# US 12,119,102 B1 · Machine-learning modeling to generate virtual bolus attributes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,119,102 B1 - Document ID: US12119102B1 - Status: Granted - Kind: B1 - Application number: 17/530,383 - Filed: 2021-11-18 - Granted: 2024-10-15 - Earliest claimed: 2021-11-18 - Assignees: Siemens Healthineers International AG - Applicants: Siemens Healthineers International AG - Inventors: Heini Hyvonen; Hannu Laaksonen; Ville Pietila - Web page: https://patentgrep.com/patent/US12119102B1 ## CPC - A61N 5/1039: {using functional images, e.g. PET or MRI} - G16H 30/40: for processing medical images, e.g. editing - A61N 5/1064: {for adjusting radiation treatment in response to monitoring} - G06T 7/0012: {Biomedical image inspection} - G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - A61N 2005/1041: {using a library of previously administered radiation treatment applied to other patients} - A61N 2005/1074: {Details of the control system, e.g. user interfaces} - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2207/20081: Training; Learning - G06T 2207/30004: Biomedical image processing - A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Disclosed herein are methods and systems for predicting a virtual bolus in order to generate a radiation therapy treatment plan comprising training, by a processor, a machine-learning model using a training dataset comprising a set of medical images corresponding to a set of previously performed radiation therapy treatments, each medical image comprising at least one planning target volume and a non-anatomical region added to the medical image; and executing, by the processor, the machine-learning model using a medical image not included within the training dataset, the machine-learning model predicting an attribute of a non-anatomical region for the medical image not included in the training dataset. ## Claims ### Claim 1 A method comprising: executing, by a processor, a machine-learning model using a medical image not used to train the machine-learning model to predict an attribute of a non-anatomical region for the medical image, the non-anatomical region being located on an outer surface of a patient, wherein the machine-learning model has been trained using a set of medical images corresponding to a set of previously performed radiation therapy treatments, each medical image comprising at least one planning target volume and an added non-anatomical region for its respective radiation therapy treatment. ### Claim 2 (depends on 1) The method of claim 1, wherein the non-anatomical region corresponds to material configured to reduce or increase radiation emitted towards the planning target volume. ### Claim 3 (depends on 1) The method of claim 1, wherein the non-anatomical region has an electron density value or a mass density value corresponding to human flesh. ### Claim 4 (depends on 1) The method of claim 1, wherein the attribute of the non-anatomical region corresponds to a thickness of the non-anatomical region. ### Claim 5 (depends on 1) The method of claim 1, wherein the attribute of the non-anatomical region corresponds to a position of the non-anatomical region in relation to the planning target volume. ### Claim 6 (depends on 1) The method of claim 1, wherein the attribute of the non-anatomical region corresponds to a position of the non-anatomical region compared to an organ of the patient. ### Claim 7 (depends on 1) The method of claim 1, wherein each medical image within the set of medical images comprises an indication of whether its corresponding non-anatomical region is acceptable. ### Claim 8 (depends on 1) The method of claim 1, further comprising: instructing, by the processor, a radiation therapy machine to adjust at least one of its attributes in accordance with the attribute of the non-anatomical region. ### Claim 9 (depends on 1) The method of claim 1, further comprising: transmitting, by the processor, the attribute of the non-anatomical region to a computing device to be displayed on a graphical user interface. ### Claim 10 (depends on 1) The method of claim 1, further comprising: transmitting, by the processor, a revised medical image that comprises the medical image not used for training the machine-learning model and a depiction of the non-anatomical region predicted by the machine-learning model. ### Claim 11 A system comprising: a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising: executing a machine-learning model using a medical image not used to train the machine-learning model to predict an attribute of a non-anatomical region for the medical image, the non-anatomical region being located on an outer surface of a patient, wherein the machine-learning model has been trained using a set of medical images corresponding to a set of previously performed radiation therapy treatments, each medical image comprising at least one planning target volume and an added non-anatomical region for its respective radiation therapy treatment. ### Claim 12 (depends on 11) The system of claim 11, wherein the non-anatomical region corresponds to material configured to reduce or increase radiation emitted towards the planning target volume. ### Claim 13 (depends on 11) The system of claim 11, wherein the non-anatomical region has an electron density value or a mass density value corresponding to human flesh. ### Claim 14 (depends on 11) The system of claim 11, wherein the attribute of the non-anatomical region corresponds to a thickness of the non-anatomical region. ### Claim 15 (depends on 11) The system of claim 11, wherein the attribute of the non-anatomical region corresponds to a position of the non-anatomical region in relation to the planning target volume. ### Claim 16 (depends on 11) The system of claim 11, wherein the attribute of the non-anatomical region corresponds to a position of the non-anatomical region compared to an organ of the patient. ### Claim 17 (depends on 11) The system of claim 11, wherein each medical image within the set of medical images comprises an indication of whether its corresponding non-anatomical region is acceptable. ### Claim 18 (depends on 11) The system of claim 11, wherein the instructions further cause the processor to instruct a radiation therapy machine to adjust at least one of its attributes in accordance with the attribute of the non-anatomical region. ### Claim 19 (depends on 11) The system of claim 11, wherein the instructions further cause the processor to transmit the attribute of the non-anatomical region to a computing device to be displayed on a graphical user interface. ### Claim 20 (depends on 11) The system of claim 11, wherein the instructions further cause the processor to transmit a revised medical image that comprises the medical not used for training the machine-learning model and a depiction of the non-anatomical region predicted by the machine-learning model.