Density cluster imaging

Abstract

A method of improving a generated image obtained from a gamma camera for medical imaging, the image being improved by excluding the contribution of low-energy and scattered gamma rays to the generated image, the method comprising the steps of: detecting gamma ray photons by the gamma camera; converting the detected gamma ray photons into one or more optical photons; detecting the resulting optical photons on the photosensitive surface; processing the detected optical photons into a primary data array comprising pixels, where the pixels map the detection of the optical photons on the photosensitive surface, where the filling pixels correspond to the detection of the optical photons on the photosensitive surface; identifying a cluster of padding pixels in the primary data array; determining a density of fill pixels in the identified clusters; classifying the clusters into high-density clusters (clusters with high-density filled pixels) and low-density clusters (clusters with low-density filled pixels); the contributions of low-density clusters to the generated image are excluded, the low-density clusters corresponding to low-energy and scattered gamma rays.

Description

Invention Field

[0001] This invention relates to density cluster imaging. In particular, but not exclusively, this invention relates to density cluster imaging using gamma rays in medical imaging, and methods for using density cluster imaging. Background of the Invention

[0002] A gamma camera (also known as a γ camera or scintillation camera) is a device used in medical imaging to image gamma radiation emitted from a radioactive isotope. Therefore, the device must be suitable for detecting low levels of gamma radiation emitted by a radioactive isotope at the dose administered to the subject, while also providing images with appropriate resolution to allow medical practitioners to perform medical analysis, diagnosis, or treatment. Thus, gamma-ray medical imaging equipment has sensitivity and resolution suitable for its function. Typically, the device needs to generate medical images of radioactive isotopes with gamma emission energies between approximately 35 keV and approximately 511 keV, where these radioactive isotopes are administered to the subject at levels between approximately 10 MBq and 1000 MBq (e.g., a typical body weight of approximately 70 kg). Ideally, the resulting images will have clinically meaningful resolution obtained within approximately 30 minutes or less. Isotopes, activity, etc., will be adjusted according to specific conditions, such as the subject's weight and the targeted tissue.

[0003] Gamma cameras are commonly used to create images using a technique called scintigraphy. In scintigraphy, radioactive isotopes are typically attached to tracers or drugs (radiopharmaceuticals) that reach specific organs or tissues, allowing these target tissues to be imaged in this way. Therefore, this technique can create visual representations of the physiological processes of target tissues and the functions of organs or tissues for clinical analysis and medical interventions. Thus, this technique not only reveals internal structures hidden from the outside of the subject, but it can also target certain organs or disease states and / or provide more information about their anatomy and function.

[0004] Gamma cameras are typically large, expensive, and stationary devices, and are therefore usually housed in dedicated rooms in hospitals. Patients are typically asked to travel to this location and be inserted into the body of the device for scanning. Smaller gamma cameras with a more limited field of view are more portable but are still relatively large and bulky, and may also lack sensitivity and / or resolution and / or a suitable field of view. Examples of gamma cameras used for medical scintigraphy include Siemens Healthineers' SymbiaIntevo Excel, Oncovision's Sentinella, and Digilad's Ergo Imaging System.

[0005] A key challenge in using gamma-ray cameras in medical imaging is ensuring that the camera essentially records only gamma-ray photons directly from the radioactive isotope administered to the subject. Gamma-ray photons that have deviated from their original path (scattered) should not contribute to the image. An ideal scenario would be... Figure 1 As shown. Figure 1 The diagram illustrates gamma-ray photons emitted by a subject (10) from a gamma-ray (γ) source (11) (shown as the dashed area in the subject diagram). The gamma-ray photons (12) pass through a collimator (e.g., a "pinhole"), strike a detector, and are detected by the detector. In this case, the detector converts the gamma-ray photons into a voltage (23), where the voltage corresponds to the energy of the gamma-ray photon. Figure 1 In the diagram, the output voltage from the device is shown as the voltage peak.

[0006] However, scattered gamma photons (e.g., gamma photons emitted from the subject, but in which the original trajectory of the gamma photons has been altered in some way) and background gamma photons can contribute unwanted noise, making the desired image worse.

[0007] exist Figure 2 An example of this situation is schematically illustrated in the diagram, where gamma-ray photons are scattered in an effect known as Compton scattering (see the inflection point shown in the dashed line (13)). In this process, the gamma photons encounter and interact with charged particles (usually electrons). The gamma photons transfer some of their energy to the charged particles and change their direction of travel. The gamma-ray photons can originate from a background source or actually be initially scattered from a radiation source. Similarly, the detector converts the scattered gamma-ray photons into a voltage. However, in this case, because the scattered gamma-ray photons have lower energy, the detector produces a lower resulting output voltage (24). Therefore, in Figure 2 In the process, scattered gamma-ray photons give voltages corresponding to lower (striated) voltage peaks (24), while gamma-ray photons directly from the radiation source correspond to larger (striated) voltage peaks (23). Gamma-ray photons may undergo multiple scattering events, losing energy and changing their trajectory each time, resulting in scattered gamma-ray photons having a wide range of photon energies and making the original "true" trajectory difficult to determine.

[0008] One type of gamma-ray imaging camera, called a Compton camera, relies on detecting scattered photons to determine the source distribution. A Compton camera must measure the energy lost in the scattering event, the total photon energy, and the angle at which the gamma-ray photons are scattered to determine their original trajectory before scattering. These types of cameras are highly specialized and generally unsuitable for medical imaging applications due to the diffuse distribution of the radioactive isotopes being imaged and the large and variable scattering effects of biological tissues.

[0009] Therefore, gamma cameras used for medical imaging are designed to capture only gamma rays directly from radioactive isotope sources, where these gamma-ray photons will have a distinct energy signature.

[0010] Therefore, ideally, gamma-ray cameras used for medical imaging need to be able to distinguish between gamma-ray photons directly originating from radioactive isotope sources and gamma-ray photons that have been scattered or come from background sources. Ignoring these lower-energy photons is advantageous, as they do not provide direct information about source distribution and only contribute to image noise.

[0011] In existing technologies, an "energy window" is used to filter out low-energy scattered gamma-ray photons. Gamma-ray photons originating from a radioactive isotope source in the true line of sight (unscattered) will have a defined energy characteristic (e.g., their energy falls within a fairly narrow energy range). This is because they lose little or no original energy when emitted from a radiation source. However, scattered gamma-ray photons lose some energy and therefore fall outside the energy window. The energy window can exclude not only scattered gamma-ray photons but also background gamma-ray photons that are similarly unsuitable for the fairly narrow energy window.

[0012] refer to Figure 2 and Figure 3 This is accomplished in a gamma-ray camera by converting the energy of gamma-ray photons into a voltage, where the amplitude of the voltage is proportional to the energy of the gamma-ray photon. The detector also records the location of the detected event as X and Y coordinates. The energy window actually includes an upper and lower voltage level, typically corresponding to approximately ±10% of the peak gamma-ray photon light (as voltage, or converted to energy units keV). Any generated signals outside this energy window are discarded and therefore not included in the final image. The gamma camera then constructs a pixelated image using only the received pulses with known X and Y coordinates. (Reference) Figure 3The energy window is shown as the vertical dashed lines on either side of the light peak (33). For example, in this case, the energy peak (light peak) of the commonly used medical isotope Tc-99m is 140 keV. Therefore, gamma-ray photons with energies between approximately 120 keV and 160 keV directly from the radiation source will be received within the energy window. However, lower-energy photons falling outside the window, such as those from "Compton scattering" (e.g., in...), will be rejected. Figure 3 The peak shown is a low-width peak; (32) is not counted and is not recorded by the gamma camera. In this way, the energy window can be used to filter out scattered gamma photons and background gamma photons. Those skilled in the art can adjust the size of the energy window to suit their needs.

[0013] This image formation process is suitable for analog (e.g., typically using scintillation detectors and photomultiplier tubes) or digital detector imaging systems that convert gamma-ray photon energy into voltage pulses, where the amplitude of the generated voltage pulse is proportional to the energy of the gamma-ray photon. It is not readily applicable to gamma cameras with other designs, such as very small gamma cameras that do not use photomultiplier tubes, or those using highly pixelated detectors where gamma-ray interactions are detected across multiple detector pixels. These types of detectors produce multiple voltage pulses, one per detector pixel (and these are typically short lines on the detector), which must be combined to produce a single voltage pulse. The process typically requires separating multiple gamma-ray interaction events and determining which detector pixels belong to which event. Various algorithms can be used; however, these are generally computationally intensive, with the computational intensity proportional to the number of interaction events to be separated (linear or nonlinear). This can limit performance (e.g. at high photon fluxes) and make implementation difficult.

[0014] Problems encountered in gamma-ray imaging still need to be addressed in this field. In particular, improved solutions to the problems of image formation and the removal of scattered gamma-ray photons remain in demand. Invention Overview

[0015] In a first aspect of the invention, a method is provided to improve a generated image obtained from a gamma camera for medical imaging, the image being improved by excluding the contribution of low-energy and scattered gamma rays to the generated image, the method comprising the steps of: - Gamma camera detects gamma-ray photons; - Convert detected gamma-ray photons into one or more optical photons; - Detection of optical photons on a photosensitive surface; - The detected optical photons are processed into a primary data array including pixels, where the pixels map the detection of optical photons on the photosensitive surface, and the filling pixels correspond to the detection of optical photons on the photosensitive surface; - Identify clusters of fill pixels in the primary data array; - Determine the density of fill pixels in the identified clusters; - Classify clusters into high-density clusters (clusters with high-density fill pixels) and low-density clusters (clusters with low-density fill pixels). - Exclude the contribution of low-density clusters to the generated image; these low-density clusters correspond to low-energy and scattered gamma rays.

[0016] Advantageously, the method of the present invention removes low-energy gamma rays during image acquisition. Advantageously, the method of the present invention can also be used with a gamma camera, wherein the gamma camera is not used to generate voltage pulses proportional to the energy of the detected gamma-ray photons. These advanced systems can be relatively small and can provide high spatial resolution.

[0017] In particular, the present invention provides an improved solution to the problems of image formation and the removal of low-energy gamma-ray photons and detector noise, where high-resolution pixelated detectors are used for gamma-ray imaging, for example, where gamma-ray interactions are detected across multiple detector pixels. Advantageously, the method of the present invention is computationally more efficient than attempting to sum the total voltage across multiple pixels (e.g., using the DBSCAN method), especially in the case of low energy resolution. In one embodiment, the method does not use DBSCAN. Advantageously, because the method of the present invention is computationally more efficient, this means that the associated hardware specifications / requirements can be reduced and / or the size can be minimized. For example, if the processing unit requires less processing, a smaller, less power-consuming, and less expensive processing unit can be used. In turn, a smaller processor cooling unit can be used or even omitted. This reduces assembly costs and any ongoing maintenance costs. Advantageously, smaller hardware components also allow for the manufacture of smaller, more portable systems. Furthermore, it is advantageous that because the method is more efficient, it uses less energy, thus reducing associated operating costs.

[0018] Advantageously, the method of the present invention provides an enhancement of the modulation transfer function (signal-to-noise ratio and / or resolution) in the resulting image. For example, when the method of the present invention is used for medical imaging, this yields improved images that are more useful in medical diagnosis and / or treatment, and may require less time to acquire the image. This improvement is achieved because the image acquired using the method of the present invention is essentially derived only from high-energy photons from the radiation source, and by eliminating the signal of lower-energy photons that may be generated by scattering of gamma-ray photons.

[0019] With some context, referencing Figure 4 Examples of the method of the present invention are discussed. Figure 4 (a) illustrates an image produced by a gamma-ray camera without using an embodiment of the invention. That is, the gamma-ray camera has detected several detection events that may be gamma rays. These detection events have been converted into optical photons. The higher the energy of the detection event, the more optical photons are generated (therefore, higher-energy gamma-ray photons will produce a larger optical photon "splash"). These optical photons are then detected on a photosensitive surface (e.g., a CMOS sensor) and can then be converted into an output image. Therefore, Figure 4 (a) shows the detection events detected by a gamma-ray camera. These detection events may originate from a desired radioactive isotope source, background noise, or scattered gamma-ray photons.

[0020] However, the method of the present invention is designed to distinguish between detection events from a desired gamma-ray source and detection events not from a gamma-ray source (e.g., low-energy photons). Figure 4 The illustrated embodiment achieves this by processing the detected optical photons into a first-level data array comprising pixels, wherein the pixels map the detection of optical photons on a photosensitive surface, and wherein filling pixels correspond to the detection of optical photons on the photosensitive surface. Figure 4 In (b), clusters in the primary data array are shown in illustrative inner boxes. Figure 4 In (c), the largest cluster is shown as covering a 9×9 kernel. Then, Figure 4 The embodiment identifies clusters of fill pixels in a primary data array and determines the density of fill pixels within each cluster. The method then classifies these clusters into high-density clusters (clusters with high-density fill pixels) and low-density clusters (clusters with low-density fill pixels). [Reference] Figure 4 (d) The method then excludes the contribution of low-density clusters to the generated image. These low-density clusters correspond to low-energy and scattered gamma rays. Therefore, a single high-density cluster, such as... Figure 4 As shown in (d).

[0021] In one embodiment, the method includes assigning each high-density cluster to the detection of a (single) gamma-ray photon and using the gamma-ray photon detection events to contribute to the generated image. (Reference) Figure 4 (e) shows that the high-density clusters have been converted into single fill pixels. Therefore, refer to Figure 4 Processing using the method of the embodiments of the present invention Figure 4 (a) shows the original, unprocessed image to give Figure 4(e) refers to a single fill pixel, where a single fill pixel corresponds to the detection of a single gamma-ray photon at an XY position on the detector. Low-density clusters corresponding to low-energy gamma-ray photons have been discarded and do not contribute to image formation. For example, the method of the present invention can be considered to utilize the size / intensity of the two-dimensional impact (“sputter”) generated when gamma-ray photons strike the detector (where the size / intensity of a substantially circular sputter increases with the energy of the gamma-ray photon); thus, “large / strong sputter” corresponds to gamma-ray photons originating from a radiation source, and “small / diffuse splashes” are discarded. In this embodiment, and in any other aspect and / or embodiment disclosed herein, “sputter” is substantially circular. In one embodiment, “sputter” is symmetrical. In one embodiment, “sputter” is largely connected. In these embodiments, only substantially circular “photon sputters” are considered relevant by the method. In one embodiment, substantially non-circular “photon sputters” are discarded. In one embodiment, substantially linear “photon sputters” are discarded. In the context of this application, a circle can be understood to take into account the pixelated nature of the recorded sputtering, and therefore these "sputterings" may not necessarily have smooth or neat edges. For example, in one embodiment, in Figure 4 The shapes selected using the square outline in (b) can be identified as substantially circular (although with slightly jagged edges); however, only one of these features was ultimately determined to originate from gamma rays, as discussed in this paper. Figure 4 (a) to Figure 4 (e) As discussed. Not wishing to be bound by theory, the circular sputtering in this paper can be considered as having a substantially the same shape from its center to any edge.

[0022] In one embodiment, a single fill pixel is located at the center of a high-density cluster (centroided). This single fill pixel is considered to correspond to the detection of a single unscattered gamma photon. The location of the fill pixel corresponds to the origin of the unscattered gamma photon and can therefore be used to create an image. For example, reference can be made to... Figure 4 (d) to Figure 4 The transformation shown in (e) is used to understand the centroidalization step, in which the center of the large cluster is determined.

[0023] In one embodiment, unfilled pixels in the primary data array correspond to photons that are not detected on the photosensitive surface. In another embodiment, unfilled pixels in the secondary data array correspond to photons that are not detected on the photosensitive surface. In practice, any pixels that do not contribute to the high-density clusters can be cleared or "zeroed out." Thus, these pixels will not contribute to the final image. This can be referenced, for example, to... Figure 4 (a) to Figure 4 To understand this transformation as shown in (e), only one pixel contributes to the resulting image (the rest of the pixels are empty or zero).

[0024] In one embodiment, the detected gamma-ray photons have energies in the range of 20 keV to 600 keV, optionally 50 keV to 500 keV, and further optionally 100 keV to 400 keV. The method of the invention can be adapted such that gamma-ray photons with a certain energy (e.g., corresponding to emissions from a radiation source used in the subject) produce clusters considered to be high-density. That is, gamma rays in this energy range will produce characteristic “photon sputtering,” and the method of the invention can be adapted to identify sputterings of a specific size / intensity as high-density clusters. Smaller clusters are discarded. Significantly larger clusters may also be discarded, for example, if these clusters are associated with cosmic rays.

[0025] In one embodiment, gamma rays are not directly converted into electrons for preparing the generated image. In one embodiment, a CZT detector is not used. In one embodiment, gamma rays are not directly digitized for preparing the generated image. It should be noted that the present invention therefore does not actually use... Figure 4 The result shown in (a) is used to create the image. This is because it would contain too much noise.

[0026] In one embodiment, the photosensitive surface has an array of photosensitive units that are activated by optical photons. In one embodiment, the photosensitive surface includes a CMOS, CCD, or EMCCD sensor.

[0027] In one embodiment, the method of improving the generated image is by increasing the signal-to-noise ratio. In one embodiment, the method of improving the generated image is by increasing the resolution. In one embodiment, the method of improving the generated image is by increasing the modulation transfer function. In one embodiment, the method of improving the generated image is by removing gamma rays that have been scattered or deflected from their original trajectory. In one embodiment, the method of improving the generated image is by removing gamma rays that have been inelastically scattered or deflected from their original trajectory.

[0028] In one embodiment, only high-density clusters are used to contribute to the generated image. In one embodiment, the generated image is improved by removing the contribution of gamma rays that cause small optical sputtering on the photosensitive surface. In another embodiment, the generated image is improved by removing the contribution of gamma rays that cause large but diffuse (low-density) optical sputtering on the photosensitive surface.

[0029] In one embodiment, a method for improving the generated image includes removing the contribution of cosmic rays to the generated image. This can be accomplished using the method of the present invention by finding very dense and / or very large clusters (i.e., much larger than the clusters produced by the radioactive isotope source used in the subject) and actually removing them from the image.

[0030] In one embodiment, the generated image includes single-frame images, which can be combined with one or more additional single-frame images to form a frame-summed image. (See reference) Figure 5 As can be seen, frames 1 through 4 (single-frame images) and actually up to “n” single-frame images can be combined to form the total frame image on the right. For example, in the schematic diagram, individual frames (41 through 44) have been combined to show the representation of the accumulation of a radioactive isotope (e.g., Tc-99m) in a patient’s thyroid gland (51). The resulting total frame image (50) can be used to identify abnormal organ function.

[0031] In one embodiment, gamma-ray photons are detected for a time period (frame period), and only gamma-ray photons detected within that time period are used to contribute to the generated image, where the generated image is a single-frame image. In one embodiment, the number of frames acquired within the frame period is optimized. In one embodiment, the number of frames captured per second (frame rate) is optimized.

[0032] In one embodiment, the frame rate is optimized such that the probability of detecting two gamma rays at the same location (causing clusters to overlap) is low, for example, less than about 1 / 10,000, optionally less than about 1 / 1000, and further optionally less than about 1 / 100. In one embodiment, the time period (frame rate) is optimized to improve the generated image. In one embodiment, the time period (frame rate) is optimized such that the resulting single-frame image does not (over)saturate. In one embodiment, the frame rate is adjusted such that fewer than 50 gamma rays are detected per frame, optionally fewer than 10 gamma rays are detected per frame, and further optionally fewer than 5 gamma rays are detected per frame (e.g., Figure 5 The first frame (41) shows the detection of 3 gamma-ray photons and their relative XY positioning / position.

[0033] In one embodiment, the generated image is a sum of frames image (e.g., see...). Figure 5 (50 in the text), the total frame image is obtained by combining two or more single-frame images (e.g., see [link to other images]). Figure 5 Formed as shown in 41 to 44). This invention can be used to generate single-frame images. In fact, the longer the "exposure window" is, the more gamma rays are detected and captured in the frame. However, if the exposure window is too long, the frame will become too saturated, clusters will begin to merge, and the final image will become saturated. The method of this invention relies on picking out low-density clusters and high-density clusters, so if these clusters begin to merge, the efficiency of the method will decrease. For example, two or more low-energy clusters may merge and incorrectly give the impression that they are high-density clusters, resulting in increased noise. In addition, two or more high-density clusters may overlap and incorrectly give the impression of a single high-density cluster, so the number of true gamma events is underestimated, and events will be recorded at incorrect locations on the detector, such as between two clusters. However, if the "exposure window" is too short, there may be too many empty frames and be unnecessarily processed by the method of this invention. This may inefficiently use computer processor time. Ideally, the method is adjusted so that each individual frame image has at least one (and up to about 10) high-density clusters. In this way, the method of the present invention can effectively identify each true high-density cluster (corresponding to the detection of unscattered gamma-ray photons, i.e., the "true" signal) in each frame image, while minimizing the false count of low-density clusters (corresponding to the detection of gamma-ray photons with reduced energy due to scattering, i.e., "noise"). These single-frame images can then be combined into a frame sum image.

[0034] In one embodiment, pixels in the primary data array are averaged or combined with one or more neighboring pixels in the primary data array (pixel binning) to form an updated primary data array to replace the previous primary data array. In the art, this process is referred to as "pixel binning" and can be used to effectively reduce the amount of information that needs to be processed. For example, see reference... Figure 6 This demonstrates how an 8x8 matrix of 64 pixels can be reduced to a 4x4 matrix of 16 pixels by using 2x2 pixel merging. Advantageously, in this case, data processing is thus reduced to a quarter.

[0035] In one embodiment, the updated primary data array is smaller than the previous primary data array. Optionally, the updated primary data array is reduced to 1 / 4 (one-quarter, e.g., 64 pixels reduced to 16 pixels), 1 / 9, 1 / 16, 1 / 25, 1 / 36, 1 / 49, 1 / 64, 1 / 81, or 1 / 100 of the pixels in the previous primary data array. In one embodiment, the aspect ratio is maintained during pixel merging. Pixel merging can also be performed without maintaining the aspect ratio, but this requires additional actions / processing. In one embodiment, the primary data array is 1024×1024, 512×512, 256×256, or 128×128 pixels. In another embodiment, the primary data array is 512×512 or 256×256 pixels.

[0036] In one embodiment, the position of each pixel in the primary data array is mapped to a relative position in the photosensitive cell array. Because the frame-summary image consists of many combined single-frame images, it is useful for each detected gamma-ray event to have a common reference orientation. For example, in Figure 5 In the image, single-frame image 44 and single-frame image 'n' have gamma-ray photons at the same XY position / location. Therefore, in the frame summation image, these positions will overlap / superimpose, giving more intensity at these positions (in the image). Figure 5 In the middle, the darker areas indicate that more gamma-ray photons were detected.

[0037] In one embodiment, the step of excluding low-density clusters includes removing / zeroing out fill pixels from the primary data array that are not associated with high-density clusters to form the secondary data array. Low-density clusters are associated with noise, so removing any contribution of these low-density clusters to the image is beneficial.

[0038] In one embodiment, pixels in the secondary data array are averaged or combined with one or more neighboring pixels in the secondary data array (pixel merging) to form an updated secondary data array to replace the previous secondary data array. In one embodiment, the updated secondary data array is smaller than the previous secondary data array; optionally, the updated secondary data array is reduced to 1 / 4 (one-quarter, e.g., 64 pixels reduced to 16 pixels), 1 / 9, 1 / 16, 1 / 25, 1 / 36, 1 / 49, 1 / 64, 1 / 81, or 1 / 100 of the pixels in the previous secondary data array. In one embodiment, the aspect ratio is maintained during pixel merging. Pixel merging can also be performed without maintaining the aspect ratio, but this requires additional actions / processing. In one embodiment, the secondary data array is 1024×1024, 512×512, 256×256, or 128×128 pixels. In one embodiment, the secondary data array is 512×512 or 256×256 pixels. In one embodiment, the position of each pixel in the secondary data array is mapped to a relative position in the primary data array. In one embodiment, the position of each pixel in the secondary data array is mapped to its relative position in the photosensitive unit array.

[0039] In one embodiment, each high-density cluster in the secondary data array is assigned to a single fill pixel in the tertiary data array. This is because, ideally, each high-density cluster is associated with a single gamma-ray event.

[0040] In one embodiment, the values ​​of pixels in the tertiary data array are averaged or combined with one or more neighboring pixels in the tertiary data array (pixel merging) to form an updated tertiary data array to replace the previous tertiary data array. In one embodiment, the updated tertiary data array is smaller than the previous tertiary data array; optionally, the updated tertiary data array is reduced to 1 / 4 (one-quarter, e.g., 64 pixels reduced to 16 pixels), 1 / 9, 1 / 16, 1 / 25, 1 / 36, 1 / 49, 1 / 64, 1 / 81, or 1 / 100 of the pixels in the previous tertiary data array. In one embodiment, the aspect ratio is maintained during pixel merging. Pixel merging can also be performed without maintaining the aspect ratio, but this requires additional actions / processing. In one embodiment, the tertiary data array is 1024×1024, 512×512, 256×256, or 128×128 pixels. In another embodiment, the tertiary data array is 512×512 or 256×256 pixels. In one embodiment, the tertiary data array is 256×256 pixels. In one embodiment, the primary, secondary, and / or tertiary data arrays are symmetrical (e.g., 256×256 pixels). In one embodiment, the primary, secondary, and / or tertiary data arrays are asymmetrical (e.g., 512×256 pixels). In one embodiment, the primary and secondary data arrays are 512×512 pixels, and the tertiary data array is 256×256 pixels. In one embodiment, the position of each pixel in the tertiary data array is mapped to a relative position in the secondary data array, and the position of a single fill pixel substantially corresponds to the center of the assigned high-density cluster. In one embodiment, the position of each pixel in the tertiary data array is mapped to a relative position in the primary data array. In one embodiment, the position of each pixel in the tertiary data array is mapped to a relative position in the photosensitive unit array.

[0041] In one embodiment, the fill pixels in the tertiary data array correspond to the detection of gamma-ray photons by the gamma camera. In another embodiment, the pixel value of the fill pixels in the tertiary data array corresponds to the number of gamma-ray photons detected. In yet another embodiment, the pixel value of the fill pixels in the tertiary data array corresponds to the number of gamma-ray photons detected at that (relative) location. For example, the tertiary data array may record a single event (binary image) at a unique XY location; and multiple hits may exist in a frame, but these hits are not in the same location.

[0042] In one embodiment, the detected gamma-ray photons in the generated image are counted to give the total number of gamma-ray photons in the generated image. This can be used to quantify the amount of radioactive isotopes in a region of the subject within the generated image, as well as a smaller region of interest within the generated image.

[0043] In one embodiment, filled pixels in the tertiary data array are used to form the generated image. Since the tertiary data array relates to detected gamma-ray events, these can be used to create images, including frame sum images. In one embodiment, unfilled pixels in the tertiary data array correspond to gamma-ray photons not detected by the gamma camera. In another embodiment, unfilled pixels in the tertiary data array correspond to gamma-ray photons not detected by the gamma camera at that (relative) location.

[0044] In one embodiment, the kernel is used to identify clusters in a primary data array.

[0045] In one embodiment, two kernels are used to identify clusters in a primary data array, wherein the two kernels include a first kernel and a last kernel, wherein the first kernel operates on the primary data array to produce a refined primary data array, and the last kernel operates on the refined primary data array to identify clusters present in the refined primary data array. In another embodiment, two or more kernels are used to identify clusters in a primary data array, wherein the two or more kernels include a first kernel and one or more subsequent kernels forming a kernel sequence, wherein the subsequent kernels are arranged to operate on an output array from a kernel preceding them in the kernel sequence such that the final output array is a refined primary data array, and the last kernel in the kernel sequence is used to identify clusters present in the refined primary data array. In one embodiment, two or more kernels are used to identify clusters in a primary data array, wherein the two or more kernels include a first kernel and one or more subsequent kernels forming a kernel sequence, wherein the subsequent kernels are arranged to operate on the output of kernels preceding them in the kernel sequence, wherein the first kernel operates on the primary data array to refine the primary data array, wherein the refined primary data array is operated on by the kernel sequence to further refine the primary data array, and wherein the last kernel (the kernel in the kernel sequence used to operate on the refined primary data array) is used to identify clusters present in the refined primary data array. In one embodiment, one or more kernels operate on a revised primary data array.

[0046] In one embodiment, clusters identified by the kernel are classified as having high-density fill pixels or low-density fill pixels. (See reference) Figure 4(c) illustrates how the illustrative kernel (mesh-like structure) operates on a group of pixels (pixels falling beneath the kernel). Essentially, the kernel evaluates the group of pixels beneath it and determines, through a system of rules, whether a cluster meets a certain criterion, in this case, whether it's high-density or low-density. For example, if the kernel is using a median rule filter, it will determine whether the median value of the pixels beneath the kernel (the value of the middle pixel when pixels are arranged in size order) is 1 (padded) or smaller (unpadded). If it's less than 1, the cluster is "low-density"; if it's 1 or greater, the cluster is "high-density".

[0047] In one embodiment, a low-density cluster is a cluster in which less than about 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, or 10% of the pixels under the kernel are fill pixels. In one embodiment, a low-density cluster is a cluster in which less than about 50% of the pixels under the kernel are fill pixels. In one embodiment, a high-density cluster is a cluster in which at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% (e.g., choosing the value at the 90th percentile) of the pixels under the kernel are fill pixels. In one embodiment, a high-density cluster is a cluster in which at least about 50% of the pixels under the kernel are fill pixels. In one embodiment, a low-density cluster is a cluster in which the median value of the pixels under the kernel is 0. In one embodiment, a high-density cluster is a cluster in which the median value of the pixels under the kernel is greater than 0.

[0048] In one embodiment, identifying clusters and classifying them as having high-density or low-density fill pixels is done in a single step.

[0049] In one embodiment, identifying clusters, classifying them as having high-density or low-density fill pixels, and excluding the contribution of low-density clusters to the generated image are done in a single step. Advantageously, this is the most efficient and saves on computer processing power.

[0050] In one embodiment, the method bypasses the use of a secondary data array. In another embodiment, the method moves directly from a primary data array to a tertiary data array in a single step, wherein in that single step, clusters are identified and classified as high-density clusters, and these high-density clusters are, for example, centroided to a single pixel corresponding to a single gamma-ray photon detection event.

[0051] In one embodiment, the kernel is an edge-preserving filter. This can be referenced, for example, to... Figure 4 (c) to Figure 4 The transformation shown in (d) can be understood as follows, where the edges of large clusters are preserved. Advantageously, this effectively separates detected events.

[0052] In one embodiment, the size and / or shape of the kernel are adjusted to improve the generated image. In another embodiment, the size and / or shape of the kernel is optimized to maximize the rejection of scattered gamma photon detection events while minimizing the rejection of unscattered gamma photon detection events. The kernel can be adjusted to suit the needs and can be adapted to the radioactive isotope used. This is because different isotopes have different characteristic energies, thus producing “sputters” of different sizes, and the sensitivity of the detector and any signal amplification in a particular device may result in smaller or larger sputters for a specific gamma photon energy. Therefore, higher-energy isotopes may require larger kernels, or the rules used for differentiation may need to be changed based on the details of the device design (e.g., the kernel size may increase or decrease depending on the number of pixels in the frame, or the number of pixels filling the kernel will need to be increased to make the cluster meet the criteria for a high-density cluster, or decreased to avoid eliminating true clusters). In one embodiment, the kernel is adapted to detect substantially circular clusters corresponding to optical sputtering.

[0053] In one implementation, the kernel has a square, rectangular, rhomboid, hexagonal, irregular, or circular shape. In one embodiment, the kernel has a plane (line) of symmetry. In one embodiment, the kernel has rotational symmetry. In one embodiment, the kernel size is approximately the size of a high-density cluster. Generally, this produces good results. In one embodiment, the kernel size is adjusted to suit the peak energy of the gamma rays used in medical imaging. In one embodiment, the kernel size is adjusted to suit the peak energy of the gamma rays used in medical imaging, and wherein the peak energy of the gamma rays is generated by gamma rays emitted from: Tc-99m, I-123, I-131, Tl-201, Lu-177, In-111, Y-90, Sc-47, Ga-67, Cr-51, Sn-177m, Cu-67, Tm-167, Ru -97, Re-188, Au-199, Pb-203, Ce-141, Co-57, F-18, Ga-68, C-11, O-15, N-13, Zr-89, Rb-82 , Cu-64, Cd-109, Cs-131, I-125, Am-241, Cd-109, Ag-109m, U-235, Pb-212, Re-186 and Ho-166.

[0054] In one embodiment, the core is tuned to suit the peak energy of the gamma rays used in medical imaging (see the first column below), and the peak energy of the gamma rays is generated by gamma rays emitted from the isotope used (see the second column below):

[0055] In one embodiment, the size of the core is adjusted to suit the peak energy of the gamma rays used in medical imaging (see the first column below), and the peak energy of the gamma rays is generated by gamma rays emitted from the isotope used (see the second column below):

[0056] In one embodiment, the core is tuned to suit the peak energy of the gamma rays used in medical imaging (see the first column below), and the peak energy of the gamma rays is generated by gamma rays emitted from the isotope used (see the second column below):

[0057] In one embodiment, the core is adjusted to suit peak energies between approximately 20 keV and 520 keV.

[0058] In one embodiment, the size and / or shape of the kernel is set to operate on approximately 5 to 1000, optionally approximately 25 to 169, and further optionally approximately 36 to 121 pixels in the data array it is operating on. In one embodiment, the size and / or shape of the kernel is set to operate on approximately 2×2 to 15×15, optionally approximately 3×3 to 11×11, and further optionally approximately 5×5 to 10×10 pixels in the data array it is operating on. In one embodiment, the size and / or shape of the kernel is set to operate on 0.001% to 0.2%, optionally 0.003% to 0.1%, and further optionally 0.005% to 0.075% of the total number of pixels in the data array it is operating on.

[0059] In one embodiment, the data array that the kernel is operating on is a first-level data array. This is a good time to identify high-density clusters. In one embodiment, the data array that the kernel is operating on is a second-level data array. In one embodiment, the data array that the kernel is operating on is a third-level data array. In one embodiment, the size and / or shape of the kernel is configured to exclude the contribution of gamma-ray photons with energies less than about 20 keV, optionally less than about 50 keV, and further optionally less than about 100 keV to the generated image. In one embodiment, the size and / or shape of the kernel is configured to exclude the contribution of gamma-ray photons with energies greater than about 550 keV, optionally greater than about 400 keV, and further optionally greater than about 250 keV to the generated image. In one embodiment, one kernel is used to exclude lower-energy gamma-ray photons, and different kernels are used to exclude higher-energy gamma-ray photons from the generated image. In one embodiment, the size and / or shape of the kernel is configured to not exclude the contribution of gamma rays with photon energies equal to or greater than about 100 keV, optionally equal to or greater than about 150 keV, and further optionally equal to or greater than about 200 keV to the generated image. In one embodiment, the size and / or shape of the kernel is configured to include gamma rays with peak photon energies equal to or greater than about 100 keV, optionally equal to or greater than about 130 keV, and further optionally equal to or greater than about 140 keV to contribute to the generated image. In one embodiment, the size and / or shape of the kernel does not exclude gamma rays with peak energies in the range of about 50 keV to 550 keV, optionally about 120 keV to 165 keV, and further optionally about 140 keV.

[0060] In one embodiment, the gamma camera includes a CCD, EMCCD, or sCMOS chip.

[0061] In one embodiment, the gamma camera is calibrated (or periodically calibrated) with a uniform reference source before use. In another embodiment, calibrating (or periodically calibrating) the gamma camera with a uniform reference source before use eliminates any artifacts present in the gamma camera, or artifacts that may be generated by the methods used to detect gamma-ray photons. Calibration before use can be useful for removing background noise or correcting errors in the sensor.

[0062] In one embodiment, the generated image is refreshed periodically. In another embodiment, snapshots of the generated image are periodically obtained. In yet another embodiment, time-lapse images of the generated image are obtained. The method of the present invention can be used to create still images, real-time images, or time-weighted / averaged images.

[0063] The method of this invention can be used with a gamma-ray camera in a variety of ways. Ideally, it should be used in a manner that reduces processing load, thereby reducing lag or excessive energy consumption. This may mean that some (or all) of the processing is performed within the camera, for example using firmware, or that some or all of the processing is fed to an auxiliary system (which may be a dedicated system) for processing, such as on a local computer, on a USB flash drive / donkey, or in the cloud. In one embodiment, the method is performed partially or entirely within a medical imaging device. In one embodiment, the method is encoded into software that is partially or entirely stored within the medical imaging device. In one embodiment, the method is encoded into software that is stored partially or entirely on memory located within the medical imaging device. In one embodiment, the method is encoded into software stored on memory, and this software may be partially or entirely installed on the medical imaging device. In one embodiment, the method is encoded as a program / application (“app”) that can run partially or entirely on the medical imaging device. In one embodiment, the method is encoded into software stored on portable storage (e.g., a USB flash drive / donkey). In one embodiment, the method is encoded into software stored in the cloud. In one embodiment, the software will need to be installed. In one embodiment, the software is self-executable and does not require installation. In one embodiment, the medical imaging device includes a computer processor to perform all or part of the methods of any aspect or embodiment of the invention. In one embodiment, the gamma camera and the computer processor are housed in the same housing. In one embodiment, the computer processor is firmware. In one embodiment, the medical imaging device is portable. In one embodiment, the medical imaging device is handheld. In one embodiment, the gamma camera and the computer processor are not housed in the same housing but are connected via a wired or wireless connection. In one embodiment, the computer processor is in the cloud. In one embodiment, the computer processor is in a desktop computer or a handheld device such as a smartphone or tablet. In one embodiment, the gamma camera and the second processor are not in the same housing. In one embodiment, the first processor and the second processor are not in the same housing. In one embodiment, the first processor and the second processor are not in the same housing and are connected via a wired or wireless connection. In one embodiment, the second processor is in a desktop computer or a handheld device such as a smartphone or tablet. In one embodiment, the gamma camera and the first processor are in the same housing. In one embodiment, the gamma camera and the first processor are in the same housing, and the first processor is firmware. In one embodiment, the first processor and the second processor are in the same housing. In one embodiment, the first processor and the second processor are in the same housing, and the processor is firmware in the gamma camera.In one embodiment, the gamma camera, the first processor, and the second processor are housed in the same enclosure. In one embodiment, the first processor and the second processor are housed in the same enclosure and are firmware within the gamma camera. In one embodiment, the gamma camera and the second processor are not housed in the same enclosure. In one embodiment, the first processor and the second processor are not housed in the same enclosure. In one embodiment, the first processor and the second processor are not housed in the same enclosure and are connected via a wired or wireless connection. In one embodiment, the second processor is located in a desktop computer or a handheld device such as a smartphone or tablet. In one embodiment, the computer processor generates a primary array, a secondary array, and a tertiary array. In one embodiment, the first computer processor generates a primary array, and the second computer processor generates a secondary array and a tertiary array. In one embodiment, the first computer processor generates a primary array and a secondary array, and the second computer processor generates a tertiary array. In one embodiment, the first computer processor generates a primary array, a secondary array, and a tertiary array, and the second computer processor performs any further processing as needed. In one embodiment, one computer processor generates a single-frame image, while the other processor generates a summed frame image.

[0064] In one embodiment, the generated image is used in conjunction with the output from another imaging modality, such as CT, MRI, US, X-ray, fluorescence, and / or optical cameras. In one embodiment, the medical imaging apparatus defined in any aspect of the invention (and any embodiment thereof) further includes an optical camera. In one embodiment, a gamma camera defined in any aspect of the invention (and any embodiment thereof) is used in conjunction with an optical camera in a medical imaging apparatus (capturing optical photons emitted directly from the subject), and wherein the optical image and the gamma image are superimposed. In one embodiment, a gamma camera defined in any aspect of the invention (and any embodiment thereof) is used in conjunction with an optical camera in a medical imaging apparatus, and wherein the optical image and the gamma image are superimposed, and wherein when the images are superimposed, the images are substantially free of parallax.

[0065] In one embodiment, the generated image is used for medical imaging. In one embodiment, the generated image is used in a medical diagnostic method or to assist in medical diagnosis. In one embodiment, the generated image is used in a medical and / or monitoring method.

[0066] On the other hand, a method is provided to improve the generated image obtained from a gamma camera for gamma-ray medical imaging, the image being improved by excluding the contribution of low-energy and scattered gamma rays to the generated image, the method comprising the following steps: - Gamma camera detects gamma rays; - Convert the detected gamma rays into one or more optical photons; - Detection of optical photons on a photosensitive surface; - Eliminate the contribution of gamma rays that generate small optical sputterings on the photosensitive surface to the generated image. These small sputterings correspond to low-energy and scattered gamma rays.

[0067] Gamma-ray medical imaging equipment is not suitable for imaging all forms of electromagnetic radiation, such as infrared, ultraviolet, and X-ray radiation. In particular, although both gamma-ray and X-ray radiation are types of ionizing radiation, it should be understood that the difference between gamma rays and X-rays is that gamma rays, like alpha and beta particles, originate from the radioactive decay of unstable atomic nuclei, while X-rays originate from electrons outside the atomic nucleus.

[0068] Therefore, gamma rays originate from radioactive material that decays over time. X-rays, on the other hand, are actively generated by an X-ray tube, where electrons are accelerated in a vacuum and bombard metal plates. Gamma rays typically have higher energies than X-rays. X-rays typically have energies in the range of 100 eV to 100,000 eV (or 100 keV), while gamma rays typically have energies greater than approximately 100 keV. Furthermore, X-ray imaging is usually rapid, with image acquisition completed within seconds or at most one to two minutes. Scintigraphic images of gamma-emitting radioactive isotopes typically take approximately five to forty minutes to acquire, posing additional challenges in imaging equipment design. This means that, in practice, equipment suitable for gamma ray detection is not suitable for X-ray imaging, and vice versa. This is because X-ray detection / imaging systems are inefficient for gamma ray detection, as higher-energy gamma rays easily pass through the system undetected. Conversely, detection / imaging systems used for gamma-ray imaging are essentially unresponsive to lower-energy X-rays. In one embodiment, the method is optimized for gamma-ray detection. In one embodiment, the method is not used for X-ray detection. In one embodiment, the method detects ionizing radiation with energies greater than about 30 keV. In one embodiment, the method detects ionizing radiation with energies greater than about 100 keV. In one embodiment, the method is adapted to detect gamma rays emitted from a radioactive isotope source. In one embodiment, the method is adapted to detect gamma rays emitted from a radioactive isotope source applied to a subject.

[0069] This document discloses an apparatus for imaging a subject using gamma rays emitted from the subject, the apparatus including means for performing the method of the present invention or embodiments thereof.

[0070] This document discloses an apparatus for imaging a subject using gamma rays emitted from the subject, the apparatus being equipped with software and / or hardware to run the method of the present invention or embodiments thereof.

[0071] In another aspect of the invention, a system is provided comprising one or more devices arranged to operate methods according to any aspect and any embodiment of the invention.

[0072] In one embodiment, the system includes one or more of the following: a display; a display monitor; a stand / frame; a movable arm; a power supply; a battery; a memory; Wi-Fi functionality; Bluetooth functionality; a communication interface; and a communication cable.

[0073] In one embodiment, the system includes one or more devices arranged to generate 3D images.

[0074] In another aspect of the invention, the use of a method according to any aspect of the invention and any embodiment thereof in imaging a subject using gamma rays is provided, wherein an imaging agent is administered to the subject.

[0075] In one embodiment, the imaging agent is a gamma-ray emitter. In another embodiment, the gamma-ray emitter comprises one or more radioactive isotopes. In one embodiment, the gamma-ray emitter is selected from one or more of the following: Tc-99m, I-123, I-131, Lu-177, In-111, Y-90, Tl-201, Sc-47, Ga-67, Cr-51, Sn-177m, Cu-67, Tm-167, Ru-97, Re-188, Au-199, Pb-203, Ce-141, Co-57, F-18, Ga-68, C-11, O-15, N-13, Zr-89, Rb-82, Cu-64, Cd-109, Cs-131, I-125, Am-241, Cd-109, Ag-109m, U-235, Pb-212, Re-186, and Ho-166. In one embodiment, the gamma-ray emitter is selected from one or more of the following: Tc-99m, I-123, I-131, Lu-177, In-111, Y-90, Sc-47, Ga-67, Cr-51, Sn-177m, Cu-67, Tm-167, Ru-97, Re-188, Au-199, Pb-203, Ce-141, Co-57. In one embodiment, the gamma-ray emitter accumulates in the cells, tissues, and / or organs to be imaged. In one embodiment, the fluorescent agent accumulates in the cell type, cells, tissues, and / or organs to be imaged. In one embodiment, the cells, tissues, and / or organs to be imaged are selected from one or more of the following: bladder, bone, blood, blood vessels, brain, colon, eye, gallbladder, heart, intestine, kidney, liver, lung, pancreas, skin, stomach, thyroid, or parathyroid gland. In one embodiment, the cells, tissues, and / or organs to be imaged include abnormal cell growth. In one embodiment, the cells, tissues, and / or organs to be imaged include cancer. In one embodiment, the cells, tissues, and / or organs are imaged to perform one or more biological functions. In one embodiment, the biological function includes a physiological function. In one embodiment, the physiological function includes filling, emptying, contracting, or relaxing. In one embodiment, the physiological function is imaged in real time.

[0076] This document discloses an analytical or diagnostic method that includes the step of imaging a subject using a method according to the present invention or any embodiment thereof.

[0077] In one embodiment, the method is used in a portable device or system and is brought to the subject to be imaged. In another embodiment, the device or system is used with an optical camera, wherein the optical image and the gamma image are superimposed, and wherein the images are substantially free of parallax when superimposed.

[0078] This document discloses a treatment or surgical method that includes the step of imaging a subject during treatment or surgery using the method of the present invention or any embodiment thereof.

[0079] In one embodiment, a subject is treated with a gamma-ray emitter that accumulates in tissue to be removed during surgery; optionally, the tissue includes cancer.

[0080] This document discloses a method for evaluating treatments or surgeries performed on a subject, including the step of imaging the subject after the treatment or surgery using a method according to the present invention or any embodiment thereof.

[0081] In one embodiment, the subject is a human or animal, or a part or tissue thereof; or the subject may be non-human or non-animal and contain or be contaminated with gamma-emitting material. In one embodiment, the method is operated in a portable device or system and is brought to the subject to be imaged.

[0082] In one embodiment, the subject is a human or animal, or a part or tissue thereof; or the subject may be non-human or non-animal and contain or be contaminated with gamma-emitting material. In one embodiment, the method is operated in a portable device or system that is brought to the object / subject to be imaged.

[0083] In another aspect of the invention, methods according to any aspect of the invention and any embodiment thereof are provided, which are encoded into software or hardware.

[0084] This document discloses that the above disclosure, including various aspects and / or embodiments of the invention, can be applied to / used for non-medical imaging, such as imaging inanimate subject subjects using appropriate gamma rays emitted from the subject, and optionally has utility in radioactive waste management, identifying small local leaks involving radioactivity, or radiation protection. Suitable applications may include: detecting / detecting / monitoring accidental spills of gamma-emitting components (e.g., spills from medical treatment components or laboratory components); detecting / detecting / monitoring / locating situations where gamma-emitting components may be unintentionally removed from storage; detecting / detecting / monitoring / locating waste / discarded low-grade gamma-emitting components for industrial use; detecting / detecting / monitoring / locating potential unintentional radioactive contamination; or monitoring the integrity of containment vessels storing / preserving gamma-emitting components for industrial use.

[0085] The embodiments or disclosures disclosed herein may be independently combined with any other embodiments, multiple embodiments, or one or more aspects of the present invention.

[0086] The invention will now be further described with reference to the following non-limiting examples and the accompanying drawings, wherein: Brief description of the attached diagram

[0087] Figure 1 A schematic diagram of a gamma-ray medical imaging device is shown, in which gamma-ray photons are emitted from the subject.

[0088] Figure 2 and Figure 1 The same, but it also shows scattered gamma-ray photons.

[0089] Figure 3 It shows that it will come from Figure 2 A schematic diagram of the output voltage mapped to the energy distribution (with Compton scattering).

[0090] Figure 4 A schematic diagram of an embodiment of the method of the present invention is shown.

[0091] Figure 5 A schematic diagram of the total frame image constructed from single-frame images is shown.

[0092] Figure 6 A schematic diagram of "pixel merging" is shown. Detailed description of the invention

[0093] Figure 1 A schematic diagram of a prior art gamma-ray medical imaging apparatus is shown, in which gamma-ray photons are emitted from a subject. The subject (10) has been treated with a radiation source (11) that emits gamma-ray photons (γ). The trajectory of the gamma rays is indicated by dashed arrows (12). These gamma-ray photons propagate from the subject and are detected by a gamma-ray detection system (20). First, the emitted gamma-ray photons pass through a collimator (21) in the detection system and strike a gamma-ray detector (22). The detector outputs a voltage (23) that is proportional to the energy of the gamma-ray photons detected by the detection system.

[0094] Figure 2 and Figure 1 The same applies, but also shows a scattered (second) gamma-ray photon. In this case, a second unwanted gamma-ray photon (γ*) also enters through the collimator (21). In this case, this occurs when the second gamma-ray photon is scattered. Scattering is depicted as an inflection point in the path (13) of the second gamma-ray photon. The second gamma-ray photon may originate from background radiation. The gamma-ray may also originate from a radiation source (11), but in this case, the original trajectory (not shown) of the gamma-ray photon has been altered, for example, by the first scattering event. It should be noted that background radiation can enter the collimator without necessarily being scattered.

[0095] In this configuration, both the first and second gamma-ray photons pass through the collimator (21). Both gamma-ray photons strike the gamma-ray detector (22). The detector outputs a first voltage (23) corresponding to the first gamma-ray photon and a second, smaller voltage (24; fringe peak) corresponding to the second (scattered) gamma-ray photon. The voltage peaks are proportional to the energy of the gamma-ray photon detected by the detection system. In this case, the energy of the second gamma-ray photon is lower than that of the first gamma-ray photon, thus giving a lower output voltage.

[0096] Figure 3 It shows that it will come from Figure 2 The output voltage is mapped to a schematic diagram of the energy distribution. It can be imagined that if the gamma-ray detection system (20) is allowed to accumulate multiple counts over time and convert the voltage into energy, an energy distribution (30) can be obtained. This energy distribution will contain a high-energy region (31) and a low-energy region (32; Compton scattering), where the high-energy region will contain the contribution of a first voltage peak (23) and the low-energy region will contain the contribution of a second low voltage peak (24). The energy window is shown as the area between dashed vertical lines on either side of the light peak (33).

[0097] Figure 4 A schematic diagram illustrating an embodiment of the method of the present invention is shown: Figure 4 (a) illustrates an image produced by a gamma-ray camera without using an embodiment of the invention. That is, the gamma-ray camera has detected several detection events that may be associated with gamma-ray photons. These detection events have been converted into optical photons within the gamma-ray camera. The higher the energy of the detection event, the more optical photons are generated (and thus, higher-energy gamma-ray photons produce a larger optical photon "sputtering"). These optical photons have been detected on a photosensitive surface (e.g., a CMOS sensor) and converted into an output image / pixel array (e.g., a first-order data array).

[0098] Figure 4 (b) shows clusters in the primary data array (as indicated by the explanatory boxes).

[0099] Figure 4 (c) shows the largest cluster located in a 9×9 pixel array. In this case, the grid can be considered as a 9×9 kernel operating on a primary data array.

[0100] Figure 4 (d) illustrates a method that uses this kernel to identify only a single high-density cluster, with the remaining pixels set to zero / unoccupied (e.g., a secondary data array). In this case, the kernel is an edge-preserving filter.

[0101] Figure 4 (e) shows that high-density clusters are centroided (e.g., to give a three-level data array). Figure 4 The single fill pixel in (e) can be considered to correspond to the detection of an unscattered gamma-ray photon with X and Y coordinates in the array, and the position of the fill pixel corresponds to the true origin of the unscattered gamma-ray photon, which can then be used to create an image. Therefore, refer to Figure 4 (a) to Figure 4 The overall transformation shown in (e) involves only one pixel contributing to the resulting image (the remaining pixels are empty / zeroed out). For example, this could be used to construct a frame summation image (e.g., as...). Figure 5 A single-frame image (as shown).

[0102] Figure 5 A schematic diagram of the total frame image (50) constructed from single-frame images (41 to 44) is shown. That is, Figure 5 The diagram illustrates how frames 1 through 4 (single-frame images) and actually up to 'n' single-frame images (where the remainder of the single-frame images is not shown in the figure) are combined to form the summed frame image (50) on the right. For example, in the schematic diagram, individual frames (41 through 'n') have been combined to show the accumulation of a radioactive isotope (e.g., Tc-99m) in a patient's thyroid gland (51). Darker areas indicate the most active thyroid gland. The resulting summed frame image can be used to identify abnormal organ function.

[0103] Figure 6 A schematic diagram of "pixel merging" is shown. That is, in Figure 6 In the image, you can see how to reduce an 8×8 matrix of 64 pixels to a 4×4 matrix of 16 pixels by using 2×2 pixel merging: Figure 6 (a) shows an empty 64-pixel array.

[0104] Figure 6 (b) shows how to fill pixels, for example, the top left pixel has a value of 1.

[0105] Figure 6 (c) shows that pixels can undergo 2x2 pixel merging, and the grid shows which pixels are merged.

[0106] Figure 6 (d) shows the result of pixel merging, for example, the top left pixel now has a value of 3, corresponding to, for example, Figure 6 The sum of the values ​​of the merged pixels indicated in (c).

Claims

1. A method for improving generated images obtained from a gamma camera for medical imaging, the images being improved by eliminating the contribution of low-energy and scattered gamma rays to the generated images, the method comprising the steps of: The gamma camera detects gamma-ray photons; Convert the detected gamma-ray photons into one or more optical photons; Detection of optical photons on a photosensitive surface; The detected optical photons are processed into a first-level data array comprising pixels, wherein the pixels map the detection of the optical photons on the photosensitive surface, and wherein filling pixels correspond to the detection of the optical photons on the photosensitive surface; Identify clusters of fill pixels in the primary data array; Determine the density of the fill pixels in the identified clusters; The clusters are classified into high-density clusters (clusters with high-density fill pixels) and low-density clusters (clusters with low-density fill pixels). The contribution of the low-density clusters to the generated image is excluded; these low-density clusters correspond to low-energy and scattered gamma rays.

2. The method according to claim 1, wherein, The method includes assigning each high-density cluster to the detection of gamma-ray photons, and using gamma-ray photon detection events to contribute to the generated image.

3. The method according to claim 1 or 2, wherein, The generated image includes a single-frame image, which can be combined with one or more additional single-frame images to form a total frame image.

4. The method according to any one of the preceding claims, wherein, The generated image is a frame summation image, which is formed by combining two or more single-frame images.

5. The method according to any one of the preceding claims, wherein, The step of excluding the low-density clusters includes removing / zeroing out fill pixels that are not associated with the high-density clusters from the primary data array to form the secondary data array.

6. The method according to claim 5, wherein, The pixels in the secondary data array are averaged or combined with one or more neighboring pixels in the secondary data array (pixel merging) to form an updated secondary data array to replace the previous secondary data array.

7. The method according to claim 5 or 6, wherein, Each high-density cluster in the secondary data array is assigned to a single fill pixel in the tertiary data array.

8. The method according to claim 7, wherein, The pixels in the three-level data array are averaged or combined with one or more neighboring pixels in the three-level data array (pixel merging) to form an updated three-level data array to replace the previous three-level data array.

9. The method according to claim 7 or 8, wherein, The position of each pixel in the tertiary data array is mapped to a relative position in the secondary data array, wherein the position of a single fill pixel substantially corresponds to the center of the assigned high-density cluster.

10. The method according to any one of claims 7 to 9, wherein, The filled pixels in the three-level data array correspond to the detection of gamma-ray photons by the gamma camera.

11. The method according to any one of claims 7 to 10, wherein, The fill pixels in the three-level data array are used to form the generated image.

12. The method according to any one of claims 7 to 11, wherein, The unfilled pixels in the three-level data array correspond to the gamma camera not detecting gamma-ray photons.

13. The method according to any one of the preceding claims, wherein, The kernel is used to identify clusters in the primary data array.

14. The method according to claim 13, wherein, Clusters identified by the kernel are classified as having high-density fill pixels or low-density fill pixels.

15. The method according to claim 14, wherein, Identifying the clusters and classifying them as having high-density or low-density fill pixels is done in a single step.

16. The method according to any one of claims 13 to 15, wherein, The kernel is an edge-preserving filter.

17. The method according to any one of claims 13 to 16, wherein, The size and / or shape of the kernel are adjusted to improve the generated image.

18. The method according to any one of claims 13 to 17, wherein, The size and / or shape of the kernel is approximately the size of a high-density cluster.

19. The method according to any one of claims 13 to 18, wherein, The size and / or shape of the core are adjusted to suit the peak energy of the gamma rays used in the medical imaging.

20. The method according to any one of claims 13 to 19, wherein, The size and / or shape of the core are adjusted to suit the peak energy of the gamma rays used in the medical imaging, and wherein the peak energy of the gamma rays is generated by gamma rays emitted from: Tc-99m, I-123, I-131, Lu-177, In-111, 201-Tl, Y-90, Sc-47, Ga-67, Cr-51, Sn-177m, Cu-67, Tm-16 7. Ru-97, Re-188, Au-199, Pb-203, Ce-141, Co-57, F-18, Ga-68, C-11, O-15, N-13, Zr-89, Rb- 82. Cu-64, Cd-109, Cs-131, I-125, Am-241, Cd-109, Ag-109m, U-235, Pb-212, Re-186 and Ho-166.

21. The method according to any one of claims 13 to 20, wherein, The size and / or shape of the kernel are set to operate on approximately 5 to 1000, optionally approximately 25 to 169, and further optionally approximately 36 to 81 pixels in the data array it is operating on.

22. The method according to any one of claims 13 to 21, wherein, The size and / or shape of the kernel are set to operate on approximately 2×2 to 15×15, optionally approximately 3×3 to 10×10, and further optionally approximately 5×5 to 9×9 pixels in the data array it is operating on.

23. The method according to any one of claims 13 to 22, wherein, The size and / or shape of the kernel are set to operate on 0.001% to 0.2%, optionally 0.003% to 0.1%, and further optionally 0.005% to 0.075% of the total number of pixels in the data array it is operating on.

24. The method according to any one of claims 13 to 23, wherein, The size and / or shape of the kernel are configured to exclude the contribution of gamma-ray photons with energies less than about 20 keV, optionally less than about 50 keV, and further optionally less than about 100 keV to the generated image.

25. A method for improving generated images obtained from a gamma camera for gamma-ray medical imaging, the images being improved by excluding the contribution of low-energy and scattered gamma rays to the generated images, the method comprising the steps of: The gamma camera detects gamma rays; Convert the detected gamma rays into one or more optical photons; Detection of optical photons on a photosensitive surface; The contribution of gamma rays generated by small optical sputtering on the photosensitive surface to the generated image is eliminated. These small sputterings correspond to low-energy and scattered gamma rays.

26. The method of claim 25, wherein, The sputtering is basically circular.

27. The method according to any one of claims 1 to 26, wherein, The method is encoded into software or hardware.

28. A system or apparatus arranged to operate the method defined in any one of claims 1 to 27.

29. The system or apparatus of claim 28, wherein it is equipped with software and / or hardware to operate the method defined in any one of claims 1 to 27.

30. The system or device according to claim 28 or 29, comprising one or more of the following: a display; a display monitor; a stand / frame; a movable arm; a power supply; a battery; a memory; Wi-Fi functionality; Bluetooth functionality; a communication interface; and a communication cable.

31. The system or device according to any one of claims 28 to 30, wherein it is portable and optionally handheld.

32. The system or apparatus according to any one of claims 28 to 31, which is arranged to generate 3D images.

33. The system or device according to any one of claims 28 to 32, comprising an optical camera, wherein, The generated optical and gamma images are superimposed, and when the images are superimposed, the images are essentially free of parallax.

34. The use of the method, system, or apparatus according to any one of claims 1 to 33 for imaging a subject using gamma rays, wherein, An imaging agent was administered to the subject.

35. The use according to claim 34, wherein, The imaging agent is selected from one or more of the following: Tc-99m, I-123, I-131, Lu-177, In-111, Y-90, Tl-201, Sc-47, Ga-67, Cr-51, Sn-177m, Cu-67, Tm-167, Ru-97, Re-188, Au-199, Pb-203, Ce-141, Co-57, F-18, Ga-68, C-11, O-15, N-13, Zr-89, Rb-82, Cu-64, Cd-109, Cs-131, I-125, Am-241, Cd-109, Ag-109m, U-235, Pb-212, Re-186, and Ho-166.

36. The use according to claim 34 or 35, wherein, The imaging agent accumulates in the cells, tissues, and / or organs to be imaged, optionally selected from one or more of the following: bladder, bone, blood, blood vessels, brain, colon, eye, gallbladder, heart, intestine, kidney, liver, lung, pancreas, skin, stomach, thyroid, or parathyroid gland.

37. An analytical or diagnostic method comprising the step of imaging a subject using a method, system, or device according to any one of claims 1 to 36.

38. A treatment or surgical method comprising the step of imaging a subject during treatment or surgery using the method, system, or device according to any one of claims 1 to 37.

39. A method for evaluating treatment or surgery performed on a subject, comprising the step of imaging the subject after the treatment or surgery using the method, system, or device according to any one of claims 1 to 38.

40. The method for evaluating treatment or surgery performed on a subject according to claim 39, wherein, The subject is a human or an animal, or a part or tissue removed from the subject.

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