Abstract: Systems and methods for reconstructing medical images based on the trained deep learning processes, and for training deep learning processes, are disclosed. In some examples, image measurement data is received. A histo-image is generated based on the image measurement data. Further, an attenuation map, such as a ?-map, is received. An attenuation histo-image is generated based on the attenuation map. Further, a trained machine learning process, such as a trained neural network, is applied to features generated from the histo-image and the attenuation histo-image. Based on the application of the machine learning process to the histo-image and the attenuation histo-image, output image data characterizing an image volume is generated. In some examples, a machine learning process is trained based on histo-images and corresponding attenuation histo-images. The trained machine learning process may be employed to reconstruct images, such as positron emission tomography (PET) images.

Abstract: An apparatus for collecting super-sampled imaging data includes an imaging scanner, a bed, and a first motor. The imaging scanner includes a plurality of detectors arranged substantially circumferentially about a scanner axis. The bed has a plane and a central axis that is generally in the plane of the bed. However, the bed is oriented so as to form a first non-zero degree angle between the central axis and the scanner axis. The first motor translates the bed along the central axis, and a second motor may translate the bed along an axis orthogonal to the central axis and scanner axis. Since the voxels pass through the scanner in different relative positions of the bed, additional information may be acquired during the scan.

Abstract: An apparatus for collecting super-sampled imaging data includes an imaging scanner, a bed, and a first motor. The imaging scanner includes a plurality of detectors arranged substantially circumferentially about a scanner axis. The bed has a plane and a central axis that is generally in the plane of the bed. However, the bed is oriented so as to form a first non-zero degree angle between the central axis and the scanner axis. The first motor translates the bed along the central axis, and a second motor may translate the bed along an axis orthogonal to the central axis and scanner axis. Since the voxels pass through the scanner in different relative positions of the bed, additional information may be acquired during the scan.