34 results about "Digitally reconstructed radiographs" patented technology

An image-guided radiosurgery method and system are presented that use 2D/3D image registration to keep the radiosurgical beams properly focused onto a treatment target. A pre-treatment 3D scan of the target is generated at or near treatment planning time. A set of 2D DRRs are generated, based on the pre-treatment 3D scan. At least one 2D x-ray image of the target is generated in near real time during treatment. The DRRs are registered with the x-ray images, by computing a set of 3D transformation parameters that represent the change in target position between the 3D scan and the x-ray images. The relative position of the radiosurgical beams and the target is continuously adjusted in near real time in accordance with the 3D transformation parameters. A hierarchical and iterative 2D/3D registration algorithm is used, in which the transformation parameters that are in-plane with respect to the image plane of the x-ray images are computed separately from the out-of-plane transformation parameters.

A system and method for registering pre-operative images of an object with an intra-operative image of the object is disclosed. Prior to an operative procedure, Digitally Reconstructed Radiographs (DRRs) are generated for the pre-operative images of each individual patient. Signatures are extracted from the DRRs. The signatures are stored in a knowledge base. During the operative procedure, a signature is extracted from the intra-operative image. The intra-operative signature is compared to the stored pre-operative signatures. A pre-operative image having a best signature match to the intra-operative signature is retrieved. The retrieved pre-operative image is registered with the intra-operative image.

A system and a method of performing a frameless image-guided biopsy uses imaging, a six-dimensional robotic couch system, a laser guidance system, an optical distance indicator, and a needle control apparatus. A planning CT scan is made of the patient with stereotactic fiduciary markers to localize and produce digitally reconstructed radiographs. Two stereoscopic images are generated using an imaging device to visualize and identify a target tumor. The images are fused with the digitally reconstructed radiographs of the planning CT scan to process tumor location. The tumor location data are communicated to the movable robotic couch to position the target tumor of the patient at a known isocenter location. A biopsy needle is guided with a laser alignment mechanism towards the isocenter at the determined depth using a needle positioning apparatus and an Optical Distance Indicator, and a biopsy sample of the target tumor is obtained.

A method and apparatus for convolutional neural network (CNN) regression based 2D/3D registration of medical images is disclosed. A parameter space zone is determined based on transformation parameters corresponding to a digitally reconstructed radiograph (DRR) generated from the 3D medical image. Local image residual (LIR) features are calculated from local patches of the DRR and the X-ray image based on a set of 3D points in the 3D medical image extracted for the determined parameter space zone. Updated transformation parameters are calculated based on the LIR features using a hierarchical series of regressors trained for the determined parameter space zone. The hierarchical series of regressors includes a plurality of regressors each of which calculates updates for a respective subset of the transformation parameters.

The invention provides a two-dimensional and three-dimensional medical image registration method, which can quickly carry out registration on an intra-operative two-dimensional X-ray image and a preoperative three-dimensional CT (Computed Tomography) image. The two-dimensional and three-dimensional medical image registration method comprises the following steps: carrying out filtering preprocessing on the three-dimensional CT image, projecting the three-dimensional CT image in two mutually-orthogonal plane systems to obtain two corresponding digitally reconstructed radiograph (DRR) sets according to a maximum intensity projection (MIP) algorithm; obtaining two two-dimensional X-ray images on another two orthogonal planes, and carrying out the filtering preprocessing on the two-dimensional X-ray image; carrying out traversal on the two DRR sets, and carrying out registration on the two DRR sets and another two orthogonal two-dimensional X-ray images so as to determine a corresponding most similar DRR as well as the in-plane position coordinate, the in-plane rotation angle and an out-of-plane rotation angle of the DRR; converting the coordinate and the angles to be under a three-dimensional CT image coordinate system to obtain six registration parameters. According to the method, the X-ray image and the CT image which is generated in advance can be subjected to accurate and quick real-time registration.

A system and method of producing virtual roadmap image of a patient is described. CT-like image data may be obtained for a patient, with or without a contrast agent, and differenced so as to form a roadmap mask. The roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment. The fluoroscopic image may be subtracted from the roadmap mask so as to more clearly visualize the position of a catheter introduced into the patient for treatment. When the orientation of the fluoroscopic image is changed during the course of treatment, the roadmap mask image for the corresponding orientation is used.

A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment. The 3D medical volume is registered to the 2D medical image using final transformation parameters resulting from a plurality of iterations.

System and method of dynamically presenting visual representations of a target volume in an anatomical context to an operator-user of a charged particle radiation therapy system. The visual representations are integrated as a part of a treatment console graphical user interface (GUI) and presented as a part of a holistic view of treatment information. The visual representations are digitally reconstructed radiographs (DRRs) constructed based on anatomical images captured before the therapy session, or alternatively during a treatment session using an imaging system attached to the radiation treatment system. In an online mode, the on-screen visual representations are rendered in synchronization with the irradiation delivery process.

The invention discloses a 2D-3D medical image parallel registration method based on combination similarity measure. The method comprises the following steps: firstly, using a CUDA (Compute Unified Device Architecture) parallel computing model to finish a quick generation process of a DRR (Digitally Reconstructed Radiograph) image; combining a SAD (Sum of Absolute Difference) with PI (pattern intensity) as new similarity measure to carry out parallel computation on GPU (Graphics Processing Unit); and finally, transferring a combination similarity measure value to CPU (Central Processing Unit), and adopting a fruit fly optimization algorithm based on bacterial chemotaxis behaviors to optimize for looking for an optimal registration parameter. An experiment verifies the performance of the method to show that the execution speed of the method is effectively improved since DRR high-speed generation and the mixed similarity measure are realized in the GPU. Meanwhile, compared with the single similarity measure, the invention adopts the mixed similarity measure to improve the accuracy of a registration result.

The invention discloses a simulated projection DRR ( digitally reconstructed radiograph) generating method based on a CUDA (compute unified device architecture) technology. The method comprises the following steps: establishing a new projection model; back-projecting each pixel point of a two-dimensional image; and finally generating a simulated projection image required by image registration through computing the sum of grey values of three-dimensional images on a back-projection line. Besides, based on the model, the invention also discloses a hardware acceleration method based on the CUDA technology. By using the method disclosed by the invention, the coupling between the projection distortion and scale variation can be effectively eliminated, the robustness and the accuracy of two-dimensional and three-dimensional image registration are increased, and the real-time and the efficiency of the algorithm are improved.

The invention provides a method for fully corresponding fusion of pre-operation CT data and intraoperative X-ray radiograph, and belongs to the field of medical image processing. Three-dimensional reconstruction of a focal part is carried out by using pre-operation CT 3D data of a patient and an MC algorithm; by taking the three-dimensional reconstructed model as information input, an digitally reconstructed three-dimensional radiograph, namely a DRR (digitally reconstructed radiograph) is simulated and generated on the basis of known boundary dimensions of a C-shaped arm type X-ray machine to be applied; a simulation emission source generated by the DRR is arranged at the position of a true emission source of the C-shaped arm type X-ray machine; an imaging plane is arranged at a true imaging plane position of the C-shaped arm type X-ray machine; the three-dimensional reconstructed focal model is arranged at the position of the patient during photographing; thus, the generation process of the DRR truly simulates the imaging process of the C-shaped arm type X-ray machine applied by the system; accordingly the CT 3D data and the X-ray radiograph are fully correspondingly fused, and the DRR is used for simulating the true X-ray radiograph.

The invention discloses an image guide method implemented by the aid of two-dimensional images. The image guide method includes steps of selecting and screening reference feature regions; searching real-time feature regions; performing positioning and computing on the basis of the reference feature regions and the real-time feature regions; estimating deformation and errors, and the like. The image guide method has the advantages that two-dimensional DRRs (digitally reconstructed radiographs) need to be generated according to three-dimensional images of lesions of patients when image guide is carried out by the aid of two-dimensional images, and then the feature regions in real-time images which are acquired in real time are compared to feature regions in the two-dimensional DRRs, so that deviation among current locations and preset locations can be determined; markers do not need to be implanted in the patients when the patients are examined by the aid of the image guide method, accordingly, suffering can be relieved for the patients, and treatment risks and the treatment cost can be reduced for the patients.

The invention discloses a method and system for positioning a soft tissue lesion based on dual-energy X-ray images. The method comprises the following steps of: generating a three-dimensional image of a patient and offline generating a soft tissue DRR (Digitally Reconstructed Radiograph) image library along a plane inside translation direction; acquiring high- and low-energy X-ray images of the patient and generating a soft tissue X-ray image of the patient; taking the soft tissue X-ray image as a registered image and estimating values of a plane inside translation parameter and a plane outside translation parameter by using a registering window including the lesion in the DRR image in the soft tissue DRR image library generated offline; adjusting the three-dimensional image by a newest parameter estimation result for the plane outside translation parameter and generating the soft tissue DRR image library along the plane outside translation direction on line; and taking the soft tissue X-ray image as the registered image and further estimating the values of the plane inside translation parameter and the plane outside translation parameter by using the registering window including the lesion in the DRR image in the soft tissue DRR image library generated on line.

Methods and systems are proposed herein for generative adaptive, multi-resolution images efficiently without intensive processing and/or memory consumption or hardware requirements. According to one aspect of the claimed subject matter, a system is provided that includes a computing workstation, communicatively coupled to both a data storage device and an image acquisition device. Real time images acquired by the image acquisition device are presented to the user along with one or more digitally reconstructed radiographs (DRRs)—generated using dynamically selected rendering techniques—from previously acquired image data. The user is able to verify the DRRs as a match to the verification image, and subsequently to dynamically generate additional DRRs more suitable by actuating a portion of the generated DRR. Based on the user actuation, a new DRR is generated and presented to the user for verification.