Toshiba Medical Systems Corporation

Kabushiki Kaisha Toshiba

University of Central Florida Research Foundation, Inc.

University of Utah Research Foundation

Canon Medical Systems Corporation

Toshiba Medical Research Institute USA

Alexander A Zamyatin

Michael D Silver

Daxin Shi

Satoru Nakanishi

Yu Zou

Be-Shan Chiang

Zhi Yang

Ilmar A Hein

Thomas Labno

Mihail Petru Dinu

Alexander Katsevich

Satoru Ohishi

Xiaolan Wang

Gengsheng Lawrence Zeng

Yuexing Zhang

Zhengyan Wang

Anusha Muthu Natarajan

Yongsheng Pan

Anusha Muthu-Natarajan

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

An image is reconstructed based upon a filtered backprojection (FBP) technique using a reconstruction filter which is customized or shaped by parameters including a weight value that is determined for weighing projection data according to a predetermined noise model. The weight value is determined based upon rays or views in the projection data. A regularization term is also combined with the noise-weighted FBP.

Method and system for generating image using filtered backprojection with noise weighting and or prior in

A method of computing statistical weights for a computed tomography (CT) iterative reconstruction process is provided. The method includes obtaining detector count data from a CT scan of an object; calculating variance data based on the count data and an electronic noise variance; transforming the calculated variance data to obtain statistical weight data; and performing the CT iterative reconstruction process using the statistical weight data and raw projection data to obtain a reconstructed CT image.

Method for statistical weights design in iterative reconstruction algorithms

Photon counting detectors are sparsely placed at predetermined positions in the fourth-generation geometry around an object to be scanned in spectral Computer Tomography (CT). An X-ray emitting source rotates radially outside the sparsely placed photon counting detectors. Furthermore, the integrating detectors are placed in the third-generation in combination to the sparsely placed photon counting detectors at predetermined positions in the fourth-generation geometry.

Method and system for spectral computed tomography (CT) with inner ring geometry

Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, generating synthetic data by forward projection of the reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and applying a scaling factor whose value is adaptively determined and optimized based upon the minimization of a predetermined cone beam artifact metric. Ultimately, the cone beam artifact is substantially reduced by generating a corrected image using the reference image and the correction image that has been optimally scaled based upon the adaptively determined scaling factor value.

Method and system for substantially reducing cone beam artifacts based upon adaptive scaling factor in circular computer tomography (CT)

Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, differentiating the reference image; generating synthetic data by forward projection of the differentiated reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and optionally applying a scaling factor. Ultimately, the cone beam artifact is substantially reduced by generating a corrected image using the reference image and the correction image that has been optimally scaled based upon the adaptively determined scaling factor value.

Method and system for substantially reducing cone beam artifacts based upon image domain differentiation in circular computer tomography (CT)

A medical imaging apparatus, processing device or specialized circuit can include an input interface to input scan data of a medical image scan of a target object, a processor to generate an output image from the input scan data, and an output interface to output the output image to, e.g., a display. The processor can execute a first reconstruction of the scan data to obtain an intermediate image of the target object, a high-density forward projection of the intermediate object to obtain generated data, a sinogram updating using both of the generated data and the scan data to obtain a high-resolution sinogram, and a second reconstruction based on the high-resolution sinogram to obtain an output image.

High density forward projector for spatial resolution improvement for medical imaging systems including computed tomography

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

The methods and systems of the present invention is an algorithm which estimates motion inside objects that change during the scan. The algorithm is flexible and can be used for solving the misalignment correction problem and, more generally, for finding scan parameters that are not accurately known. The algorithm is based on Local Tomography so it is faster and is not limited to a source trajectory for which accurate and efficient inversion formulas exist.

Method for estimating scan parameters from tomographic data

Photon starvation causes streaks and noise and seriously impairs the diagnostic value of the CT imaging. To reduce streaks and noise, a new scheme of adaptive Gaussian filtering relies on the diffusion-derived scale-space concept in one embodiment of the current invention. In scale-space view, filtering by Gaussians of different sizes is similar to decompose the data into a sequence of scales. As the scale measure, the variance of the filter linearly relates to the noise standard deviation of a predetermined noise model in the new filtering method. The new filter has only one optional parameter that remains stable once tuned. Although single-pass processing using the new filter generally achieves desired results, iterations are optionally performed.

Method and system utilizing parameter-less filter for substantially reducing streak and or noise in computer tomography (CT) images

The CT imaging system optimizes its image generation by updating an image with the current application of a data fidelity update and a regularization update together in a single step in an iterative reconstruction algorithm. The iterative reconstruction algorithm includes the ordered subsets simultaneous algebraic reconstruction technique (OSSART) and the simultaneous algebraic reconstruction technique (SART). The data fidelity update and the regularization update are independently obtained using some predetermined statistical information such as noise and or error in matching the real data.

Concurrent update iterative reconstruction (IR) method and system

Spatial resolution is substantially improved by simulating a system blur kernel including an angle variable in the forward projection during a predetermined iterative reconstruction technique. The iterative reconstruction acts as a deconvolution, which overcomes certain restrictions of system optics. In general, resolution is substantially improved with cone beam and helical data without a large increase in noise.

Spatial resolution improvement in computer tomography (CT) using iterative reconstruction

Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.

Method and system for estimating noise level

Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, generating synthetic data by forward projection of the reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and substantially reducing the cone beam artifacts by generating a corrected image using the reference image and the correction image.

Method and system for substantially reducing artifacts in circular cone beam computer tomography (CT)

Streak artifacts arise in helical CT reconstruction with cone beam weighting (CBW) with helical pitch ratio between 0.5 and 1.0 in a prevalent 2PI mode. The sreak artifacts are substantially removed by applying upsampling to the measured data in the segment direction before weighting. Furthermore, by making the upsampling adaptive to the view Z-position, an amount of extra processing is greatly reduced to near 1%.

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Hawthorn Woods, IL, United States of America

Alexander A Zamyatin