Abstract: A projection dataset from a cone beam computed tomography (CBCT) can be input into a first set of one or more neural networks trained for at least one of saturation correction, truncation correction, and scatter correction. Reconstruction can then be performed on the output projection dataset to produce an image dataset. Thereafter, this image dataset can be input into a second set of one or more neural networks trained for at least one of noise reduction and artefact reduction, thereby generating a higher quality CBCT image.

Abstract: An X-ray diagnostic apparatus according to an embodiment includes processing circuitry configured to improve quality of fourth data corresponding to a fourth number of views that is smaller than a first number of views by inputting the fourth data to a learned model generated by performing machine learning with second data corresponding to a second number of views as input learning data, and third data corresponding to a third number of views that is larger than the second number of views as output learning data, the second data and the third data being acquired based on first data corresponding to the first number of views. The fourth data is data acquired by tomosynthesis imaging.

Abstract: A framework for isolating an aneurysm and parent vessel in volumetric image data is provided herein. In accordance with one aspect, the framework generates a refined mask by performing region growing starting at an aneurysm dome point to eliminate vessels that are indirectly connected to an aneurysm or parent vessel in volumetric image data. A final mask may be generated based at least in part on the refined mask by eliminating any kissing vessel connected with the aneurysm from the refined mask. The final mask may then be used for segmentation of the aneurysm and the parent vessel in the volumetric image data.

Abstract: A framework for isolating an aneurysm and parent vessel in volumetric image data is provided herein. In accordance with one aspect, the framework generates a refined mask by performing region growing starting at an aneurysm dome point to eliminate vessels that are indirectly connected to an aneurysm or parent vessel in volumetric image data. A final mask may be generated based at least in part on the refined mask by eliminating any kissing vessel connected with the aneurysm from the refined mask. The final mask may then be used for segmentation of the aneurysm and the parent vessel in the volumetric image data.

Abstract: A framework for isolating an aneurysm and parent vessel in volumetric image data is provided herein. In accordance with one aspect, the framework generates a refined mask by performing region growing starting at an aneurysm dome point to eliminate vessels that are indirectly connected to an aneurysm or parent vessel in volumetric image data. A final mask may be generated based at least in part on the refined mask by eliminating any kissing vessel connected with the aneurysm from the refined mask. The final mask may then be used for segmentation of the aneurysm and the parent vessel in the volumetric image data.

Abstract: A framework for isolating an aneurysm and parent vessel in volumetric image data is provided herein. In accordance with one aspect, the framework generates a refined mask by performing region growing starting at an aneurysm dome point to eliminate vessels that are indirectly connected to an aneurysm or parent vessel in volumetric image data. A final mask may be generated based at least in part on the refined mask by eliminating any kissing vessel connected with the aneurysm from the refined mask. The final mask may then be used for segmentation of the aneurysm and the parent vessel in the volumetric image data.

Abstract: A method (100) of aneurysm analysis (110) and virtual stent simulation (120) for endovascular treatment of sidewall intracranial aneurysms.

Abstract: A system erases features in a displayable three dimensional (3D) medical image volume comprising multiple image slices. An erasure cursor coordinate detector detects two dimensional (2D) location coordinates identifying location of a movable erasure cursor in a displayed image within the medical image volume. A data processor translates the detected erasure cursor 2D location coordinates to corresponding 2D pixel coordinates within an image slice in the 3D medical image volume. The data processor sets luminance values of pixels corresponding to the 2D pixel coordinates of the image slice to a background luminance value of the image slice to provide erased pixels corresponding to erasure cursor locations having an identifier. A display processor generates data representing a display image showing the image slice with the set background luminance values of the erased pixels.

Abstract: A method (100) of aneurysm analysis (110) and virtual stent simulation (120) for endovascular treatment of sidewall intracranial aneurysms.