Mayo Foundation for Medical Education and Research

Ge Precision Healthcare LLC

General Electric Company

Zhoubo Li

Cynthia H McCollough

Shuai Leng

Lifeng Yu

Armando Manduca

Jiang Hsieh

Brian Edward Nett

Jiahua Fan

Roy A Nilsen

Mingye Wu

Joel G Fletcher

Ryan James Lemminger

Meghan Lynn Yue

Priti Madhav

Rajeshwari Karthikeyan

Adam Cohen

Methods and systems are provided for calibrating a medical imaging system. In one example, a method includes identifying a calibration vector to be updated based on an artifact in an image acquired with the medical imaging system, determining calibration data to be obtained with the medical imaging system based on the calibration vector, obtaining the calibration data with the medical imaging system, and updating the calibration vector based on the calibration data.

Systems and methods for image artifact mitigation with targeted modular calibration

A system and method for determining stenosis severity in a subject's vasculature using multi-energy computer tomography (MECT) imaging is provided. In some aspects, the method includes acquiring MECT data using a CT system, performing a material decomposition process on acquired MECT data to generate one or more material density maps, and selecting, using the one or more material density maps, one or more regions of interest (ROIs) encompassing at least one vessel cross-section. The method also includes measuring an iodine content in the one or more ROIs, and determining a stenosis severity based on the measured iodine content. The method further includes generating a report indicating the stenosis severity associated with the subject's vasculature.

System and method for quantifying luminal stenosis using multi-energy computed tomography imaging

Described here are systems and methods for optimization techniques for automatically selecting x-ray beam spectra, energy threshold, energy bin settings, and other imaging technique parameters for photon-counting detector computed tomography ('PCCT'). The techniques described here are generally based on subject or object size, material of interest, and location of the target material. Advantageously, the optimizations can be integrated with different PCCT systems to automatically select optimal imaging technique parameters before scanning a particular subject or object.

Methods for optimizing imaging technique parameters for photon-counting computed tomography

A system and method is provided for performing material decomposition using a computed tomography (CT) system. The method includes acquiring CT imaging data of an object including data subsets corresponding to at least two different energy spectral bins and using the CT imaging data at each of the at least two different energy spectral bins to form a series of equations for basis material decomposition. The method also includes using a general physical constraint, which quantifies how each basis material in the object is mixed together to form the object, within the series of equations. The method also includes determining at least one basis material density of the object using the physical constraint and the CT imaging data and generating an image of the object using the CT imaging data and the mass densities of at least one basis material.

System and method for basis material decomposition with general physical constraint for multi-energy computed tomography

System and methods are provided for producing computed tomography (CT) images. In some aspects, a method includes obtaining medical image data sets acquired using the multiple energies of irradiating radiation and analyzing the medical image data sets for spatial and spectral features. The method also includes comparing the spatial and spectral features of the medical image data sets to identify similarities and using the similarities, weighting the medical image data sets to generate images of the subject having reduced noise compared to images of the subject produced from the medical image data sets without weighting.

System and method for controlling noise in multi-energy computed tomography images based on spatio-spectral information

Methods and systems are provided for dual energy imaging. In one embodiment, a method for a dual energy imaging system comprises determining a first tube potential and a second tube potential according to a size of a subject, and controlling the dual energy imaging system with the first tube potential and the second tube potential to generate lower energy x-rays and higher energy x-rays respectively to image the subject. In this way, image quality may be increased while minimizing dose during dual energy imaging of a particular imaging subject.

Systems and methods for automatic tube potential selection in dual energy imaging

Methods and systems are provided for direct monochromatic image generation for spectral computed tomography. In one embodiment, a method comprises acquiring projection data during a scan of a subject, reconstructing a low energy image and a high energy image from the projection data, and generating a monochromatic image from the low energy image and the high energy image. In this way, a monochromatic image may be generated directly from low and high energy images with a substantial reduction in image noise, especially when compared to a monochromatic image generated indirectly from material density images.

Direct monochromatic image generation for spectral computed tomography

A system and method is provided for estimating the local noise of CT images and denoising the images using a modified non-local means (NLM) algorithm that is adaptive to local variations of noise levels. A strategy for efficiently estimating the local noise of CT images is also described.

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Libertyville, IL, United States of America

Zhoubo Li