Canon Medical Systems Corporation

University of California

Toshiba Medical Systems Corporation

Wenyuan Qi

Evren Asma

Yi Qiang

Jeffrey Kolthammer

Peng Peng

Xiaoli Li

Chung Chan

Yujie Lu

Tiantian Li

Zhaoheng Xie

Zhou Yu

Wenli Wang

Kent C Burr

Manabu Teshigawara

Liang Cai

Karthikayan Balakrishnan

Ryo Okuda

Ting Xia

Xiaochun Lai

Qiang Yi

A method, apparatus, and non-transitory computer-readable storage medium for image denoising whereby a deep image prior (DIP) neural network is trained to produce a denoised image by inputting the second medical image to the DIP neural network and combining a converging noise and an output of the DIP network during the training such that the converging noise combined with the output of the DIP network approximates the first medical image at the end of the training, wherein the output of the DIP network represents the denoised image.

Apparatus and methods for unsupervised image denoising using double over-parameterization

A method, system, and computer readable medium to perform nuclear medicine scatter correction estimation, sinogram estimation and image reconstruction from emission and attenuation correction data using deep convolutional neural networks. In one embodiment, a Deep Convolutional Neural network (DCNN) is used, although multiple neural networks can be used (e.g., for angle-specific processing). In one embodiment, a scatter sinogram is directly estimated using a DCNN from emission and attenuation correction data. In another embodiment a DCNN is used to estimate a scatter-corrected image and then the scatter sinogram is computed by a forward projection.

Scatter estimation for PET from image-based convolutional neural network

A PET scanner includes gamma-ray detector rings that form a bore through which an imaging subject is translated, a length of the bore defining an axial length of the PET scanner, the gamma-ray detector rings being movable along the axial length, the gamma-ray detector rings including gamma-ray detector modules therein, and processing circuitry configured to receive PET data associated with a plurality of transaxial slices of the imaging subject, the PET data including a first set of spatial information and timing information corresponding to a first data acquisition period for the gamma-ray detector modules in a first axial position and a second set of spatial information and timing information corresponding to a second data acquisition period for the gamma-ray detector modules in a second axial position, and reconstruct a PET image based on the first set of spatial and timing information and the second set of spatial and timing information.

Pet reconstruction for adjustable pet system

An apparatus for reconstructing a positron emission tomography (PET) image, comprising processing circuitry configured to extract, from raw data obtained from a PET scanner, energy data and timing data associated with a plurality of annihilation events, the extracted energy data and the extracted timing data for each annihilation event corresponding to interactions between each of a pair of gamma rays generated by each annihilation event and one or more gamma ray detectors of the PET scanner, classify each annihilation event based on respective extracted energy data and respective extracted timing data, determine, for each annihilation event and based on a calculated timing resolution of the annihilation event, a width of a time-of-flight kernel, and reconstruct, by processing circuitry, the PET image based on the obtained raw data from the PET scanner and the determined width of the time-of-flight kernel associated with each annihilation event.

Methods and systems for reconstructing a positron emission tomography image

A method is provided for count loss correction based on detector dead time in a radiation diagnosis apparatus. The method includes acquiring scan data from a scan of a patient performed using the radiation diagnosis apparatus; determining, from the acquired scan data, a first count rate occurring in a first energy window spanning a first energy range; determining, from the acquired scan data, a second count rate occurring in a second window spanning a second energy range, the second energy range being different from the first energy range; calculating a particular count loss correction factor based on the determined first count rate and the determined second count rate; and reconstructing an image based on the acquired scan data and the calculated particular count loss correction factor.

Method to detect energy-dependent count loss

Upon receiving list-mode data by detecting radiation emitted from a radiation source positioned with a field of view of a medical imaging scanner, each photon included in the list-mode data can be classified according to one or more interaction properties, such as energy or number of crystals interacted with. Grouped pairs of photons can be generated based on the classifying, and a corresponding interaction-property-specific correction kernel (e.g., a corresponding interaction-property-specific point spread function correction kernel) can be selected for each group. Corresponding interaction-property-specific correction kernels can then be utilized to generate higher quality images.

Event property-dependent point spread function modeling and image reconstruction for PET

A method is provided for determining a scatter fraction for a radiation diagnosis apparatus. The method includes acquiring an energy spectrum from list mode data obtained from a scan performed using the radiation diagnosis apparatus; determining, from the acquired list mode data, a first number of events occurring in a first energy window spanning a first energy range; determining, from the acquired list mode data, a second number of events occurring in a second window, the second energy window spanning a second energy range different from the first energy range; calculating a singles scatter fraction based on the determined first number of events and the determined second number of events; and reconstructing an image based on the acquired list mode data and the calculated singles scatter fraction.

Energy-based scatter fraction estimation and scatter correction

Existing, low quality images can be restored using reconstruction or a combination of post-reconstruction techniques to generate a real patient phantom. The real patient phantom (RPP) can then be simulated in Monte Carlo simulations of a higher performance system and a lower performance system. Alternatively, the RPP can be simulated in the higher performance system, and a real scan can be performed by an existing, lower performance system. The higher performance system can be differentiated from the lower performance system in a variety of ways, including a higher resolution time of flight measurement capability, a greater sensitivity, smaller detector crystals, or less scattering. A neural network can be trained using the images produce by the higher performance system as the target, and the images produced by the lower performance system as the input. After training, the trained neural network can be used to output input images taken in a lower performance system with higher performance system characteristics.

Neural network for improved performance of medical imaging systems

A guided pairing method includes generating a singles list by detecting a plurality of singles at a plurality of detector elements in a detector array, the plurality of singles falling within a plurality of detection windows; for each detection window of the plurality of detection windows in the singles list having exactly two singles of the plurality of singles, determining the line of responses (LORs) for each of the two singles of the plurality of singles; for each detection window of the plurality of detection windows in the singles list having more than two singles of the plurality of singles, determining all coincidences possible based on the more than two singles; generating a weight for said each coincidence of the coincidences based on the determined LORs for said each of the two singles of the plurality of singles; and pairing the more than two singles based on the generated weight for said each coincidence of the coincidences.

Method and apparatus for guided pairing of multi-coincidences for time of flight positron emission tomography

A method of normalizing detector elements in an imaging system is described herein. The method includes a line source that is easier to handle for a user, and decouples the normalization of the detector elements into a transaxial domain and an axial domain in order to isolate errors due to positioning of the line source. Additional simulations are performed to augment the real scanner normalization. A simulation of a simulated line source closely matching the real line source can be performed to isolate errors due to physical properties of the crystals and position of the crystals in the system, wherein the simulated detector crystals are otherwise modeled uniformly. A simulation of a simulated cylinder source can be performed to determine errors due to other effects stemming from gaps between the detector crystals.

Method and system for PET detector efficiency normalization

The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.

Total SPECT scatter estimation and correction using radiative transfer equation

Multiple interactions, such as Compton scattering, inside a PET detector are used to predict an incident photon's direction for identifying true coincidence events versus scatter/random coincidence events by creating a cone shaped shell projection defining a range of possible flight directions for the incident photon. The disclosed techniques can be used as prior information to improve the image reconstruction process. The disclosed techniques can be implemented in a LYSO/SiPM-based layer stacked detector, which can precisely register multiple interactions' 3D position.

Scatter and random coincidence rejection

The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

Method and apparatus for scatter estimation in positron emission tomography

A method of imaging includes obtaining a plurality of dynamic sinograms, each dynamic sinogram representing detection events of gamma rays at a plurality of detector elements, summing the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays; reconstructing, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms, and generating, using the plurality of dynamic sinograms and the activity map, at least one parametric image.

Growing community of inventors

Vernon Hills, IL, United States of America

Wenyuan Qi