Abstract: A method for detecting an anomaly related to a medical imaging device includes acquiring data from a plurality of detectors of the medical imaging device, applying the acquired data to a first autoencoder, and detecting, based on outputs from the first autoencoder, an anomaly related to the medical imaging device.

Abstract: 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.

Abstract: A method for signal separation includes obtaining list mode data representing radiation detected during an imaging scan, the list mode data being affected by quasi-periodic motion of an imaging object; dividing the list mode data into first non-overlapping frames of a first frame length, and process the first frames to determine a cardiac cycle length; determining a second frame length, longer than the first frame length, based on the determined cardiac cycle length; re-binning the list mode data into overlapping frames having the second frame length, based on the non-overlapping frames having the first frame length; applying a principal component analysis (PCA) process on the re-binned list mode data having the second frame length to determine a respiratory waveform; determining a cardiac waveform using the determined respiratory waveform; and reconstructing an image based on the list mode data using the determined respiratory waveform and the determined cardiac waveform.

Abstract: A method for performing single gating in a positron emission tomography (PET) system includes: receiving list-mode data acquired by scanning an imaging object using the PET system, the list-mode data being affected by quasi-periodic motion of the imaging object; producing a plurality of vectors based on the received list-mode data; generating a reference vector based on the produced plurality of vectors; selecting, from the produced plurality of vectors, a set of vectors corresponding to a single gate, based on respective differences compared with the generated reference vector; and generating an image of the imaging object based on the selected set of vectors.

Abstract: A method is provided for generating an attenuation map for PET image reconstruction. The method includes training a deep convolutional neural network (DCNN) model by minimizing a loss function between initial input image data and the attenuation map generated by a spectral CT scan as supervised data. Further, the method includes obtaining PET data from a scan of a subject and reconstructing a PET image from the PET data and an attenuation map output from the DCNN. The initial input image data can be from a conventional CT scan with or without beam-hardening correction or the input image data can be from a phantom, a simulation, or a SPECT image.

Abstract: 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.

Abstract: 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.

Abstract: A positron emission tomography (PET) scanner is provided having a plurality of detector subsystems, including processing circuitry to determine, for each detector subsystem of the plurality of detector subsystems, a singles count loss correction factor of the detector subsystem; determine, for each detector subsystem pair of a plurality of pairs of the detector subsystems, a coincidence count loss correction factor for the detector subsystem pair; calculate a scanner coincidence count loss correction factor for the PET scanner based on the coincidence count loss correction factors determined for the plurality of pairs of the detector subsystems; and reconstruct an image based on the calculated scanner count loss correction factor and scan data acquired from a scan of a patient performed using the PET scanner.

Abstract: A dynamic frame reconstruction apparatus and method for medical image processing is disclosed which reduces the computationally expensive reconstruction of images but which retains the accuracy of the image reconstruction. A convolutional neural network is used to cluster the dynamic data into groups of frames, each group sharing similar radiotracer distribution. In one embodiment, groups of frames that have similar reconstruction parameters are determined, and scatter and random estimations are computed once and shared among each of the frames in the same frame group.

Abstract: 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.

Abstract: A method of generating an image, including: receiving, via a monitoring device, time-dependent data corresponding to a patient parameter; obtaining emission data representing radiation detected during a medical imaging scan; identifying time frames during the medical imaging scan to exclude from the obtained emission data, the identified time frames corresponding to a stressed emotional state for the patient based on the patient parameter; modifying the obtained emission data to exclude the emission data corresponding to the time frames corresponding to the stressed emotional state; and generating an emotional-state-corrected image based on the modified emission data excluding the emission data corresponding to the time frames corresponding to the stressed emotional state.

Abstract: 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.

Abstract: A neural network is initially trained to remove errors and is later fine tuned to remove less-effective portions (e.g., kernels) from the initially trained network and replace them with further trained portions (e.g., kernels) trained with data after the initial training.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.