Toshiba Medical Systems Corporation

Canon Medical Systems Corporation

Kabushiki Kaisha Toshiba

Xiaofeng Niu

Wenli Wang

Xiaohui Zhan

Hongwei Ye

Ilmar A Hein

Daniel Gagnon

Evren Asma

Wenyuan Qi

Ting Xia

Ruoqiao Zhang

Ilmar Hein

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 photon counting computed tomography (CT) method including receiving a first forward model including a set of first parameters and a set of second parameters corresponding to a plurality of materials and path lengths by scanning a slab at plural tube voltages and plural tube currents of an X-ray tube; evaluating an image quality of a material decomposition image reconstructed by the set of first parameters and the set of second parameters; and updating at least one second parameters from the set of second parameters if the image quality of the material decomposition image does not satisfy a predetermined threshold, wherein the updating is achieved by updating the at least one second parameter from the set of second parameters to an energy dependent parameter from a constant value.

Iterative method for material decomposition calibration in a full size photon counting computed tomography system

A method and a system for providing calibration for a photon counting CT detector forward model for material decomposition. Various slabs of predetermined material and path lengths are placed in the photon counting CT detector and scanned using one or more stationary X-rays to obtain calibration data and parametrize the forward model.

Material decomposition calibration method and apparatus for a full size photon counting CT system

A method and a system for providing calibration for a photon counting detector forward model for material decomposition. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at each tube voltage setting for each detector pixel.

Two-step material decomposition calibration method for a full size photon counting computed tomography system

A method and a system for a two-step calibration method for the polychromatic semiconductor-based PCD forward counting model, to account for various pixel summing readout modes for imaging at different resolutions. 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. To correct the variation of the detector response due to different PCD sub-pixel summing schemes, the embodiments calibrate forward model parameters based on the various pixel readout modes.

Pixel summing scheme and methods for material decomposition calibration in a full size photon counting computed tomography system

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 for generating crystal efficiency correction factors by performing a normalization calibration based on delayed data. The method and apparatus obtain delayed data from a scan of a patient using a Positron Emission Tomography (PET) scanner, generate a sinogram from the obtained delayed data, determine, using a processing circuit, mean fan and block line of response sensitivities from the generated sinogram, determine, using the processing circuit, mean detector efficiency based on the determined mean fan and block line of response sensitivities, determine, using the processing circuit, an individual crystal efficiency based on the determined mean fan and block line of response sensitivities and the mean detector efficiency for each module, and calculate the crystal efficiency correction factors based on the determined individual crystal efficiency of each module.

Method and system of random-event-based count-rate adaptive normalization for PET detector efficiency

An apparatus for performing a non-local means (NLM) filter is described. The pixel of the NLM-filtered image are weighted averages of pixels from a noisy image, where the weights are a measure of the similarity between patches of the noisy image. The similarity weights can be calculated using a Kullback-Leibler or a Euclidean distance measure. The similarity weights can be based on filtered patches of the noisy image. The similarity weights can be based on a similarity measure between patches of an anatomical image corresponding to the noisy image. The similarity weights can be calculated using a time series of noisy images to increase the statistical sample size of the patches. The similarity weights can be calculated using a weighted sum of channel similarity weights calculated between patches of noisy image that have been band-pass filtered. The NLM-filtered image can also be blended with a non-NLM-filtered image.

Apparatus and method for robust non-local means filtering of tomographic images

A method and system for determining a position of a source including obtaining prompt data and related delayed data from a Positron Emission Tomography (PET) scanner, generating a sinogram from the prompt data, generating crystal efficiency correction factors by performing a normalization calibration based on the obtained delayed data, performing normalization correction on the generated sinogram based on the crystal efficiency correction factors to generate a corrected sinogram, rebinning the corrected sinogram to generate a plurality of two-dimensional sinogram slices, and determining a central axis for each of the plurality of two-dimensional sinogram slices using a center estimation process.

Cylinder source software-positioning method for PET calibration and image quality assurance

A method and apparatus for reducing random events in positron emission tomography (PET) list mode data, the method including the steps of obtaining, for a PET scanner having a given reconstruction field of view (FOV), time-of-flight (TOF) prompt list-mode count data that includes TOF information, the TOF prompt list-mode count data including a plurality of entries; and filtering the obtained prompt list-mode count data by removing those entries in the obtained prompt list-mode count data that represent emission points lying outside a tangential TOF mask to obtain filtered list-mode count data.

Random coincidence reduction in positron emission tomography using tangential time-of-flight mask

A method is provided for estimating scatter in a positron emission tomography (PET) scan at multiple bed positions, the method comprising calculating a first scatter sinogram based on scatter data obtained at a first bed position, calculating a second scatter sinogram based on scatter data obtained at a second bed position, and deriving a third scatter sinogram for a third bed position between the first bed position and the second bed position, wherein the third scatter sinogram is derived from the first scatter sinogram according to a first percentage of overlap of the first bed position with the third bed position, and from the second scatter sinogram according to a second percentage of overlap of the second bed position with the third bed position.

Method and apparatus for estimating scatter in a positron emission tomography scan at multiple bed positions

A method of estimating random events in positron emission tomography list mode data, including obtaining time-of-flight (TOF) list mode count data that includes TOF information; converting the obtained TOF list mode count data into four-dimensional (4D) raw sinogram count data, without using the TOF information, wherein the 4D raw sinogram count data includes random count values; interpolating the 4D raw sinogram count data to generate 4D interpolated sinogram count data; low-pass filtering the 4D interpolated sinogram count data to remove noise; converting the low-pass filtered 4D interpolated sinogram count data into filtered 4D raw sinogram count data; and generating, by a processor, five-dimensional (5D) TOF raw sinogram count data from the filtered 4D raw sinogram count data by effectively applying a TOF mask filter to the filtered 4D raw sinogram count data.

Random estimation in positron emission tomography with tangential time-of-flight mask

A method and apparatus for smoothing random event data obtained from a Positron Emission Tomography (PET) scanner. The method includes obtaining initial random event data u(s, &#x3c6;, t=0)=u(s, &#x3c6;), corresponding to t=0, calculating second-order central differences u, uwith respect to s, &#x3c6;, calculating a gradient u, using u=2(u+u)&#x2212;&#x3bb;(u&#x2212;u), where &#x3bb; is a constant parameter, and updating the random event data using u(s, &#x3c6;, t)=u(s, &#x3c6;, t)+&#x394;t u, where &#x394;t=t&#x2212;t, t=0 in a first iteration, and &#x394;t is greater than 0. The method repeats the steps of calculating the second-order central differences, calculating the gradient, and updating the random event data until a change in u(s, &#x3c6;, t) from a previous iteration is less than a predetermined threshold value.

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Xiaofeng Niu