Abstract: Disclosed herein are methods for radiotherapy treatment plan optimization for irradiating one or more target regions using both an internal therapeutic radiation source (ITRS) and an external therapeutic radiation source (ETRS). One variation of a method comprises iterating through ITRS radiation dose values and ETRS radiation dose values to attain a cumulative dose that meets prescribed dose requirements. In some variations, an ITRS is an injectable compound that has a targeting backbone and a radionuclide, and images acquired using an imaging compound that has the same targeting backbone as the injectable compound can be used to calculate the radiation dose deliverable using the injectable ITRS, and also to calculate firing filters for delivering radiation using a biologically-guided radiation therapy (BGRT) system. Image data acquired from a previous treatment session may be used to adapt the dose provided by an ITRS and/or ETRS for a future treatment session.

Abstract: Described herein are methods for beam station delivery of radiation treatment, where the patient platform is moved to a series of discrete patient platform locations or beam stations that are determined during treatment planning, stopped at each of these locations while the radiation source rotates about the patient delivering radiation to the target regions that intersect the radiation beam path, and then moving to the next location after the prescribed dose of radiation (e.g., in accordance with a calculated fluence map) for that location has been delivered to the patient.

Abstract: Disclosed herein are methods for patient setup and patient target region localization for the irradiation of multiple patient target regions in a single treatment session. Virtual localization is a method that can be used to register a patient target region without requiring that the patient is physically moved using the patient platform. Instead, the planned fluence is updated to reflect the current location of the patient target region by selecting a localization reference in the localization image, calculating a localization function based on the localization reference point, and calculating the delivery fluence by convolving the localization function with a shift-invariant firing filter. Mosaic multi-target localization partitions a planned fluence map for multiple patient target regions into sub-regions that can be individually localized.

Abstract: An architecture and techniques for providing a generalizable machine learning recommendation in connection with medical protocols such as radiology protocols. In response to receipt of a medical examination order request in a standardized input format, the system can, based on a machine learning technique, output a recommended protocol according to a standardized output format. The system can then perform a mapping procedure that maps site-specific data to the standardized input format and the standardized output format. The site-specific data can comprise information that is specific to an entity that provides the medical examination order request.

Abstract: Various methods and systems are provided for automatically recommending one or more radiology protocols based on an imaging examination order which includes both structured and unstructured data. In one example, a method includes receiving an imaging examination order requesting an imaging examination, wherein the imaging examination order comprises structured data and unstructured text, converting the unstructured text into one or more feature vectors, mapping the structured data and the one or more feature vectors to a standardized radiology protocol representation using an imaging examination order classifier, and mapping the standardized radiology protocol representation to a site-specific radiology protocol using a site-specific radiology protocol translator.

Abstract: Disclosed herein are methods for patient setup and patient target region localization for the irradiation of multiple patient target regions in a single treatment session. Virtual localization is a method that can be used to register a patient target region without requiring that the patient is physically moved using the patient platform. Instead, the planned fluence is updated to reflect the current location of the patient target region by selecting a localization reference in the localization image, calculating a localization function based on the localization reference point, and calculating the delivery fluence by convolving the localization function with a shift-invariant firing filter. Mosaic multi-target localization partitions a planned fluence map for multiple patient target regions into sub-regions that can be individually localized.

Abstract: Disclosed herein are methods for patient setup and patient target region localization for the irradiation of multiple patient target regions in a single treatment session. Virtual localization is a method that can be used to register a patient target region without requiring that the patient is physically moved using the patient platform. Instead, the planned fluence is updated to reflect the current location of the patient target region by selecting a localization reference in the localization image, calculating a localization function based on the localization reference point, and calculating the delivery fluence by convolving the localization function with a shift-invariant firing filter. Mosaic multi-target localization partitions a planned fluence map for multiple patient target regions into sub-regions that can be individually localized.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

Abstract: Described herein are methods for beam station delivery of radiation treatment, where the patient platform is moved to a series of discrete patient platform locations or beam stations that are determined during treatment planning, stopped at each of these locations while the radiation source rotates about the patient delivering radiation to the target regions that intersect the radiation beam path, and then moving to the next location after the prescribed dose of radiation (e.g., in accordance with a calculated fluence map) for that location has been delivered to the patient.

Abstract: Disclosed herein are methods for radiotherapy treatment plan optimization for irradiating one or more target regions using both an internal therapeutic radiation source (ITRS) and an external therapeutic radiation source (ETRS). One variation of a method comprises iterating through ITRS radiation dose values and ETRS radiation dose values to attain a cumulative dose that meets prescribed dose requirements. In some variations, an ITRS is an injectable compound that has a targeting backbone and a radionuclide, and images acquired using an imaging compound that has the same targeting backbone as the injectable compound can be used to calculate the radiation dose deliverable using the injectable ITRS, and also to calculate firing filters for delivering radiation using a biologically-guided radiation therapy (BGRT) system. Image data acquired from a previous treatment session may be used to adapt the dose provided by an ITRS and/or ETRS for a future treatment session.

Abstract: Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

Abstract: Disclosed herein are systems and methods for guiding the delivery of therapeutic radiation using incomplete or partial images acquired during a treatment session. A partial image does not have enough information to determine the location of a target region due to, for example, poor or low contrast and/or low SNR. The radiation fluence calculation methods described herein do not require knowledge or calculation of the target location, and yet may help to provide real-time image guided radiation therapy using arbitrarily low SNR images.

Abstract: Described herein are methods for beam station delivery of radiation treatment, where the patient platform is moved to a series of discrete patient platform locations or beam stations that are determined during treatment planning, stopped at each of these locations while the radiation source rotates about the patient delivering radiation to the target regions that intersect the radiation beam path, and then moving to the next location after the prescribed dose of radiation (e.g., in accordance with a calculated fluence map) for that location has been delivered to the patient.

Abstract: Disclosed herein are methods for radiotherapy treatment plan optimization for irradiating one or more target regions using both an internal therapeutic radiation source (ITRS) and an external therapeutic radiation source (ETRS). One variation of a method comprises iterating through ITRS radiation dose values and ETRS radiation dose values to attain a cumulative dose that meets prescribed dose requirements. In some variations, an ITRS is an injectable compound that has a targeting backbone and a radionuclide, and images acquired using an imaging compound that has the same targeting backbone as the injectable compound can be used to calculate the radiation dose deliverable using the injectable ITRS, and also to calculate firing filters for delivering radiation using a biologically-guided radiation therapy (BGRT) system. Image data acquired from a previous treatment session may be used to adapt the dose provided by an ITRS and/or ETRS for a future treatment session.

Abstract: Disclosed herein are systems and methods for guiding the delivery of therapeutic radiation using incomplete or partial images acquired during a treatment session. A partial image does not have enough information to determine the location of a target region due to, for example, poor or low contrast and/or low SNR. The radiation fluence calculation methods described herein do not require knowledge or calculation of the target location, and yet may help to provide real-time image guided radiation therapy using arbitrarily low SNR images.

Abstract: Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.