Mayo Foundation for Medical Education and Research

The University of Chicago

Other

Lifeng Yu

Cynthia H McCollough

Joel G Fletcher

Shuai Leng

Xiaochuan Pan

Chien-min Kao

Zhoubo Li

Yu Zou

Armando Manduca

Maryellen L Giger

Howard J Halpern

Emil Sidky

Dan Xia

Charles A Pelizzari

Martin King

Seungryong Cho

Charles Anthony Mistretta

Hua Li

Xin Liu

Joshua D Trzasko

Hao Gong

David M Hough

Andrew D Missert

Systems and methods are provided for synthesizing information from multiple image series of different kernels into a single image series, and also for converting a single baseline image series of a kernel reconstructed by a CT scanner to image series of various other kernels, using deep-learning based methods. For multi-kernel synthesis, a single set of images with desired high spatial resolution and low image noise can be synthesized from multiple image series of different kernels. The synthesized kernel is sufficient for a wide variety of clinical tasks, even in circumstances that would otherwise require many separate image sets. Kernel conversion may be configured to generate images with arbitrary reconstruction kernels from a single baseline kernel. This would reduce the burden on the CT scanner and the archival system, and greatly simplify the clinical workflow.

Systems and methods for multi-kernel synthesis and kernel conversion in medical imaging

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

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

A fully image-based framework for CT image, or other medical image, quality evaluation and virtual clinical trial using deep-learning techniques is provided. This framework includes deep learning-based noise insertion, lesion insertion, and model observer, which enable efficient, objective, and quantitative image quality evaluation and virtual clinical trial directly performed on patient images.

Deep learning-based medical image quality evaluation and virtual clinical trial

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

A method for creating an energy series of images acquired using a multi-energy computed tomography (CT) imaging system having a plurality of energy bins includes acquiring, with the multi-energy CT imaging system, a series of energy data sets, where each energy data set is associated with at least one of the energy bins. The method includes producing a conglomerate image using at least a plurality of the energy data sets and, using the conglomerate image, reconstructing an energy series of images, each image in the energy series of images corresponding to at least one of the energy data sets.

System and method for improved energy series of images using multi-energy CT

A system and method for acquiring an image of a region of interest (ROI) of subject using a computed tomography (CT) system includes a) performing a scout scan of the subject using the CT system to yield scout data related to the ROI and b) determining an initial contrast volume form at least the scout data. The method also includes c) prescribing a scanning protocol to be implemented using the computed tomography system to image the ROI and d) determining a size of the subject about the ROI. The method further includes e) determining a computed tomography dose related to volume (CTDIvol) based on at least the size determined at step d) and f) adjusting the scanning protocol prescribed in step b) to match at least one of a desired radiation dose and a relative intravenous (IV) contrast dose to a reference CTDIvol. The method includes g) acquiring imaging data from the ROI using the CT system by using the adjusted scanning protocol.

Method for controlling radiation dose and intravenous contrast dose in computed tomography imaging

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.

System and method for denoising medical images adaptive to local noise

A method and apparatus for reconstruction of a region of interest for an object is provided. The reconstruction of the object may be based on chords which may fill a part, all, or more than all of the region of interest. Using chords for reconstruction may allow for reducing data acquired and/or processing for reconstructing a substantially exact image of the ROI. Moreover, various methodologies may be used in reconstructing the image, such as backprojection-filtration, and modified filtration backprojection.

Imaging system

Projection data acquired with an x-ray CT system is filtered using a bilateral filter to reduce image noise and enable the acquisition at lower x-ray dose without the loss of image diagnostic quality. The bilateral filtering is performed before image reconstruction by producing a noise equivalent data set from the acquired projection data and then converting the bilateral filtered values back to a projection data set suitable for image reconstruction.

Projection-space denoising with bilateral filtering in computed tomography

A method for CT imaging that utilizes an automatic tube potential selection for individual subjects and diagnostic tasks. The method quantifies the relative radiation dose of different tube potentials for achieving a specific image quality. This allows the selection of a tube potential that provides a reduced radiation dose while still providing CT images of a sufficient quality.

System and method for automatic tube potential selection for dose reduction in medical imaging

A system and method for the accurate quantitative evaluation of dual-energy computed tomography (CT) projection data that is acquired in a dual-source helical scan includes employing a dual-source z-axis helical interpolation method. The method includes transforming the two helical projection data sets, where corresponding projections of high- and low-energy data sets are shifted with respect to one another by 90 degrees or another angle, into corresponding non-helical projection data sets. A dual-source helical interpolation algorithm allows for projection space dual-energy processing by realigning the high- and low-energy datasets based on the z-axis interpolation. This algorithm may be implemented using a variety of interpolation schemes and can be extended from single slice to multi-slice data acquisitions. Subsequent to the registration of the non-helical projection data sets, projection space processing allows for accurate material quantification and virtual monochromatic images in which beam hardening artifacts have been substantially suppressed.

Method for creating images indicating material decomposition in dual energy, dual source helical computed tomography

A system and method for decomposing more than two materials in an imaging object includes performing a CT imaging acquisition of a portion of an imaging object using at least two energy levels to acquire imaging data associated with each of the at least two energy levels. A total mass attenuation of the imaging data is expressed as a weighted sum of constituent element mass attenuation coefficients and an effective atomic number and density of the constituent elements in the portion of the imaging object is determined by one of a number of methods. Accordingly, concentration of the constituent elements in imaged object is determined by solve the expression using known material attenuation coefficients and the measured CT data.

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Byron, MN, United States of America

Lifeng Yu