Abstract: Systems and methods for generating radiotherapy plans are provided. One or more medical images are received. One or more anatomical objects are segmented from the one or more medical images. Radiotherapy configurations for one or more objectives are determined based on the one or more medical images and the segmentations of the one or more anatomical objectives. A candidate radiotherapy plan is generated based on the radiotherapy configurations. A quality assessment of the candidate radiotherapy plan is performed. The one or more objectives are updated based on results of the quality assessment and the determining, the generating, and the performing are repeated for one or more iterations using the updated one or more objectives as the radiotherapy objectives to generate the candidate radiotherapy plan for a last iteration as a final radiotherapy plan. The final radiotherapy plan is output. Since the pipeline is automatic, the plan generation can be scaled. The data can serve large-scale AI model training.

Abstract: Provided herein are systems for predicting a location of a gross target volume in a patient. In examples, systems include one or more processors that are configured to obtain image data associated with a plurality of two-dimensional (2D) images. The one or more processors can be configured to determine a location of one or more lesions at the first point in time, and determine a future location of the one or more lesions at a second point in time based on the location of the one or more lesions and a trajectory representing expected motion of the one or more lesions. In examples, the one or more processors can be configured to generate a control signal to cause a linear accelerator to move based on the future location of the one or more lesions.

Abstract: Provided herein are systems for predicting a location of a gross target volume in a patient. In examples, systems include one or more processors that are configured to obtain image data associated with a plurality of two-dimensional (2D) images. The one or more processors can be configured to determine a location of one or more lesions at the first point in time and determine a future location of the one or more lesions at a second point in time based on the location of the one or more lesions and a trajectory representing expected motion of the one or more lesions. In examples, the one or more processors can be configured to generate a control signal to cause a linear accelerator to move based on the future location of the one or more lesions.

Abstract: Provided herein are systems for determining a location of a gross target volume of a patient. In some examples, systems can include one or more processors that are configured to obtain image data associated with a plurality of images of a lesion of a patient. For each image, the one or more processors can be configured to backproject points representing the lesion into the 3D space to determine a plurality of distribution confidence values for a subset of voxels within the three-dimensional space. The one or more processors can be configured to determine a three-dimensional confidence distribution based on confidence values from the plurality of distribution confidence values corresponding to each voxel of the 3D space and determine a position of the lesion within the 3D space based on the 3D confidence distribution.

Abstract: Systems and methods for a deep learning reconstruction network with computationally light and efficient CNN architecture and a training strategy tailored to image reconstruction of dynamic multi-coil GRASP MRI. The configuration of the size of the network used in training time may be adjusted, which allows for higher accelerations and different hardware constraints.

Abstract: For reconstruction in sampling-based imaging, such as reconstruction in MR imaging, an iterative, multiple-mapping based hierarchal machine-learned network reconstruction may produce artifact corrected images based on under-sampled scans. Two or more mappings may be used to reduce the presence of artifacts, in some cases including localized low-noise-contribution artifacts, relative to reconstructions based on fully-sampled scans.

Abstract: Systems and methods for reconstruction for a medical imaging system. Non-Cartesian k-space data is acquired using a dynamic MR sequence. A time compression network compresses the non-Cartesian data. The compressed data is used for reconstruction of an image. The time compression network is configured to reduce the (time and memory) complexity of the reconstruction process.

Abstract: Systems and methods for determining a multi-field dose for radiation therapy are provided. 1) one or more medical images of a patient and 2) a fluence map for each of a plurality of fields for radiation therapy of the patient are received. Fluence-related information is determined for each of the plurality of fields based on the fluence maps. The fluence-related information for the plurality of fields are aggregated. A multi-field dose for the patient is determined based on the one or more medical images and the aggregated fluence-related information using a machine learning based dose prediction network. The multi-field dose is output.

Abstract: Systems and methods reconstruction for a medical imaging system using a quasi-newton method. An unrolled iterative reconstruction process is used to reconstruct an image from the scan data. The unrolled iterative reconstruction process includes a plurality of cascades that include at least a data-consistency step and a regularization step. The data-consistency step is modified based at least in part on information of already calculated gradients of one or more previous cascades of the plurality of cascades using a quasi-newton computation.

Abstract: Systems and methods for reconstruction for a medical imaging system. A scaling factor is used during the reconstruction process to adjust a step size of a gradient update. The adjustment of the step size of the gradient provides the ability to adjust a level of denoising by the reconstruction process.

Abstract: One or more tractograms of a global tractography of a tissue of interest are determined. At least one instance of diffusion magnetic resonance imaging data of the tissue of interest is obtained. A trained machine-learning algorithm generates the one or more tractograms based on the at least one instance of the diffusion magnetic resonance imaging data.

Abstract: Provided herein are systems for planning radiotherapy treatments. Systems can include one or more processors to receive radiotherapy data associated with a set of treatments administered to a set of patients; generate a beam eye view (BEV) projection for each patient of the set of patients; and for each patient of the set of patients, provide treatment data associated with the patient and data associated with the BEV projections corresponding to the patient to a model to train the model to generate an output. The output can represent a fluence map. Systems and method for generating leaf sequences are also provided.

Abstract: Systems and methods for reconstruction for a medical imaging system. An adapter is used to adapt scan data so that different quantities of repetitions or directions may be used to train and implement a single multichannel backbone network.

Abstract: Provided herein are methods and systems for planning radiation therapy. In examples, at least one processor can be programmed to: receive data associated with a first planning target volume and a first radiation map, provide the first planning target volume and the first radiation map to a first model to cause the first model to output data associated with at least one first beam position and least one first beam strength, generate a second radiation map, and provide the first planning target volume and the second radiation map to a second model to cause the second model to output data associated with at least one second beam position and least one second beam strength. At least one processor can be further programmed to: transmit data associated with the at least one second beam position and the least one second beam strength to cause a linear accelerator to deliver radiation.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, scanning is accelerated by under-sampling. In iterative reconstruction, the input to the regularizer is altered provide for correlation of non-local aliasing artifacts. Duplicates of the input image are shifted by different amounts based on the level of acceleration. The resulting shifted images are used to form the input to the regularizer. Providing an input based on shifts allows the regularization to suppress non-local as well as local aliasing artifacts.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. To further maintain computational complexity while improving robustness, auxiliary information is used in the regularization and corresponding reconstruction. The auxiliary information is in put to the machine-learned network.

Abstract: Techniques are provided for determining magnetic resonance images showing different contrasts in an examination. Magnetic resonance data for all magnetic resonance images are acquired using the same acquisition technique and the magnetic resonance images are reconstructed from their magnetic resonance data sets using at least one reconstruction algorithm. The reconstruction comprises at least one de-noising step. After acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and de-noising strengths for the de-noising step are chosen individually for each contrast depending on the respective at least one noise strength measure.

Abstract: For reconstruction, a machine-learned model is adapted to allow for reconstruction based on the repetitions available in some scanning. The reconstruction for one or more subsets is performed during the scanning. The machine-learned model is trained to reconstruction separately or independently for each repetition or to use information from previous repetitions without requiring waiting for completion of scanning. The reconstructed image may be displayed much more rapidly after completion of the acquisition since the reconstruction begins during the reconstruction.

Abstract: A computer-implemented method includes, based on an input dataset defining an input image, determining a reconstructed image using a reconstruction algorithm, and executing a data-consistency operation for enforcing consistency between the input image and the reconstructed image. The data-consistency operation determines, for multiple K-space positions at which the input dataset comprises respective source data, a contribution of respective K-space values associated with the input dataset to a K-space representation of the reconstructed image.

Abstract: Systems and methods for a deep learning reconstruction network with computationally light and efficient CNN architecture and a training strategy tailored to image reconstruction of dynamic multi-coil GRASP MRI. The configuration of the size of the network used in training time may be adjusted, which allows for higher accelerations and different hardware constraints.