Parameterized model reconstruction is used for internal dose tomography. The parameterized model, solved for within the reconstruction, models the dose level and may account for diffusion, isotope half-life, and/or biological half-life. Using the detected emissions from different scans (e.g., from different scan sessions in a given cycle) as input for the one reconstruction, the parameterized model reconstruction determines the biodistribution of dose at any time.

The present disclosure may provide a system for radiotherapy planning. The system may obtain planning information relating to at least one beam to be delivered to a subject in a treatment of the subject. The system may also generate an input of a fluence map generation model based on the planning information. For each of the at least one beam, the system may further generate at least one deliverable fluence map relating to at least one segment of the beam based on the input and the fluence map generation model.

Systems and methods can include a method for training a deep convolutional neural network to provide a patient radiation treatment plan, the method comprising collecting patient data from a group of patients, the patient data including at least one image of patient anatomy and a prior treatment plan, wherein the treatment plan includes predetermined machine parameters, and training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data to determine a new treatment plan. Systems and methods can also include a method of using a deep convolutional neural network to provide a radiation treatment plan, the method comprising retrieving a trained deep convolution neural network previously trained on patient data from a group of patients, collecting new patient data, wherein the new patient data includes at least one image of patient anatomy, and determining a new treatment plan for the new patient using the trained deep convolutional neural network for regression, wherein the new treatment plan has a new set of machine parameters.

In radiation treatment planning, a plurality of optimization loops are performed. In each optimization loop computes a dose distribution (60) in a patient represented by a planning image (42) with regions of interest (ROIs) defined in the planning image. Weights (64) for objective functions (50) are determined from objective function value (OFV) goals (52) for the objective functions. An optimized dose distribution is produced by adjusting the plan parameters to optimize the computed dose distribution respective to composite objective function (62). At least one optimization loop may include updating (70) at least one OFV goal to be used in at least the next performed optimization loop. At least one optimization loop may include updating an objective function quantifying compliance with a target dose for a target ROI based on a comparison of a metric of coverage of the target ROI and a desired coverage of the target ROI.

A method for delivering radiation treatment may include defining a preliminary trajectory including a plurality of control points. Each control point may be associated with position parameters of a gantry and a couch. The method may also include generating a treatment plan based on the preliminary trajectory by optimizing an intensity and position parameters of a collimator and MLC leaves for each control point. The method may also include decomposing the treatment plan into a delivery trajectory including the plurality of control points. Each of the plurality of control points may be further associated with the optimized intensity, the optimized position parameters of the collimator and the MLC leaves, an output rate, and a motion parameter of each of the gantry, the couch, the collimator, and the MLC leaves. The method may further include instructing a radiation delivery device to deliver the treatment plan according to the delivery trajectory.

An example method for a radiation therapy system that includes a movable couch to perform radiation therapy has been disclosed. One method includes based on the X-ray images of an anatomical region of the patient that includes a target volume, reconstructing a digital volume of the anatomical region and based on a user input indicating a location of a patient origin in the digital volume, determining one or more shift values for repositioning the patient origin at an isocenter of the radiation therapy system with respect to a coordinate system. The method also includes obtaining a treatment plan that is based on the location of the patient origin and is associated with the target volume, based on the treatment plan, repositioning the movable couch so that the patient origin is disposed at the isocenter, and while the patient origin is disposed at the isocenter, directing a treatment beam to the patient origin in accordance with the treatment plan associated with the target volume.

A correlation between a CT value and a water equivalent thickness ratio distribution for each patient can be corrected without increasing a treatment time, and more accurate treatment can be realized. A treatment planning system 112 which generates a treatment plan for irradiating an irradiation target with a particle beam calculates a correction amount of a water equivalent thickness ratio of a first treatment plan created in advance, calculates a water equivalent thickness ratio distribution based on the correction amount and the first treatment plan, and creates a second treatment plan from the water equivalent thickness distribution.

Systems and methods for tracking a target volume, e.g., tumor, in real-time during radiation treatment are provided. The system includes a memory to store a pre-acquired 3D image of the anatomy of interest in a first reference frame and a processor, operative coupled with the memory, to receive, from an ultrasound probe, a set-up ultrasound image of the anatomy of interest in a second reference frame. The processor further to establish a transformation between the first and second reference frames by registering the set-up ultrasound image with the pre-acquired 3D image and receive, from the ultrasound probe, an intrafraction ultrasound image of the anatomy of interest. The processor further to register the intrafraction ultrasound image with the set-up ultrasound image and track motion of the anatomy of interest based on the registered intrafraction ultrasound image.

The invention relates to a method for synthesizing ultrasmall silica nanoparticles, useful in particular for diagnostics and/or therapy. More specifically, a method for synthesizing silica nanoparticles, said method comprising the mixing of at least one silane which is negatively charged at physiological pH with at least one silane which is neutral at physiological pH, and/or at least one silane which is positively charged at physiological pH, wherein: —the molar ratio A of neutral silane(s) to negatively charged silane(s) is defined as follows: 0≤A≤6, —the molar ratio B of positively charged silane(s) to negatively charged silane(s) is defined as follows: 0≤B≤5, —the molar ratio C of neutral and positively charged silanes to negatively charged silane(s) is defined as follows: 0<C≤8. The invention also relates to the obtained ultrasmall silica nanoparticles.

Systems and methods are disclosed for performing operations comprising: accessing a current structural estimate of a region of interest; generating a first simulated X-ray measurement based on the current structural estimate of the region of interest; receiving a first real X-ray measurement; and generating an update to the current structural estimate of the region of interest as a function of the first simulated X-ray measurement and the first real X-ray measurement, the update being generated invariant on the current structural estimate.