Calibration of active concentration uptake

Abstract

A framework to calibrate active concentration uptake. The framework generates a pattern representing a volume of interest. A three-dimensional (3D) motif may be printed based on the pattern. Active concentration uptake in the volume of interest can then be calibrated by using the 3D motif.

Description

Technical Field

[0001] The present disclosure relates generally to medical devices and, more particularly, to calibration of activity concentration uptake. Background Art

[0002] The field of medical imaging has made significant advances since X-rays were first used to see inside the human body. Nuclear medicine and molecular imaging are types of medical imaging used to visualize how the body functions. During nuclear medicine and molecular imaging procedures, radioactive tracers are introduced into the body where they bind to specific tissues, organs, or processes and are then detected by an external device (e.g., a single photon emission computed tomography, or SPECT scanner) to provide information about organ function and cellular activity.

[0003] Radiomics is seen as a bridge between medical imaging and personalized medicine. See Lambin, P., Leijenaar, R., Deist, T. et al., Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14, 749–762 (2017), which is incorporated herein by reference. Radiomics is a method for extracting a large number of quantitative radiomic features from medical images using data characterization algorithms. Radiomic features capture tissue and lesion properties, such as heterogeneity and shape, which can be used for clinical problem solving.

[0004] However, medical imaging systems can produce structured noise patterns that can drown out the signal in radiomics. For example, SPECT images have particularly poor resolution, which makes it difficult to actually detect any structure at the cellular level. Similarly, positron emission tomography (PET) produces low resolution in the millimeter range, similar in order of magnitude to SPECT. The underlying biological structure may be several orders of magnitude smaller than the resolution provided by these modalities.

[0005] For example, a tumor, regardless of stage, can be considered as a convex object with an impenetrable wall that contains a uniform distribution of activity A in a certain volume V. The activity A' measured based on reconstructed SPECT (or PET) images may be underestimated due to partial volume effects. Typically, due to the partial volume effect, the measured activity A' is less than the actual activity A. The partial volume effect can be corrected based on anatomical information. See, for example, U.S. Patent No. US10699445B2, which is incorporated herein by reference. The effective value is introduced to account for the fact that the activity is not uniformly distributed in the tumor.

[0006] In fibroblast activation protein inhibitor (FAPI) imaging, activity is associated with specific cells in clustered structures or fibers. These structures are well below the resolution of the imaging modality, but they can have an impact on partial volume correction estimates and contribute to structural noise, thereby confounding or compromising radiomic features. Unless structural noise is compensated for in radiomic analyses, it is unlikely that much derivative voxel information will be generated beyond simple 0th and 1st order moments. Summary of the invention

[0007] This article describes a framework for calibrating activity concentration uptake. The framework generates a pattern representing a volume of interest. A three-dimensional (3D) phantom can be printed based on the pattern. The activity concentration uptake in the volume of interest can then be calibrated by using the 3D phantom. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] A more complete understanding of the present disclosure and its many attendant aspects will be readily obtained as it becomes better understood by reference to the following detailed description when considered in conjunction with the accompanying drawings.

[0009] Figure 1 is a block diagram illustrating an exemplary system;

[0010] Figure 2 An exemplary calibration method is shown;

[0011] Figure 3a An exemplary graphical representation of normal tissue is shown;

[0012] Figure 3b An exemplary graphical representation of tumor tissue is shown;

[0013] Figure 4 exemplary reconstructed images showing different orders of fractal objects; and

[0014] Figure 5 Exemplary zoomed-in reconstructed images of a portion of a fractal object of different orders are shown. DETAILED DESCRIPTION

[0015] In the following description, many specific details are set forth, such as examples of specific components, devices, methods, etc., in order to provide a thorough understanding of the implementation of the present framework. However, it is apparent to those skilled in the art that these specific details do not need to be used to practice the implementation of the present framework. In other cases, in order to avoid unnecessarily obscuring the implementation of the present framework, well-known materials or methods are not described in detail. Although the present framework is susceptible to various modifications and alternative forms, its specific embodiments are shown by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that this is not intended to limit the present invention to the specific form disclosed; on the contrary, the present invention is intended to cover all modifications, equivalents, and alternatives that fall within the spirit and scope of the present invention. In addition, for ease of understanding, certain method steps are described as separate steps; however, these separately described steps should not be interpreted as necessarily relying on order in their execution. Independent of the use of grammatical terms, individuals with male or female gender identities are also included in the terms.

[0016] The term "x-ray image" as used herein may mean a visible x-ray image (e.g., displayed on a video screen) or a digital representation of an x-ray image (e.g., a file corresponding to the pixel output of an x-ray detector). The term "on-treatment x-ray image" as used herein may refer to an image captured at any point in time during the treatment delivery phase of an interventional or therapeutic procedure, which may include times when a radiation source is turned on or off. Sometimes, for ease of description, CT imaging data (e.g., cone beam CT imaging data) may be used herein as an exemplary imaging modality. However, it should be understood that data from any type of imaging modality may also be used in various implementations, including but not limited to x-rays, MRI, PET (positron emission tomography), PET-CT, SPECT, SPECT-CT, MR-PET, 3D ultrasound images, and the like.

[0017] Unless otherwise indicated, it will be apparent from the following discussion that it will be understood that terms such as "segmenting," "generating," "recording," "determining," "aligning," "locating," "processing," "computing," "selecting," "estimating," "detecting," "tracking," and the like may refer to the actions and processes of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (e.g., electronic) quantities within registers and memories of a computer system into other data similarly represented as physical quantities within memories or registers of a computer system or other such information storage, transmission, or display device. Embodiments of the methods described herein may be implemented using computer software. If written in a programming language that conforms to an identified standard, a sequence of instructions designed to implement the method may be compiled so as to be executed on a variety of hardware platforms and interfaced with a variety of operating systems. In addition, the implementation of the present framework is not described with reference to any particular programming language. It will be understood that a variety of programming languages ​​may be used.

[0018] As used herein, the term "image" refers to multidimensional data composed of discrete image elements (e.g., pixels for 2D images and voxels for 3D images). The image may be, for example, a medical image of an object collected by computed tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to those skilled in the art. The image may also be provided from a non-medical environment, such as, for example, a remote sensing system, an electron microscope, etc. Although an image may be considered to be obtained from an R 3 to R functions, or to R 3 The method is not limited to such images and can be applied to images of any dimensionality, such as 2D pictures or 3D volumes. For 2D or 3D images, the domain of the image is typically a 2D or 3D rectangular array in which each pixel or voxel can be addressed with reference to a set of 2 or 3 mutually orthogonal axes. The terms "digital" and "digitized" as used herein refer to an image or volume in a digital or digitized format acquired via a digital acquisition system or via conversion from an analog image.

[0019] One aspect of the present framework enables calibration of meaningful partial volume corrections of biological tissue and the effects of potential but not directly measurable structures. Such calibration can be performed using printed three-dimensional (3D) phantoms that act as negative volumes for active concentrations. The 3D phantoms can have fractal patterns that resemble actual tumor structures observed under high-resolution imaging. Such 3D phantoms can be used for simulations as well as measurements. They can be used to evaluate the noise patterns and image responses of medical imaging systems and their image reconstruction methods, thereby facilitating calibration for radiomics use. These and other features and advantages will be described in more detail herein.

[0020] Figure 11 is a block diagram illustrating an exemplary system 100. System 100 includes a computer system 101 for implementing the framework described herein. In some implementations, computer system 101 operates as a standalone device. In other implementations, computer system 101 can be connected (e.g., using a network) to other machines, such as a medical scanner 102, a workstation 103, and a 3D printer 150. In a networked deployment, computer system 101 can operate in the capacity of a server (e.g., a thin-client server), a cloud computing platform, a client user machine in a server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.

[0021] In some implementations, the computer system 101 includes a processor or central processing unit (CPU) 104 coupled to one or more non-transitory computer-readable media 105 (e.g., computer storage or memory devices), a display device 110 (e.g., a monitor), and various input devices 111 (e.g., a mouse or keyboard) via an input-output interface 121. The computer system 101 may also include support circuits such as caches, power supplies, clock circuits, and communication buses. Various other peripheral devices, such as additional data storage devices and printing devices, may also be connected to the computer system 101.

[0022] The present technology can be implemented in various forms of hardware, software, firmware, dedicated processors, or a combination thereof, or as part of microinstruction code, or as part of an application or software product, or a combination thereof, which is executed via an operating system. In some implementations, the technology described herein is implemented as computer-readable program code tangibly embodied in a non-transitory computer-readable medium 105. Specifically, the present technology can be implemented by a calibration module 107.

[0023] The non-transitory computer readable medium 105 may include random access memory (RAM), read-only memory (ROM), magnetic floppy disk, flash memory and other types of memory, or a combination thereof. The computer readable program code is executed by the CPU 104 to process data retrieved from, for example, medical equipment 120, workstation 103, and database 109. In this way, the computer system 101 is a general-purpose computer system, which becomes a special-purpose computer system when the computer readable program code is executed. The computer readable program code is not intended to be limited to any particular programming language and its implementation. It should be understood that various programming languages ​​and their encodings can be used to implement the teachings of the disclosure contained herein.

[0024] The same or different computer readable medium 105 can be used to store a database (or data set) 109. Such data can also be stored in an external storage device or other memory. The external storage device can be implemented using a database management system (DBMS) managed by the CPU 104 and resident on a memory such as a hard disk, RAM, or removable media. The external storage device can be implemented on one or more additional computer systems. For example, the external storage device can include a data warehouse system resident on a separate computer system, a cloud platform or system, a picture archiving and communication system (PACS), or any other hospital, medical institution, medical office, testing facility, pharmacy, or other medical patient record storage system.

[0025] The workstation 103 may include a tablet, mobile device, laptop, or computer intended for use by a medical technician. The workstation 103 may include a graphical user interface to receive user input via an input device (e.g., a keyboard, mouse, touch screen, voice or video recognition interface) to enter medical data 122. The workstation 103 may operate in conjunction with the overall system 100. For example, the workstation 103 may communicate directly or indirectly with the medical scanner 102 and / or the 3D printer 150.

[0026] The medical scanner 102 is a radiological scanner that acquires medical image data 120 associated with a patient. The medical scanner 102 may acquire the medical image data 120 using techniques such as, but not limited to, high resolution computed tomography (HRCT), magnetic resonance (MR) imaging, computed tomography (CT), spiral CT, X-ray, angiography, positron emission tomography (PET), fluoroscopy, single photon emission computed tomography (SPECT), or a combination thereof.

[0027] The 3D printer 150 is used to create a phantom for medical applications. The phantom is an artificially manufactured structure that has properties similar to biological tissue (e.g., geometry, softness), including normal and diseased tissue (e.g., tumor-embedded tissue). The 3D printer 150 receives data 152 from the calibration module 107 to print the phantom. It should be understood by those skilled in the art that many different models of 3D printers 150 can be used to achieve 3D printing of phantoms that can be used in medical applications. In addition, it should be understood that several types of 3D printing processes can be used to produce these parts. One 3D printing process that can be used is selective laser sintering (SLS). Other 3D printing processes such as stereolithography (SLA), fused deposition modeling (FDM), selective laser melting (SLM), electron beam melting (EBM), or laminated object manufacturing (LOM) can also be used. Other 3D printing processes can also be used as long as they can produce a 3D printed phantom that is liquid-fillable and impermeable (i.e., leak-proof) and has photon interaction properties similar to the corresponding biological tissue of interest.

[0028] It should also be understood that because some of the constituent system components and method steps described in the accompanying drawings can be implemented in software, the actual connections between the system components (or process steps) can be different depending on how the present framework is programmed. Given the teachings provided herein, one of ordinary skill in the relevant art will be able to envision these and similar implementations or configurations of the present framework.

[0029] Figure 2 An exemplary method 200 of calibration is shown. It should be understood that the steps of method 200 may be performed in the order shown or in a different order. Additional, different, or fewer steps may also be provided. In addition, method 200 may be used Figure 1 The system 100 may be implemented by a different system or a combination thereof.

[0030] At 202, the calibration module 107 generates a fractal pattern representing a volume of interest. The fractal pattern contains detailed structures that are much smaller (e.g., in the nanometer range) than the spatial resolution (e.g., 0.3 to 0.6 mm) of the medical scanner 102. The volume of interest may be, for example, tissue in which a tumor is embedded, a breast nodule, a liver, or any other organ or tissue identified for further study. The fractal pattern may represent, for example, vasculature or epithelial structures within the volume of interest.

[0031] A fractal pattern is a geometric shape that contains detailed structures that appear self-similar at different scales. Tumor growth at any scale in nature seems to follow similar laws. Therefore, fractal patterns can well describe certain specific growths of tumors. Examples of fractal patterns include, but are not limited to, fractal trees, polygons, Koch snowflakes or curves, Hilbert curves (or Hilbert space-filling curves), Peano curves, or combinations thereof. For example, a Hilbert curve is a continuous fractal space-filling curve that is constructed as the limit of a piecewise linear curve. Fractal patterns can be generated by applying an iterative process to simple equations, for example using an iterated function system or quaternion algebra by generating recursion. Other fractal generation algorithms can also be used.

[0032] Fractal patterns can be characterized using fractal dimensions. Fractal dimension is an index that characterizes a fractal pattern or set by quantifying its complexity as the ratio of changes in detail to changes in scale. Fractal dimension is different from our intuitive concept of topological dimension and can be a non-integer value. The more irregular and complex an object is, the higher its fractal dimension is relative to its topological dimension. The invariance of a fractal pattern can be scaled using the fractal dimension.

[0033] Fractal dimension may be related to the progression of tumor tissue. See, e.g., Mauro Tambasco, Anthony M. Magliocco, Relationship between tumor grade and computed architectural complexity in breast cancer specimens, Human Pathology, Vol. 39, No. 5, 2008, pp. 740-746, which is incorporated herein by reference. Tumor progression may be represented by tumor grade (e.g., grade 1, 2, or 3), which describes how abnormal cancer cells and tissues look and how fast they may grow. Tumor progression may also be represented by tumor staging, which measures the size and extent of cancer in the body.

[0034] Figure 3a An exemplary illustration of normal tissue 300 is shown, and Figure 3b An exemplary illustration of a tumor tissue 310 is shown. Normal tissue 300 includes blood vessels 302 and surrounding tissue 304. Tumor tissue 310 includes blood vessels 312 and surrounding tissue 314. It can be observed that blood vessels 312 are more irregular and tortuous than blood vessels 302 in normal tissue 300. The higher the tumor grade, the higher the irregularity of the blood vessels in the tumor tissue.

[0035] In order to generate an accurate representation of the volume of interest, the fractal dimension of the fractal pattern can be determined based on the tumor progression within the volume of interest. The fractal dimension can be a non-integer value, for example, between 1 and 3. In some implementations, the fractal dimension increases with the tumor grade of the volume of interest. For example, the fractal dimensions for tumor grades 1, 2, and 3 can be 1.28, 1.40, and 1.56, respectively. Other types of correlations are also possible.

[0036] Back to Figure 2 At 204, the calibration module 107 starts the 3D printer 150 to print a 3D phantom based on the fractal pattern. The 3D phantom defines a negative volume around the fractal pattern for active concentrations. The fractal pattern can be enlarged to accurately represent the volume of interest. The 3D phantom can have solid or open boundaries. The material of the 3D phantom includes, for example, polylactic acid (PVA). Other types of materials are also useful as long as they are compatible with liquid source imaging and photon interaction properties similar to those of biological tissue. In some implementations, the 3D phantom represents tumor tissue. As previously described, a pattern can be generated by determining a fractal dimension based on the progression of the tumor. Advantageously, such a 3D phantom enables more accurate partial volume correction for tumor tissue than a phantom based on a "clean" geometric volume.

[0037] At 206, the calibration module 107 calibrates the activity concentration uptake in the volume of interest by using the 3D phantom. The 3D phantom can be used to calibrate the partial volume as a function of tumor structure according to staging (or biopsy) or Hires imaging. Controlling the negative space by printing the 3D phantom advantageously enables the study and calibration of patterns, which helps in quantitative imaging of small scale structures with uneven partial volume correction. The 3D phantom can also be used for simulation as well as measurement, and also allows the evaluation of noise patterns.

[0038] In some implementations, the 3D phantom is filled with a radioactive tracer of known test activity concentration. The radioactive tracer fills a positive volume defined by a fractal pattern. Medical image acquisition is performed by a medical scanner 102 to acquire raw image data. For example, SPECT or PET emission detection is performed on the 3D phantom to generate raw image data representing measurements or counts of emissions. Image reconstruction is performed based on the raw image data to generate a reconstructed image of the 3D phantom. Forward projection can be used for image reconstruction. Calibration of activity concentration uptake can be performed based on the reconstructed image.

[0039] In some implementations, calibration of activity concentration uptake is performed by scaling and comparing the reconstructed image to a reference (or expected) image to determine a correction for partial volume effects. The calibration factor for correcting for partial volume effects can be represented by determining a comparison function value (e.g., difference) of the activity concentration in the reconstructed image to a known test activity concentration. The boundaries of the fractal pattern cause partial volume effects. The comparison provides a correction to offset the partial volume effect. For example, see U.S. Patent No. US10699445B2, which is incorporated herein by reference.

[0040] Figure 4 Exemplary reconstructed images (404a-c, 406a-c, 408a-c) of fractal objects of different orders 402a-c are shown. The fractal objects 402a-c can be based on a Koch snowflake (or curve) that is iteratively generated in a series of phases. As shown, the second, third, and fourth iterations can generate different first, second, and third fractal objects (402a, 402b, 402c). The fractal dimension and / or angle can be adjusted to produce different objects.

[0041] 3D phantoms based on fractal objects (402a, 402b, 402c) can be printed and used to characterize and calibrate activity concentrations in a volume of interest. The positive volume (or background) of the 3D phantom is filled with a known concentration of radioactive tracer. Image acquisition is performed in the medical scanner 102 to acquire raw image data. Image reconstruction is performed based on the raw image data to generate reconstructed images (404a-c, 406a-c, 408a-c) of the phantom. As shown, high-resolution images 404a-c, limited (or low) resolution images 406a-c, and low-resolution and detection response images 408a-c are generated for the first, second, and third fractal objects (402a, 402b, 402c). As the area becomes larger and larger at higher orders of magnitude, the number of counts should be higher if the concentration of the radioactive tracer remains the same. However, the blurred low-resolution images 408a-c appear to be the same, which means that a partial volume correction (PVC) calibration factor may be required to retrieve the correct counts.

[0042] Figure 5 Exemplary magnified reconstructed images (504a-c, 506a-c, 508a-c) of a portion 502a-c of a fractal object of different order 402a-c are shown. By magnifying the portions 502a-c, it can be observed that small features may be lost and therefore miscounted unless a PVC calibration factor is present.

[0043] One or more PVC calibration factors can be determined based on the reconstructed image of the 3D phantom to correct for partial volume effects. Such calibration factors can be determined by comparing the activity concentration after forward projection in the reconstructed image with a known test activity concentration. Any comparison function can be used, such as a mathematical difference (subtraction) or a ratio (e.g., a ratio of a known test activity concentration to a reconstructed activity concentration after forward projection). Other comparison functions and / or other terms can be included, such as a constant or variable that can be used to adjust the overall correction level.

[0044] The PVC calibration factors may have a spatial distribution and thus may provide a region map for partial volume correction. The PVC region map provides a distribution of gains that counteract partial volume effects. The gains may be additive or multiplicative. For ratios, the gains may be multiplicative. The PVC calibration factors or region map may be used to reconstruct detected emissions from a medical scanner 102 of a patient's volume of interest. Such image reconstruction may include, for example, multiplying the detected emissions by the PVC calibration factors in a reconstructed forward projection.

[0045] Although the present framework has been described in detail with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various modifications and substitutions may be made thereto without departing from the spirit and scope of the present invention as set forth in the appended claims. For example, within the scope of the present disclosure and the appended claims, elements and / or features of different exemplary embodiments may be combined with each other and / or substituted with each other.

[0046] The following is a list of non-limiting exemplary embodiments disclosed herein:

[0047] Illustrative embodiment 1: One or more non-transitory computer-readable media containing an instruction program executable by a machine to perform calibration operations, the operations comprising: generating a fractal pattern representing a volume of interest, wherein the fractal pattern is characterized by a fractal dimension; printing a three-dimensional (3D) phantom based on the fractal pattern; and calibrating the activity concentration uptake in the volume of interest by using the 3D phantom.

[0048] Illustrative Embodiment 2: The one or more non-transitory computer readable media of Illustrative Embodiment 1, wherein generating the fractal pattern comprises determining a fractal dimension based on a tumor progression of the volume of interest.

[0049] Illustrative Embodiment 3: The one or more non-transitory computer readable media of Illustrative Embodiment 2, wherein tumor progression is represented by tumor grade.

[0050] Illustrative Embodiment 4: The one or more non-transitory computer readable media of any one of Illustrative Embodiments 1-3, wherein determining the fractal dimension comprises increasing the fractal dimension with tumor grade.

[0051] Illustrative embodiment 5: One or more non-transitory computer-readable media of any one of illustrative embodiments 1-4, wherein the instruction program is executable by a machine to calibrate activity concentration uptake in a volume of interest by: performing medical image acquisition of a 3D phantom filled with a radioactive tracer of known test activity concentration to generate raw image data, performing image reconstruction based on the raw image data to generate a reconstructed image, and determining one or more calibration factors for correcting partial volume effects based on the reconstructed image.

[0052] Illustrative Embodiment 6: A calibration method, comprising: generating a pattern representing a volume of interest; printing a three-dimensional (3D) phantom based on the pattern; and calibrating activity concentration uptake in the volume of interest by using the 3D phantom.

[0053] Illustrative Embodiment 7: The method of Illustrative Embodiment 6, wherein the pattern comprises a fractal pattern.

[0054] Illustrative Embodiment 8: The method of Illustrative Embodiment 7, wherein the fractal pattern is characterized by a fractal dimension.

[0055] Illustrative Embodiment 9: The method of Illustrative Embodiment 8, wherein generating the pattern comprises determining a fractal dimension based on tumor progression of the volume of interest.

[0056] Illustrative Example 10: The method of Illustrative Example 9, wherein tumor progression is indicated by tumor grade.

[0057] Illustrative Embodiment 11: The method of Illustrative Embodiment 10, wherein determining the fractal dimension comprises increasing the fractal dimension with tumor grade.

[0058] Illustrative Embodiment 12: The method of Illustrative Embodiment 7, wherein generating the fractal pattern comprises generating a Hilbert curve.

[0059] Illustrative Embodiment 13: The method of any of Illustrative Embodiments 6-12, wherein printing the 3D phantom comprises selective laser sintering, stereolithography, fused deposition modeling, selective laser melting, electron beam melting, or laminated object manufacturing.

[0060] Illustrative Example 14: The method of any one of Illustrative Examples 6-13, wherein calibrating the activity concentration uptake in the volume of interest by using a 3D phantom includes: performing medical image acquisition of a 3D phantom filled with a radioactive tracer with a known test activity concentration to generate raw image data; performing image reconstruction based on the raw image data to generate a reconstructed image; and determining one or more calibration factors for correcting partial volume effects based on the reconstructed image.

[0061] Illustrative Embodiment 15: The method of Illustrative Embodiment 14, wherein determining one or more calibration factors comprises determining a comparison function value of the activity concentration in the reconstructed image to a known test activity concentration.

[0062] Illustrative Embodiment 16: The method of Illustrative Embodiment 15, wherein determining the comparison function value comprises determining a difference.

[0063] Illustrative Embodiment 17: The method of Illustrative Embodiment 15, wherein determining the comparison function value comprises determining a ratio.

[0064] Illustrative embodiment 18: A system comprising: a non-transitory memory for storing computer-readable program code; and a processor in communication with the non-transitory memory, the processor operating together with the computer-readable program code to perform a calibration method, comprising: generating a pattern representing a volume of interest; printing a three-dimensional (3D) phantom based on the pattern; and calibrating activity concentration uptake in the volume of interest by using the 3D phantom.

[0065] Illustrative Embodiment 19: The system of Illustrative Embodiment 18, wherein the pattern comprises a fractal pattern.

[0066] Illustrative Embodiment 20: The system of Illustrative Embodiment 19, wherein the fractal pattern is characterized by a fractal dimension.

Claims

1. One or more non-transitory computer-readable media containing a program of instructions executable by a machine to perform operations of calibration, the operations comprising: generating a fractal pattern representing a volume of interest, wherein the fractal pattern is characterized by a fractal dimension; Printing three-dimensional (3D) phantoms based on fractal patterns; as well as Active concentration uptake in the volume of interest was calibrated by using a 3D phantom. 2 . The one or more non-transitory computer-readable media of claim 1 , wherein generating the fractal pattern comprises determining a fractal dimension based on tumor progression of the volume of interest.

3. The one or more non-transitory computer-readable media of claim 2, wherein tumor progression is represented by tumor grade. 4 . The one or more non-transitory computer-readable media of claim 3 , wherein determining the fractal dimension comprises increasing the fractal dimension with tumor grade.

5. The one or more non-transitory computer readable media of claim 1, wherein the program of instructions is executable by the machine to calibrate activity concentration uptake in a volume of interest by: performing medical image acquisition of a 3D phantom filled with a radiotracer of known test activity concentration to generate raw image data, performing image reconstruction based on the original image data to generate a reconstructed image, and One or more calibration factors for correcting for partial volume effects are determined based on the reconstructed image.

6. A calibration method comprising: generating a pattern representing a volume of interest; printing a three-dimensional (3D) phantom based on the pattern; as well as Active concentration uptake in the volume of interest was calibrated by using a 3D phantom. The method of claim 6 , wherein the pattern comprises a fractal pattern. The method of claim 7 , wherein the fractal pattern is characterized by a fractal dimension.

9. The method of claim 8, wherein generating the pattern comprises determining a fractal dimension based on tumor progression of the volume of interest.

10. The method of claim 9, wherein tumor progression is represented by tumor grade. The method of claim 10 , wherein determining the fractal dimension comprises increasing the fractal dimension with tumor grade.

12. The method of claim 7, wherein generating a fractal pattern comprises generating a Hilbert curve.

13. The method of claim 6, wherein printing the 3D phantom comprises selective laser sintering, stereolithography, fused deposition modeling, selective laser melting, electron beam melting, or laminated object manufacturing.

14. The method of claim 6, wherein calibrating activity concentration uptake in the volume of interest by using a 3D phantom comprises: performing medical image acquisition of a 3D phantom filled with a radiotracer having a known test activity concentration to generate raw image data; performing image reconstruction based on the original image data to generate a reconstructed image; and One or more calibration factors for correcting for partial volume effects are determined based on the reconstructed image.

15. The method of claim 14, wherein determining one or more calibration factors comprises determining a comparison function value of the activity concentration in the reconstructed image to a known test activity concentration. The method of claim 15 , wherein determining the comparison function value comprises determining a difference. The method of claim 15 , wherein determining the comparison function value comprises determining a ratio.

18. A system comprising: a non-transitory memory for storing computer readable program code; as well as a processor in communication with a non-transitory memory, the processor operating with a computer readable program code to perform a calibration method comprising: generating a pattern representing a volume of interest; printing a three-dimensional (3D) phantom based on the pattern; and Active concentration uptake in the volume of interest was calibrated by using a 3D phantom.

19. The system of claim 18, wherein the pattern comprises a fractal pattern.

20. The system of claim 19, wherein the fractal pattern is characterized by a fractal dimension.

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