Abstract: A reconstructed volume of a region of patient anatomy is processed to reduce motion artifacts in the reconstructed volume. Autosegmentation of high-contrast structures present in an initial reconstructed volume is performed to generate a 3D representation of the high-contrast structures. 2D mask projections are generated by performing forward projection on the 3D representation, where each 2D mask projection includes location information indicating pixels that correspond to the high-contrast structures during the forward projection process. The acquired 2D projections are modified via in-painting to generate corrected 2D projections, where the acquired 2D projections are modified using information from the 2D mask projections. For example, pixels in the acquired 2D projections that are associated with high-contrast moving structures are replaced with low-contrast pixels.

Abstract: A reconstructed volume of a region of patient anatomy is processed to reduce motion artifacts in the reconstructed volume. Autosegmentation of high-contrast structures present in an initial reconstructed volume is performed to generate a 3D representation of the high-contrast structures. 2D mask projections are generated by performing forward projection on the 3D representation, where each 2D mask projection includes location information indicating pixels that correspond to the high-contrast structures during the forward projection process. The acquired 2D projections are modified via in-painting to generate corrected 2D projections, where the acquired 2D projections are modified using information from the 2D mask projections. For example, pixels in the acquired 2D projections that are associated with high-contrast moving structures are replaced with low-contrast pixels.

Abstract: A method of imaging a region of patient anatomy having a target volume includes performing an autosegmentation of a high-contrast portion of a first reconstructed volume of the region to generate a three-dimensional (3D) representation of the high-contrast portion disposed within the region and generating a set of two-dimensional (2D) mask projections of the region by performing a forward projection process on the 3D representation, wherein each 2D mask projection in the set of 2D mask projections includes location information indicating pixels that are blocked by the high-contrast portion during the forward projection process performed on the 3D representation.

Abstract: This invention relates to a system (100) and method for selecting a signal processing program to be executed by a signal processor in a hearing aid. The system (100) comprises a single layer of neurons (200) each having one or more neighbours and being arranged in a memory device (106), wherein each neuron comprises a neuron vector referring to a specific acoustic situation and a neuron label referring to signal processing program associated with said specific acoustic situation. The system (100) further comprises a sensor adapted to detect an external acoustic situation and to define a sensor vector associated with said external acoustic situation, and wherein a processor (102) calculates a vector difference between said sensor vector and each of the neuron vectors, identifies that neuron providing the smallest vector difference, and selects a signal processing program referred to by a neuron label of that neuron.

Abstract: This invention relates to a system (100) and method for selecting a signal processing program to be executed by a signal processor in a hearing aid. The system (100) comprises a single layer of neurons (200) each having one or more neighbours and being arranged in a memory device (106), wherein each neuron comprises a neuron vector referring to a specific acoustic situation and a neuron label referring to signal processing program associated with said specific acoustic situation. The system (100) further comprises a sensor adapted to detect an external acoustic situation and to define a sensor vector associated with said external acoustic situation, and wherein a processor (102) calculates a vector difference between said sensor vector and each of the neuron vectors, identifies that neuron providing the smallest vector difference, and selects a signal processing program referred to by a neuron label of that neuron.