# US 2021/0330993 A1 · Automated magnetic resonance image segmentation for ultrasound thermal therapy control > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0330993 A1 - Document ID: US20210330993A1 - Status: Published application - Kind: A1 - Application number: 16/859,740 - Filed: 2020-04-27 - Published: 2021-10-28 - Earliest claimed: 2020-04-27 - Assignees: Profound Medical Inc. - Applicants: Profound Medical Inc. - Inventors: Pavel Falkovskiy - Web page: https://patentgrep.com/patent/US20210330993A1 ## CPC - A61N 7/02: Localised ultrasound hyperthermia - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - A61B 2090/061: {for measuring dimensions, e.g. length} - A61B 2090/374: {NMR or MRI} - A61N 2007/0073: {using multiple frequencies} - A61N 2007/0095: {by modifying an excitation signal} - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - 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) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A system and method for acquiring magnetic resonance (MR) images, with an MR system, of tissue proximal to a target treatment region in a patient, the tissue including a non-spatially uniform subcutaneous fat layer; in a computer comprising a hardware-based processor, automatically determining a thickness of the subcutaneous fat layer using a trained neural network, the neural network trained using manually-segmented MR images from previous patients; in the computer, automatically adjusting a treatment parameter based on the thickness of the subcutaneous fat layer; and delivering thermal therapy to the target treatment region with a high-intensity focused ultrasound system based on the adjusted treatment parameter. ## Claims ### Claim 1 A method for controlling delivery of thermal therapy, comprising: acquiring magnetic resonance (MR) images, with an MR imaging system, of tissue proximal to a target treatment region in a patient, the tissue including a non-spatially uniform subcutaneous fat layer; in a computer comprising a hardware-based processor, automatically determining an actual thickness of the subcutaneous fat layer using a trained neural network, the neural network trained using manually-segmented MR images from previous patients; in the computer, automatically adjusting a set of treatment parameters based on the actual thickness of the subcutaneous fat layer; and delivering thermal therapy to the target treatment region with a high-intensity focused ultrasound (HIFU) system based on the adjusted treatment parameters. ### Claim 2 (depends on 1) The method of claim 1, wherein the set of treatment parameters comprises an input to a safety control algorithm. ### Claim 3 (depends on 2) The method of claim 2, wherein the safety control algorithm ensures a predetermined maximum temperature and/or a predetermined maximum thermal dose of tissue between the target treatment region and a therapeutic applicator of the HIFU system. ### Claim 4 (depends on 2) The method of claim 2, wherein the safety control algorithm increases a cooldown time between thermal therapy sonications when the actual thickness of the subcutaneous fat layer is higher than a predetermined thickness, the predetermined thickness corresponding to an average subcutaneous fat layer thickness for the gender of the patient. ### Claim 5 (depends on 2) The method of claim 2, wherein the safety control algorithm decreases a cooldown time between thermal therapy sonications when the actual thickness of the subcutaneous fat layer is lower than the predetermined thickness. ### Claim 6 (depends on 1) The method of claim 1, wherein the set of treatment parameters comprises a frequency of ultrasound energy generated by the HIFU system. ### Claim 7 (depends on 1) The method of claim 1, wherein the set of treatment parameters comprises a power of ultrasound energy generated by the HIFU system. ### Claim 8 (depends on 7) The method of claim 7, wherein the power of the ultrasound energy increases when the actual thickness of the subcutaneous fat layer is higher than a predetermined thickness, the predetermined thickness corresponding to an average subcutaneous fat layer thickness for the gender of the patient. ### Claim 9 (depends on 8) The method of claim 8, wherein the power of the ultrasound energy decreases when the actual thickness of the subcutaneous fat layer is lower than the predetermined thickness. ### Claim 10 (depends on 1) The method of claim 1, wherein the set of treatment parameters comprises a relative phase of ultrasound energy generated by each ultrasound transducer element in an array of ultrasound transducer elements. ### Claim 11 A system for delivering ultrasound thermal therapy comprising: a magnetic resonance (MR) imaging system; a high-intensity focused ultrasound (HIFU) system comprising an array of ultrasound transducer elements; a computer comprising a hardware-based processor, the computer operatively coupled to the MR imaging system and to the HIFU system; and a non-transitory storage medium comprising computer-readable instructions that, when executed by the processor, cause the processor to: send a first output signal to the MR system to acquire MR images of tissue proximal to a target treatment region in a patient, the tissue including a non-spatially uniform subcutaneous fat layer, automatically determine an actual thickness of the subcutaneous fat layer using a trained neural network, the neural network trained using manually-segmented MR images from previous patients, adjust a set of treatment parameters based on the actual thickness of the subcutaneous fat layer, and send a second output signal to the HIFU system to deliver thermal therapy to the target treatment region based on the adjusted treatment parameter. ### Claim 12 (depends on 11) The system of claim 11, wherein the set of treatment parameters comprises an input to a safety control algorithm. ### Claim 13 (depends on 12) The system of claim 12, wherein the safety control algorithm ensures a predetermined maximum temperature and/or a predetermined maximum thermal dose of tissue between the target treatment region and a therapeutic applicator of the HIFU system. ### Claim 14 (depends on 12) The system of claim 12, wherein the safety control algorithm increases a cooldown time between thermal therapy sonications when the actual thickness of the subcutaneous fat layer is higher than a predetermined thickness, the predetermined thickness corresponding to an average subcutaneous fat layer thickness for the gender of the patient. ### Claim 15 (depends on 14) The system of claim 14, wherein the safety control algorithm decreases a cooldown time between thermal therapy sonications when the actual thickness of the subcutaneous fat layer is lower than the predetermined thickness. ### Claim 16 (depends on 11) The system of claim 11, wherein the set of treatment parameters comprises a frequency of ultrasound energy generated by the HIFU system. ### Claim 17 (depends on 11) The system of claim 11, wherein the set of treatment parameters comprises a power of ultrasound energy generated by the HIFU system. ### Claim 18 (depends on 17) The system of claim 17, wherein the power of the ultrasound energy increases when the thickness of the subcutaneous fat layer is higher than a predetermined thickness, the predetermined thickness corresponding to an average subcutaneous fat layer thickness for the gender of the patient. ### Claim 19 (depends on 18) The system of claim 18, wherein the power of the ultrasound energy decreases when the thickness of the subcutaneous fat layer is lower than the predetermined thickness. ### Claim 20 (depends on 11) The system of claim 11, wherein the set of treatment parameters comprises a relative phase of ultrasound energy generated by each ultrasound transducer element in the array of ultrasound transducer elements.