Leland Stanford Junior University

Ge Precision Healthcare LLC

Adam S Wang

Norbert Joseph Pelc

Debashish Pal

Sen Wang

Abdullah-Al-Zubaer Imran

Evan Zucker

Bhavik Natvar Patel

Zhye Yin

Yirong Yang

Systems/techniques that facilitate generation of image denoising training data via photon-count splitting are provided. In various embodiments, a system can access a set of sinograms generated by a photon-counting computed tomography scanner. In various aspects, the system can split the set of sinograms into a first reduced-photon-count set of sinograms and a second reduced-photon-count set of sinograms. In various instances, the system can convert, via image reconstruction, the first reduced-photon-count set of sinograms into at least one training input image and the second reduced-photon-count set of sinograms into at least one training output image. In various cases, the system can train a deep learning neural network based on the at least one training input image and the at least one training output image.

Generation of image denoising training data via photon-count splitting

Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Patient anatomy and task specific automatic exposure control in computed tomography

An x-ray apparatus includes an x-ray source capable of producing an x-ray beam with a focal spot having a spatial shape that is selected to pre-amplify predetermined spatial frequencies exceeding half a cutoff frequency as compared to spatial frequencies below half the cutoff frequency; an x-ray detector capable of detecting the x-ray beam; and a processor adapted to reconstruct an image from the detected x-ray beam using a filter that compensates the pre-amplified predetermined spatial frequencies. The spatial shape comprises two or more disconnected regions that preferably have widths less than that of a nominal focal spot, combined widths greater than or equal to that of a nominal focal spot, and are separated by less than three times a width of a nominal focal spot.

Focal spot shape for computed tomography

A method for determining a composition of an object using a spectral x-ray system is provided. X-ray photons of at least two different energies are transmitted through the object. The energy of each detected x-ray photon using a detector in the x-ray system is estimated. A first weighted sum of the number of detected photons of each energy is found using a first weighting function, wherein the first weighting function is dependent on the attenuation coefficient function of a first material. In another embodiment, the photons are binned into two energy bins wherein there is a gap between the energy bins.

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Stanford, CA, United States of America

Adam S Wang