Methods and systems for scatter correction

Various methods and systems are provided for scatter correction in nuclear medicine imaging systems. In one embodiment, a method for NM imaging comprises acquiring, with a plurality of detectors, imaging data separated into a high energy window and a low energy window, removing photopeak photons from the imaging data in the low energy window to obtain a corrected scatter distribution, correcting the imaging data based on the corrected scatter distribution, and outputting a scatter-corrected image reconstructed from the corrected imaging data. In this way, fast and accurate scatter correction for CZT-based gamma cameras may be performed, and image quality as well as quantitative accuracy may be increased.

FIELD

Embodiments of the subject matter disclosed herein relate to non-invasive diagnostic imaging, and in particular to scatter correction for nuclear medicine (NM) imaging systems.

BACKGROUND

Nuclear medicine (NM) imaging systems, such as positron emission tomography (PET) imaging systems and single photon emission computed tomography (SPECT) imaging systems, include multiple detectors or detector heads for detecting radiation emitted from within a subject in order to image the internal structure of the subject. For example, PET imaging systems acquire data that represent the distribution of positron-emitting nuclides within the body of a patient. When a positron interacts with an electron by annihilation, the entire mass of a positron-electron pair is converted into two 511-keV photons. The photons are emitted in opposite directions along a line of response (LOR). The annihilation photons are detected by detectors that are placed on both sides of the LOR, in a configuration such as a detector ring. Coincidence occurs when these annihilation photons arrive and are detected at the detector elements at the same time. An image is then generate based on the acquired image data that includes the annihilation photon detection information.

Compton scattering occurs when one or both annihilation photons interact with matter, change direction, and lose energy. The detection of such scattered photons causes errors and/or image artifacts. NM imaging systems are typically configured with scatter correction methods to account for Compton scattering.

BRIEF DESCRIPTION

In one embodiment, a method for NM imaging comprises acquiring, with a plurality of detectors, imaging data separated into a high energy window and a low energy window, removing photopeak photons from the imaging data in the low energy window to obtain a corrected scatter distribution, correcting the imaging data based on the corrected scatter distribution, and outputting a scatter-corrected image reconstructed from the corrected imaging data. In this way, fast and accurate scatter correction for CZT-based gamma cameras and PET may be performed, consequently increasing image quantitative accuracy.

DETAILED DESCRIPTION

The following description relates to various embodiments of nuclear medicine (NM) imaging. In particular, methods and systems are provided for scatter correction for NM imaging systems, such as a PET or SPECT imaging system. An example of a PET imaging system that may be used to acquire images processed in accordance with the present techniques is shown inFIGS. 1 and 2. Another example of an NM imaging system that may be used to acquire images processed in accordance with the present techniques, in particular a SPECT imaging system, is shown inFIG. 3. A PET imaging system may be configured with low energy resolution detectors while a SPECT imaging system may be configured with CZT detectors which provide an improved energy resolution in comparison to more traditional NaI detectors. Further, Compton scattering occurs when one or both annihilation photons interact with matter, change direction, and lose energy. The detection of such scattered photons causes errors and/or image artifacts. The number of scattered photons may be substantial, as depicted by the example distributions of scattered photons inFIG. 4. NM imaging systems typically include scatter correction methods. Systems configured with NaI detectors, for example, may use measurements of scattered photons with an additional energy window for performing scatter correction. However, for systems configured with CZT detectors, the photopeak resolution is often asymmetric due to incomplete charge collection within the detector, resulting in many photopeak events being incorrectly sorted into lower energy bins. This “tailing” effect caused by such incorrectly binned photopeak photons, as depicted inFIG. 5, contaminates the true scatter signal in the lower energy bins, thereby negatively impacting scatter correction methods such as the multiple energy window technique mentioned hereinabove that rely on estimates of scatter from the spectra. Methods for scatter correction, such as the methods shown inFIGS. 6 and 7, include removing such tailed photopeak photons from the measurements of scattered photons to thereby obtain corrected scatter estimation. The correction methods provided herein allow for scatter measurements obtained with CZT detectors to be consistent with scatter measurements obtained with NaI detectors, as depicted inFIGS. 8 and 9. Further, the methods are applicable in projection space as well as image space, as depicted inFIGS. 10 and 11.

Various embodiments of the invention provide a multi-modality imaging system10as shown inFIGS. 1 and 2. Multi-modality imaging system10may be any type of imaging system, for example, different types of medical imaging systems, such as a Positron Emission Tomography (PET), a Single Photon Emission Computed Tomography (SPECT), a Computed Tomography (CT), an ultrasound system, Magnetic Resonance Imaging (MRI), or any other system capable of generating tomographic images. The various embodiments are not limited to multi-modality medical imaging systems, but may be used on a single modality medical imaging system such as a stand-alone PET imaging system or a stand-alone SPECT imaging system, for example. Moreover, the various embodiments are not limited to medical imaging systems for imaging human subjects, but may include veterinary or non-medical systems for imaging non-human objects.

Referring toFIG. 1, the multi-modality imaging system10includes a first modality unit11and a second modality unit12. The two modality units enable the multi-modality imaging system10to scan an object or patient in a second modality using the second modality unit12. The multi-modality imaging system10allows for multiple scans in different modalities to facilitate an increased diagnostic capability over single modality systems. In one embodiment, multi-modality imaging system10is a Computed Tomography/Positron Emission Tomography (CT/PET) imaging system10, e.g., the first modality11is a CT imaging system11and the second modality12is a PET imaging system12. The CT/PET system10is shown as including a gantry13representative of a CT imaging system and a gantry14that is associated with a PET imaging system. As discussed above, modalities other than CT and PET may be employed with the multi-modality imaging system10.

The gantry13includes an x-ray source15that projects a beam of x-rays toward a detector array18on the opposite side of the gantry13. Detector array18is formed by a plurality of detector rows (not shown) including a plurality of detector elements which together sense the projected x-rays that pass through a medical patient22. Each detector element produces an electrical signal that represents the intensity of an impinging x-ray beam and hence allows estimation of the attenuation of the beam as it passes through the patient22. During a scan to acquire x-ray projection data, gantry13and the components mounted thereon rotate about a center of rotation.

FIG. 2is a block schematic diagram of the PET imaging system12illustrated inFIG. 1in accordance with an embodiment of the present invention. The PET imaging system12includes a detector ring assembly40including a plurality of detector crystals. The PET imaging system12also includes a controller or processor44, to control normalization, image reconstruction processes and perform calibration. Controller44is coupled to an operator workstation46. Controller44includes a data acquisition processor48and an image reconstruction processor50, which are interconnected via a communication link52. PET imaging system12acquires scan data and transmits the data to data acquisition processor48. The scanning operation is controlled from the operator workstation46. The data acquired by the data acquisition processor48is reconstructed using the image reconstruction processor50.

The detector ring assembly40includes a central opening, in which an object or patient, such as patient22may be positioned using, for example, a motorized table24(shown inFIG. 1). The motorized table24is aligned with the central axis of detector ring assembly40. This motorized table24moves the patient22into the central opening of detector ring assembly40in response to one or more commands received from the operator workstation46. A PET scanner controller54, also referred to as the PET gantry controller, is provided (e.g., mounted) within PET system12. The PET scanner controller54responds to the commands received from the operator workstation46through the communication link52. Therefore, the scanning operation is controlled from the operator workstation46through PET scanner controller54.

During operation, when a photon collides with a crystal62on a detector ring40, it produces a scintillation event on the crystal. Each photomultiplier tube or photosensor produces an analog signal that is transmitted on communication line64when a scintillation event occurs. A set of acquisition circuits66is provided to receive these analog signals. Acquisition circuits66produce digital signals indicating the three-dimensional (3D) location and total energy of the event. The acquisition circuits66also produce an event detection pulse, which indicates the time or moment the scintillation event occurred. These digital signals are transmitted through a communication link, for example, a cable, to an event locator circuit68in the data acquisition processor48.

The data acquisition processor48includes the event locator circuit68, an acquisition CPU70, and a coincidence detector72. The data acquisition processor48periodically samples the signals produced by the acquisition circuits66. The acquisition CPU70controls communications on a back-plane bus74and on the communication link52. The event locator circuit68processes the information regarding each valid event and provides a set of digital numbers or values indicative of the detected event. For example, this information indicates when the event took place and the position of the scintillation crystal62that detected the event. An event data packet is communicated to the coincidence detector72through the back-plane bus74. The coincidence detector72receives the event data packets from the event locator circuit68and determines if any two of the detected events are in coincidence. Coincidence is determined by a number of factors. First, the time markers in each event data packet must be within a predetermined time period, for example, 12.5 nanoseconds, of each other. Second, the line-of-response (LOR) formed by a straight line joining the two detectors that detect the coincidence event should pass through the field of view in the PET imaging system12. Events that cannot be paired are discarded. Coincident event pairs are located and recorded as a coincidence data packet that is communicated through a physical communication link78to a sorter/histogrammer80in the image reconstruction processor50.

The image reconstruction processor50includes the sorter/histogrammer80. During operation, sorter/histogrammer80generates a data structure known as a histogram. A histogram includes a large number of cells, where each cell corresponds to a unique pair of detector crystals in the PET scanner. Because a PET scanner typically includes thousands of detector crystals, the histogram typically includes millions of cells. Each cell of the histogram also stores a count value representing the number of coincidence events detected by the pair of detector crystals for that cell during the scan. At the end of the scan, the data in the histogram is used to reconstruct an image of the patient. The completed histogram containing all the data from the scan is commonly referred to as a “result histogram.” The term “histogrammer” generally refers to the components of the scanner, e.g., processor and memory, which carry out the function of creating the histogram.

The image reconstruction processor50also includes a memory module82, an image CPU84, an array processor86, and a communication bus88. During operation, the sorter/histogrammer80counts all events occurring along each projection ray and organizes the events into 3D data. This 3D data, or sinogram, is organized in one exemplary embodiment as a data array90. Data array90is stored in the memory module82. The communication bus88is linked to the communication link52through the image CPU84. The image CPU84controls communication through communication bus88. The array processor86is also connected to the communication bus88. The array processor86receives data array90as an input and reconstructs images in the form of image array92. Resulting image arrays92are then stored in memory module82.

The images stored in the image array92are communicated by the image CPU84to the operator workstation46. The operator workstation46includes a CPU94, a display96, and an input device98. The CPU94connects to communication link52and receives inputs, e.g., user commands, from the input device98. The input device98may be, for example, a keyboard, mouse, a touch-screen panel, and/or a voice recognition system, and so on. Through input device98and associated control panel switches, the operator can control the operation of the PET imaging system12and the positioning of the patient22for a scan. Similarly, the operator can control the display of the resulting image on the display96and can perform image-enhancement functions using programs executed by the workstation CPU94.

The detector ring assembly40includes a plurality of detector units. The detector unit may include a plurality of detectors, light guides, scintillation crystals and analog application specific integrated chips (ASICs). For example, the detector unit may include twelve SiPM devices, four light guides, 144 scintillation crystals, and two analog ASICs.

As another example,FIG. 3is a schematic illustration of a NM imaging system such as a SPECT imaging system300having a plurality of imaging detectors302mounted on a gantry304.

The imaging detectors302may be configured to rotate around a fixed pivot. The movement of the imaging detectors302is controlled to reduce the likelihood or avoid collision among the moving imaging detectors and/or reduce the likelihood of one imaging detector obstructing the field of view of another imaging detector. For example, the SPECT imaging system300in some embodiments provides coordinated swinging or rotating motion of a plurality of imaging detectors302or detector heads.

In particular, a plurality of imaging detectors302are mounted to a gantry304and/or a patient support structure (not shown) (e.g., under a patient table320), which may define a table support for a patient table320. In the illustrated embodiment, the imaging detectors302are configured as a detector array306positioned around the subject310(e.g., a patient), as viewed inFIG. 3. The detector array306may be coupled directly to the gantry304, or may be coupled via support members312thereto, to allow movement of the entire array306relative to the gantry304(e.g., rotational movement in the clockwise or counter-clockwise direction as viewed inFIG. 3). Additionally, each of the imaging detectors302includes a detector unit314, at least some of which are mounted to a movable detector carrier316(e.g., a support arm or actuator that may be driven by a motor to cause movement thereof) that extends from the gantry304. In some embodiments, the detector carriers316allow movement of the detector units314towards and away from the subject310, such as linearly. Thus, in the illustrated embodiment the detector array306is around the subject310and may allow linear movement of the detector units314, such as towards or away from the patient table320in one embodiment. However, other configurations and orientations are possible as described herein, as well as different types of movements (e.g., transverse or perpendicular movement relative to the patient table320). It should be noted that the movable detector carrier316may be any type of support that allows movement of the detector units314relative to the support member312and/or gantry304, which in various embodiments allows the detector units314to move linearly towards and away from the support member312, such as radially inward and outwards for positioning adjacent the subject310. For example, as described herein, the detector units314may be controlled to move independently of each other towards or away from the subject310, as well as capable of rotational, pivoting, or tilting movement in some embodiments.

Each of the imaging detectors302in various embodiments is smaller than a conventional whole body or general purpose imaging detector. A conventional imaging detector may be large enough to image most or all of a width of a patient's body at one time and may have a diameter of approximately 50 cm or more. In contrast, each of the imaging detectors302may include one or more detector units314coupled to a respective detector carrier316and having dimensions of 4 cm to 20 cm and may be formed of Cadmium Zinc Telluride (CZT) tiles or modules. For example, each of the detector units314may be 8×8 cm in size and be composed of a plurality of CZT pixelated modules (not shown). For example, each module may be 4×4 cm in size and have 16×16=256 pixels. In some embodiments, each detector unit314includes a plurality of modules, such as an array of 1×7 modules. However, different configurations and array sizes are contemplated including, for example, detector units314having multiple rows of modules.

It should be understood that the imaging detectors may be different sizes and/or shapes with respect to each other, such as square, rectangular, circular, or another shape. An actual field of view (FOV) of each of the imaging detectors302may be directly proportional to the size and shape of the respective imaging detector.

The gantry304may be formed with an aperture318(e.g., opening or bore) therethrough as illustrated. The patient table320is configured with a support mechanism, such as the patient support structure, to support and carry the subject310in one or more of a plurality of viewing positions within the aperture318and relative to the imaging detectors302. Alternatively, the gantry304may comprise a plurality of gantry segments (not shown), each of which may independently move a support member312or one or more of the imaging detectors302.

The gantry304may also be configured in other shapes, such as a “C”, “H”, and “L”, for example, and may be rotatable about the subject310. For example, the gantry304may be formed as a closed ring or circle, or as an open arc or arch which allows the subject310to be easily accessed while imaging and facilitates loading and unloading of the subject310, as well as reducing claustrophobia in some subjects310. For example, in some embodiments the gantry304may be arc shaped and the support members312movable along the arc to position the detector units314at different locations along the gantry304. In some embodiments, the detector units314may also be independently movable along the gantry304.

Additional imaging detectors (not shown) may be positioned to form rows of detector arrays or an arc or ring around the subject310. By positioning multiple imaging detectors302at multiple positions with respect to the subject310, such as along an imaging axis (e.g., head to toe direction of the subject310), image data specific for a larger FOV may be acquired more quickly.

Each of the imaging detectors302has a radiation detection face, which is directed towards the subject310or a region of interest within the subject310. The radiation detection faces may be covered by or have coupled thereto a collimator322. The actual FOV for each of the imaging detectors302may be increased, decreased, or relatively unchanged by the type of collimator322. In one embodiment, the collimator322is a multi-bore collimator, such as a parallel-hole collimator. However, other types of collimators, such as converging or diverging collimators may optionally or alternatively be used. Other examples for the collimator322include pinhole, parallel-beam converging, diverging fan-beam, converging or diverging cone-beam, multi-bore converging, multi-bore converging fan-beam, multi-bore converging cone-beam, multi-bore diverging, or other types of collimators.

Optionally, multi-bore collimators may be constructed to be registered with pixels of the detector units314, which in one embodiment are CZT detectors. However, other materials may be used. Registered collimation may improve spatial resolution by forcing photons going through one bore to be collected primarily by one pixel. Additionally, registered collimation may improve sensitivity and energy response of pixelated detectors as detector area near the edges of a pixel or in between two adjacent pixels may have reduced sensitivity or decreased energy resolution or other performance degradation. Having collimator septa directly above the edges of pixels reduces the chance of a photon impinging at these degraded performance locations, without decreasing the overall probability of a photon passing through the collimator.

A controller unit330may control the movement and positioning of the patient table320, imaging detectors302, gantry304, and/or the collimators322. A range of motion before or during an acquisition, or between different image acquisitions, is set to maintain the actual FOV of each of the imaging detectors302directed, for example, towards or “aimed at” a particular area or region of the subject310or along the entire subject310.

The controller unit330may have a gantry motor controller332, table controller334, detector controller336, pivot controller338, and collimator controller340. The controllers330,332,334,336,338,340may be automatically commanded by a processing unit350, manually controlled by an operator, or a combination thereof. The gantry motor controller332may move the imaging detectors302with respect to the subject310, for example, individually, in segments or subsets, or simultaneously in a fixed relationship to one another. For example, in some embodiments, the gantry controller332may cause the imaging detectors302and/or one or more of the support members312to rotate about the subject310, which may include motion of less than or up to 180 degrees (or more).

The table controller334may move the patient table320to position the subject310relative to the imaging detectors302. The patient table320may be moved in up-down directions, in-out directions, and right-left directions, for example. The detector controller336may control movement of each of the imaging detectors302to move closer to and farther from a surface of the subject310, such as by controlling translating movement of the detector carriers316linearly towards or away from the subject310(e.g., sliding or telescoping movement). Optionally, the detector controller336may control movement of the detector carriers316to allow coordinated movement of the detector array306.

The pivot controller338may control pivoting, rotating, or swinging movement of the detector units314at ends of the detector carriers316, and/or the detector carrier316. For example, one or more of the detector units314or detector carriers316may be rotated or swung about at least one axis to view the subject310from a plurality of angular orientations. The collimator controller340may adjust a position of an adjustable collimator, such as a collimator with adjustable strips (or vanes) or adjustable pinhole(s).

It should be noted that motion of one or more imaging detectors302may be in directions other than strictly axially or radially, and optionally, motions in several motion directions may be used. Moreover, the motions of the imaging detectors302are coordinated in various embodiments as described herein. Therefore, the term “motion controller” may be used to indicate a collective name for all motion controllers. It should be noted that the various controllers may be combined, for example, the detector controller336and pivot controller338may be combined to provide the different movements described herein.

Prior to acquiring an image of the subject310or a portion of the subject310, the imaging detectors302, gantry304, patient table320, and/or collimators322may be adjusted as discussed in more detail herein, such as to first or initial imaging positions, as well as subsequent imaging positions. The imaging detectors302may each be positioned to image a portion of the subject310. Alternatively, one or more of the imaging detectors302may not be used to acquire data, such as the imaging detectors302at ends of the detector array306, which as illustrated inFIG. 3are in a protracted position towards the subject310. Positioning may be accomplished manually by the operator and/or automatically, which may include using other images acquired before the current acquisition, such as by another imaging modality such as CT, MM, X-ray, PET, or ultrasound.

After the imaging detectors302, gantry304, patient table320, and/or collimators322are positioned, one or more images are acquired by one or more of the imaging detectors302being used, which may include pivoting or swinging motion of one or more of the detector units314, which may pivot, rotate, or swing to different degrees or between different ranges of angles. The image data acquired by each imaging detector302may be combined and reconstructed into a composite image, which may comprise two-dimensional (2D) images, a three-dimensional (3D) volume, or a 3D volume over time (4D).

In one embodiment, the imaging detectors302, gantry304, patient table320, and/or collimators322remain stationary after being initially positioned. In another embodiment, an effective field of view for one or more of the imaging detectors may be increased by movement such as pivoting, rotating, or swinging one or more of the imaging detectors302, rotating the detector array306with the gantry304, adjusting one or more of the collimators322, or moving the patient table320.

In various embodiments, a data acquisition system (DAS)360receives electrical signal data produced by the imaging detectors302and converts this data into digital signals for subsequent processing. An image reconstruction device362and a data storage device364may be provided in addition to the processing unit350. It should be noted that one or more functions related to one or more of data acquisition, motion control, data processing, and image reconstruction may be accomplished through hardware, software, and/or by shared processing resources, which may be located within or near the imaging system300, or may be located remotely. Additionally, a user input device366may be provided to receive user inputs (e.g., control commands), as well as a display368for displaying images.

Additionally, a detector position controller365is also provided, which may be implemented in hardware, software, or a combination thereof. For example, as shown inFIG. 3, the detector position controller365may form part of or operate in connection with the processing unit350. In some embodiments, the detector position controller365may be a module that operates to control the movement of the imaging detectors302, including the detector units314, such that coordinated or synchronized movement is provided as described herein. It should be noted that movement of a plurality of the imaging detectors302and/or detector units314may be performed at the same time (e.g., simultaneously or concurrently) or at different times (e.g., sequentially or step-wise, such as back and forth between two detector units314). It also should be understood that when referring to a detector head, such a detector head may include one or multiple detector modules.

As mentioned hereinabove, Compton scattering occurs when one or both annihilation photons interact with matter (e.g., the patient22or the subject310), change direction, and lose energy. The detection of such scattered photons causes errors and/or image artifacts. An NM imaging system such as the PET imaging system12or the SPECT imaging system300may therefore be configured to perform scatter correction to reduce errors or image artifacts caused by scattered photons. Scatter correction may be based on measurements of scattered photons or scatter simulations based on emission and attenuation information. For example, one approach to scatter correction is based on the use of multiple energy windows, wherein the information from other windows is used to estimate the scatter within the photopeak window, and the estimated scatter is subtracted from the photopeak window. This approach works relatively well for NaI detectors with a modest 10% energy resolution, for example. However, for CZT gamma cameras which offer significantly improved energy resolution in comparison to traditional NaI detectors, the photopeak resolution is often asymmetric due to incomplete charge collection within the detector, resulting in many photopeak events being incorrectly sorted into lower energy bins. This “tailing” effect caused by such incorrectly binned photopeak photons contaminates the true scatter signal in the lower energy bins, thereby negatively impacting scatter correction methods such as the multiple energy window technique mentioned hereinabove that rely on estimates of scatter from the spectra.

As described further herein, systems and methods are provided for scatter correction based on a decomposition of photopeak and scattered photons acquired in two or more energy windows. As an illustrative example of how photopeak and scattered photons may be distributed over a range of energies and thus decomposed as described further herein,FIG. 4shows a graph400illustrating example distributions of scattered photons and photopeak photons acquired or measured with PET detectors during a scan according to an embodiment of the invention. Graph400includes a plot of the total distribution405of photon counts, including both scattered photons and photopeak photons. Graph400further includes a plot of the photopeak distribution407of the photopeak photons, a plot of the first scatter distribution410for first-order scattered photons, a plot of the second scatter distribution420for second-order scattered photons, a plot of the third scatter distribution430for third-order scattered photons, a plot of the fourth scatter distribution440for fourth-order scattered photons, a plot of the fifth scatter distribution450for fifth-order scattered photons, and a plot of the sixth scatter distribution460for sixth-order scattered photons.

The photopeak distribution407clearly illustrates the so-called photopeak comprising the primary gamma photon energy, which comprises 511 keV in the example but may comprise a different energy in other examples, such as for SPECT, depending on the radionuclide(s). Notably, the total distribution405at the photopeak energy is higher than the photopeak distribution407, due to contributions from scattered photons.

At lower energies, the total distribution405is substantial but is mostly composed of scattered photons as indicated by the distributions410,420,430,440,450, and460. However, the photopeak distribution407is non-zero in this scatter region due to the tailing effect mentioned hereinabove, and so the total distribution405at lower energies includes contributions from photopeak photons in the photopeak distribution407.

As described further herein, the photopeak photons may be removed from the lower energy bins, for example at a scatter window or low energy window positioned near 120 keV in the case of SPECT Tc99m imaging, in order to obtain corrected scatter data. This corrected scatter data in turn may be used to correct the photopeak window or high energy window, or the photopeak distribution407near the photopeak energy, such that a scatter-free image may be obtained.

FIG. 5shows a graph500illustrating example distributions of scattered and photopeak photons acquired or measured with CZT detectors in a SPECT imaging system, such as the SPECT imaging system300, according to an embodiment. In particular, graph500includes a plot of the total distribution505of the photon counts, including both scattered and photopeak photons, as well as a plot of the photopeak distribution510of the photopeak photons. Both distributions505and510clearly illustrate the photopeak comprising the primary gamma photon energy. The total distribution505further clearly depicts the tailing effect of scattered photons on the distribution when compared to the pure photopeak distribution510.

As an illustrative example of how the photopeak distribution and the scatter distributions may be decomposed, the total amount of photons HW in the high energy window may be expressed as:
HW=P+β*S,
while the total number of photons LW in the low energy window may be expressed as:
LW=α*P+S,
where P is the number of photopeak photons in the high energy window, S is the number of scattered photons in the low energy window, β*S is the number of scattered photons in the high energy window, and α*P is the number of photopeak photons in the low energy window. The scaling factors α and β are both less than one. The scaling factor α may be measured during calibration of the detector without scattered media, while the scaling factor β may be measured during calibration on phantom data based on the width and position of the high and low energy windows.

The distribution of scattered photons SD may thus be obtained by multiplying the total number of photons HW in the high energy window by the scaling factor α and subtracting the result from the total number of photons LW in the low energy window, such that:
SD=LW−α*HW=(1−α*β)*S.

Since both scaling factors α and β are less than one, the product of α and β is less than one, and the factor (1−α*β) is therefore greater than 0 and less than one. An estimate of the amount of photons S in the scatter window may thus be obtained by dividing the resulted amount of scattered photons in SD by the factor (1−α*β).

Various methods are provided herein for improved scatter correction by considering tailed photopeak photons. As an example,FIG. 6shows a high-level flow chart illustrating an example method600for scatter correction according to an embodiment of the invention. In particular, method600relates to removing photopeak photons from a scatter window to improve scatter correction of a photopeak window in an acquired imaging dataset. Method600is described with regard to the systems and components ofFIGS. 1-3, though it should be appreciated that the method600may be implemented with other systems and components without departing from the scope of the present disclosure. Method600may be implemented as executable instructions in memory, such as non-transitory memory of the memory module82, and executed by one or more processors such as the acquisition CPU70, the image CPU84, and the array processor86, as an illustrative and non-limiting example. As another illustrative and non-limiting example, method600may be implemented as executable instructions in memory, such as non-transitory memory of the data storage device364, and executed by one or more processors such as the processing unit350and/or the image reconstruction device362.

Method600begins at605. At605, method600acquires imaging data with at least two energy windows. For example, method600acquires the imaging data in accordance with a nuclear medicine imaging protocol such as a single-photon emission computed tomography (SPECT) imaging protocol or a positron emission tomography (PET) imaging protocol. In some examples, method600acquires the imaging data with a wide energy window, wherein the wide energy window is sufficiently wide enough (e.g., includes a large enough range of energy bins) to cover at least two energy windows that do not overlap. One energy window of the at least two energy windows comprises a photopeak window including a threshold range of energies around the photopeak energy. A second window of the at least two energy windows comprises a scatter window including a threshold range of energies, wherein the threshold range of energies is displaced away from the photopeak energy such that the threshold range of energies for the scatter window does not overlap with the threshold range of energies for the photopeak window. As the photopeak energy is typically near the higher end of the range of energy bins measured during a scan, the photopeak window may also be referred to herein as a high energy window. Similarly, scatter photons typically have a lower energy in comparison to photopeak photons due to the scattering, and so the scatter window is also referred to herein as a low energy window. Continuing at610, method600separates the imaging data with a high energy window and a low energy window. The high energy window may comprise the photopeak window, while the low energy window may comprise the scatter window as discussed hereinabove.

At615, method600removes the photopeak photon distribution known from the imaging data in the high energy window from the imaging data in the low energy window to obtain a scattered photon distribution. That is, method600estimates the distribution of photopeak photons in the low energy window based on the imaging data in the high energy window that primarily contains photopeak photons, and removes the photopeak photons from the imaging data in the low energy window. Such an approach is possible because photopeak photons have the same spatial distribution in both the high and low energy windows. In this way, the corrected imaging data in the low energy window corresponds to scatter photons only, and thus may be used for a more accurate scatter correction of the imaging data in the high energy window. In some examples, method600uses an image-based subtraction method to remove the photopeak photon distribution from the imaging data of the low energy window. An example image-based subtraction method is described further herein with regard toFIG. 7. In other examples, method600may remove the photopeak photon distribution from the low energy window by applying a correction of the two datasets during iterative reconstruction. In yet other examples, method600may remove the photopeak photon distribution from the low energy window by applying a correction to the low energy window during iterative post-reconstruction processing. For example, to apply the correction during iterative reconstruction, method600may reconstruct the pure scatter image with an iterative algorithm such as Expectation-Maximization (EM). During reconstruction, method600may add weighted projections of the high energy peak to calculated forward projections of the low energy peak:

where REC is the reconstructed pure scatter image, n is the iteration number, NORM is a normalization factor, LPP is low peak projections, Forward(Recn−1) is the forwarded projections of the (n−1) iteration, and w is the weight of the high peak projection (HPP).

As another example, to apply the correction during iterative post-reconstruction processing, method600may reconstruct low energy images (LEI) and high energy images (HEI) separately, and, starting with LEI, iteratively calculate a “pure” scatter image S:

At620, method600corrects the imaging data in the wide energy window or the high energy window based on the scattered photon distribution. In some examples, method600may correct the imaging data by using an image-based or a projection-based subtraction technique, during the process of iterative reconstruction, or in iterative post-processing. For example, the corrected imaging data of the lower energy window (i.e., the corrected scatter projections) may be subtracted from the imaging data or the projection data of the high energy window. As another example, the corrected scatter projections may be used to estimate the scatter in the high energy window, for example by scaling or weighting the corrected scatter projections, and then this estimated scatter in the high energy window may be used during iterative reconstruction by adding the estimated scatter to an estimated scatter-free projection to match the acquired projections or imaging data of the high energy window.

At625, method600outputs a scatter-corrected image reconstructed from the corrected imaging data. For example, method600may obtain the scatter-corrected image at620when correcting the imaging data in the high energy window based on the scattered photon distribution, and so method600may output the scatter-corrected image to a display device, such as display96, or to a non-transitory memory for storage and later retrieval. As another example, if corrected projection data for the high energy window is obtained at620, method600may reconstruct the corrected projection data based on an image reconstruction technique such as iterative reconstruction, analytic reconstruction (e.g., filtered back projection), or a deep learning image reconstruction model. Method600then returns.

FIG. 7shows a high-level flow chart illustrating an example method700for scatter correction according to an embodiment of the invention. In particular, method700relates to an image-based technique for correcting a scatter estimate in order to improve scatter correction. Method700is described with regard to the systems and components ofFIGS. 1-3, though it should be appreciated that the method700may be implemented with other systems and components without departing from the scope of the present disclosure. Method700may be implemented as executable instructions in memory, such as non-transitory memory of the memory module82, and executed by one or more processors such as the acquisition CPU70, the image CPU84, and the array processor86, as an illustrative and non-limiting example. As another illustrative and non-limiting example, method600may be implemented as executable instructions in memory, such as non-transitory memory of the data storage device364, and executed by one or more processors such as the processing unit350and/or the image reconstruction device362.

Method700begins at705. At705, method700acquires imaging data with at least two energy windows. For example, method700acquires the imaging data in accordance with a nuclear medicine imaging protocol such as a single-photon emission computed tomography (SPECT) imaging protocol or a positron emission tomography (PET) imaging protocol. As discussed hereinabove, one energy window of the at least two energy windows comprises a photopeak window including a threshold range of energies around the photopeak energy. A second window of the at least two energy windows comprises a scatter window including a threshold range of energies, wherein the threshold range of energies is displaced away from the photopeak energy such that the threshold range of energies for the scatter window does not overlap with the threshold range of energies for the photopeak window. As the photopeak energy is typically near the higher end of the range of energy bins measured during a scan, the photopeak window may also be referred to herein as a high energy window. Similarly, scatter photons typically have a lower energy in comparison to photopeak photons due to the scattering, and so the scatter window is also referred to herein as a low energy window.

At710, method700reconstructs a first image from the imaging data in the high energy window with a contrast and resolution matched to scattered photons. For example, method700may reconstruct the first image by perform iterative reconstruction with the imaging data of the high energy window for a limited number of iterations, such as two or three iterations, such that the first image is reconstructed at a low resolution with a primarily low frequency signal. As typical scattered photon distributions do not have high frequencies and are primarily low frequency, the contrast and resolution is thus matched to the scattered photon distribution.

Similarly, at715, method700reconstructs a second image from the imaging data in the low energy window with a contrast and resolution matched to scattered photons. For example, method700may perform iterative reconstruction with the imaging data of the low energy window for a limited number of iterations, such as two or three iterations, such that the second image is reconstructed with the low resolution matched to a typical scattered photon distribution.

At720, method700corrects the second image according to a photopeak photon distribution known from the first image to obtain a scattered photon distribution image. For example, method700may subtract the first image from the second image to obtain the scattered photon distribution image. In particular, method700may perform a weighted subtraction, wherein the first image is weighted by the scaling factor α described hereinabove to obtain a scattered photon distribution image SD from the first image HW and the second image LW:
SD=LW−α*HW,
where the scaling factor α is obtained from detector calibration without scatter media, as discussed hereinabove.

Continuing at725, method700processes the scattered photon distribution image (e.g., the image SD obtained via the subtraction method at720). For example, method700may scale the image by multiplying the image by the inverse of the factor (1−α*β) described hereinabove.

At730, method700performs scatter correction on the imaging data based on the scattered photon distribution image to obtain a scatter-corrected image. In some examples, method700may correct the imaging data by using an image-based or a projection-based subtraction technique, during the process of iterative reconstruction, or in iterative post-processing. For example, the corrected imaging data of the lower energy window (i.e., the corrected scatter projections) may be subtracted from the imaging data or the projection data of the high energy window. As another example, the corrected scatter projections may be obtained by transforming the processed scattered photon distribution image to projection space to obtain processed scattered photon distribution projections, and these corrected scatter projections may be used to estimate the scatter in the high energy window, for example by scaling or weighting the corrected scatter projections, and then this estimated scatter in the high energy window may be used during iterative reconstruction by adding the estimated scatter to an estimated scatter-free projection to match the acquired projections or imaging data of the high energy window.

At735, method700outputs the scatter-corrected image, for example to the display96or to storage for later retrieval. Method700then returns.

FIG. 8shows a set of images800illustrating example scatter images with and without scatter correction according to an embodiment of the invention. Each of the scatter images800are acquired of a same subject (e.g., a same phantom) and are reconstructed with a same technique from a same energy window, and depict a same region of interest (ROI)815overlaid on each image. in particular, the set of images800includes a scatter image810acquired with a sodium iodine (NaI) gamma camera, such that the scatter image810does not include tailed photons. The correction methods described hereinabove therefore may not be applicable to the scatter image810, as the scatter image810does not include photopeak photons. The set of images800further includes a scatter image820acquired with a CZT camera that includes scattered and tailed photopeak photons. A hot spot is clearly visible in the scatter image820caused by the tailed photopeak photons. The set of images800further includes a corrected scatter image830corresponding to the scatter image820but corrected for tailed photopeak photons according to the methods described herein. The hot spot clearly visible in the scatter image820is reduced in the corrected scatter image830, such that the corrected scatter image830is more consistent with the pure scatter distribution depicted in the scatter image810.

For clarity,FIG. 9shows a graph900illustrating example distributions for the example scatter images ofFIG. 8. In particular, the graph900depicts the distribution of photon counts as a function of pixel location in the scatter images800. The graph900includes a plot of the distribution910for the scatter image810, a plot of the distribution920for the scatter image820, and a plot of the distribution930for the corrected scatter image830.

The distribution920for the un-corrected scatter image820clearly depicts the hot spot caused by the tailed photons. The distribution930for the corrected scatter image830indicates that the correction techniques described herein accurately correct the scatter distribution such that the distribution830resembles the pure scatter distribution910. That is, by correcting the scatter data to remove tailed photopeak photons, the scatter estimates for CZT detectors may approach the accuracy of scatter estimates of NaI detectors. Scatter correction techniques that were previously unavailable for NM imaging systems configured with CZT gamma cameras due to the tailing effect, such as the dual energy window technique, may thus be performed with such NM imaging systems.

To illustrate the efficacy of the methods provided herein for correcting scatter distributions,FIGS. 10 and 11depict example images. For example,FIG. 10shows a set of images1000illustrating an example scatter correction according to an embodiment. In particular, the first image1010depicts high energy projections as acquired while the second image1020depicts low energy projections as acquired. The second image1020clearly suggests the presence of tailed photopeak photons, as the second image1020resembles the first image1010at a lower resolution. Notably, the circular dark object in the first image1010is visible in the second image1020. The third image1030depicts corrected scatter projections corresponding to the projections of the second image1020with the correction technique provided herein applied thereto. The third image1030thus depicts “pure” scatter projections of the second image1020with the tailed photopeak photons removed. It should be appreciated that attempting to perform scatter correction of the projection data of the high energy window depicted in the first image1010based on the projection data of the low energy window depicted in the second image1020would result in the removal of photopeak photons from the high energy window, thereby reducing the accuracy of the scatter correction and lowering the image quality of the final image overall.

Similarly,FIG. 11shows a set of images1100illustrating another example scatter correction according to an embodiment. The set of images1100includes a first image1110comprising a low-resolution reconstructed high energy window image, a second image1120comprising a low-resolution reconstructed low energy window image, and a third image1130comprising a pure scatter image corrected for tailed photons. It should be appreciated that attempting to correct the first image1110based on the second image1120would degrade the overall accuracy and image quality of the first image1110, as the second image1120includes photopeak photons. In contrast, correcting the first image1110based on the corrected or pure scatter distribution of the third image1130would result in a more accurate scatter correction and thus an improved image quality.

A technical effect of the disclosure includes an improved image quantitative and qualitative accuracy for images acquired with CZT detectors. Another technical effect of the disclosure includes the increased accuracy of scatter correction for NM imaging systems. Yet another technical effect of the disclosure includes the reduction or elimination of photopeak photons from scatter distributions. Another technical effect of the disclosure includes the display of a reconstructed image acquired with CZT detectors with accurate scatter correction applied thereto. Yet another technical effect of the disclosure includes the reduction of computational complexity for accurate scatter correction for images acquired with CZT detectors, as the present methods and systems do not require computationally expensive scatter simulations for scatter correction.

In one embodiment, a method for NM imaging comprises acquiring, with a plurality of detectors, imaging data separated into a high energy window and a low energy window, removing photopeak photons from the imaging data in the low energy window to obtain a corrected scatter distribution, correcting the imaging data based on the corrected scatter distribution, and outputting a scatter-corrected image reconstructed from the corrected imaging data.

In a first example of the method, the method further comprises reconstructing a first image from the imaging data in the high energy window with contrast and resolution matched to scattered photons, and reconstructing a second image from the imaging data in the low energy window with contrast and resolution matched to the scattered photons. In a second example of the method optionally including the first example, reconstructing the first image and the second image with the contrast and the resolution matched to the scattered photons comprises performing iterative reconstruction of the imaging data in the high energy window and the low energy window, respectively, for a reduced number of iterations. In a third example of the method optionally including one or more of the first and second examples, removing the photopeak photon distribution from the imaging data in the low energy window comprises subtracting the first image from the second image to obtain a scatter image of the corrected scatter distribution. In a fourth example of the method optionally including one or more of the first through third examples, correcting the imaging data based on the corrected scatter distribution comprises correcting the imaging data based on the scatter image. In a fifth example of the method optionally including one or more of the first through fourth examples, the method further comprises processing the scatter image by at least scaling the scatter image based on calibration of the plurality of detectors and a calibration of a width and a position of the high energy window and the low energy window. In a sixth example of the method optionally including one or more of the first through fifth examples, the method further comprises weighting the first image with a scaling factor obtained from calibration of the plurality of detectors prior to subtracting the first image from the second image. In a seventh example of the method optionally including one or more of the first through sixth examples, correcting the imaging data based on the corrected scatter distribution comprises using the corrected scatter distribution during iterative reconstruction as a scatter estimate. In an eighth example of the method optionally including one or more of the first through seventh examples, the plurality of detectors comprise CZT detectors.

In another embodiment, a method for NM imaging comprises acquiring, with a plurality of detectors, imaging data separated into a high energy window and a low energy window, reconstructing a first image from the imaging data in the high energy window with a low resolution, reconstructing a second image from the imaging data in the low energy window with a low resolution, correcting the second image according to a photopeak photon distribution known from the first image to obtain a scattered photon distribution image, correcting the imaging data based on the scattered photon distribution image, and outputting a scatter-corrected image reconstructed from the corrected imaging data.

In a first example of the method, correcting the second image according to the photopeak photon distribution known from the first image comprises scaling the first image and subtracting the scaled first image from the second image to obtain the scattered photon distribution image. In a second example of the method optionally including the first example, scaling the first image comprises multiplying the first image by a scaling factor obtained from calibrating the plurality of detectors. In a third example of the method optionally including one or more of the first and second examples, reconstructing the first image from the imaging data in the high energy window comprises performing iterative reconstruction of the imaging data in the high energy window for a limited number of iterations, and reconstructing the second image from the imaging data in the high energy window comprises performing iterative reconstruction of the imaging data in the low energy window for the limited number of iterations. In a fourth example of the method optionally including one or more of the first through third examples, the method further comprises reconstructing the scatter-corrected image by performing iterative reconstruction of the corrected imaging data for a number of iterations greater than the limited number of iterations.

In yet another embodiment, a system comprises a detector array including a plurality of detectors, and a computing device communicatively coupled to the detector array and configured with instructions in non-transitory memory that when executed cause the computing device to: acquire, via the detector array, imaging data separated into a high energy window and a low energy window; remove photopeak photons from the imaging data in the low energy window to obtain a corrected scatter distribution; correct the imaging data based on the corrected scatter distribution; and output a scatter-corrected image reconstructed from the corrected imaging data.

In a first example of the system, the computing device is further configured with instructions in the non-transitory memory that when executed cause the computing device to reconstruct a first image from the imaging data in the high energy window with contrast and resolution matched to scattered photons, and reconstruct a second image from the imaging data in the low energy window with contrast and resolution matched to the scattered photons. In a second example of the system optionally including the first example, reconstructing the first image and the second image with the contrast and the resolution matched to the scattered photons comprises performing iterative reconstruction of the imaging data in the high energy window and the low energy window, respectively, for a reduced number of iterations. In a third example of the system optionally including one or more of the first and second examples, the computing device is further configured with instructions in the non-transitory that when executed cause the computing device to remove the photopeak photons from the imaging data in the low energy window by subtracting the first image from the second image to obtain a scatter image of the corrected scatter distribution, and to correct the imaging data based on the corrected scatter distribution by correcting the imaging data based on the scatter image. In a fourth example of the system optionally including one or more of the first through third examples, the plurality of detectors comprise CZT detectors. In a fifth example of the system optionally including one or more of the first through third examples, the plurality of detectors comprise PET detectors with low energy resolution. In a sixth example of the system optionally including one or more of the first through fifth examples, the system further comprises a display device communicatively coupled to the computing device, wherein the computing device outputs the scatter-corrected image to the display device.