Abstract: An image segmentation method is disclosed. The method includes receiving a plurality of atlases and a subject image, each atlas including an atlas image showing a structure of interest and associated structure delineations, the subject image being acquired by an image acquisition device and showing the structure of interest. The method further includes calculating, by an image processor, mapped atlases by registering the respective atlases to the subject image, and determining, by the image processor, a first structure label map for the subject image based on the mapped atlases. The method also includes training, by the image processor, a structure classifier using a subset of the mapped atlases, and determining, by the image processor, a second structure label map for the subject image by applying the trained structure classifier to one or more subject image points in the subject image.

Abstract: A deformable radiotherapy phantom can be produced using an additive manufacturing process, based on a medical image of the patient. The deformable phantom can include dosimeters for measuring radiation dose distribution. A smart material can allow deformation in response to an applied stimulus. Among other things, the phantom can be used to validate radiation dose warping, a radiotherapy treatment plan, to determine a maximum acceptable deformation of the patient, to validate a cumulative accuracy of dose warping and deformable image registration, or the like.

Abstract: Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.

Abstract: Embodiments of the disclosure may be directed to a system for generating a motion target volume representative of shape changes of a target region in a patient. The system may comprise at least one computer system configured to receive a plurality of electronic medical images that include the target region, and each of the plurality of images may have been taken at a different time point. The computer system may be configured to define a three-dimensional volume containing the target region in each of the plurality of images, and the three-dimensional volume may be different in at least two of the plurality of images due to differences in shape of the target region in the at least two images. The computer system may also be configured to co-register the three-dimensional volumes and generate the motion target volume, wherein the motion target volume encompasses each of the three-dimensional volumes.

Abstract: Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical images. Embodiments of the present disclosure may relate to a method for segmenting medical images. The method may be implemented by a processor device executing a plurality of computer executable instructions. The method may comprise receiving an image from a memory, and identifying at least one landmark point within the image. The method may further comprise selecting an image point in the image, and determining at least one feature for the image point relative to the at least one landmark point. The method may also comprise associating the image point with an anatomical structure by using a classification model based on the at least one determined feature.

Abstract: Systems and methods for performing radiation treatment planning are provided. An exemplary system may include a processor device communicatively coupled to a memory device and configured to perform operations when executing instruction stored in the memory device. The operations may include receiving a reference treatment plan including one or more dose constraints and determining, based on the reference treatment plan, segment information of a plurality of radiation beams. The operations may also include determining a fluence map for each of the plurality of radiation beams based on the one or more dose constraints using a fluence map optimization algorithm. The operations may also include determining a dose distribution based on the fluence maps of the plurality of radiation beams. The operations may also include determining at least one beam modulation property of a new treatment plan using a warm-start optimization algorithm based on the segment information and the dose distribution.

Abstract: Systems and methods can include training a deep convolutional neural network model to provide a beam model for a radiation machine, such as to deliver a radiation treatment dose to a subject. A method can include determining a range of parameter values for at least one parameter of a beam model corresponding to the radiation machine, generating a plurality of sets of beam model parameter values, wherein one or more individual sets of beam model parameter values can include a parameter value selected from the determined range of parameter values, providing a plurality of corresponding dose profiles respectively corresponding to respective individual sets beam model parameter values in the plurality of sets of beam model parameter values, and training the neural network model using the plurality of beam models and the corresponding dose profiles.

Abstract: The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image.

Abstract: An image segmentation method is disclosed. The method includes receiving a plurality of atlases and a subject image, each atlas including an atlas image showing a structure of interest and associated structure delineations, the subject image being acquired by an image acquisition device and showing the structure of interest. The method further includes calculating, by an image processor, mapped atlases by registering the respective atlases to the subject image, and determining, by the image processor, a first structure label map for the subject image based on the mapped atlases. The method also includes training, by the image processor, a structure classifier using a subset of the mapped atlases, and determining, by the image processor, a second structure label map for the subject image by applying the trained structure classifier to one or more subject image points in the subject image.

Abstract: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.

Abstract: Apparatus and techniques are described herein for nuclear magnetic resonance (MR) projection imaging. Such projection imaging may be used to control radiation therapy delivery to a subject, such as including receiving reference imaging information, generating a two-dimensional (2D) projection image using imaging information obtained via nuclear magnetic resonance (MR) imaging, the 2D projection image corresponding to a specified projection direction, the specified projection direction including a path traversing at least a portion of an imaging subject, determining a change between the generated 2D projection image and the reference imaging information, and controlling delivery of the radiation therapy at least in part using the determined change between the obtained 2D projection image and the reference imaging information.

Abstract: Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.

Abstract: An adaptive therapy delivery system can receive imaging information including a volumetric image comprising a target such as a tumor or one or more other structures, and can receive imaging information corresponding to one or more imaging slices comprising different portions of the target, such as imaging slices acquired at different times after acquisition of the volumetric image. The system can spatially register information from an earlier-acquired image with a portion of the target included in a later-acquired one of the imaging slices. The system can then determine an updated location of the target indicated by the spatially-registered information. Using the updated location, the system can generate an updated therapy protocol to control delivery of a therapy beam.

Abstract: An image-guided therapy delivery system includes a therapy generator configured to generate a therapy beam directed to a time-varying therapy locus within a therapy recipient, an imaging input configured to receive imaging information about a time-varying target locus within the therapy recipient, and a therapy controller. The therapy generator includes a therapy output configured to direct the therapy beam according to a therapy protocol. The therapy controller is configured to automatically generate a predicted target locus using information indicative of an earlier target locus extracted from the imaging information, a cyclic motion model, and a specified latency, and automatically generate an updated therapy protocol to align the time-varying therapy locus with the predicted target locus.

Abstract: The present disclosure relates to methods and systems for radiotherapy. Embodiments of the present disclosure may receive a medical image including images of a target and an organ at risk (OAR). Some embodiments may also receive a target dose and a constraint on an OAR dose. Some embodiments may also generate a dose application plan based on the target dose and the constraint. Generation of the dose application plan may include determining a placement of an arc along which radiation is to be applied based on the target dose and a location of the target; dividing the arc into a plurality of segments; determining a dose rate associated with each segment; calculating a predicted OAR dose based on the determined dose rate; and comparing the predicted OAR dose with the constraint on the OAR dose to determine whether the predicted OAR dose satisfies the constraint on the OAR dose.

Abstract: Systems and methods for performing radiation treatment planning are provided. An exemplary system may include a processor device communicatively coupled to a memory device and configured to perform operations when executing instruction stored in the memory device. The operations may include receiving a reference treatment plan including one or more dose constraints and determining, based on the reference treatment plan, segment information of a plurality of radiation beams. The operations may also include determining a fluence map for each of the plurality of radiation beams based on the one or more dose constraints using a fluence map optimization algorithm. The operations may also include determining a dose distribution based on the fluence maps of the plurality of radiation beams. The operations may also include determining at least one beam modulation property of a new treatment plan using a warm-start optimization algorithm based on the segment information and the dose distribution.

Abstract: The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image.

Abstract: 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.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate and track a moving, three-dimensional (3D ) target in a patient undergoing image-guided radiation therapy. For example, an adaptive filter model for a region of interest in the patient may be received, wherein the adaptive filter model is based on the target to be tracked. An image acquisition device may obtain a two-dimensional (2D) slice of a region of interest in the patient. A processor may then apply the adaptive filter model to the 2D slice, wherein the adaptive filter model includes an offset value. The processor may also determine a location of the target in the 2D slice based on the adaptive filter model. The processor may also estimate a potential location of the target based on the offset value. The processor may then repeat one or more of the above steps to track the moving target during image-guided radiation therapy of the patient.