Patent Description:
CT scanning and PET scanning are well known methods for diagnostic medical imaging. CT scanning employs multiple X-ray images taken in multiple directions to generate a <NUM>-dimensional image or multiple tomographic image "slices. " PET scanning employs a gamma-emitting radiopharmaceutical ingested by a patient or injected into a patient. Multiple gamma ray images are taken in multiple directions to generate a <NUM>-dimensional PET image or multiple slices. CT and PET scanning provide different information. For example, CT scanning generally has higher resolution and is superior for providing structural data such as the structure of bones, organs, etc. PET scanning generally has lower resolution but provides more useful information regarding the functional condition of body tissues and systems such as the cardiovascular system. PET is superior for indicating the presence of soft tissue tumors or decreased blood flow to certain organs or areas of the body, for example. The complementary strengths of CT and PET scanning can be provided simultaneously by performing both methods in a single apparatus and imaging session.

PET scanners with longer axial length are getting increasingly popular for whole body imaging. The longer axial length of a PET scanner reduces the scan time for whole body imaging as well as increase the overall sensitivity of the scanner. A CT scan that covers the same scan range as that of the PET field of view (FOV) is used to generate a robust mu-map for attenuation compensation. In the present disclosure, the term CT and mu-map can be used interchangeably.

On the other hand, for organ specific imaging, e.g. cardiac imaging, the axial length of the long PET scanner or multi-bed PET scans is generally longer than the region of the organ of interest in the patient's body and will cover the organ of interest as well as other regions around it. Currently, when PET scanners with long axial FOV are used, the region corresponding to the entire (e.g. full) axial FOV of the scanner is scanned by CT to generate the mu-maps. For example, in a brain scan using a PET scanner with an axial FOV of <NUM>, because the scanner FOV is longer than the patient's brain the region outside the cranium also gets scanned by CT to generate an accurate mu-map. Hence, for PET scanners with longer axial FOV, the current approach results in having to conduct CT scan regions that are not of clinical interest when the region of interest is smaller than the PET scanner FOV. This means that use of longer FOV PET scanners expose the patients to higher CT radiation dose as well as irradiating organs that are adjacent to the region of interest although those organs are not the subject of the clinical test. A process for obtaining an attenuation map from a truncated transmission scan of an imaged object is known from <CIT>. A method for using non-attenuation corrected PET emission images to compensate for incomplete anatomic images is known from <CIT>. A maximum-a-posteriori algorithm for estimating the missing part of the attenuation map from the PET emission data is known from "<NPL>.

Accordingly, there is a need in the art for improved methods for combined PET and CT scanning. It would be particularly beneficial to provide a method for combined PET and CT scanning that can eliminate the need for CT scanning the regions outside the region of interest.

The present invention is defined by the enclosed claims. According to an exemplary aspect of the present disclosure, a method for minimizing a patient's exposure to CT scan radiation during the mu-map generation process in a PET scan is disclosed. The disclosed method is useful in PET scanners whose axial field of view (FOV) is particularly longer compared to the volume of interest (VOI) in the patient. The method comprises performing a full axial FOV PET scan of a patient and generating a PET data; performing a truncated FOV CT scan of a VOI in the patient's body in which an organ of interest lies; generating a truncated mu-map covering the truncated FOV of the CT scan, wherein the truncated FOV of the CT scan is shorter than the full axial FOV of the PET scan; generating a truncated PET data that corresponds to the truncated mu-map and reconstructing a PET image of the VOI using the truncated PET data; and generating a mu-map for the full axial FOV of the PET scan by extending the truncated mu-map generated from the truncated FOV CT scan by estimating the missing mu-map data using the PET data.

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

Disclosed herein is a method that allows generation of a mu-map for the corresponding PET scan using a CT scan whose FOV is truncated to be limited to the organ of interest only and then extending the truncated mu-map by estimating the mu-map for the regions that are outside the truncated CT FOV, but still within the PET scan range, using TOF PET data. In other words, CT scan for generating mu-map is truncated to the region in which the organ of interest resides so that the region of the patient's body exposed to radiation during CT scan is minimized. This, however, results in a truncated mu-map that is missing data for the regions outside the CT FOV but still within the PET scan range. Thus, the method of the present disclosure extends the truncated mu-map by estimating the missing mu-map data using TOF PET data.

<FIG> illustrates one embodiment of a nuclear imaging system <NUM> in which the methods of the present disclosure can be implemented. The nuclear imaging system <NUM> includes at least a first imaging modality <NUM> provided in a first gantry 16a. The first imaging modality <NUM> may include any suitable modality, such as, for example, a computed-tomography (CT) modality, a positron-emission tomography (PET) modality, a single-photon emission computerized tomography (SPECT) modality, etc. The first imaging modality <NUM> may include a long axial FOV or a short axial FOV. A patient <NUM> lies on a movable patient bed <NUM> that may be movable with respect to the first gantry 16a. In some embodiments, the nuclear imaging system <NUM> includes a second imaging modality <NUM> provided in a second gantry 16b. The second imaging modality <NUM> can be any suitable imaging modality, such as, for example, a CT modality, a PET modality, a SPECT modality and/or any other suitable imaging modality. The second modality <NUM> may include a long axial FOV or a short axial FOV. Each of the first imaging modality <NUM> and/or the second imaging modality <NUM> can include one or more detectors <NUM> arranged, for example, in one or more rings. Each of the detectors <NUM> is configured to detect an annihilation photon, gamma ray, and/or other nuclear imaging event.

Scan data from the first imaging modality <NUM> and/or the second imaging modality <NUM> is stored at one or more computer databases <NUM> and processed by one or more computer processors <NUM> of a computer system <NUM>. The graphical depiction of computer system <NUM> in <FIG> is provided by way of illustration only, and computer system <NUM> may include one or more separate computing devices, for example, as described with respect to <FIG>. The scan data may be provided by the first imaging modality <NUM>, the second imaging modality <NUM>, and/or may be provided as a separate data set, such as, for example, from a memory coupled to the computer system <NUM>. The computer system <NUM> can include one or more processing electronics for processing a signal received from one of the plurality of detectors <NUM>.

<FIG> illustrates a computer system <NUM> configured to implement one or more processes, in accordance with some embodiments. The system <NUM> is a representative device and can include a processor subsystem <NUM>, an input/output subsystem <NUM>, a memory subsystem <NUM>, a communications interface <NUM>, and a system bus <NUM>. In some embodiments, one or more than one of the system <NUM> components can be combined or omitted such as, for example, not including an input/output subsystem <NUM>. In some embodiments, the system <NUM> can comprise other components not shown in <FIG>. For example, the system <NUM> can also include, for example, a power subsystem. In other embodiments, the system <NUM> can include several instances of a component shown in <FIG>. For example, the system <NUM> can include multiple memory subsystems <NUM>. For the sake of conciseness and clarity, and not limitation, one of each component is shown in <FIG>.

The processor subsystem <NUM> can include any processing circuitry operative to control the operations and performance of the system <NUM>. In various aspects, the processor subsystem <NUM> can be implemented as a general purpose processor, a chip multiprocessor (CMP), a dedicated processor, an embedded processor, a digital signal processor (DSP), a network processor, an input/output (I/O) processor, a media access control (MAC) processor, a radio baseband processor, a co-processor, a microprocessor such as a complex instruction set computer (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, and/or a very long instruction word (VLIW) microprocessor, or other processing device. The processor subsystem <NUM> also can be implemented by a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), and so forth.

In various aspects, the processor subsystem <NUM> can be arranged to run an operating system (OS) and various applications. Examples of an OS comprise, for example, operating systems generally known under the trade name of Apple OS, Microsoft Windows OS, Android OS, Linux OS, and any other proprietary or open source OS. Examples of applications comprise, for example, network applications, local applications, data input/output applications, user interaction applications, etc..

In some embodiments, the system <NUM> can include a system bus <NUM> that couples various system components including the processing subsystem <NUM>, the input/output subsystem <NUM>, and the memory subsystem <NUM>. The system bus <NUM> can be any of several types of bus structure(s) including a memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, <NUM>-bit bus, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect Card International Association Bus (PCMCIA), Small Computers Interface (SCSI) or other proprietary bus, or any custom bus suitable for computing device applications.

In some embodiments, the input/output subsystem <NUM> can include any suitable mechanism or component to enable a user to provide input to system <NUM> and the system <NUM> to provide output to the user. For example, the input/output subsystem <NUM> can include any suitable input mechanism, including but not limited to, a button, keypad, keyboard, click wheel, touch screen, motion sensor, microphone, camera, etc..

In some embodiments, the input/output subsystem <NUM> can include a visual peripheral output device for providing a display visible to the user. For example, the visual peripheral output device can include a screen such as, for example, a Liquid Crystal Display (LCD) screen. As another example, the visual peripheral output device can include a movable display or projecting system for providing a display of content on a surface remote from the system <NUM>. In some embodiments, the visual peripheral output device can include a coder/decoder, also known as Codecs, to convert digital media data into analog signals. For example, the visual peripheral output device can include video Codecs, audio Codecs, or any other suitable type of Codec.

The visual peripheral output device can include display drivers, circuitry for driving display drivers, or both. The visual peripheral output device can be operative to display content under the direction of the processor subsystem <NUM>. For example, the visual peripheral output device can be able to play media playback information, application screens for application implemented on the system <NUM>, information regarding ongoing communications operations, information regarding incoming communications requests, or device operation screens, to name only a few.

In some embodiments, the communications interface <NUM> can include any suitable hardware, software, or combination of hardware and software that is capable of coupling the system <NUM> to one or more networks and/or additional devices. The communications interface <NUM> can be arranged to operate with any suitable technique for controlling information signals using a desired set of communications protocols, services or operating procedures. The communications interface <NUM> can include the appropriate physical connectors to connect with a corresponding communications medium, whether wired or wireless.

Vehicles of communication comprise a network. In various aspects, the network can include local area networks (LAN) as well as wide area networks (WAN) including without limitation Internet, wired channels, wireless channels, communication devices including telephones, computers, wire, radio, optical or other electromagnetic channels, and combinations thereof, including other devices and/or components capable of/associated with communicating data. For example, the communication environments comprise in-body communications, various devices, and various modes of communications such as wireless communications, wired communications, and combinations of the same.

Wireless communication modes comprise any mode of communication between points (e.g., nodes) that utilize, at least in part, wireless technology including various protocols and combinations of protocols associated with wireless transmission, data, and devices. The points comprise, for example, wireless devices such as wireless headsets, audio and multimedia devices and equipment, such as audio players and multimedia players, telephones, including mobile telephones and cordless telephones, and computers and computer-related devices and components, such as printers, network-connected machinery, and/or any other suitable device or third-party device.

Wired communication modes comprise any mode of communication between points that utilize wired technology including various protocols and combinations of protocols associated with wired transmission, data, and devices. The points comprise, for example, devices such as audio and multimedia devices and equipment, such as audio players and multimedia players, telephones, including mobile telephones and cordless telephones, and computers and computer-related devices and components, such as printers, network-connected machinery, and/or any other suitable device or third-party device. In various implementations, the wired communication modules can communicate in accordance with a number of wired protocols. Examples of wired protocols can include Universal Serial Bus (USB) communication, RS-<NUM>, RS-<NUM>, RS-<NUM>, RS-<NUM> serial protocols, FireWire, Ethernet, Fibre Channel, MIDI, ATA, Serial ATA, PCI Express, T-<NUM> (and variants), Industry Standard Architecture (ISA) parallel communication, Small Computer System Interface (SCSI) communication, or Peripheral Component Interconnect (PCI) communication, to name only a few examples.

Accordingly, in various aspects, the communications interface <NUM> can include one or more interfaces such as, for example, a wireless communications interface, a wired communications interface, a network interface, a transmit interface, a receive interface, a media interface, a system interface, a component interface, a switching interface, a chip interface, a controller, and so forth. When implemented by a wireless device or within wireless system, for example, the communications interface <NUM> can include a wireless interface comprising one or more antennas, transmitters, receivers, transceivers, amplifiers, filters, control logic, and so forth.

In various aspects, the communications interface <NUM> can provide data communications functionality in accordance with a number of protocols. Examples of protocols can include various wireless local area network (WLAN) protocols, including the Institute of Electrical and Electronics Engineers (IEEE) <NUM>. xx series of protocols, such as IEEE <NUM>1a/b/g/n/ac, IEEE <NUM>, IEEE <NUM>, and so forth. Other examples of wireless protocols can include various wireless wide area network (WWAN) protocols, such as GSM cellular radiotelephone system protocols with GPRS, CDMA cellular radiotelephone communication systems with 1xRTT, EDGE systems, EV-DO systems, EV-DV systems, HSDPA systems, and so forth. Further examples of wireless protocols can include wireless personal area network (PAN) protocols, such as an Infrared protocol, a protocol from the Bluetooth Special Interest Group (SIG) series of protocols (e.g., Bluetooth Specification versions <NUM>, <NUM>, <NUM>, legacy Bluetooth protocols, etc.) as well as one or more Bluetooth Profiles, and so forth. Yet another example of wireless protocols can include near-field communication techniques and protocols, such as electro-magnetic induction (EMI) techniques. An example of EMI techniques can include passive or active radio-frequency identification (RFID) protocols and devices. Other suitable protocols can include Ultra Wide Band (UWB), Digital Office (DO), Digital Home, Trusted Platform Module (TPM), ZigBee, and so forth.

In some embodiments, at least one non-transitory computer-readable storage medium is provided having computer-executable instructions embodied thereon, wherein, when executed by at least one processor, the computer-executable instructions cause the at least one processor to perform embodiments of the methods described herein. This computer-readable storage medium can be embodied in memory subsystem <NUM>.

In some embodiments, the memory subsystem <NUM> can include any non-transitory machine-readable or computer-readable media capable of storing data, including both volatile/non-volatile memory and removable/non-removable memory. The memory subsystem <NUM> can include at least one non-volatile memory unit. The non-volatile memory unit is capable of storing one or more software programs. The software programs can contain, for example, applications, user data, device data, and/or configuration data, or combinations therefore, to name only a few. The software programs can contain instructions executable by the various components of the system <NUM>.

In various aspects, the memory subsystem <NUM> can include any non-transitory machine-readable or computer-readable media capable of storing data, including both volatile/non-volatile memory and removable/non-removable memory. For example, memory can include read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDR-RAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory (e.g., ferroelectric polymer memory), phase-change memory (e.g., ovonic memory), ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, disk memory (e.g., floppy disk, hard drive, optical disk, magnetic disk), or card (e.g., magnetic card, optical card), or any other type of media suitable for storing information.

In one embodiment, the memory subsystem <NUM> can contain an instruction set, in the form of a file for executing various methods, such as methods including A/B testing and cache optimization, as described herein. The instruction set can be stored in any acceptable form of machine readable instructions, including source code or various appropriate programming languages. Some examples of programming languages that can be used to store the instruction set comprise, but are not limited to: Java, C, C++, C#, Python, Objective-C, Visual Basic, or. NET programming. In some embodiments a compiler or interpreter is comprised to convert the instruction set into machine executable code for execution by the processing subsystem <NUM>.

The flowchart <NUM> of <FIG> summarizes an embodiment of the method. First, a full axial FOV PET scan is performed on a patient, thus, generating a PET sinogram data. (See Step <NUM>). The full axial FOV scan can mean one bed position or multiple bed positions. The method then includes performing a truncated FOV CT scan of a region in the patient's body in which the organ of interest lies (also referred to as a volume-of-interest (VOI) in the patient). (See step <NUM>). Next, the method includes generating a truncated mu-map covering the truncated FOV of the CT scan, wherein the truncated FOV of the CT scan is shorter than the full axial FOV of the PET scan. (See Step <NUM>). In this embodiment, the truncated CT FOV is fully within the single FOV of the PET scan. Because the mu-map generated from the truncated FOV CT scan does not match the full FOV of the long axial FOV PET scan TOF data, the mu-map data from the truncated FOV CT scan is missing data before a mu-map for the full PET FOV can be generated. The method of the present disclosure thus includes generating a mu-map for full axial FOV of the PET scan by extending the truncated mu-map generated from the truncated FOV CT scan by estimating the missing mu-map data using the entire measured TOF PET data from step <NUM>. (See Step <NUM>). The method can further include reconstructing a PET image using the estimated mu-map for full axial FOV of the PET scan (i.e., the mu-map generated in step <NUM>), (See Step <NUM>). The reconstruction step <NUM> comprises allocating different weights to the information content from the different regions in the estimated mu-map during the PET image reconstruction process. Different weight is assigned to the portions of the full axial FOV mu-map that were generated from the measured truncated FOV CT scan vs. the portions of the full axial FOV mu-map that were generated by estimating the missing mu-map data for the regions between the VOIs that were scanned by the truncated FOV CT scans (i.e. the regions that were not CT scanned).

This approach would be ideal for cases such as cardiac imaging where the cardiac region is located at the center of the single bed FOV and the CT is used to scan only the cardiac region while the mu-values of the rest of the body are jointly estimated using PET data. Some examples of other VOI imaging where this concept of organ specific CT scans can be used are breast scans, brain/prostate/pancreas/liver imaging.

Estimating the missing mu-map data in Step <NUM> can comprise using a combination of, prior predictions, numerical methods, CT scout scans or artificial intelligence type algorithms. In the examples discussed herein, CT is the modality used for obtaining the anatomical information, it is within the scope of the present disclosure to encompass embodiments where the anatomical information is obtained by using MRI, ultrasound or any other imaging modality or any combination of different modalities.

Estimating the missing mu-map data in Step <NUM> can also comprise calculating the mean TOF PET emission values within the truncated FOV of the CT scan as well as those outside the truncated FOV of the CT scan and using the mean TOF PET emission values to segment norm corrected PET images by identifying voxels that are above uptake threshold for fat, muscle and lungs and generating a mask used to detect the support of the mu-map in the region of the truncated FOV of the CT scan. This is illustrated using some examples below.

The process of generating a mu-map for the full axial FOV of the PET scan from the truncated mu-map from the truncated FOV CT scan will now be described in more detail. <FIG> shows PET scan projection sinogram of a clinical cardiac scan obtained using Siemens Biograph Vision scanner. The projection data is <NUM> pixels along the radial direction, <NUM> angular views, <NUM> TOF time bins and <NUM> pixels along the axial direction. <FIG> shows a PET scan projection of the same patient from <FIG> after the list-mode data that passes through regions outside the CT based VOI has been turned off to simulate a truncated PET scan projections of a VOI corresponding to a truncated FOV CT scan of the VOI. The single angular view projection data shown in <FIG> and <FIG> were obtained by rebinning the list-mode data to 520x815 (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) using span <NUM>. The patient was injected with <NUM> MBq of FDG and scanned <NUM> minutes post injection. A two minute Step and Shoot (S&S) scan was performed over the cardiac region to study the uptake in the myocardial region. The list-mode data was rebinned for a maximum ring difference (MRD) of <NUM>, with a TOF mashing factor of <NUM>.

<FIG> shows the mu-map of the patient in transaxial, coronal, and sagittal views. The axial length of the mu-map in <FIG> is the same as the axial length of the Biograph Vision scanner (<NUM>) used generate the PET scan projection of <FIG>. Then, to study the effects of using a shorter truncated FOV CT scan, <NUM> slices were removed from the bottom and <NUM> slices from the top of the CT image. This is done by turning off the list-mode data that passes through regions outside the VOI based CT scan. The resulting PET scan projection sinogram is shown in <FIG>. Thus, the sinogram shown in <FIG> shows what the PET projection data that matches the truncated FOV CT would look like. This simulates a truncated FOV CT scan with a shorter axial length compared to a single bed PET scan.

<FIG> is a mu-map of a CT scan corresponding to the axial scan length of a PET scanner with an axial length of <NUM> slices. <FIG> is a mu-map of a truncated FOV CT scan generated by removing <NUM> slices of data from the top and <NUM> slices of data from the bottom of the CT images to simulate a truncated CT scan with a shorter axial length compared to a single bed PET scan. The dark regions <NUM> in the images are the result of the removed data.

Four approaches were studied for the validation. The PET data were reconstructed using:.

<FIG> shows the detector crystal efficiencies of a PET scanner. This particular example is from the clinical <NUM>-ring Biograph Vision scanner (798x80). The crystal efficiencies represent the detection efficiencies of each of the crystals in the scanner to the incident photons. For illustration purpose, <FIG> illustrates the crystal efficiencies of the PET scanner's detector rings where the group of rings have been unfurled flat. To generate truncated sinograms that matches the truncated FOV CT, the line of response (LOR) outside the CT scan range was removed during the rebinning step by modifying the crystal efficiencies of the scanner. This will be explained below with reference to <FIG>. By generating truncated sinograms that exactly match the truncated CT could, in theory, reconstruct converged images without any bias. The effects of the missing data were modeled in the norm during reconstruction.

In <FIG>, the detector crystal efficiencies of the detector crystals outside the truncated CT axial FOV are turned off. The missing data from the detector crystal rings outside the truncated CT axial FOV are shown as the dark bands <NUM> along the top and bottom of <FIG>. By turning off the crystal efficiencies, the LORs that passes through regions (not measured by the CT) are not considered during reconstruction. In the illustrated example, a band of <NUM> detector crystal rings at the top and <NUM> detector crystal rings at the bottom of the scanner were turned off to correspond to the <NUM> slices and <NUM> slices of truncated data in the CT image shown in <FIG>.

<FIG> shows the transaxial, coronal, and sagittal slices through a CT scan corresponding to one Biograph Vision scanner bed with an axial length of <NUM> slices. To determine the support (e.g. the outline) of the mu-map in the truncated region, the norm corrected PET data is back-projected using non-attenuation correction (NAC) algorithm. The resulting transaxial, coronal, and sagittal slices of the corrected time-of-flight (TOF) back-projected images are shown in <FIG>.

The missing slices of the mu-map are estimated by calculating the mean TOF PET emission values within the truncated FOV of the CT scan as well as those outside the truncated FOV of the CT scan. These mean TOF PET emission values were used to segment the norm corrected PET images from <FIG> by identifying the voxels above the uptake threshold for fat, muscle and lungs and generate the mask (e.g. the outline, or a rough approximate region where the attenuating region could be) used to detect the support of the mu-map in the truncated region which is shown in <FIG>. Thus, <FIG> is the extended mu-map which now includes the attenuation data for the regions that are within the PET scan FOV but outside the truncated FOV of the CT. The missing part of the CT/attenuation map can be estimated using a combination of segmentation, numerical methods, PET data or Artificial Intelligence (AI). Further another approach is to use the scout scan obtained by the CT image to estimate the outline as well as the attenuation along the LOR. The scout scan could be just one view or multiple views. The information from the scout scan can be integrated with the estimation step to generate the estimate mu-map.

The initial support of the mu-map obtained is slightly bigger than the true mu-map measured by CT. This initial support of the mu-map serves as an ideal starting point for advanced joint estimation algorithms such as maximum likelihood reconstruction of attenuation activity (MLAA) and maximum likelihood attenuation correction factor (MLACF) in conjunction with AI based approaches that can be used to determine the extended mu-map.

Modified Ordinary Poisson Maximum Likelihood Expectation Maximization (OP-MLEM) algorithm modeling TOF as well as point-spread-function (PSF) (<NUM> iterations and <NUM> subset) was used for the PET reconstruction. The modified OP-MLEM update equation is given as the following Equation (<NUM>): <MAT> where λn is the image after n iterations, Y is measured data with the missing data modeled accurately during the TOF mashing step, BPTOF is the TOF back projection and FPTOF is TOF forward projection, R is Randoms, S is scatter, N is norm and A is attenuation, the subscript 'c' stands for complete data and the subscript 'v' stands for virtual gantry generated corresponding to the truncated organ specific CT FOV. This update equation uses multiple sinograms, multiple mu-maps, multiple scatter, and multiple norm for the same image update. Alpha can be multi-dimensional. The above equation is an example to explain the modified OP-MLEM update equation for the reconstruction where the truncated sinogram from the virtual gantry and completely measured data are used along with the truncated mu-map as well as the expanded mu map are used to generate the reconstructed image. Note that the norm, randoms, attenuation and scatter sinograms of the virtual gantry based on the truncated CT data (<FIG>) and measured sinograms can be different and will be modeled accordingly for the reconstruction. In some embodiments of the present disclosure, the above modified OP-MLEM update equation can also be written as, but not limited to, the following Equation (<NUM>): <MAT> When
the truncated sinogram shown in <FIG> was used, the drop in sensitivity due to the missing LORs were modeled during the norm expansion step so as to accurately compensate for the missing data (the missing data represented in <FIG>).

<FIG>, row (a) shows reconstructed images using complete data (i.e. untruncated sinogram shown in <FIG>), and untruncated mu-map (shown in <FIG>). This image was used as the baseline to calculate the bias and variance in the reconstructed images obtained using the other approaches. The reconstructed image using truncated mu-map of <FIG> and truncated sinogram of <FIG> that has been rebinned to same axial length as truncated mu-map is shown in <FIG>, row (b). This image has higher statistical variation throughout the image and the noisy voxels are more evident at the edge of the organ specific truncated axial CT FOV as noted by the arrows. The increased noise in the reconstructed image is due to the rebinning of the original data such that the LORs that pass outside the truncated short axial CT FOV are removed. This effect can also be seen in the absolute percentage bias image in <FIG>, row (a). Here, the voxel by voxel absolute percentage bias was calculated between the images shown in <FIG>, row (b) and <FIG>, row (a). Further, not modeling the effects of attenuation in the region outside the CT FOV resulted in high bias at the edge slices as shown in the examples in <FIG>, rows (a) and (b).

Reconstructing the image using truncated mu-map and untruncated original sinogram resulted in less noise as well as bias (compare <FIG>, row (c) to <FIG>, row (b)). Note that less noise as well as fewer artifacts are seen at the edge of CT FOV as the entire sinogram was used in the reconstruction. The robustness of the reconstructed image arises from the fact that with the improved TOF timing resolution of <NUM> picoseconds, the effects of mismatch in the mu-map is very local and hence the error does not propagate much into the PET reconstructed image (within the CT sampled region). Finally, <FIG>, row (d) shows reconstructed image obtained using the extended mu-map (shown in <FIG>) and untruncated original Sinogram (<FIG>). The percentage bias as illustrated in <FIG>, row (c) was found to be least when the extended mu-map was used and the bias in the myocardial region was found to be less than <NUM>%. The arrows in <FIG>, row (b) shows the high intensity voxels due to truncation of the sinogram and lower counts that are measured from that region while the arrows in <FIG>, row (c) and row (d) show that the values in those region are more accurate with less noise as the more of the PET data is used during the reconstruction.

By using organ specific truncated FOV CT scan(s) whose axial length is shorter than the full axial FOV PET scan as disclosed, one can reduce the radiation dose to the organs that are outside the VOI during the mu-map generation CT scan. Furthermore, by rebinning the original PET data (full axial FOV data), a virtual gantry can be generated that has the same dimensions as that of the truncated short axis FOV CT (<FIG> and <FIG>). Reconstructing the corresponding truncated sinogram data provided an unbiased reconstructed image without any systemic artifacts as the short axial CT exactly matched the PET data.

According to some embodiments, the method of flowchart <NUM> can be implemented in the nuclear imaging system <NUM> of <FIG> and <FIG>. Such system can comprise a PET/CT scanner modalities <NUM>, <NUM>, a non-transitory machine-readable storage medium <NUM>, tangibly embodying a program of instructions executable by a processor <NUM> to cause the processor to perform an operation comprising:.

In some embodiments of the present disclosure, the method of the flowchart <NUM> can be applied to cases where the full axial FOV PET scan is conducted by a multi-bed scan or a CBM scan. Such method is summarized by the flowchart <NUM> shown in <FIG>. In some embodiments, the method comprises: performing a full axial FOV PET scan of a patient (the full axial FOV PET scan can be, but not limited to, single-bed scan, multi-bed scan, or CBM scans) which generates a full axial FOV PET scan data, (see Step <NUM>); performing multiple truncated FOV CT scan of different regions in the patient's body in which the organ of interest lies, (see Step <NUM>); generating truncated mu-maps covering the regions scanned by the truncated CT FOV (<FIG>), (see Step <NUM>); generating truncated PET sinogram data corresponding to the truncated mu-maps covering the region scanned by the truncated CT FOV by rebinning the full axial FOV PET scan data to match the limited axial length (i.e. the truncated FOV) of the CT scan and reconstructing PET image of the truncated regions using the truncated PET sinogram data, (see Step <NUM>); reconstructing PET images of the truncated regions using the measured (i.e., truncated scan) CT data and truncated PET sinogram data, (see Step <NUM>); estimating the mu-map using a combination of segmentation, numerical methods, PET data or Artificial Intelligence, (see Step <NUM>); and reconstructing the whole body (i.e., full PET FOV) PET image using a combination of truncated rebinned PET scan data, truncated CT scan data, estimated CT data of the non-scanned region, other correction factors and untruncated measured PET scan data, (see Step <NUM>).

According to some embodiments, the method of flowchart <NUM> can be implemented in the nuclear imaging system <NUM> of <FIG> and <FIG>. Such system can comprise a PET/CT scanner modalities <NUM>, <NUM>, a non-transitory machine-readable storage medium <NUM>, tangibly embodying a program of instructions executable by a processor <NUM> to cause the processor to perform an operation comprising the steps outlined in flowchart <NUM> described above.

According to some embodiments, the method of flowchart <NUM> can further comprise the following steps: reconstructing a full FOV PET image using the full axial FOV PET scan data and the mu-map for full axial FOV of the PET scan while the patient is on the patient bed; and if the full FOV PET image exhibits any abnormal uptake in any region that was not scanned by the truncated FOV CT scan, conducting a truncated FOV CT scan of the region of the abnormal uptake and performing the following to reconstruct a PET image of the region of the abnormal uptake: (i) generating a truncated mu-map from the truncated FOV CT scan of the region of the abnormal uptake; (ii) generating a truncated PET data that corresponds to the truncated mu-map covering the region of the abnormal uptake by rebinning the PET scan data to match the truncated FOV of the truncated FOV CT scan; and (iii) reconstructing PET image of the region of the abnormal uptake using the truncated PET data.

Furthermore, according to another embodiment of the present disclosure, the truncated mu-map generated in the embodiments of the process outlined in flowchart <NUM> or flowchart <NUM> can be used to improve the scatter sinogram data obtained using the estimated mu-map. <FIG> is a flowchart <NUM> outlining such method for improving the scatter sinogram data. A first scatter sinogram data is generated using the truncated mu-maps (i.e. the mu-maps generated from the truncated FOV CT scan) (e.g. <FIG>) and the truncated rebinned measured PET sinogram data (e.g. <FIG>), (See Step <NUM>). Then, a second scatter sinogram data is generated for the whole body using the complete measured full axial FOV PET sinogram data (e.g. <FIG>), and the estimated mu-map for the full axial FOV (e.g. <FIG>) with or without the truncated mu-map. (See Step <NUM>). Next, the second scatter sinogram data is compared against the first scatter sinogram data to determine if the second scatter sinogram data (the whole body scatter) is consistent with the first scatter sinogram data. (See Step <NUM>). Since the mu-map for the full axial FOV includes portions that are estimated, the scatter sinogram from the estimated portion of the mu-map could be different. If the second scatter sinogram data is not consistent with the first scatter sinogram data, use the first scatter sinogram data (the scatter data from the truncated mu-map (e.g. <FIG>)) and truncated rebinned PET sinogram data (e.g. <FIG>) to improve or correct the second scatter sinogram data (the whole body scatter data). (See Step <NUM>). The correction could be, but not limited, to improve the scatter scaling, improve the scatter shape, scatter modelling, and improve the overall quantification of the scatter sinogram obtained using the estimated mu-map. The improved/corrected scatter sinogram data can then be used to improve the estimation of (i.e. re-estimate) the mu-map for the truncated FOV CT scanned region of the patient (See Step <NUM>). We can then use the scatter from the re-estimated mu-map to improve the scatter from the truncated mu-map, such as, but not limited to, improving the scatter in the edge slices of the truncated mu-map as well as determining the scatter with less statistical variation.

Referring to the flowchart <NUM> in <FIG>, in some embodiments, the concept of using truncated CT scan can be used to detect errors in scanner parameters and fix them. The method outlined in flowchart <NUM> can be used to check that the PET scanner's imaging parameters are within the tolerance range. The method comprises first performing a full axial FOV PET scan of a patient generating a full axial FOV PET data. (See Step <NUM>). The full axial FOV can be, but not limited to, single bed, multi-bed, or CBM scans. The method then includes performing a truncated FOV CT scan for each of one or more VOIs (each VOI being a region in the patient's body in which an organ of interest lies) (See Step <NUM>). Next, a truncated mu-map is generated for each of the truncated FOV CT scanned region (i.e. the VOI) from the truncated FOV CT scan data. (See Step <NUM>). The measured CT regions (i.e., the truncated FOV CT scanned regions) could be multiple regions in the body apart from each other. e.g. the CT in one scan could be of the brain, heart and pelvis with regions of space not sampled in between. An example of such truncated mu-map is illustrated in <FIG> shows a complete mu-map of a patient that was acquired from head to thigh. <FIG> shows a mu-map from a truncated CT scan that acquired scans of just the brain, the cardiac region, and the pelvic region of the patient. The method outlined in the flowchart <NUM> also includes using the full axial FOV PET data to estimate the mu-map corresponding to the full axial PET FOV including the region(s) scanned by the truncated FOV CT scan. (See step <NUM>). Next, the method includes comparing the estimated/generated mu-map with the truncated mu-maps (which were generated from the measured truncated FOV CT scan data) to see if the measured full axial FOV PET data is consistent and does not have any artifacts such as time offset error or gantry offset. (See step <NUM>). If any mismatch is observed, one can perform a quality check of the PET scanner system. Further, the data obtained using the measured CT and PET can be used to correct any inconsistencies in the PET scanner system's parameters as well as improve the measured PET scan data. (See step <NUM>). The improved PET scan data can now in turn be used to re-estimate the mu-maps in the truncated FOV CT scanned regions of the patient (i.e. the VOIs). (See step <NUM>).

Another option is to use the truncated mu-map generated from the measured truncated FOV CT scan and compare it against the estimated mu-map to see if the PET scanner imaging parameters (e.g. time offset) are within the desired tolerance range. If the scanner imaging parameters are not within the desired tolerance range, a new calibration should be run for the PET scanner to update the calibration. Thus, an out-of-calibration condition for the PET scanner can be determined without the need to conduct separate QC (quality control) studies.

According to some embodiments, the list-mode data from the full axial FOV PET scan can be used directly in the methods of the present disclosure instead of the PET sinogram data. <FIG> is a flowchart <NUM> that outlines the concept. Only the list-mode data <NUM> that passes through the region measured by the truncated FOV CT scan are identified and used to generate reconstructed PET images corresponding to the region measured by the truncated FOV CT scan. In cases where there are more than one VOI and, thus, a multiple truncated FOV CT scans are performed, one truncated CT scan for each of the VOIs, the same rule is applied for each of the VOI region. In other words, for each of the VOI region, only the list-mode data that passes through that region are identified and used to generate reconstructed PET images corresponding to that VOI region. The remaining list-mode data can be used to estimate the missing mu-map for the regions that the truncated FOV CT scan did not measure. After one or more organ specific truncated FOV CT scans are performed and corresponding truncated mu-maps are generated for each of the truncated FOV CT scanned region (See <NUM>), multiple sets of sinograms are generated. (See <NUM>), one set for each of the CT scanned region, from the list-mode data <NUM> and the truncated mu-maps. Then, sinograms matching the entire PET FOV (i.e. the full axial FOV PET) are generated. (See <NUM>). From the sinograms matching the entire PET FOV, the full FOV PET image can be reconstructed. (See <NUM>). The multiple list-mode data and sinograms may or may not be limited to the regions measured by the truncated FOV CT. The MRD and span angles used during the reconstruction of the region that is measured by the truncated FOV CT scan as well as the estimated region can be different from each other for the different sinograms as well as the entire PET FOV list-mode data and sinograms.

As shown in flowchart <NUM> in <FIG>, the proposed approach of utilizing the list-mode data and multiple organ specific truncated CT scan data for reconstructing the final PET image is outlined. The reconstruction uses the sinograms/list-mode data that matches the full axial FOV of the PET (See <NUM>), multiple sinograms matching the full axial FOV (See <NUM>), multiple organ specific truncated FOV CT scan of VOIs (See <NUM>), and the correction factors (See <NUM>). The reconstruction of the PET image can be performed either as one closed form equation or as a summation of multiple individual reconstructions. (See <NUM>). Furthermore, if the clinicians observe any abnormalities in the PET image during the scan time, then they can perform an additional CT scan of just the region of the abnormalities (See step <NUM>). For example, if the user desires to CT scan another region in the patient body while the patient is still on the patient bed, the new information from the CT can be used along with previously acquired data and reconstructed. The scanner correction factors <NUM> such as norm, scatter, randoms can be calculated at the same MRD and span angle as the PET data <NUM> and they can be different for the various organ specific CT scan region.

According to another embodiment, a method is disclosed that comprises: performing a full axial FOV PET scan of a patient; performing a truncated FOV CT scan of a VOI; generate a mu-map of the CT scanned region; estimate a mu-map for the regions not scanned by the CT; reconstructing a PET image of the full axial FOV, while the patient is still on the patient bed, using a mu-map that is a combination of the mu-map generated from the measured truncated FOV CT scan and the estimated mu-maps while the patient is still on the patient bed; and if the clinician finds any abnormal uptake in any region that was not measured by the CT, then a new truncated FOV CT scan can be performed on just the abnormal region and a mu-map is generated from the new truncated FOV CT scan; then reconstructing a PET image using an updated mu-map for the full axial FOV which now incorporates the mu-map generated from the new truncated FOV CT scan. The updated mu-map for the full axial FOV is a combination of the mu-map generated from the measured new truncated FOV CT scan and estimated mu-maps.

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 method comprising the steps of:
(a) performing (<NUM>) a full axial field of view (FOV) PET scan of a patient and generating a PET data;
(b) performing (<NUM>) a truncated FOV CT scan of a volume of interest (VOI) in the patient's body;
(c) generating (<NUM>) a truncated mu-map covering the truncated FOV of the CT scan, wherein the truncated FOV of the CT scan is shorter than the full axial FOV of the PET scan;
(d) generating a truncated PET data that corresponds to the truncated mu-map and reconstructing a PET image of the VOI using the truncated PET data; and
(e) generating (<NUM>) a mu-map for full axial FOV of the PET scan by extending the truncated mu-map generated from the truncated FOV CT scan by estimating the missing mu-map data using the PET data;
characterized by
(f) reconstructing (<NUM>) a PET image using the mu-map for full axial FOV of the PET scan by allocating different weights to the information content from the different regions in the mu-map.