Covidien LP

Siemens Computer Aided Diagnosis Ltd.

Siemens Computer Aided Disgnosis Ltd.

Nicolas J Merlet

Oren P Weingarten

Ariel Birenbaum

Guy Alexandroni

Ron Barak

Alexander Y Nepomniashchy

Evgeni Kopel

Philippe Nathan Bamberger

Isaac Leichter

A system for registering a luminal network to a 3D model of the luminal network includes a computing device configured to identify potential matches in the 3D model with location data of a location sensor, assigning one of the potential matches a registration score based on a deformation model applied to the 3D model, and displaying the potential match having the highest registration score.

Deformable registration of computer-generated airway models to airway trees

Systems and methods of estimating movement of anatomical structures of a new patient include learning one or more deformation models from preoperative and intraoperative imaging data of a set of other patients and estimating movement of anatomical structures of the new patient based on the one or more deformation models and preoperative imaging data of the new patient. Estimating movement of the anatomical structures may include applying deformation models to map data derived from preoperative imaging data of a new patient to obtain deformed map data for each deformation model, and determining the deformation model that best fits the intraoperative imaging data of the new patient. Applying a deformation model to map data may include applying a transformation to the deformation model and interpolating to obtain a deformed map data. Registration is computed between locations of a medical device sampled during navigation of the medical device through the new patient and the original and deformed map data. The map data which fits the 3D locations the best is applied to the targets of the new patient.

Systems and methods for estimating the movement of a target using a universal deformation model for anatomic tissue

The disclosure is directed to a method for generating a three dimensional (3D) volume including a treatment target including receiving a plurality of two dimensional (2D) input images of a patient, determining a metal artifact in each of the plurality of 2D input images, removing the metal artifacts from the plurality of 2D input images based on the determination of the metal artifact, and replacing metal artifacts with alternative pixel data to generate a plurality of filtered 2D images. A 3D volume is generated from the plurality of filtered 2D images. The plurality of 2D input images including a treatment target.

Imaging reconstruction system and method

Methods and systems for registering three-dimensional (3D) CT image data with two-dimensional (2D) fluoroscopic image data using a plurality of markers are disclosed. In the methods and systems, a lateral angle and a cranial angle are searched for and a roll angle is computed. 3D translation coordinates are also computed. The calculated roll angle and 3D translation coordinates are computed for a predetermined number of times successively. After performing the calculations, the 3D CT image data is overlaid on the 2D fluoroscopic image data based on the lateral angle, the cranial angle, the roll angle, and the 3D translation coordinates.

Fluoroscopic pose estimation

A system and method for constructing virtual radial ultrasound images from CT data includes receiving CT data, calculating a gradient of the CT data, and obtaining 2D slices by interpolation in 3D. The method further includes calculating transmission and reflection values on each radial line away from a center of each 2D slice to create a lesion image, converting the lesion image to polar, generating random noise in the polar image, and adding random comet tails to the polar image. A background image is created by computing the mean profile along the radius from a series of polar lesion images. The background image is merged with the polar lesion image and random noise is generated in the background of the merged image. Density variations are simulated in the merged image and coordinates of the merged image are transformed to Cartesian to construct a virtual radial ultrasound image.

System and method for constructing virtual radial ultrasound images from CT data and performing a surgical navigation procedure using virtual ultrasound images

A method of updating a model of a luminal network. The method includes performing first imaging of a target, generating 3D image data including locations of fiducial markers using images captured in the first imaging, performing second imaging of the target from a first viewpoint and a second viewpoint, generating 2D image data including locations of the fiducial markers from the first viewpoint and the second viewpoint using images captured in the second imaging, determining locations of the fiducial markers in 3D space according to locations of the fiducial markers within the 2D image data of the first viewpoint, locations of the fiducial markers within the 2D image data of the second viewpoint, and the known angle, and registering the 3D image data generated using images captured in the first imaging with the determined locations of the fiducial markers in 3D space. The first viewpoint and the second viewpoint are oriented at a known angle relative to each other.

System and method of generating and updatng a three dimensional model of a luminal network

A method for adapting a detection algorithm developed for usage with data generated by a first system to data generated with a second system, the algorithm for fail safe diagnosis of irregularities including a first procedure of detecting irregularities with minimal type I errors and a second procedure of filtering out type II errors; the data generated including values for selected features characterizing the irregularities; the method including applying an intermediate step of linearly transposing the values of each selected feature generated by the first procedure, prior to applying the second procedure to those values.

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Jerusalem, Israel

Nicolas J Merlet