Patent Description:
The article "<NPL>) discloses a training of a convolutional neural network to generate patient-specific transmission data from T1 weighted MRI for attenuation correction in PET. Nuclear imaging systems can employ various technologies to capture images. For example, some nuclear imaging systems employ positron emission tomography (PET) to capture images. PET is a nuclear medicine imaging technique that produces tomographic images representing the distribution of positron emitting isotopes within a body. Some nuclear imaging systems employ computed tomography (CT), for example, as a co-modality. CT is an imaging technique that uses x-rays to produce anatomical images. Magnetic Resonance Imaging (MRI) is an imaging technique that uses magnetic fields and radio waves to generate anatomical and functional images. Some nuclear imaging systems combine images from PET and CT scanners during an image fusion process to produce images that show information from both a PET scan and a CT scan (e.g., PET/CT systems). Similarly, some nuclear imaging systems combine images from PET and MRI scanners to produce images that show information from both a PET scan and an MRI scan.

Typically, these nuclear imaging systems capture measurement data, and process the captured measurement data using mathematical algorithms to reconstruct medical images. For example, reconstruction can be based on the models that can include analytic or iterative algorithms or, more recently, deep learning algorithms. These conventional models, however, can have several drawbacks. Many of these nuclear imaging systems, for example, have high memory and computational requirements to reconstruct a medical image. Moreover, many image formation processes employed by at least some of these systems rely on approximations to compensate for detection loss. The approximations, however, can cause inaccurate and lower quality medical images. As such, there are opportunities to address deficiencies in nuclear imaging systems.

Systems and methods for generating attenuation maps based on background radiation to reconstruct medical images are disclosed.

The following will be apparent from elements of the figures, which are provided for illustrative purposes and are not necessarily drawn to scale.

This description of the exemplary embodiments is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description.

The exemplary embodiments are described with respect to the claimed systems as well as with respect to the claimed methods. Furthermore, the exemplary embodiments are described with respect to methods and systems for image reconstruction, as well as with respect to methods and systems for training functions used for image reconstruction. Features, advantages, or alternative embodiments herein can be assigned to the other claimed objects and vice versa. For example, claims for the providing systems can be improved with features described or claimed in the context of the methods, and vice versa. In addition, the functional features of described or claimed methods are embodied by objective units of a providing system. Similarly, claims for methods and systems for training image reconstruction functions can be improved with features described or claimed in context of the methods and systems for image reconstruction, and vice versa.

Various embodiments of the present disclosure can employ machine learning methods or processes to provide clinical information from nuclear imaging systems. For example, the embodiments can employ machine learning methods or processes to reconstruct images based on captured measurement data, and provide the reconstructed images for clinical diagnosis. In some embodiments, machine learning methods or processes are trained, to improve the reconstruction of images.

Quantitative Positron Emission Tomography (PET) generally requires an attenuation map to calculate the number of photons that have either been lost for a sinogram bin (i.e., attenuation correction) or wrongly assigned to another sinogram bin (i.e., scatter correction). In systems that combine PET and computed tomography (CT), linear attenuation coefficients may be generated based on the CT images, and used to determine PET corrections. For a system which combines PET and magnetic resonance (MR), this is not possible and hence other methods need to be applied in order to correct the PET data for scatter and attenuation. Nonetheless, accurate attenuation/scatter correction is a fundamental requirement for state-of-the-art PET and PET/MR systems. These corrections allow for quantitative and artifact-free PET images that can be used for clinical diagnosis.

In some embodiments, background radiation generated by PET crystals of a PET/MR imaging system is detected. PET crystals can be located on a gantry of the PET/MR imaging system, and can include, for example, lutetium oxyorthosilicate scintillator (LSO) crystals or lutetium yttrium orthosilicate (LYSO) crystals. Further, a machine learning model, such as a neural network, can be trained to generate attenuation maps based on the detected background radiation and corresponding MR images. In some examples, during a PET/MR workflow, only a short MR sequence (e.g., a high-resolution Dixon VIBE protocol) is acquired and used as an input for the machine learning model to generate a transmission-based attenuation map. In some embodiments, the machine learning model can be trained based on radiation detected from PET measurement data and corresponding MR measurement data captured from a PET/MR system using volunteer subjects. In some embodiments, the machine learning model is trained and/or updated based on radiation detected from PET measurement data and corresponding MR measurement data captured for a patient. Once the machine learning model is trained, the PET/MR system can be employed for clinical imaging.

Among other advantages, the embodiments allow for the acquisition of ground-truth image data based on machine learning models trained on attenuation maps generated based on the detection of background radiation, and MR measurement data. For example, the embodiments may allow for crystal (e.g., LSO) background transmission scans and reconstruction using an MR prior image (e.g. Dixon scan) to improve low count rates and compute attenuation maps. As such, the machine learning model can be trained without providing a radiation dose to a subject. Moreover, in some examples, MR scan and deep learning neural network are employed to generate a transmission image from the MR as an attenuation map. In addition, in some examples, the embodiments allow a patient to be scanned with a PET/MR imaging system rather than a PET/CT imaging system. The patient may feel more comfortable with the PET/MR imaging system as whole-body MR scans can be performed with the patient's arms down, while CT scans may require the patient to hold their arms up. Moreover, although crystals in PET scanners are usually either made from LSO or LYSO, the embodiments can also be used any suitable PET crystals, independent of the crystal material, or with an independent source of radiation.

In some embodiments, a scanning device, such as a PET/MR scanner, provides PET measurement data, such as three-dimensional (3D) time-of-flight sinograms (e.g., measurement data). The PET/MR scanner can include crystal material, such as LSO or LYSO crystals, that, due to radioactive decay, emits gamma rays. For example, the PET/MR scanner can include crystal material along a gantry. The emitted gamma rays can be captured by other crystals, such as crystals along the gantry located across the emitting crystals, and detected by the PET/MR scanner. The PET/MR scanner can also detect gamma rays emitted from a patient being scanned. For example, the patient can be injected with radioactive material, where the radioactive material emits gamma rays that are captured by the crystals, and detected by the PET/MR scanner. The PET/MR scanner can provide PET measurement data to a computing device based on the detected gamma rays.

The PET/MR scanner can also capture MR images, and provide corresponding MR measurement data to the computing device. The computing device can reconstruct the MR images based on the MR measurement data, and provide the MR images to a trained neural network, such as a trained deep learning neural network. The trained neural network can generate an attenuation map (e.g., a predicted attenuation map) based on the reconstructed MR image. Further, the computing device can generate an image volume (e.g., a <NUM> dimensional image) based on the generated attenuation map and the PET measurement data.

In some embodiments, the neural network is trained based on attenuation maps generated from PET measurement data, and reconstructed MR images generated from MR measurement data, where the PET measurement data and MR measurement data are received from the PET/MR scanner for one or more volunteers. In some examples, the volunteers are not injected with radioactive material. In some examples, the volunteers are injected with radioactive material.

As an example, the PET/MR scanner scans a volunteer who has not been injected with radioactive material. The PET/MR scanner generates PET measurement data based on PET scans of the volunteer (e.g., captured gamma rays as the PET/MR scanner scans the volunteer), and further generates MR measurement data (e.g., an MRI sequence using high-resolution Dixon volume-interpolated breathhold examination (VIBE)) based on MR imaging scans of the volunteer. Because the volunteer was not injected with radioactive material, the PET images are generated based gamma rays captured from "background" radiation. The computing device receives the MR measurement data, and reconstructs an MR image based on the MR measurement data using any suitable method as known in the art.

Further, the computing device generates the attenuation maps based on the PET measurement data and a "background" radiation of the PET/MR scanner. To determine the "background" radiation, the PET/MR scanner is operated with no patient (e.g., no patient on a patient table within the PET/MR scanner's field of view, blank scan), and the PET/MR scanner generates PET measurement data based on gamma rays generated by the crystal material of the PET/MR scanner itself. The computing device receives the PET measurement data identifying the captured "background" radiation, and stores the PET measurement data in memory. The PET measurement data can be captured for a period of time and aggregated in memory, and the computing device can determine a background level of radiation based on the aggregated PET measurement data. For example, the computing device can determine an average level of radiation as captured by various portions of crystal material along a gantry of the PET/MR scanner.

The computing device can then generate the attenuation maps based on the received PET measurement data and the determined background levels of radiation. The background level of radiation can be used as a "reference level" from which the attenuation correction as identified by the attenuation map is measured from. For example, the computing device can generate the attenuation maps based on a difference between the PET measurement data obtained for each of the volunteers and the PET measurement data identifying the background level of radiation. In some examples, the computing device generates the attenuation maps based on the PET measurement data obtained for each of the volunteers, the corresponding reconstructed MR images, and the PET measurement data identifying the background level of radiation. The reconstructed MR images can provide information about the shape of a person's body as well as tissue boundaries inside the patient, for example. As such, the embodiments may employ crystal background transmission scans and reconstruction using an MR prior image (e.g., Dixon scan) to improve low count rates and compute attenuation maps. In some examples, the embodiments employ a deep learning neural network to generate an attenuation map from an MR scan.

The attenuation correction for PET is not the only application for this approach, however. A similar problem can present itself during radiotherapy planning when using MR data. By adopting a final energy level, the described pipeline as well as the acquired data could be used for MR based radiotherapy planning as well.

In some examples, the computing device scales the generated attenuation maps to a corresponding energy window. For example the energy window may be defined by a lower energy value, and an upper energy value. The energy window is used to distinguish events from different processes (e.g., PET emission events from <NUM> to <NUM> keV) and transmission events (e.g., transmission events between a range of electronvolts, such as between <NUM> and <NUM> keV).

The computing device can then train the neural network based on the reconstructed MR images and corresponding attenuation maps. For example, the computing device may store a threshold amount of reconstructed MR images and corresponding attenuation maps generated for one or more volunteers within memory. Once the threshold amount of reconstructed MR images and corresponding attenuation maps is obtained, the computing device can retrieve the stored reconstructed MR images and corresponding attenuation maps from the memory, and train the neural network with the reconstructed MR images and corresponding attenuation maps. For training, the MR images can be labelled as input, and the corresponding attenuation maps can be labelled as output, for example. The neural network is trained to predict an attenuation map given a reconstructed MR image. For example, offline collection and training of the neural network may be based on pairs of MR and attenuation maps generated from background crystal transmissions (e.g., LSO or LYSO crystal transmissions). Once trained, online (e.g., with a real patient) prediction of attenuation maps from measured MR images can be based on the output from the trained neural network.

In some examples, multiple neural networks are trained based on one or more attributes of patients. For example, the reconstructed MR images and corresponding attenuation maps may be categorized according to one or more of a person's age, weight, height, and medical condition. As an example, a first neural network can be trained based on reconstructed MR images and corresponding attenuation maps generated for persons under the age of <NUM>. In addition, a second neural network can be trained based on reconstructed MR images and corresponding attenuation maps generated for persons between the ages of <NUM> and <NUM>, and a third neural network can be trained based on reconstructed MR images and corresponding attenuation maps generated for persons above the age of <NUM>. During diagnosis of a patient, the appropriate neural network may be employed by the computing device to generate image volumes, as described herein. In addition, the additional parameters of age could be used as additional input parameters to one large network from a single combined training batch.

In some examples, the computing device validates the trained neural network during a validation period. For example, the computing device can apply the neural network to MR measurement data obtained from a validation test data set, generate a reconstructed MR image, and apply the trained neural network to the reconstructed MR image to generate a predicted attenuation map. The computing device can further determine a loss between the predicted attenuation map and an expected attenuation map (e.g., the expected attenuation map could have been generated based on prior art processes). Training of the neural network can be complete with the loss has been minimized to at least a threshold.

Once trained, the computing device can apply the neural network to reconstructed MR images to generate attenuation maps (e.g., predicted attenuation maps). For example, the PET/MR scanner can capture MR scans and PET scans of a patient (e.g., a patient injected with radioactive material), and can transmit corresponding MR measurement data and PET measurement data to the computing device. The computing device reconstructs an MR image based on the MR measurement data, and further applies the trained neural network to the reconstructed MR image to generate an attenuation map. The computing device the reconstructs an image volume based on the attenuation map and the reconstructed MR image. The computing device may display the image volume to a physician for evaluation and diagnosis, for example.

In some embodiments, a computing device generates an attenuation map for performing the attenuation correction of acquired PET measurement data. The computing device generates the attenuation map based on synthetic transmission images (e.g., synthetic <NUM> keV transmission images) captured from a PET system, such as a PET/MR system or PET/CT system, and background radiation determined based on blank scans.

In some examples, the computing device generates the synthetic transmission images using a trained neural network, such as a deep learning neural network. In some examples, the neural network is trained using co-registered, previously acquired MR and transmission images. In some examples, the synthetic transmission images are generated based on the background radiation generated by PET crystals of the PET system. In some examples, the PET crystals are LSO crystals or LYSO crystals. In some examples, the computing device reconstructs the background radiation based transmission images using corresponding MR images.

In some examples, the generated attenuation maps are applied to acquired PET measurement data (e.g., PET emission data) to perform attenuation correction of the acquired PET measurement data, and to generate an attenuation corrected PET image. In some examples, the PET measurement data is acquired using the PET modality of a combined PET/MR system that allows acquisition of PET and MR measurement data. In some examples, the PET data is acquired using the PET modality of a combined PET/CT system that allows acquisition of PET and CT measurement data.

<FIG> illustrates one embodiment of a nuclear imaging system <NUM>. As illustrated, nuclear imaging system <NUM> includes image scanning system <NUM> and image reconstruction system <NUM>. Image scanning system <NUM> in this example is a PET/MR scanner, but in other examples, can be a PET/CT scanner (e.g., with CT as the corresponding co-modality instead of MR). Image scanning system <NUM> can capture MR images (e.g., of a person), and generate MR measurement data <NUM> based on the MR scans. Image scanning system <NUM> can also capture PET images (e.g., of the person), and generate PET measurement data <NUM> (e.g., sinogram data) based on the captured PET images. The PET measurement data <NUM> can represent anything imaged in the scanner's field-of-view (FOV) containing positron emitting isotopes. For example, the PET measurement data <NUM> can represent whole-body image scans, such as image scans from a patient's head to thigh. Image scanning system <NUM> can transmit the MR measurement data <NUM> and the PET measurement data <NUM> to image reconstruction system <NUM>.

In some examples, all or parts of image reconstruction system <NUM> are implemented in hardware, such as in one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more state machines, one or more computing devices, digital circuitry, or any other suitable circuitry. In some examples, parts or all of image reconstruction system <NUM> can be implemented in software as executable instructions such that, when executed by one or more processors, cause the one or more processors to perform respective functions as described herein. The instructions can be stored in a non-transitory, computer-readable storage medium, for example.

For example, <FIG> illustrates a computing device <NUM> that can be employed by the image reconstruction system <NUM>. Computing device <NUM> can implement, for example, one or more of the functions of image reconstruction system <NUM> described herein.

Computing device <NUM> can include one or more processors <NUM>, working memory <NUM>, one or more input/output devices <NUM>, instruction memory <NUM>, a transceiver <NUM>, one or more communication ports <NUM>, and a display <NUM>, all operatively coupled to one or more data buses <NUM>. Data buses <NUM> allow for communication among the various devices. Data buses <NUM> can include wired, or wireless, communication channels.

Processors <NUM> can include one or more distinct processors, each having one or more cores. Each of the distinct processors can have the same or different structure. Processors <NUM> can include one or more central processing units (CPUs), one or more graphics processing units (GPUs), application specific integrated circuits (ASICs), digital signal processors (DSPs), and the like.

Processors <NUM> can be configured to perform a certain function or operation by executing code, stored on instruction memory <NUM>, embodying the function or operation. For example, processors <NUM> can be configured to perform one or more of any function, method, or operation disclosed herein.

Instruction memory <NUM> can store instructions that can be accessed (e.g., read) and executed by processors <NUM>. For example, instruction memory <NUM> can be a non-transitory, computer-readable storage medium such as a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory, a removable disk, CD-ROM, any non-volatile memory, or any other suitable memory. For example, instruction memory <NUM> can store instructions that, when executed by one or more processors <NUM>, cause one or more processors <NUM> to perform one or more of the functions of image reconstruction system <NUM>, such as one or more of the encoding segment <NUM> functions, one or more of the Radon inversion layer <NUM> functions, or one or more of the refinement and scaling segment <NUM> functions.

Processors <NUM> can store data to, and read data from, working memory <NUM>. For example, processors <NUM> can store a working set of instructions to working memory <NUM>, such as instructions loaded from instruction memory <NUM>. Processors <NUM> can also use working memory <NUM> to store dynamic data created during the operation of computing device <NUM>. Working memory <NUM> can be a random access memory (RAM) such as a static random access memory (SRAM) or dynamic random access memory (DRAM), or any other suitable memory.

Input-output devices <NUM> can include any suitable device that allows for data input or output. For example, input-output devices <NUM> can include one or more of a keyboard, a touchpad, a mouse, a stylus, a touchscreen, a physical button, a speaker, a microphone, or any other suitable input or output device.

Communication port(s) <NUM> can include, for example, a serial port such as a universal asynchronous receiver/transmitter (UART) connection, a Universal Serial Bus (USB) connection, or any other suitable communication port or connection. In some examples, communication port(s) <NUM> allows for the programming of executable instructions in instruction memory <NUM>. In some examples, communication port(s) <NUM> allow for the transfer (e.g., uploading or downloading) of data, such as MRI measurement data <NUM> and attenuation maps <NUM>.

Display <NUM> can display user interface <NUM>. User interfaces <NUM> can enable user interaction with computing device <NUM>. For example, user interface <NUM> can be a user interface for an application that allows for the viewing of final image volumes <NUM>. In some examples, a user can interact with user interface <NUM> by engaging input-output devices <NUM>. In some examples, display <NUM> can be a touchscreen, where user interface <NUM> is displayed on the touchscreen.

Transceiver <NUM> allows for communication with a network, such as a Wi-Fi network, an Ethernet network, a cellular network, or any other suitable communication network. For example, if operating in a cellular network, transceiver <NUM> is configured to allow communications with the cellular network. Processor(s) <NUM> is operable to receive data from, or send data to, a network via transceiver <NUM>.

Referring back to <FIG>, image reconstruction system <NUM> includes neural network engine <NUM>, MR image reconstruction engine <NUM>, and image volume reconstruction engine <NUM>. MR image reconstruction engine <NUM> operates on MR measurement data <NUM> (e.g., MR raw data) to generate reconstructed MR image <NUM>. MR image reconstruction engine <NUM> can generate reconstructed MR images <NUM> based on corresponding MR measurement data <NUM> using any suitable method known in the art. Further, neural network engine <NUM> receives reconstructed MR images <NUM>, and applies a trained neural network, such as a trained deep learning neural network as described herein, to the reconstructed MR images <NUM> to generate attenuation maps <NUM>. For example, the neural network could have been trained based on reconstructed MR images and measured attenuation maps (e.g., ground truth data) during a training period, and further validated during a validation period (e.g., based on test data comprising MR images). The generated attenuation map <NUM> can identify density differences of a patient's body that can be used to correct for the absorption of photons emitted from radioactive decay (e.g., radioactive decay of crystal material of image scanning system <NUM>).

Image volume reconstruction engine <NUM> obtains PET measurement data <NUM> (e.g., PET raw data) and the generated attenuation map <NUM>, and reconstructs a final image volume <NUM>. For example, image volume reconstruction engine <NUM> applies the attenuation map <NUM> to PET measurement data <NUM> to generate the final image volume <NUM>. Final image volume <NUM> can include image data that can be provided for display and analysis, for example.

<FIG> and <FIG> illustrate exemplary portions of image scanning system <NUM> including a gantry <NUM> and a patient table <NUM> located within the gantry <NUM>. Gantry <NUM> may include crystal material <NUM>, <NUM>, such as LSO or LYSO crystals. Radioactive decay of crystal material <NUM> can cause gamma ray emissions, which can be detected by other crystal material <NUM>. While <FIG> illustrates a patient <NUM> located on patient table <NUM>, <FIG> includes no patient.

As described herein, image reconstruction system <NUM> can determine background levels of radiation generated by crystals <NUM> when no patient is located on patient table <NUM>, as illustrated in <FIG>, based on gamma emissions captured by crystals <NUM>. Further, to train a neural network, such as the neural network of neural network engine <NUM>, image scanning system <NUM> captures MR scans and corresponding PET scans with patient <NUM> located on patient table <NUM>, as illustrated in <FIG>. The patient <NUM> has no injected radioactivity, and thus detected activity (e.g., detected counts) is based on radioactive decay of crystals <NUM>, <NUM>. Image scanning system <NUM> can provide MR measurement data <NUM> and PET measurement data <NUM> to image reconstruction system <NUM> based on the MR scans and PET scans, respectively.

Image reconstruction system <NUM> can reconstruct MR images based on the MR measurement data <NUM>, and generate attenuation maps, such as attenuation maps <NUM>, based on the reconstructed MR images and the detected background levels of radiation. Image reconstruction system <NUM> can train a neural network, such as the neural network of neural network engine <NUM>, based on matching pairs of the attenuation maps and reconstructed MR images.

For example, <FIG> illustrates image reconstruction system <NUM> receiving MR measurement data <NUM> and PET measurement data <NUM> from image scanning system <NUM>. Computing device <NUM> can reconstruct MR images <NUM> based on the received MR measurement data <NUM> according to any suitable method, and can store reconstructed MR images <NUM> in database <NUM>. Database <NUM> can be a local or remote storage device, such as a cloud-based server, a disk (e.g., a hard disk), a memory device on another application server, a networked computer, or any other suitable data storage device.

Further, image reconstruction system <NUM> can receive PET measurement data <NUM> when no patient is within image scanning system <NUM> (e.g., blank scan as illustrated in <FIG>), and can store PET measurement data without patient <NUM> in database <NUM>. Computing device <NUM> can determine a background level of radiation based on PET measurement data without patient <NUM>. Further, image reconstruction system <NUM> can also receive PET measurement data <NUM> when a patient is within image scanning system <NUM> (e.g., as illustrated in <FIG>), and store PET measurement data with patient <NUM> in database <NUM>.

Computing device <NUM> can generate attenuation maps, such as attenuation maps <NUM>, based on PER measurement data with patient <NUM> and a background level of radiation as identified by PET measurement data without patient <NUM>. For example, computing device <NUM> can generate attenuation correction data <NUM> that identifies and characterizes the attenuation maps, and can store the attenuation correction data <NUM> within database <NUM>. In some examples, computing device <NUM> generates the attenuation maps based on PER measurement data with patient <NUM>, the background level of radiation as identified by PET measurement data without patient <NUM>, and reconstructed MR images <NUM>. The MR images <NUM> can provide information about a patient's body as well as tissue boundaries within the patient, for example. In some examples, computing device <NUM> scales the attenuation maps to a corresponding energy window identified by energy window data <NUM>. The energy window may identify a range of electronvolts, such as <NUM> - <NUM> kev. For example, and based on energy window data <NUM>, attenuation maps may be scaled to an energy level, such as <NUM> kev. Computing device <NUM> can train the neural network based on the generated attenuation maps and corresponding MR images <NUM>.

<FIG> illustrates the generation of a final image volume <NUM> based on a trained neural network. The trained neural network can generate a predicted attenuation map based on an MR image. As illustrated, MR image reconstruction engine <NUM> receives MR measurement data <NUM>, and generates an MR image <NUM> according to any suitable method. Neural network engine <NUM> receives the MR image <NUM> from MR image reconstruction engine <NUM>, and applies a trained neural network to MR image <NUM> to generate an attenuation map <NUM>. Image volume reconstruction engine <NUM> receives PET measurement data <NUM> from <NUM>, where the PET measurement data <NUM> corresponds to the received MR measurement data <NUM> (e.g., PET measurement data <NUM> and MR measurement data <NUM> are based on simultaneous PET and MR scans, respectively, of a same person). Image volume reconstruction engine <NUM> further receives the generated attenuation map <NUM>, and adjusts (e.g., corrects) PET measurement data <NUM> based on attenuation map <NUM> to generate the final image volume <NUM>.

<FIG> is a flowchart of an example method <NUM> to train a neural network. The method can be performed by one or more computing devices, such as computing device <NUM>. Beginning at step <NUM>, first PET measurement data is received from an image scanning system. No volunteer (e.g., patient) is located within the image scanning system. For example, image reconstruction system <NUM> can receive the first PET measurement data, such as PET measurement data <NUM>, from image scanning system <NUM>. Image reconstruction system <NUM> can determine a background radiation level of the image scanning system based on the first PET measurement data. At step <NUM>, MR measurement data and corresponding second PET measurement data is received from the image scanning system. The MR measurement data and corresponding second PET measurement data are captured with a volunteer located within the image scanning system. For example, image reconstruction system <NUM> can receive MR measurement data <NUM> and corresponding PET measurement data <NUM> from image scanning system <NUM> based on MR scans and PET scans performed for the volunteer.

Further, at step <NUM>, an attenuation correction is determined based on the first PET measurement data (e.g., the background radiation level) and the second PET measurement data. An attenuation map can identify the attenuation correction. For example, image reconstruction system <NUM> can generate an attenuation map <NUM> based on PET measurement data <NUM> and a previously determined background level of radiation of image scanning system <NUM>, such as a background level identified by PET measurement data without patient <NUM> stored in database <NUM>. At step <NUM>, a neural network is trained based on the attenuation correction and the received MR measurement data. For example, image reconstruction system <NUM> can train a neural network of neural network engine <NUM> based on generated attenuation maps <NUM> and corresponding reconstructed MR images <NUM>. In some examples, the trained neural network is stored in a database, such as database <NUM>.

<FIG> is a flowchart of an example method <NUM> to generate an image volume, and can be carried out by one or more computing device such as, for example, computing device <NUM>. Beginning at step <NUM>, MR measurement data and PET measurement data (e.g., sinogram data) is received from an image scanning system. The MR measurement data and PET measurement data correspond to MR and PET scans of a patient. For example, image reconstruction system <NUM> can receive MR measurement data <NUM> and PET measurement data <NUM> from image scanning system <NUM> for a patient. At step <NUM>, a trained neural network is applied to the MR measurement data to generate an attenuation map. The neural network could have been trained in accordance with method <NUM>. As an example, neural network engine <NUM> can apply a trained neural network to reconstructed MR images <NUM> to generate attenuation map <NUM>.

Proceeding to step <NUM>, image volume data is generated based on the attenuation map and the received PET measurement data. The image volume data can identify and characterize an image volume (e.g., a 3D image volume). For example, image reconstruction system <NUM> can generate final image volume <NUM> based on attenuation maps <NUM> and corresponding PET measurement data <NUM>. At step <NUM>, the final image volume is stored in a database. For example, image reconstruction system <NUM> can store the generated final image volume <NUM> in database <NUM>.

The apparatuses and processes are not limited to the specific embodiments described herein. In addition, components of each apparatus and each process can be practiced independent and separate from other components and processes described herein.

Claim 1:
A computer-implemented method (<NUM>) comprising:
receiving (<NUM>) first positron emission tomography (PET) measurement data from an image scanning system (<NUM>), the first PET measurement data being obtained with no patient (<NUM>) within the image scanning system (<NUM>);
determining a reference level of radiation of the image scanning system (<NUM>) based on the first PET measurement data;
receiving (<NUM>) magnetic resonance (MR) measurement data and second PET measurement data from the image scanning system (<NUM>), the second PET measurement data being obtained with a patient (<NUM>) within the image scanning system (<NUM>);
generating a first attenuation map based on the first PET measurement data and the second PET measurement data;
training a neural network with the first attenuation map (<NUM>) and the MR measurement data; and
storing the trained neural network in a database (<NUM>).