# US 2019/0318497 A1 · System and method for assisted patient positioning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0318497 A1 - Document ID: US20190318497A1 - Status: Published application - Kind: A1 - Application number: 15/950,435 - Filed: 2018-04-11 - Published: 2019-10-17 - Earliest claimed: 2018-04-11 - Assignees: Siemens Healthcare GmbH - Applicants: Siemens Healthcare GmbH - Inventors: Zhuokai Zhao; Yao-jen Chang; Ruhan Sa; Kai Ma; Jianping Wang; Vivek Kumar Singh; Terrence Chen; Andreas Wimmer; Birgi Tamersoy - Web page: https://patentgrep.com/patent/US20190318497A1 ## CPC - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - A61B 5/1128: {using image analysis (A61B5/1127 takes precedence)} - A61B 5/70: {Means for positioning the patient in relation to the detecting, measuring or recording means} - A61B 5/704: {Tables} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/037: {Emission tomography} - A61B 6/0407: {Supports, e.g. tables or beds, for the body or parts of the body} - A61B 6/0487: {Motor-assisted positioning} - A61B 6/547: {involving tracking of position of the device or parts of the device} - G06T 7/285: using a sequence of stereo image pairs - G06T 7/593: from stereo images - G06T 2207/10012: Stereo images - G06T 2207/10072: Tomographic images - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/10104: Positron emission tomography [PET] - G06T 2207/10108: Single photon emission computed tomography [SPECT] - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/20221: Image fusion; Image merging - G06T 2207/30004: Biomedical image processing - G06T 2207/30196: Human being; Person - G06T 2210/41: Medical - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality. ## Claims ### Claim 1 A method for generating a medical image, comprising: obtaining, via a camera, at least one surface image of a patient; determining, from the at least one surface image, a pose of the patient using at least one spatial information module; positioning, via a moveable bed, the patient at an imaging start position; and obtaining, via a medical image modality, a medical image of the patient. ### Claim 2 (depends on 1) The method of claim 1, wherein the at least one spatial information module comprises at least one of a single-frame analytics network or a multi-frame analytics network. ### Claim 3 (depends on 2) The method of claim 2, wherein the single-frame analytics network comprises a fully convolutional neural network. ### Claim 4 (depends on 1) The method of claim 1, wherein the at least one spatial information module comprises a Dense Block Hourglass (DBHG) network. ### Claim 5 (depends on 1) The method of claim 1, comprising generating a composite image from two or more surface images of the patient, wherein the composite image is generated by a temporal information module in signal communication with the spatial information module. ### Claim 6 (depends on 5) The method of claim 5, wherein the temporal information module comprises a relative motion recovery module and an image synthesis module. ### Claim 7 (depends on 6) The method of claim 6, wherein the relative motion recovery module comprises a synchronized patient bed position reading to infer a relative motion between the patient and the camera. ### Claim 8 (depends on 6) The method of claim 6, wherein the relative motion recovery module comprises a structure from motion module, a simultaneous localization and mapping module, or a combination thereof. ### Claim 9 (depends on 6) The method of claim 6, wherein the image synthesis module comprises a depth fusion module. ### Claim 10 (depends on 9) The method of claim 9, wherein the depth fusion method aggregates the depth map for each frame estimated from multi-view stereo matching process. ### Claim 11 (depends on 9) The method of claim 9, wherein the image synthesis module synthesizes an extended field of view based on a coarse geometry fitting to the patient. ### Claim 12 (depends on 1) The method of claim 1, comprising preprocessing the at least one surface image of the patient prior to determining a pose of the patient. ### Claim 13 (depends on 1) The method of claim 1, wherein the temporal information module comprises a recurrent neural network. ### Claim 14 A system of generating a medical image, comprising: an imaging modality configured to obtain a medical image of a patient; an imaging device configured to obtain at least one surface image of a patient; and a processor configured to implement at least one of a spatial information module, a temporal information module, or a combination thereof, wherein the processor is configured to receive the at least one surface image of the patient and verify at least one of a patient pose or a body region, and wherein the processor is configured to execute a medical imaging procedure using the imaging modality when the patient pose or body region is verified. ### Claim 15 (depends on 14) The system of claim 14, wherein the spatial information module comprises a fully convolutional neural network. ### Claim 16 (depends on 14) The system of claim 14, wherein the spatial information module comprises a Dense Block Hourglass (DBHG) network. ### Claim 17 (depends on 14) The system of claim 14, wherein the temporal information module is configured to generate a composite image from two or more surface images of the patient. ### Claim 18 (depends on 17) The system of claim 17, wherein the temporal information module comprises a relative motion recovery module and an image synthesis module. ### Claim 19 (depends on 18) The system of claim 18, wherein the relative motion recovery module comprises a simultaneous localization and mapping module, a synchronized bed position module configured to infer a relative motion between the patient and the camera, or a combination thereof. ### Claim 20 A non-transitory computer-readable medium encoded with computer executable instructions, the computer executable instructions, when executed by a computer in a system for reviewing neural scans, cause the system for reviewing neural scans to execute the steps of: receiving at least one surface image of a patient; determining a pose of the patient from the at least one surface image using at least one spatial information module; positioning, via a moveable bed, the patient at an imaging start position; and obtaining, via a medical image modality, a medical image of the patient.