Abstract: A method comprising: receiving (i) a first OCT scan, and (ii) a second, subsequent, OCT scan, each comprising one or more two-dimensional (2D) scan images of an eye of a subject; receiving a first and second retinal images of the eye of the subject, associated with the first and second OCT scans; matching corresponding first and second pixel column patches, each comprising one or more pixel columns, associated respectively with the scan images of the first and second OCT scans, based, at least in part, on performing image registration between the scan images of the first and second OCT scans; and using the matched corresponding first and second pixel column patches as input for a trained machine learning model, wherein an output of the trained machine learning model classifies the second pixel column patches as representing retinal atrophy or not representing retinal atrophy.

Abstract: A system and method of predicting a condition of a fetus may include receiving a magnetic resonance imaging (MRI) scan of the fetus, comprising a sequence of slices; detecting a volume of interest (VOI) representing a location of a brain of the fetus; segmenting one or more slices comprised in the VOI to a set of brain structures selected from a right hemisphere, a left hemisphere, a right lateral ventricle, and a left lateral ventricle; based on said segmentation, calculating at least one ventricle; and predicting a condition of the fetus based on the at least one ventricle metric.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: Described herein is a computer implemented method that includes receiving at a data processor two or more digital data files representing medical images of a same modality; performing group-wise 3D registration of the digital data files representing medical images of a same modality; and parallel lesion detection and analysis on the digital data files representing the medical images.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: There is provided a method of locating a tip of a metallic instrument inserted in a body, utilizing a baseline sinogram comprising projections in N exposure directions and derived from a prior computerized tomography (CT) scanning of the body, the method comprising: performing three-dimensional Radon space registration of a sparse repeat sinogram to the baseline sinogram, the repeat CT scanning having the metallic instrument inserted into the body, the metallic instrument having an attached marker located at a known distance from the instrument tip; subtracting the baseline sinogram from the repeat sinogram in accordance with the registration parameters to obtain projection difference images; and using the projection difference images and the known distance of the attached marker from the metallic instrument tip to determine a three-dimensional location of the metallic instrument tip.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: There is provided a method of locating a tip of a metallic instrument inserted in a body, utilizing a baseline sinogram comprising projections in N exposure directions and derived from a prior computerized tomography (CT) scanning of the body, the method comprising: performing three-dimensional Radon space registration of a sparse repeat sinogram to the baseline sinogram, the repeat CT scanning having the metallic instrument inserted into the body, the metallic instrument having an attached marker located at a known distance from the instrument tip; subtracting the baseline sinogram from the repeat sinogram in accordance with the registration parameters to obtain projection difference images; and using the projection difference images and the known distance of the attached marker from the metallic instrument tip to determine a three-dimensional location of the metallic instrument tip.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: There is provided a method of computer tomography (CT) volume reconstruction based on baseline sinograms, the method comprising: obtaining partial scan sinograms in a number of directions smaller than the number of directions in the baseline scan; aligning baseline sinograms and partial scan sinograms utilizing rigid registration in three-dimensional Radon space, generating configuration data informative of rays to be cast in a further repeat scan in an un-scanned direction, wherein the generating configuration data comprises: identifying, from a partial scan sinogram, rays with intensities that significantly differ from intensities of corresponding rays in an aligned baseline sinogram, identifying regions of the scanned object in which identified changed rays intersect, determining scan angles and associated rays to be cast in a further repeat scan according to the identified regions; obtaining sinograms of a repeat scan performed according to generated configuration data and processing the sinograms into an

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: A robot-guided system to assist orthopedic surgeons in performing orthopedic surgical procedures on pre-positioned inserts, including for the fixation of bone fractures, and especially for use in long bone distal intramedullary locking procedures. The system provides a mechanical guide for drilling the holes for distal screws in intramedullary nailing surgery. The drill guide is automatically positioned by the robot relative to the distal locking nail holes, using data derived from only a small number of X-ray fluoroscopic images. The system allows the performance of the locking procedure without trial and error, thus enabling the procedure to be successfully performed by less experienced surgeons, reduces exposure of patient and operating room personnel to radiation, shortens the intra-operative time, and thus reduces post-operative complications.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: There are provided a method of CT volume reconstruction based on a baseline sinogram obtained by a prior scanning an object in B directions, and a system thereof.

Abstract: A novel image-guided system for precise automatic targeting in minimally invasive keyhole neurosurgery. The system consists of a miniature robot fitted with a mechanical guide for needle, probe, or catheter insertion. Intraoperative, the robot is directly affixed to a head clamp or to the patient skull. It automatically positions itself with respect to predefined entry points and targets in a preoperative CT/MRI image following an anatomical registration with an intraoperative 3D surface scan of the patient facial features and a registration jig. The registration procedure is a novel three-way scheme, in which the intraoperative surface scan including the registration jig is matched to a model generated from the preoperative CT/MRI image, the robot position is known in relation to the registration jig, and the entry and target points are known from the preoperative CT/MRI image, such that the robot position can be related to the entry and target points.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: A method for using an assembled three-dimensional image to construct a three-dimensional model for determining a path through a lumen network to a target. The three-dimensional model is automatically registered to an actual location of a probe by tracking and recording the positions of the probe and continually adjusting the registration between the model and a display of the probe position. The registration algorithm becomes dynamic (elastic) as the probe approaches smaller lumens in the periphery of the network where movement has a bigger impact on the registration between the model and the probe display.

Abstract: A robot-guided system to assist orthopaedic surgeons in performing orthopaedic surgical procedures on pre-positioned inserts, including for the fixation of bone fractures, and especially for use in long bone distal intramedullary locking procedures. The system provides a mechanical guide for drilling the holes for distal screws in intramedullary nailing surgery. The drill guide is automatically positioned by the robot relative to the distal locking nail holes, using data derived from only a small number of X-ray fluoroscopic images. The system allows the performance of the locking procedure without trial and error, thus enabling the procedure to be successfully performed by less experienced surgeons, reduces exposure of patient and operating room personnel to radiation, shortens the intra-operative time, and thus reduces post-operative complications.

Abstract: A robot-guided system to assist orthopaedic surgeons in performing orthopaedic surgical procedures on pre-positioned inserts, including for the fixation of bone fractures, and especially for use in long bone distal intramedullary locking procedures. The system provides a mechanical guide for drilling the holes for distal screws in intramedullary nailing surgery. The drill guide is automatically positioned by the robot relative to the distal locking nail holes, using data derived from only a small number of X-ray fluoroscopic images. The system allows the performance of the locking procedure without trial and error, thus enabling the procedure to be successfully performed by less experienced surgeons, reduces exposure of patient and operating room personnel to radiation, shortens the intra-operative time, and thus reduces post-operative complications.

Abstract: A novel image-guided system for precise automatic targeting in minimally invasive keyhole neurosurgery. The system consists of a miniature robot fitted with a mechanical guide for needle, probe, or catheter insertion. Intraoperative, the robot is directly affixed to a head clamp or to the patient skull. It automatically positions itself with respect to predefined entry points and targets in a preoperative CT/MRI image following an anatomical registration with an intraoperative 3D surface scan of the patient facial features and a registration jig. The registration procedure is a novel three-way scheme, in which the intraoperative surface scan including the registration jig is matched to a model generated from the preoperative CT/MRI image, the robot position is known in relation to the registration jig, and the entry and target points are known from the preoperative CT/MRI image, such that the robot position can be related to the entry and target points.