Abstract: A generative adversarial network including a generator portion and a discriminator portion is constructed. The network is configured such that the network operates to enhance intensity images, wherein an intensity image is obtained by illuminating an object with an energy pulse and measuring the return strength of the energy pulse, and wherein a pixel of the intensity image corresponds to the return strength. As a part of the configuring, a loss function of the generative adversarial network is minimized, the loss function comprising a mean square error loss measurement of a noisy intensity image relative to a mean square error loss measurement of a corresponding clean intensity image. An enhanced intensity image is generated by applying the minimized loss function of the network to an original intensity image, the applying improving an image quality measurement of the enhanced intensity image relative to the original intensity image.

Abstract: A method of generating a 4D model includes capturing imagery of a catheter and a vessel as the catheter is directed through the vessel to a location of interest, wherein the catheter is disposed on a guidewire, constructing a 3D time varying reference curve describing a trajectory of the guidewire, and constructing a time varying 3D model of the artery using the reference curve.

Abstract: A technique relates to coronary reconstruction. Angiogram data associated with cardiac catheterization of an artery for a subject is received, where a contrasting agent is used during the cardiac catheterization. Frames that match from the angiogram data are selected, the frames being from different views. Two-dimensional ordered point clouds for the frames are formed. A three-dimensional ordered point cloud of a reconstructed coronary artery for the subject from the two-dimensional ordered point clouds of the frames is formed. Observed contrast motion caused by the contrasting agent onto the reconstructed coronary artery is mapped, the reconstructed coronary artery thereby providing a three-dimensional model of the artery of the subject during the cardiac catheterization.

Abstract: A catheter including a monitoring body and a pair of markers disposed on the monitoring body, each marker of the pair of markers encircling the monitoring body, wherein the pair of markers are configured to indicate, through their appearance from a given point of view, an orientation of the monitoring body.

Abstract: A generative adversarial network including a generator portion and a discriminator portion is constructed. The network is configured such that the network operates to enhance intensity images, wherein an intensity image is obtained by illuminating an object with an energy pulse and measuring the return strength of the energy pulse, and wherein a pixel of the intensity image corresponds to the return strength. As a part of the configuring, a loss function of the generative adversarial network is minimized, the loss function comprising a mean square error loss measurement of a noisy intensity image relative to a mean square error loss measurement of a corresponding clean intensity image. An enhanced intensity image is generated by applying the minimized loss function of the network to an original intensity image, the applying improving an image quality measurement of the enhanced intensity image relative to the original intensity image.

Abstract: A generative adversarial network including a generator portion and a discriminator portion is constructed. The network is configured such that the network operates to enhance intensity images, wherein an intensity image is obtained by illuminating an object with an energy pulse and measuring the return strength of the energy pulse, and wherein a pixel of the intensity image corresponds to the return strength. As a part of the configuring, a loss function of the generative adversarial network is minimized, the loss function comprising a mean square error loss measurement of a noisy intensity image relative to a mean square error loss measurement of a corresponding clean intensity image. An enhanced intensity image is generated by applying the minimized loss function of the network to an original intensity image, the applying improving an image quality measurement of the enhanced intensity image relative to the original intensity image.

Abstract: From a first image using a model, a first uncertainty map is generated. An uncertainty level of a location in the first uncertainty map corresponds to a detection of a known structure in a portion of the first image. A first weighted image corresponding to the first uncertainty map is generated, the generating including assigning a first weight to a pixel of the first image, the first weight corresponding to the uncertainty level of a location in the first uncertainty map corresponding to the pixel. From a second image using a model, a second uncertainty map is generated. A second weighted image corresponding to the second uncertainty map is generated. The first image and the second image are combined to form a composite image, each image participating in the composite image according to the corresponding weighted image.

Abstract: A technique relates to coronary reconstruction. Angiogram data associated with cardiac catheterization of an artery for a subject is received, where a contrasting agent is used during the cardiac catheterization. Frames that match from the angiogram data are selected, the frames being from different views. Two-dimensional ordered point clouds for the frames are formed. A three-dimensional ordered point cloud of a reconstructed coronary artery for the subject from the two-dimensional ordered point clouds of the frames is formed. Observed contrast motion caused by the contrasting agent onto the reconstructed coronary artery is mapped, the reconstructed coronary artery thereby providing a three-dimensional model of the artery of the subject during the cardiac catheterization.

Abstract: A generative adversarial network including a generator portion and a discriminator portion is constructed. The network is configured such that the network operates to enhance intensity images, wherein an intensity image is obtained by illuminating an object with an energy pulse and measuring the return strength of the energy pulse, and wherein a pixel of the intensity image corresponds to the return strength. As a part of the configuring, a loss function of the generative adversarial network is minimized, the loss function comprising a mean square error loss measurement of a noisy intensity image relative to a mean square error loss measurement of a corresponding clean intensity image. An enhanced intensity image is generated by applying the minimized loss function of the network to an original intensity image, the applying improving an image quality measurement of the enhanced intensity image relative to the original intensity image.

Abstract: From a first image using a model, a first uncertainty map is generated. An uncertainty level of a location in the first uncertainty map corresponds to a detection of a known structure in a portion of the first image. A first weighted image corresponding to the first uncertainty map is generated, the generating including assigning a first weight to a pixel of the first image, the first weight corresponding to the uncertainty level of a location in the first uncertainty map corresponding to the pixel. From a second image using a model, a second uncertainty map is generated. A second weighted image corresponding to the second uncertainty map is generated. The first image and the second image are combined to form a composite image, each image participating in the composite image according to the corresponding weighted image.

Abstract: A method of generating a 4D model includes capturing imagery of a catheter and a vessel as the catheter is directed through the vessel to a location of interest, wherein the catheter is disposed on a guidewire, constructing a 3D time varying reference curve describing a trajectory of the guidewire, and constructing a time varying 3D model of the artery using the reference curve.

Abstract: A method of generating a 4D model includes capturing imagery of a catheter and a vessel as the catheter is directed through the vessel to a location of interest, wherein the catheter is disposed on a guidewire, constructing a 3D time varying reference curve describing a trajectory of the guidewire, and constructing a time varying 3D model of the artery using the reference curve.

Abstract: A method of generating a 4D model includes capturing imagery of a catheter and a vessel as the catheter is directed through the vessel to a location of interest, wherein the catheter is disposed on a guidewire, constructing a 3D time varying reference curve describing a trajectory of the guidewire, and constructing a time varying 3D model of the artery using the reference curve.

Abstract: A catheter including a monitoring body and a pair of markers disposed on the monitoring body, each marker of the pair of markers encircling the monitoring body, wherein the pair of markers are configured to indicate, through their appearance from a given point of view, an orientation of the monitoring body.

Abstract: A method of generating a 4D model includes capturing imagery of a catheter and a vessel as the catheter is directed through the vessel to a location of interest, wherein the catheter is disposed on a guidewire, constructing a 3D time varying reference curve describing a trajectory of the guidewire, and constructing a time varying 3D model of the artery using the reference curve.

Abstract: A method of generating a 4D model includes capturing imagery of a catheter and a vessel as the catheter is directed through the vessel to a location of interest, wherein the catheter is disposed on a guidewire, constructing a 3D time varying reference curve describing a trajectory of the guidewire, and constructing a time varying 3D model of the artery using the reference curve.