Toshiba Medical Systems Corporation

Kabushiki Kaisha Toshiba

University of Central Florida Research Foundation Inc

Canon Medical Systems Corporation

Daxin Shi

Alexander A Zamyatin

Yu Zou

Mihail Petru Dinu

Zhou Yu

Michael D Silver

Alexander Katsevich

Thomas Labno

A method and apparatus is provided to reconstruct a computed tomography image using iterative reconstruction (IR) that is accelerated using various combinations of ordered subsets, conjugate gradient, preconditioning, resetting/restarting, and/or gradient approximation techniques. For example, when restarting criteria are satisfied the IR algorithm can be reset by setting conjugate-gradient parameters to initial values and/or by changing the number of ordered subsets. The IR algorithm can be accelerated by approximately calculating the gradients, by using a diagonal or Fourier preconditioner, and by selectively updating the preconditioner based on the regularization function. The update direction and step size can be calculated using the preconditioner and a surrogate function, which is not necessarily separable.

Method and apparatus for acceleration of iterative reconstruction of a computed tomography image

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

A computed tomography (CT) method and apparatus including a radiation source configured to produce radiation directed to an object space, and a plurality of detector elements configured to detect the radiation produced from the radiation source through the object space and generate projection data. A rotation mount is configured to rotate the radiation source around the object space. Processing circuitry is configured to cause the rotation mount to rotate the radiation source, and to receive the projection data. The projection data includes a plurality of projection data sets. The processing circuitry calculates a set of weights based on the projection data sets, calculates a set of pre-weights based on the weights, and minimizes a penalized weighted least-squares cost function to produce a reconstructed image. The cost function is a sum of a weighted least-squares term, weighted using the weights, and a penalty term weighted using the pre-weights.

Computed tomography apparatus and empirical pre-weighting method for decreasing image noise nonuniformity

A method of tomographic imaging the real part of the refractive index of the internal structure of an image volume and a corresponding apparatus for performing the method. The method includes a two-step process of first retrieving phase projections of an image volume from projective intensity measurements through the image volume. The second step of the method includes iterative image reconstruction of an image of the image volume using phase projections taken at multiple projection angles. The first step of intensity measurements and subsequent phase retrieval can use one of many possible phase retrieval methods including the Bronnikov phase retrieval method. The second step of iterative image reconstruction can be performed using the total variation minimization method, the algebraic reconstruction technique method, or any other iterative image reconstruction method.

Iterative reconstruction scheme for phase contrast tomography

A spectral computed tomography (CT) ordered subsets (OS) reconstruction method using two sets of projection data having a different number of views is provided. The method includes obtaining a first set of projection data that includes single-energy CT data for a first number of views, obtaining a second set of projection data that includes spectral CT data for a second number of views, wherein the first and second numbers are integers and the second number is smaller than the first number, partitioning the first set of projection data into N equal-sized subsets of projection data, including the second set of projection data in each of the N subsets of projection data to generate N expanded subsets of projection data, and performing OS reconstruction with the N expanded subsets of projection data to generate a reconstructed CT image.

Ordered subset scheme in spectral CT

In the CT imaging system, a one-direction condition improves noise uniformity in the denoised images and avoids over smoothing in the low noise regions in an image, assuming that the image originally has an unequal noise distribution. On the other hand, the discrete gradients of total variation (DTV) minimization results in an improved edge preserving effects in comparison to the conventional total variation (TV) minimization. Using DTV, the pixel values on an edge will not be substantially affected after a certain denoising treatment. The difference between the DTV minimization and the conventional TV minimization is substantially negligible for strong edges while the DTV minimization substantially improves in preserving the weak edges. DTV keeps the original values while TV slightly smoothes the values for the pixel near the bottom and top of edges.

Method and system for image denoising using discrete total variation (TV) minimization with one-direction condition

Noise map for CT images have been estimated by generalizing the prior art even-and-odd views approach. One example is to estimate a noise map from images reconstructed from three sets of independent views. A second example is to estimate a noise map from images reconstructed by using two sets of correlated views. A third example is to estimate a noise map from noise map from two images reconstructed from two sets of independent views while the number of views in each set is unequal. Physical phantom data were employed to validate our proposed noise map estimation methods. In comparison to the existing method, our alternative methods yield reasonably accurate noise map estimation.

Alternative noise map estimation methods for 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

The image generation method and system generates an image using a predetermined iterative reconstruction technique from cone beam data that has been expanded by adding additional data, and an instance of the iteration process is weighted according to a corresponding validation weight during the reconstruction. Optionally, an instance of the iteration process is weighted according to a combination of weights during the reconstruction. The predetermined combination of the weights includes axial weights based upon a validity value of the expanded data and statistical weights.

Method and system for expanding axial coverage in iterative reconstruction in computer tomography (CT)

The image reconstruction method and system reconstructs a multi-scale image using a set of predetermined nested 3D grids, and a zoomed image is reconstructed without losing details. The multiple steps uses a decreasing grid size to reconstruct an ultimately zoomed image in region of interest without degrading image quality.

Method and system for supplementing detail image in successive multi-scale reconstruction

The image generation method and system generates an image using a predetermined iterative reconstruction technique, and an instance of the iteration process is weighted according to a predetermined combination of weights during the reconstruction. The predetermined combination of the weights includes weights based upon a predetermined noise model and a predetermined window function to improve image quality.

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Vernon Hills, IL, United States of America

Daxin Shi