WO2026055893A1 - Procédé de positionnement de corps humain pour examen par transmission de rayons x - Google Patents

Procédé de positionnement de corps humain pour examen par transmission de rayons x

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Publication number
WO2026055893A1
WO2026055893A1 PCT/CN2024/118636 CN2024118636W WO2026055893A1 WO 2026055893 A1 WO2026055893 A1 WO 2026055893A1 CN 2024118636 W CN2024118636 W CN 2024118636W WO 2026055893 A1 WO2026055893 A1 WO 2026055893A1
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WIPO (PCT)
Prior art keywords
model
bone
target
patient
standard
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PCT/CN2024/118636
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English (en)
Chinese (zh)
Inventor
王朔
张丽
陈志强
邢宇翔
邓智
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Tsinghua University
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Tsinghua University
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Priority to PCT/CN2024/118636 priority Critical patent/WO2026055893A1/fr
Publication of WO2026055893A1 publication Critical patent/WO2026055893A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras

Definitions

  • This disclosure relates to the interdisciplinary field of medicine and engineering, and in particular to a method for locating the human body in X-ray transmission examination.
  • X-ray transmission examination methods include: 1) Fluoroscopy: Fluoroscopy equipment is used during surgery to guide the surgeon through real-time imaging with penetrating X-rays. This method is typically used in orthopedic surgery and the placement of internal fixation devices. 2) Digital subtraction angiography (DSA): In interventional procedures, surgeons can introduce contrast agents into the patient's blood vessels and, combined with X-ray imaging technology, monitor the intravascular conditions in real time to guide the interventional procedure. These methods provide real-time X-ray transmission imaging information during surgery, assisting surgeons in accurate positioning and manipulation, and playing an important auxiliary role in some surgeries requiring precise positioning and manipulation.
  • DSA Digital subtraction angiography
  • Fluoroscopy X-ray equipment (also known as X-ray fluoroscopy examination equipment) is widely used for real-time dynamic imaging during surgery, mainly including C-arms, O-arms, and G-arms.
  • a C-arm X-ray device typically consists of a fixed articulated arm and a movable X-ray detector, shaped like the letter "C". This device is widely used in orthopedic surgery, trauma surgery, cardiac catheterization, and other surgical and treatment procedures to provide real-time X-ray imaging.
  • An O-arm X-ray device is an X-ray device that rotates around the patient and is typically used for imaging and guiding surgery within the operating room. O-arm devices can provide high-resolution 3D imaging, aiding surgeons in accurate positioning and surgical planning during complex procedures.
  • G-arm X-ray devices are similar to C-arms but slightly different in construction, and are also commonly used for X-ray imaging and guiding surgery within the operating room. They provide multi-angle X-ray imaging, helping surgeons to observe and guide the procedure in real time.
  • X-ray transmission aims to obtain prior information about the location and shape of the organ being examined. Due to the radiation hazards associated with X-ray transmission, the maximum number of scans a patient can safely undergo within a given timeframe is limited. Because of this limitation, physicians must carefully plan the imaging protocol before surgery. Any solution that facilitates rapid and accurate localization of the target organ without excessive beam out-of-focus imaging will greatly assist clinicians and reduce the risk of radiation damage to the patient. For example, in radiation therapy (such as cancer treatments that use high-dose radiation to kill cancer cells and shrink tumors), it is common practice to use a patient-specific mold (usually made of plastic or plaster) to keep the patient in the exact same posture during preoperative organ scans and subsequent treatments.
  • a patient-specific mold usually made of plastic or plaster
  • This customized mold helps to accurately target the organ region without rescanning the patient and repositioning the tumor before each treatment.
  • this method limits the clinician's operational space and incurs considerable time and financial costs.
  • Existing technologies also propose in vivo organ deformation models based on different subject postures, which can extract organ shape representations for specific patients and predict their deformable shapes based on different postural parameters.
  • FEM Finite Element Method
  • This disclosure presents a method, system, electronic device, and computer-readable storage medium for human body localization during X-ray transmission examination to rapidly locate target organs preoperatively based on target bone landmarks.
  • the first aspect of this disclosure provides a method for human body localization in X-ray transmission examination, comprising:
  • a corresponding patient parametric model is obtained based on the visible light image, and a standard skeletal model is obtained by fitting the patient parametric model.
  • a personalized skeletal model is obtained by segmenting and reconstructing the preoperative medical images.
  • the personalized bone model is fused with the standard bone model to obtain a target fusion image, thereby achieving target organ localization for the patient.
  • the step of fitting a standard bone model based on the patient parametric model includes: generating bone wrapping surface data and obtaining skin surface data based on the patient parametric model; and fitting a standard bone model based on the bone wrapping surface data and the skin surface data.
  • the standard bone model is obtained by fitting the bone wrapping surface data and the skin surface data using a nonlinear least squares method.
  • the step of fitting the standard bone model based on the bone wrapping surface data and the skin surface data using a nonlinear least squares method includes: setting deformable surface parameters, obtaining energy based on the deformable surface parameters, the bone wrapping surface data and the skin surface data, and minimizing the energy using a nonlinear least squares method to obtain the standard bone model.
  • the step of fusing the personalized skeletal model and the standard skeletal model based on target bone markers to obtain a target fused image includes: selecting a first set of target bone markers of the personalized skeletal model in a preset order and a preset number; selecting a second set of target bone markers of the standard skeletal model in a preset order and a preset number; and fusing the first set of target bone markers and the second set of target bone markers in a corresponding order to obtain the target fused image.
  • fusing the first target bone marker set and the second target bone marker set in a corresponding order to obtain the target fused image includes: matching each first target bone marker in the first target bone marker set and the second target bone marker set in a corresponding order; obtaining a spatial transformation combination; and adjusting the personalized skeleton model based on the spatial transformation combination so that the adjusted personalized skeleton model is consistent with the coordinate system of the standard skeleton model, thereby obtaining the target fused image.
  • a human body positioning system for X-ray transmission examination comprising:
  • the image acquisition module is used to acquire visible light images of the patient in the hospital bed and preoperative medical images of the patient obtained using fluoroscopic examination equipment.
  • the skeletal modeling module is used to obtain a corresponding patient parametric model based on the visible light image, and to fit a standard skeletal model based on the patient parametric model.
  • the reconstruction module is used to segment and reconstruct the preoperative medical images to obtain a personalized skeletal model.
  • the fusion module is used to fuse the personalized bone model with the standard bone model based on the target bone landmarks to obtain a target fusion image, so as to realize the target organ localization of the patient.
  • the bone modeling module when used to fit a standard bone model based on the patient parametric model, is specifically used to: generate bone wrapping surface data and obtain skin surface data based on the patient parametric model; and fit a standard bone model based on the bone wrapping surface data and the skin surface data.
  • a third aspect of this disclosure provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; and the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of this disclosure.
  • a fourth aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method proposed in the first aspect of this disclosure.
  • the present disclosure provides a method, system, electronic device, and storage medium for human body localization in X-ray transmission examination. This involves acquiring visible light images of a patient in bed and preoperative medical images of the same patient obtained using fluoroscopy equipment; obtaining a corresponding parametric model of the patient based on the visible light images; fitting a standard skeletal model based on the patient parametric model; segmenting and reconstructing the preoperative medical images to obtain a personalized skeletal model; and fusing the personalized skeletal model with the standard skeletal model based on target bone landmarks to obtain a target fused image, thereby achieving target organ localization.
  • the position and shape of the target organ in the patient's posture corresponding to the visible light images can be obtained from the bone positions in the target fused image. This enables rapid preoperative localization of the target organ based on target bone landmarks, thereby better assisting physicians in developing preoperative imaging plans and reducing the scanning radiation dose during intraoperative X-ray transmission examination.
  • Figure 1 is a schematic flowchart of a human body positioning method for X-ray transmission examination provided in an embodiment of this disclosure
  • Figure 2 is a schematic diagram of the shooting scene provided in the embodiment of this disclosure.
  • Figure 3 is a schematic diagram of a visible light image and a three-dimensional model of an SMPL patient provided in an embodiment of this disclosure
  • Figure 4 is a schematic diagram of the skeletal landmarks and three-dimensional body surface model provided in the embodiments of this disclosure.
  • Figure 5 is a schematic diagram of fitting a standard skeletal model based on a three-dimensional model of an SMPL patient provided in an embodiment of this disclosure
  • Figure 6 is a schematic diagram of preoperative medical images and segmentation and reconstruction results provided in an embodiment of this disclosure.
  • Figure 7 is a schematic diagram of the selection of bone landmarks provided in an embodiment of this disclosure.
  • Figure 8 is a schematic diagram of the effect after fusing the reconstructed spinal model and the standard skeletal model provided in the embodiments of this disclosure.
  • Figure 9 is a block diagram of a human body positioning system for X-ray transmission examination provided in an embodiment of this disclosure.
  • This disclosure provides a method for human body localization during X-ray transmission examination to quickly locate target organs preoperatively based on target bone landmarks.
  • Figure 1 is a schematic flowchart of a human body positioning method for X-ray transmission examination provided in an embodiment of this disclosure.
  • this method for locating the human body during X-ray transmission examination includes the following steps:
  • Step S101 Acquire visible light images of the patient in the hospital bed and preoperative medical images of the patient obtained using fluoroscopic examination equipment.
  • the visible light image can be captured by a visible light camera arranged on the X-ray fluoroscopy equipment. Specifically, a visible light camera is installed near the detector plane of the X-ray fluoroscopy equipment, and a visible light image of the patient on the hospital bed is captured using the visible light camera.
  • preoperative medical images can be obtained by scanning with fluoroscopy equipment.
  • the main scanning methods for preoperative medical images include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography-CT (PET-CT), and ultrasound imaging.
  • FIG 2 is a schematic diagram of the imaging scenario provided in an embodiment of this disclosure.
  • the fluoroscopic examination device is an X-ray fluoroscopic examination device.
  • the X-ray fluoroscopic examination device obtains preoperative medical images of the patient on the hospital bed through an X-ray source and a detector.
  • a visible light camera is installed near the detector plane of the X-ray fluoroscopic examination device to capture visible light images of the patient on the hospital bed.
  • Step S102 Obtain the corresponding patient parameterized model based on the visible light image, and fit the standard bone model based on the patient parameterized model.
  • SMPL Skinned Multi-Person Linear
  • M( ⁇ , ⁇ ) mapping the shape parameter vector ⁇ and pose parameter vector ⁇ to the vertices of the human body, where ⁇ controls the shape of the human body and ⁇ controls the pose.
  • SMPL-H is a variant of the SMPL model that adds details to the hand bones, making the generated human model more realistic.
  • SMPL-X is an extended version of the SMPL model, suitable for people of various body types, muscle mass, and body shape characteristics, and can simulate more realistic human shapes and movements.
  • STAR Synchronization Trained Articulated Human Body Regressor
  • SMPL and BlendSCAPE transforming pose-related deformation factors into a set of sparse spatial local pose correction hybrid shape functions. Each joint only affects a sparse subset of mesh vertices, enabling the reconstruction of 3D human poses from a single image or video, and the generation and editing of animations.
  • the patient parameterized model obtained based on the visible light image in step S102 is the SMPL patient three-dimensional model.
  • the specific steps for obtaining the corresponding 3D model of the SMPL patient based on the visible light image include: (1) Pose estimation: The joint position and pose of the patient in the visible light image are estimated by computer vision and deep learning technology to obtain the joint position and pose estimation results; (2) Shape modeling: The 3D shape of the patient is estimated by using the joint position and pose estimation results and adopting the existing human shape modeling method to obtain the patient's shape parameter vector ⁇ and pose parameter vector ⁇ ; (3) Alignment and matching: The learned SMPL is obtained, and the obtained patient's shape parameter vector ⁇ and pose parameter vector are used as input data to the learned SMPL output patient vertices. All the vertices form the 3D model of the SMPL patient, thereby realizing the alignment and matching of the estimated patient's 3D shape with the SMPL model.
  • Figure 3 is a schematic diagram of a visible light image and a three-dimensional model of an SMPL patient provided in an embodiment of this disclosure.
  • (a) is a visible light image of the patient
  • (b) and (c) are the front view and side view of the three-dimensional model of the SMPL patient obtained by the method of this disclosure, respectively.
  • a standard bone model is obtained by fitting a patient parametric model, including: generating bone wrapping surface data and obtaining skin surface data based on the patient parametric model; and fitting a standard bone model based on the bone wrapping surface data and skin surface data.
  • the standard bone model is obtained by fitting the bone wrapping surface data and skin surface data using a nonlinear least squares method.
  • a standard skeletal model is obtained by fitting nonlinear least squares data based on skeletal surface data and skin surface data.
  • the process includes: setting deformable surface parameters, obtaining energy based on deformable surface parameters, skeletal surface data and skin surface data, and minimizing energy using nonlinear least squares to obtain the standard skeletal model.
  • the process of fitting a standard skeletal model based on the 3D model of an SMPL patient is as follows:
  • Figure 4 is a schematic diagram of the skeletal landmarks and three-dimensional body surface model provided in the embodiments of this disclosure
  • Figure 5 is a schematic diagram of fitting a standard skeletal model based on a three-dimensional model of an SMPL patient provided in the embodiments of this disclosure.
  • the 3D pose is projected onto a 2D image plane.
  • specific joint points or skeletal landmarks (referred to as bone landmarks) are extracted, such as 2D skeletal landmarks in the head, shoulder, elbow, and wrist, as shown in Figure 4(a), which includes multiple bone landmarks in the head, shoulder, elbow, wrist, hip, knee, and ankle.
  • These bone landmarks when connected, form the initial spatial configuration of the standard skeletal model.
  • a watertight Genus-0 surface referred to as the bone wrapping surface W (i.e., bone wrapping surface data)
  • This bone wrapping surface is a smooth, impermeable 2D manifold surface that does not include areas such as the inside of the ribs, nor does it include the pelvis or the small opening between the ulna and radius.
  • a body surface 3D model is obtained (as shown in Figure 4(b)).
  • This body surface 3D model is the skin surface data, also known as the skin surface M.
  • a standard skeletal model B is generated based on the skin surface M.
  • the initial vertex configuration of the deformable surface X i.e., the deformable surface parameters
  • a nonlinear least squares method is used to minimize the energy, which consists of a fitting term and a regularization term.
  • the fitting term is used to attract the deformable surface X to the skeletonized surface W;
  • the regularization term is used to prevent the deformable surface X from deviating from its initial vertex configuration. Deformation in a physically unreasonable way:
  • B is the standard skeleton model (also known as energy)
  • ⁇ fit is the weight of the fitting term
  • ⁇ reg is the weight of the regularization term.
  • Regularization is expressed as a discrete bending energy that penalizes changes in the mean curvature:
  • the fitting term penalizes the squared distance between vertex x_i ⁇ X and target position t_i ⁇ W:
  • the target position t ⁇ sub>i ⁇ /sub> is a point on the skeleton wrapping surface W, which has three types: nearest point correspondence, fixed correspondence, or collision target.
  • the weight ⁇ sub> i ⁇ /sub> is determined by the type of the target position t ⁇ sub> i ⁇ /sub> (0.1 for nearest point correspondence, 1 for fixed correspondence, and 100 for collision target).
  • the final standard skeleton model B is obtained.
  • This standard skeleton model is a three-dimensional skeleton model.
  • Figure 5(a) shows the three-dimensional model of the SMPL patient with skeleton markers
  • Figure 5(b) shows the obtained standard skeleton model B.
  • Step S103 Segment and reconstruct the preoperative medical images to obtain a personalized skeletal model.
  • the preoperative medical images can target the human spine, thus obtaining a personalized skeletal model that is a reconstructed spinal model. It should be noted that the preoperative medical images can also target different parts such as the arm, head, and pelvis as needed, thereby obtaining a reconstructed model of the corresponding bones.
  • Figure 6 is a schematic diagram of the preoperative medical image and segmentation and reconstruction results provided in this embodiment of the disclosure.
  • a CT scan image of the human spine taken before surgery is obtained.
  • the CT scan image is segmented and reconstructed to obtain the reconstructed spinal model shown in Figure 6(b).
  • the reconstructed spinal model includes the lumbar vertebrae, thoracic vertebrae, and cervical vertebrae.
  • Step S104 Based on the target bone landmarks, the personalized bone model is fused with the standard bone model to obtain a target fusion image, so as to realize the localization of the patient's target organ.
  • step S104 based on the target bone markers, the personalized bone model and the standard bone model are fused to obtain a target fused image.
  • This includes: selecting a first set of target bone markers from the personalized bone model according to a preset order and a preset number; selecting a second set of target bone markers from the standard bone model according to a preset order and a preset number; and fusing the first and second target bone marker sets in a corresponding order to obtain the target fused image.
  • fusing the first and second target bone marker sets in a corresponding order to obtain the target fused image includes: matching each first target bone marker in the first and second target bone marker sets one-to-one according to the corresponding order; obtaining a spatial transformation combination; and adjusting the personalized bone model based on the spatial transformation combination to make the coordinate system of the adjusted personalized bone model consistent with that of the standard bone model, thereby obtaining the target fused image.
  • the target bone landmarks are selected from the lumbar, thoracic, and cervical vertebrae.
  • the preset number is, for example, 5, and the preset order is, for example, the first cervical vertebra, the seventh cervical vertebra, the seventh thoracic vertebra, the fourth lumbar vertebra, and the fifth lumbar vertebra.
  • Figure 7 is a schematic diagram of the selection of bone landmarks provided in the embodiments of this disclosure.
  • the reconstructed spinal model includes lumbar, thoracic, cervical, and sacral vertebrae, wherein the cervical vertebrae include the first cervical vertebra C1, the second cervical vertebra C2, the third cervical vertebra C3, ..., the seventh cervical vertebra C7.
  • the thoracic vertebrae include the first thoracic vertebra T1, the second thoracic vertebra T2, ..., the seventh thoracic vertebra T7, ..., the eleventh thoracic vertebra T11, and the twelfth thoracic vertebra T12.
  • the lumbar vertebrae include the first lumbar vertebra L1, the second lumbar vertebra L2, ..., the fourth lumbar vertebra L4, and the fifth lumbar vertebra L5.
  • the sacral vertebrae include the first sacral vertebra S1, etc.
  • the five primary target bone landmarks are selected from the reconstructed spinal model: m1 (center of the first cervical vertebra C1), m2 (center of the seventh cervical vertebra C7), m3 (center of the seventh thoracic vertebra T7), m4 (center of the fourth lumbar vertebra L4), and m5 (center of the fifth lumbar vertebra L5).
  • LM1 ⁇ m1 , m2 , m3 , m4 , m5 ⁇ .
  • These primary target bone landmarks are chosen primarily because of their anatomical specificity and identifiability.
  • the seventh cervical vertebra C7 is the lowest cervical vertebra, characterized by a prominent spinous process, known as a prominent vertebra.
  • a second set of target bone landmarks is obtained according to a preset order and a preset number.
  • the second set of target bone landmarks LM2 ⁇ n1 , n2 , n3 , n4 , n5 ⁇ , where the five second target bone landmarks n1 , n2 , n3 , n4 , and n5 are the center points of the first cervical vertebra C′1 , the seventh cervical vertebra C′7 , the seventh thoracic vertebra T′7 , the fourth lumbar vertebra L′ 4 , and the fifth lumbar vertebra L′ 5 in the standard skeletal model, respectively.
  • l > 0 is the scaling factor
  • R ⁇ SO (3) is the three-dimensional rotation matrix
  • It is a three-dimensional translation vector
  • We model the unknown additive noise assuming it follows a zero-mean isotropic Gaussian distribution with a standard deviation of ⁇ sub> i ⁇ /sub> .
  • we optimize the scaling factor l * , rotation matrix R * , and translation vector t * under the condition of maximum likelihood estimation, we optimize the scaling factor l * , rotation matrix R * , and translation vector t * :
  • l * , R * , and t * constitute a spatial transformation combination.
  • the spatial transformation combination obtained by optimization is applied to the reconstructed spinal model to adjust the coordinate system of the reconstructed spinal model, thereby making the coordinate system of the adjusted reconstructed spinal model consistent with that of the standard skeletal model.
  • Figure 8 is a schematic diagram of the effect after fusing the reconstructed spinal model and the standard skeletal model provided in the embodiments of this disclosure.
  • Figure 8(a) shows the fused image obtained by fusing the reconstructed spinal model and the standard skeletal model.
  • Figures 8(b) and (c) show the side view and front view of the fused image of the SMPL patient 3D model corresponding to the reconstructed spinal model and the standard skeletal model.
  • the target fusion image may include a first fusion image obtained by fusing the personalized bone model and the standard bone model.
  • the target fusion image may also include a second fusion image of the patient parameterized model corresponding to the personalized bone model and the standard bone model, a third fusion image of the preoperative medical image corresponding to the personalized bone model and the standard bone model, etc.
  • step S104 since the relative positions of bones and target organs are fixed in the personalized skeletal model, fusing the personalized skeletal model with the standard skeletal model maps the relative positions of bones and target organs onto the standard skeletal model.
  • This allows the target fusion image to include the position and shape information of the target organ in the patient's posture in the visible light image. Therefore, based on the target fusion image, the position and shape of the human body, especially the target organ, can be quickly located during X-ray transmission examination. This assists doctors in developing imaging plans before surgery and reduces scanning radiation dose.
  • this disclosure also proposes a human body positioning system for X-ray transmission examination.
  • Figure 9 is a block diagram of a human body positioning system for X-ray transmission examination provided in an embodiment of this disclosure.
  • this human positioning system for X-ray transmission examination includes an image acquisition module, a skeleton modeling module, a reconstruction module, and a fusion module, wherein:
  • the image acquisition module is used to acquire visible light images of the patient in the hospital bed and preoperative medical images of the patient obtained using fluoroscopic examination equipment.
  • the skeletal modeling module is used to obtain the corresponding patient parametric model based on the visible light image, and to fit the standard skeletal model based on the patient parametric model.
  • the reconstruction module is used to segment and reconstruct preoperative medical images to obtain personalized bone models
  • the fusion module is used to fuse personalized bone models with standard bone models based on target bone landmarks to obtain a target fusion image, thereby enabling the localization of the patient's target organs.
  • the bone modeling module obtains a standard bone model based on the patient parametric model by: generating bone wrapping surface data and obtaining skin surface data based on the patient parametric model; and obtaining a standard bone model based on the bone wrapping surface data and the skin surface data.
  • a standard bone model is obtained by fitting the bone wrapping surface data and skin surface data using a nonlinear least squares method.
  • the bone modeling module uses nonlinear least squares to fit a standard bone model based on bone wrapping surface data and skin surface data, including: setting deformable surface parameters, obtaining energy based on deformable surface parameters, bone wrapping surface data and skin surface data, and minimizing energy using nonlinear least squares to obtain a standard bone model.
  • the target bone landmarks in the fusion module are selected from the lumbar, thoracic, and cervical vertebrae.
  • the fusion module is specifically used to: select a first target bone marker set of a personalized skeletal model according to a preset order and a preset number; select a second target bone marker set of a standard skeletal model according to a preset order and a preset number; and fuse the first target bone marker set and the second target bone marker set in a corresponding order to obtain a target fused image.
  • the fusion module fuses the first target bone landmark set and the second target bone landmark set in a corresponding order to obtain a target fused image, including: matching each first target bone landmark in the first target bone landmark set and the second target bone landmark set in a corresponding order; obtaining a spatial transformation combination; and adjusting the personalized skeleton model based on the spatial transformation combination so that the adjusted personalized skeleton model is consistent with the coordinate system of the standard skeleton model, thereby obtaining the target fused image.
  • a visible light image of the patient in bed and a preoperative medical image of the patient obtained using a fluoroscopic examination device are acquired.
  • a corresponding parametric model of the patient is obtained based on the visible light image, and a standard skeletal model is fitted based on the patient parametric model.
  • a personalized skeletal model is obtained by segmenting and reconstructing the preoperative medical image. Based on target bone landmarks, the personalized skeletal model and the standard skeletal model are fused to obtain a target fused image, thereby achieving target organ localization for the patient.
  • the position and shape of the target organ in the patient's posture corresponding to the visible light image can be obtained based on the bone position in the target fused image. This enables rapid preoperative localization of the target organ based on target bone landmarks, thereby better assisting doctors in developing preoperative imaging plans and reducing the scanning radiation dose during subsequent intraoperative X-ray transmission examinations.
  • this disclosure also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.
  • this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.
  • this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.
  • the terms “one embodiment,” “some embodiments,” “example,” “specific example,” or “some examples,” etc. refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure.
  • the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.
  • the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
  • those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
  • first and second are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.
  • a feature defined as “first” or “second” may explicitly or implicitly include at least one of that feature.
  • a plurality of means at least two, such as two, three, etc., unless otherwise explicitly specified.
  • computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM).
  • the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
  • the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module.
  • the integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
  • the storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

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Abstract

L'invention concerne un procédé de positionnement de corps humain pour un examen par transmission de rayons X. Le procédé consiste à : acquérir une image en lumière visible d'un patient sur un lit d'hôpital, et une image médicale préopératoire du patient obtenue à l'aide d'un dispositif d'examen fluoroscopique (S101); sur la base de l'image en lumière visible, obtenir un modèle de patient paramétré correspondant, et sur la base du modèle de patient paramétré, effectuer un ajustement pour obtenir un modèle de squelette standard (S102); effectuer une segmentation et une reconstruction sur l'image médicale préopératoire, de façon à obtenir un modèle de squelette personnalisé (S103); et sur la base d'un repère osseux cible, fusionner le modèle de squelette personnalisé et le modèle de squelette standard pour obtenir une image fusionnée cible, de façon à positionner un organe cible du patient (S104). En utilisant le procédé de la présente divulgation, un organe cible peut être rapidement positionné de manière préopératoire sur la base d'un repère osseux cible.
PCT/CN2024/118636 2024-09-12 2024-09-12 Procédé de positionnement de corps humain pour examen par transmission de rayons x Pending WO2026055893A1 (fr)

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