Abstract: Disclosed is an operating method of an apparatus operated by at least one processor, which includes: receiving RF data obtained from tissue through an arbitrary ultrasound probe; extracting a generalized quantitative feature to a probe domain from the RF data; and reconstructing the generalized quantitative feature to generating a quantitative ultrasound image.

Abstract: Disclosed is a method of an imaging device operated by at least one processor. The method includes: receiving pulse-echo data obtained from a tissue; and outputting a quantitative image of an objective variable using a B-mode image generated from the pulse-echo data, and a quantitative style feature extracted from the pulse-echo data.

Abstract: A method of operating an image device operated by at least one processor is provided. The method of operating an image device comprises generating beamformed RF-mode images including phase shift information for each incident angle by using multi-angle ultrasound-echo data, generating phase shift maps representing a displacement of speckle pattern between adjacent beamformed RF-mode images, and obtaining a speed-of-sound distribution image corresponding to the phase shift maps by using a deep neural network which is trained to reconstruct speed-of-sound distribution of a tissue from training phase shift maps.

Abstract: A method of operating an image device operated by at least one processor is provided. The method of operating an image device comprises generating beamformed RF-mode images including phase shift information for each incident angle by using multi-angle ultrasound-echo data, generating phase shift maps representing a displacement of speckle pattern between adjacent beamformed RF-mode images, and obtaining a speed-of-sound distribution image corresponding to the phase shift maps by using a deep neural network which is trained to reconstruct speed-of-sound distribution of a tissue from training phase shift maps.

Abstract: The method of operating an image device comprises receiving an input of virtual tissues modeled with an arbitrary shape and a quantitative feature, simulating a TOF change or a signal strength change of ultrasound data having penetrated the virtual tissues modeled with a speed-of-sound distribution or an attenuation coefficient distribution in a first and in a second direction, and creating an image pair representing the TOF change or the signal strength change, creating a speed-of-sound distribution image or an attenuation coefficient distribution image of each of the virtual tissues as a ground truth of an image pair created in the corresponding virtual tissue, and training a first neural network that reconstruct the speed-of-sound distribution image from an input image pair or training a second neural network that reconstructs the attenuation coefficient distribution image from the input image pair.