A method for optimizing image capture of a scene by a swarm of drones including a root drone and first and second level-1 drones involves the root drone following a predetermined trajectory over the scene, capturing one or more root keyframe images, at a corresponding one or more root drone orientations and root drone-to-scene distances. For each root keyframe image: the root drone generates a ground mask image for that root keyframe image, and applies that ground mask image to the root keyframe image to generate a target image. The root drone then analyzes the target image to generate first and second scanning tasks for the first and second level-1 drones to capture a plurality of images of the scene at a level-1 drone-to-scene distance smaller than the root drone-to-scene distance; and the first and second level-1 drones carry out the first and second scanning tasks respectively.

An apparatus comprises processing circuitry configured to receive a plurality of training image data sets and a plurality of predetermined displacements. The processing circuitry is further configured to use the training image data sets and predetermined displacements to train a transformation regressor in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

An apparatus comprises processing circuitry configured to receive a plurality of training image data sets and a plurality of predetermined displacements. The processing circuitry is further configured to use the training image data sets and predetermined displacements to train a transformation regressor in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

Systems and methods include control of a nuclear imaging scanner to acquire nuclear imaging scan data of a body, control of a computed tomography scanner to acquire computed tomography scan data of the body, determination of a scanning speed, of the nuclear imaging scanner, associated with each of a plurality of scanning coordinates based on locations of one or more internal volumes associated with radioactivity greater than a threshold level, a classification determined for each of the one or more of the internal volumes indicating a degree of clinical interest based at least in part on the radioactivity associated with the internal volume, and an attenuation coefficient map based on the computed tomography scan data, and control of the nuclear imaging scanner to scan the body over each of the scanning coordinates at the associated scanning speed.

Systems and methods include control of a nuclear imaging scanner to acquire nuclear imaging scan data of a body, control of a computed tomography scanner to acquire computed tomography scan data of the body, determination of a scanning speed, of the nuclear imaging scanner, associated with each of a plurality of scanning coordinates based on locations of one or more internal volumes associated with radioactivity greater than a threshold level, a classification determined for each of the one or more of the internal volumes indicating a degree of clinical interest based at least in part on the radioactivity associated with the internal volume, and an attenuation coefficient map based on the computed tomography scan data, and control of the nuclear imaging scanner to scan the body over each of the scanning coordinates at the associated scanning speed.

An information processing device includes a processor configured to: acquire information related to image periodicity included in data of a first image and data of a second image to be compared; and correct predetermined information for aligning the data of the first image and the data of the second image on a basis of the information related to image periodicity.

Various methods and systems are provided for x-ray imaging. In one embodiment, a method includes acquiring, with an x-ray detector, an x-ray image of a subject, determining a transformation that minimizes anti-scatter grid artifacts in the x-ray image, correcting the x-ray image according to the transformation to generate a corrected image, and outputting the corrected image. In this way, artifacts arising from a misalignment of an anti-scatter grid between the calibration and the acquisition may be reduced.

Various methods and systems are provided for x-ray imaging. In one embodiment, a method includes acquiring, with an x-ray detector, an x-ray image of a subject, determining a transformation that minimizes anti-scatter grid artifacts in the x-ray image, correcting the x-ray image according to the transformation to generate a corrected image, and outputting the corrected image. In this way, artifacts arising from a misalignment of an anti-scatter grid between the calibration and the acquisition may be reduced.

An ultrasound imaging device (10) with an ultrasound probe (12) acquires a live ultrasound image which is displayed with a contour (62) or reference image (60) registered with the live ultrasound image using a composite transform (42). To update the composite transform, the ultrasound imaging device acquires a baseline three-dimensional ultrasound (3D-US) image (66) tagged with a corresponding baseline orientation of the ultrasound probe measured by a probe tracker, and one or more reference 3D-US images (70) each tagged with a corresponding reference orientation. Transforms (54) are computed to spatially register each reference 3D-US image with the baseline 3D-US image. A closest reference 3D-US image is determined whose corresponding orientation is closest to a current orientation of the ultrasound probe as measured by the probe tracker. The composite transform is updated to include the transform to spatially register the closest reference 3D-US image to the baseline 3D-US image.

An ultrasound imaging device (10) with an ultrasound probe (12) acquires a live ultrasound image which is displayed with a contour (62) or reference image (60) registered with the live ultrasound image using a composite transform (42). To update the composite transform, the ultrasound imaging device acquires a baseline three-dimensional ultrasound (3D-US) image (66) tagged with a corresponding baseline orientation of the ultrasound probe measured by a probe tracker, and one or more reference 3D-US images (70) each tagged with a corresponding reference orientation. Transforms (54) are computed to spatially register each reference 3D-US image with the baseline 3D-US image. A closest reference 3D-US image is determined whose corresponding orientation is closest to a current orientation of the ultrasound probe as measured by the probe tracker. The composite transform is updated to include the transform to spatially register the closest reference 3D-US image to the baseline 3D-US image.