Kabushiki Kaisha Toshiba

Toshiba Medical Systems Corporation

Canon Medical Systems Corporation

Toshiba Corporation

Ilmar A Hein

Alexander A Zamyatin

Katsuyuki Taguchi

Aleksandr Zamyatin

Zhou Yu

Michael D Silver

Xiaohui Zhan

Xiaofeng Niu

Yu Zou

Tzu-Cheng Lee

Ting Xia

Sachin Moghe

A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

Counting response and beam hardening calibration method for a full size photon-counting CT system

A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

Apparatus and method combining deep learning (DL) with an X-ray computed tomography (CT) scanner having a multi-resolution detector

An apparatus, system and method for calibrating an x-ray apparatus including acquiring sinogram data by scanning a symmetrical phantom using a plurality of detector channels; generating mirror-copied sinogram data by mirror-copying at least one of first sinogram data and second sinogram data of the acquired sinogram data, wherein the first sinogram data and the second sinogram data are generated by dividing the sinogram data at a center detector channel of the plurality of detector channels; outputting a first reconstructed image by reconstructing the mirror-copied sinogram data; and determining a calibration parameter based on the first reconstructed image.

Self calibration method and apparatus for correcting offset angle in a photon counting computed tomography system

A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g., convolutional neural network can be trained to perform the image processing).

Apparatus and method for artifact detection and correction using deep learning

A CT imaging apparatus has processing circuitry that is configured to obtain projection data collected by a CT detector during a scan of an object. The processing circuitry is also configured to perform iterative reconstruction of the projection data to generate a current image. The iterative reconstruction includes filtering forward-projected data during backprojection or filtering image data prior to forward projection to model system optics. The processing circuitry is also configured to combine the current image with a previously-obtained image to generate an updated image.

Iterative reconstruction with system optics modeling using filters

Iterative reconstruction (IR) algorithms are advantageous over standard filtered backprojection (FBP) algorithms by improving resolution and noise performance. In this regard, model-based IR algorithms (MBIR) have been developed to incorporate accurate system models into IR and result in a better image quality than IR algorithms without a system model. System optics are included in both forward and backprojection (IRSOM-FPBP).

System optics in at least in one of backprojection and forward projection for model-based iterative reconstruction

A method of computed-tomography and a computed-tomography apparatus in which x-ray projection data is acquired at a number of views for a scan of an object. Partial images are created from data for a desired number of said views. Full scan images are created from plural ones of the partial images. Non-overlapping time images are created from the full-scan images. Gradient images are also created. An improved image is created by weighting respective ones of the full scan and non-overlapping time images using the gradient image. The improved image has increased sharpness with reduced noise.

Image domain based noise reduction for low dose computed tomography fluoroscopy

Embodiments and processes of computer tomography perform tasks associated with selecting a portion of projection or emission data that contain the least amount of motion based upon a predetermined motion index, a derivative of plane integral (DPI). Other embodiments and processes of computer tomography perform tasks associated with determining an amount of direction-dependent motion in an object based upon a comparison of the DPIs in predetermined directions.

Motion index for medical imaging data based upon Grangeat's formula

A method of computed-tomography and a computed-tomography apparatus where a flying focal spot x-ray interpolation interlacing is used. Weighted or non-weighted interlacing of zero values is performed, or interpolation interlacing is performed. The interpolation interlacing may be implemented as part of backprojection and or may be a separate process prior to backprojection. In both cases interlacing is performed on post-logged convolved data. The interpolation interlacing may also be incorporated into different parts of the processing chain, such as before convolution.

Interpolation interlacing based data upsampling algorithm for cone-beam x-ray CT flying focal spot projection data

A method of computed-tomography and a computed-tomography apparatus where a interpolation kernel width is adaptively determined as a function of the distance from the x-ray source to the reconstruction pixel. The width of the kernel is the projection of the reconstruction pixel on the detector. The method can be implemented in the channel direction. The method can also be implemented in the segment direction, or in the channel and segment directions at the same time. Backprojection is performed using the adaptive kernel width and may by used with helical and circular scanning, and with cone-beam or fan beam x-ray CT.

Computed tomography device and method using circular-pixel position-adaptive interpolation

The CT imaging system optimizes its image generation by substantially reducing artifacts caused by a known amount of readout time lag in the X-ray detectors or data acquisition system. Although each detector row takes the same amount of time to read out the signals, the time lag cumulates over the rows as each row is sequentially read out. The back-projection coordinates are correspondingly corrected based upon the above described delay.

Method and system for reducing artifact due to time delay in data acquisition system in computer tomography

An X-ray computed tomography apparatus includes an X-ray tube () which generates X-rays, a multi-channel X-ray detector () which detects X-rays transmitted through an object to be examined, a rotating frame () which is equipped with the X-ray tube and the X-ray detector, a reconstruction unit () which reconstructs an image on the basis of an output from the X-ray detector, and a control unit () which alternately shifts the X-ray focal spot of the X-ray tube along the rotating direction on the anode by a distance +&#x3c3; and a distance &#x2212;&#x3c3; for each view. The distance &#x3c3; is set to where SOD is the distance between the X-ray tube and the rotation center, and &#x394;&#x3b2; is the angle difference between adjacent views, such that X-ray focal spots in the adjacent views overlap each other at almost the same position.

X-ray computed tomography apparatus

A method of removing an imaging artifact in a medical image, including obtaining a first plurality of images, the first plurality of images collectively defining a first image volume; filtering the first plurality of images to create a second plurality of images, each image in the second plurality of images comprising an average of at least two images in the first plurality of images; selecting a first image from the first plurality of images; adding a lost noise image to a second image in the second plurality of images to create a noise restored image, the second image in the second plurality of images corresponding to the first image in the first plurality of images; determining a gradient image based on pixel values in the second plurality of images, the gradient image comprising a gradient value at each pixel location in the second image; and combining, based on the determined gradient image, the first image and the noise restored image to obtain a corrected image that does not contain the imaging artifact.

Method for helical windmill artifact reduction with noise restoration for helical multislice CT

An X-ray computed tomography apparatus includes a helical scanning device configured to collect projection data while at least one of a gantry and a couch moves along a body axial direction of an object on the couch when at least one of the gantry and the couch is tilted, the helical scanning device including an X-ray source configured to generate X-rays, and a detector disposed opposite the X-ray source and having detector elements arranged in a plurality of rows along the body axial direction, and a reconstructing device configured to reconstruct an image based on the projection data using cone-beam Feldkamp reconstruction.

Tilted gantry helical cone-beam Feldkamp reconstruction for multislice CT

An x-ray computed tomography system, method, and computer product that generates an x-ray beam with an x-ray generator and detects with an x-ray detector at least one characteristic of the x-ray beam generated by the x-ray generator, after the x-ray beam has passed through an object, the system including a converting unit configured to obtain analog projection data outputted by the x-ray detector and to convert the analog projection data to digital projection data, and a processing unit configured to obtain the digital projection data from the converting unit, to detect overflow digital projection data that overflows a measuring range of the computed tomography system, and to correct the overflow digital projection data of the digital projection data by using a curve fitting function.

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Chicago, IL, United States of America

Ilmar A Hein