Siemens Healthineers International Ag

Varian Medical Systems International Ag

Varian Medical Systems, Inc.

Dieter Marc Seghers

Pascal Paysan

Adam Michal Strzelecki

Mathieu Plamondon

Igor Peterlik

Mathias Lehmann

Janne Iimari Nord

Benjamin M Haas

Joakim Pyyry

Sami Petri Perttu

Marcus Brehm

Balazs Nagy

Alexander Heinz

Philippe Messmer

Josh Star-Lack

Adam James Wang

Peter Munro

Luis Melo De Carvalho

A reconstructed volume of a region of patient anatomy is processed to reduce motion artifacts in the reconstructed volume. Autosegmentation of high-contrast structures present in an initial reconstructed volume is performed to generate a 3D representation of the high-contrast structures. 2D mask projections are generated by performing forward projection on the 3D representation, where each 2D mask projection includes location information indicating pixels that correspond to the high-contrast structures during the forward projection process. The acquired 2D projections are modified via in-painting to generate corrected 2D projections, where the acquired 2D projections are modified using information from the 2D mask projections. For example, pixels in the acquired 2D projections that are associated with high-contrast moving structures are replaced with low-contrast pixels. These corrected 2D projections are used to produce an improved reconstructed volume with fewer and/or less visually prominent motion artifacts.

Motion artifact reduction in computed tomography

A computer-implemented method of reducing scatter in an X-ray projection image of an object, the method comprising: generating an initial X-ray projection image of an object with an imaging beam produced by an imaging system; based on a first transmission indicator for the object and on a second transmission indicator for at least one element of the imaging system, selecting a kernel for convolution of the initial projection image; convolving the initial X-ray projection image with the kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

Estimating scatter in X-ray images caused by imaging system components using kernels based on beam hardening

A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

Estimating scatter in X-ray images caused by imaging system components using spatially-dependent kernels

A computer-implemented method of segmenting a reconstructed volume of a region of patient anatomy includes: determining an anatomical region associated with the reconstructed volume; detecting one or more metal objects disposed in an initial 3D metal object mask associated with the reconstructed volume; for each of the one or more metal objects disposed in the initial 3D metal object mask, determining a volume associated with the metal object; determining a value for at least one segmentation parameter based on the anatomical region and on the volume associated with the one or more metal objects; and generating a final 3D metal object mask associated with the reconstructed digital volume using the value for the segmentation parameter.

Adaptive auto-segmentation in computed tomography

A computer-implemented method for modifying X-ray projection images of a subject region includes: generating a set of combined two-dimensional (2D) projections of a subject region, wherein each combined 2D projection includes one or more mask-bordering pixels and one or more mask-edge pixels; forming a three-dimensional (3D) matrix of the set of combined 2D projections; based on the 3D matrix, generating a linear algebraic system for determining pixel values for pixels indicated in a set of 2D projection metal masks, wherein a first change in slope of pixel value associated with a mask-edge pixel of a combined 2D projection is constrained to equal a second change in slope of pixel value associated with a mask-bordering pixel of a combined 2D projection; determining values for a variable vector of the linear algebraic system; and generating a set of inpainted 2D projections by modifying initial 2D projections with values for the variable vector.

Metal artifact reduction in computed tomography

Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.

Tomographic image analysis using artificial intelligence (AI) engines

Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.

Tomographic image reconstruction using artificial intelligence (AI) engines

Reconstruction of projection images of a CBCT scan is performed by generating simulated projection data, comparing the simulated projection data to the projection images of the CBCT scan, determining a residual volume based on the comparison, and using the residual volume to determine an accurate reconstructed volume. The reconstructed volume can be used to segment a tumor (and potentially one or more organs) and align the tumor to a planning volume (e.g., from a CT scan) to identify changes, such as shape of the tumor and proximity of the tumor to an organ. These changes can be used to update a radiation therapy procedure, such as by altering a radiation treatment plan and fine-tuning a patient position.

Iterative image reconstruction in image-guided radiation therapy

One example method to reduce image artifacts, which may include obtaining measured projection data acquired using an imaging system. The measured projection data is associated with a target object and an artifact source within a radiation field of the imaging system. The method may also include generating virtual projection data associated with the artifact source by forward projecting a model representing one or more physical properties of the artifact source. The method may further include generating corrected projection data based on the measured projection data and the virtual projection data; and reconstructing the corrected projection data into reconstructed volume image data to reduce image artifacts caused by the artifact source.

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Zürich, Switzerland

Dieter Marc Seghers