-dimensional distribution corresponding to each placement deviation scene based on the dose three-dimensional distribution of the patient randomly moved in the maximum placement deviation range; for each round of circulation of robustness optimization, performing optimization calculation on the dose three-dimensional distribution of the unbiased scene to obtain optimized dose distribution, and calculating the sum of difference values between the optimized dose distribution and the dose three-dimensional distribution of each biased scene to obtain a target function value; when robustness optimization iteration reaches a preset number of times, optimization is ended, and a final optimization plan and corresponding final dose three-dimensional distribution are obtained; and for the final optimization plan, moving the final dose three-dimensional distribution in the maximum positioning deviation range, obtaining a deviation scene DVH curve, and evaluating the final optimization plan.

This application relates to the field of CT imaging technology. A collimation device on a variable incident light angle X-ray attenuation system includes: a X-ray filter; an A-level collimator disposed at the incident end of the X-ray filter; and a B-level collimator disposed at the exit end of the X-ray filter. The opening centers of the A-level and B-level collimators are symmetrically arranged along the perpendicular bisector of the imaging field range perpendicular to the light plane, based on the X-ray source focal spot, to compensate for X-ray divergence deviation caused by the X-ray source focal spot and limit the X-ray incident width. While ensuring CT scan imaging quality, this application achieves the dual technical effects of improved X-ray utilization efficiency and reduced patient dose, effectively solving a series of scattering-related technical problems associated with traditional X-ray filtering devices alone.

The utility model discloses a Parkinson's disease patient dose visualization partition medicine box, belongs to the technical field of partition medicine boxes, and comprises a partition medicine box and a partition plate, the partition plate is fixedly connected in the partition medicine box, the interior of the partition medicine box is divided into two partition medicine chambers through the partition plate, and storage assemblies are installed in the two partition medicine chambers. The corresponding storage chambers are opened through the handles on the first cover plate, medicine of different types and doses can be accurately placed in the storage chambers according to the single dosage of a patient, medicine taken once is stored in each storage chamber, confusion is avoided, after filling, the positioning grooves and the positioning rods are connected in a clamped mode, the first cover plate is fixed in an abutting mode through the positioning plates, the medicine can be effectively prevented from being spilled out, and the medicine taking efficiency is improved. A second medicine taking groove is matched with a first medicine taking groove of a target storage chamber by rotating a rotating plate, medicine can smoothly slide into the medicine taking chamber, a second cover plate is opened through mutual cooperation of a first magnetic block and a second magnetic block, the medicine box is obliquely separated, a patient can conveniently take out the medicine, and the whole medicine taking process is easy and convenient to operate.

Systems and methods are provided for identifying therapeutic zones where there is glycemic dysfunction of a specific type that can be addressed by making strategic changes to behavior and / or therapy parameters. Systems and methods described herein evaluate large historical data sets to: identify a therapeutic zone or zones with glycemic dysfunction that are most readily addressable; quantify the glycemic impact of a plurality of different therapeutic adjustments in terms of either adjustments to historical doses or the parameters of a prospective dosing strategy to determine the highest possible improvement; and / or identify patient dosing strategies to provide therapy recommendations adapted for the patient's preferred behavioral dosing strategy.

The invention provides a VMAT plan patient dose verification prediction method and system based on Bayesian model uncertainty quantification, and is applied to the technical field of medical data processing. According to the method, a multi-modal classification model is constructed, a radiation field complexity feature and a flux graph are used as input, one-dimensional and two-dimensional branches are used for feature extraction and then fusion, a full connection layer is used for output, an Adam optimizer is used for training, variational Monte Carlo is combined, and data, the model and total uncertainty are calculated through sampling. Based on classification model output, through LASSO dimensionality reduction, in combination with classification probability and uncertainty, MLP prediction GPR is constructed, self-defined weighted MAE training is used, test samples are classified according to clinical thresholds, intervention is triggered by exceeding thresholds, and a patient dose verification prediction result is generated.

Systems and methods are provided for identifying therapeutic zones where there is glycemic dysfunction of a specific type that can be addressed by making strategic changes to behavior and / or therapy parameters. Systems and methods described herein evaluate large historical data sets to: identify a therapeutic zone or zones with glycemic dysfunction that are most readily addressable; quantify the glycemic impact of a plurality of different therapeutic adjustments in terms of either adjustments to historical doses or the parameters of a prospective dosing strategy to determine the highest possible improvement; and / or identify patient dosing strategies to provide therapy recommendations adapted for the patient's preferred behavioral dosing strategy.

In a method to determine a patient (radiation) dose distribution (13), it is provided to calculate, from a measured signal (7) of a detector (6) placed behind a region of interest, a dose distribution (11) for a patient-shaped water equivalent phantom (12) and to compute from this a patient dose distribution (13) that takes into account inhomogeneities of a matter distribution of the patient (4).

Systems and methods are provided for identifying a treatment zone in which a particular type of glycemic dysfunction exists that can be addressed by a change in a behavior and / or treatment parameter strategy. The systems and methods described herein evaluate large historical datasets to: identify one or more treatment zones in which glycemic dysfunction is most easily addressed; quantify the impact of a variety of different treatment adjustments on glycemia according to adjustments to historical doses or parameters of an expected dosing strategy to determine the best way to improve; and / or determine a patient dosing strategy to provide a treatment recommendation that is tailored to a patient's preferred behaviorally dosing strategy.

A system according to at least one embodiment of the present disclosure comprises: an imaging source; an imaging detector; a processor; and a memory coupled to the processor and having stored thereon data that, when executed by the processor, enables the processor to: initiate a multi-dimensional scan of a patient's anatomy, the multi-dimensional scan including the imaging source emitting a radiation beam received by the imaging detector; receiving radiation beam information associated with the radiation beam received by the imaging detector; comparing the radiation beam information to a beam threshold; and instructing the imaging source to adjust at least one beam parameter of the radiation beam based on the comparison such that the radiation beam information conforms to the beam threshold.

The invention relates to radiographic imaging. In order to improve effective dose estimation, a computer-implemented method (400) for determining an effective dose of a subject from a radiographic examination is provided in which a radiographic imaging device having a radiation source and a radiation detector is used to acquire a two-dimensional (2D) radiographic image of the subject, the method comprising: a) providing (410) a three-dimensional (3D) patient model; b) determining (420) a set of non-rigid registration parameters for adapting the 3D patient model to the object to map points in a 2D radiographic image of the object to points in the 3D patient model; c) registering (430) the 3D patient model to the subject using the determined set of non-rigid registration parameters, and d) determining (440) the effective dose based on the registered 3D patient model of the body part, geometry data of the radiographic imaging device, and generator data of the radiation source.