Relative electron density mapping from magnetic resonance images

Abstract

Systems, computer software, and methods are disclosed for generating a relative electron density map (RED) from a magnetic resonance imaging (MRI) scan. This may include acquiring an MRI scan of a portion of a patient and segmenting a first region and a second region in the MRI scan. A RED map may then be generated from the MRI scan by assigning a first RED to the first region, assigning a second RED to the second region, and assigning a RED to unsegmented regions in the MRI scan based on their intensity in the MRI scan.

Description

[Technical Field]

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 417,978, filed October 20, 2022, entitled "Relative Electron Density Mapping from Magnetic Resonance Images," which is incorporated by reference. [Background technology]

[0002] Magnetic resonance imaging (MRI), or nuclear magnetic resonance imaging, is a non-invasive imaging technique that uses the interaction of radio frequency pulses and a strong magnetic field (varied by weak gradient fields applied across it to specifically encode or decode its phase and frequency) with body tissue to obtain projections, spectral signals, and planar or volumetric images from within a patient's body. Magnetic resonance imaging is particularly useful in imaging soft tissues and may be used in conjunction with interventional procedures such as radiation therapy or image-guided surgery for the diagnosis of disease. Summary of the Invention

[0003] Systems, computer software, and methods are disclosed for generating a relative electron density (RED) map from a magnetic resonance imaging (MRI) scan. This may include obtaining an MRI scan of a portion of a patient and segmenting the MRI scan into a first region and a second region. A RED map may then be generated from the MRI scan by assigning a first RED to the first region, assigning a second RED to the second region, and assigning RED to unsegmented regions in the MRI scan based on intensity in the MRI scan.

[0004] In some variations, the first region is cortical bone and the second region is gas, and the first and second regions have substantially similar intensities in an MRI scan but have significantly different relative electron densities.

[0005] In some variations, the assignment of the first RED to the first region and the assignment of the second RED to the second region may include the assignment of known average values ​​corresponding to the compositions of the first and second regions, or may include the assignment of measured values ​​corresponding to the compositions of the first and second regions.

[0006] In some variations, assigning REDs to unsegmented regions may involve identifying intensity ranges in the MRI scan and assigning REDs based on the intensity ranges.

[0007] In some variations, the assignment of RED may involve associating a composition with an identified intensity range and assigning a known average value of RED corresponding to the composition.

[0008] In some variations, the assignment of a RED may involve utilizing a RED for compositions expected to be seen in an MRI scan.

[0009] In some variations, the calculation may include determining a first sub-region within the first region having an intensity within a first range, determining a second sub-region within the first region having an intensity within a second range, assigning a first RED corresponding to the composition of the first sub-region, and assigning a third RED corresponding to the composition of the second sub-region.

[0010] In some variations, the first and third REDs may be based on known average values ​​of composition, CT / MR intensity correlation, or measurements from a CT scan.

[0011] In some variations, the determination of the first and second sub-regions may utilize a thresholding technique.

[0012] In some variations, an MRI scan may be obtained of the patient within the MRI-guided radiation therapy system, and the computing may further comprise determining a radiation treatment plan utilizing the relative electron density map while the patient is within the MRI-guided radiation therapy system, and controlling the MRI-guided radiation therapy system to deliver treatment to the patient while the patient is within the MRI-guided radiation therapy system.

[0013] In some variations, the relative electron density map may be generated without a CT scan or may be generated from a single MRI scan.

[0014] In some variations, the MRI scan may be a balanced fast imaging MRI scan with steady-state free precession, or may be a T2 weighted scan.

[0015] Implementations of the present subject matter may include, but are not limited to, methods consistent with the description provided herein and articles comprising tangibly embodied machine-readable media operable to cause one or more machines (e.g., computers, etc.) to perform operations that implement one or more of the described features. Similarly, computer systems, which may include one or more processors and one or more memories coupled to the one or more processors, are also contemplated. The memory, which may include a computer-readable storage medium, may contain, encode, store, etc., one or more programs that cause the one or more processors to perform one or more of the described operations. A computer-implemented method consistent with one or more implementations of the present subject matter may be performed by one or more data processors in a single computing system or in multiple computing systems. Such multiple computing systems may be connected and may exchange data and / or commands or other instructions, etc., via one or more connections, including, but not limited to, connections over a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, etc.) via a direct connection between one or more of the multiple computing systems, etc.

[0016] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the following description. Other features and advantages of the subject matter described herein will be apparent from the specification and drawings, and from the claims. While certain features of the presently disclosed subject matter are described for illustrative purposes with respect to particular implementations, it should be readily understood that such features are not intended to be limiting. The claims following this disclosure are intended to define the scope of the protected subject matter. [Brief explanation of the drawings]

[0017] The accompanying drawings, which are disclosed in and constitute a part of this specification, illustrate certain aspects of the presently disclosed subject matter and, together with the description, help to explain certain principles associated with the disclosed implementations. [Figure 1] FIG. 1 is a diagram illustrating an exemplary implementation of a magnetic resonance guided radiation therapy system (MRgRT system) that couples a magnetic resonance imaging system (MRI) and a radiation therapy source according to certain aspects of the present disclosure. [Figure 2A] FIG. 2A illustrates an MRI image showing various anatomical regions according to certain aspects of the present disclosure. [Figure 2B] FIG. 2B is a table including exemplary compositions, relative electron densities, and how the compositions may appear in an MRI, according to certain embodiments of the present disclosure. [Figure 3] FIG. 3 is a flow diagram illustrating an exemplary process for generating a relative electron density map according to certain aspects of the present disclosure. [Figure 4A] FIG. 4A is a diagram illustrating segmentation of regions in an MRI scan according to an embodiment of the present disclosure. [Figure 4B] FIG. 4B is a diagram illustrating an exemplary partial RED map according to certain aspects of the present disclosure. [Figure 5A] FIG. 5A is a diagram illustrating an exemplary process for RED allocation according to certain aspects of the present disclosure. [Figure 5B]FIG. 5B is a diagram illustrating an exemplary process for assigning RED based at least in part on a range of intensities in an MRI scan according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a process flow diagram illustrating an exemplary process for adaptive radiation therapy utilizing RED allocation according to certain aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0018] The present disclosure provides systems, methods, and software for creating relative electron density maps from MRI images. One particular application for such mapping is in the planning and delivery of MRI-guided radiation therapy. FIG. 1 illustrates one implementation of a magnetic resonance-guided radiation therapy system 100 (MRgRT system) that couples a magnetic resonance imaging system 101 and a radiation therapy source 150 consistent with certain aspects of the present disclosure. In FIG. 1, the MRI 101 includes a main electromagnet 102, a gradient coil assembly 104, and an RF coil system 106. A patient couch 108 on which a patient 110 can lie is located within the MRI 101. The exemplary main electromagnet 102 depicted in FIG. 1 is a gapped solenoid electromagnet separated by a buttress 114, leaving a gap 116. Other MRI configurations, such as an ungapped magnet, a dipole magnet, etc., may also be used.

[0019] FIG. 1 also depicts a simplified exemplary radiation therapy device 150 for delivering radiation therapy. Examples of radiation therapy devices may include, for example, a linear accelerator (LINAC) for delivering high-energy photons (e.g., X-rays, gamma rays), a particle beam source (e.g., protons, heavy ions, neutrons, electrons, etc.), etc. The radiation therapy device 150 may be configured to move to different positions around the patient to deliver radiation from various angles. For example, the radiation therapy device may be mounted on a rotatable gantry positioned between the two halves of an MRI magnet, and the gantry may rotate around the patient to enable MRI imaging while delivering radiation from varying gantry angles. In other embodiments, the radiation therapy device 150 may be mounted on a robotic arm or may be in a fixed position.

[0020] As used herein, the phrase "MRgRT system" refers to hardware and / or software associated with the operation of a magnetic resonance imaging system and associated radiation therapy equipment. The more general phrase "system," as used throughout this disclosure, includes any hardware and / or software required to implement the disclosed concepts. The use of the term "a / the system" includes a processor and / or computer program (and, where appropriate, an MRgRT system) that enables the disclosed concepts. This disclosure contemplates that relative electron density mapping techniques may be utilized in conjunction with an MRgRT system or separate from an MRgRT system.

[0021] FIG. 2A illustrates a typical MRI image with various anatomical regions labeled. Magnetic resonance imaging uses the magnetization of atoms in a patient to create detailed anatomical images. A magnetic field is used to align protons in hydrogen nuclei in the region of interest. Radio frequency (RF) energy pulses are then used to excite the protons into different spin positions. When the protons relax to their initial orientation, they emit RF energy that is detected and measured. The measured RF energy may be analyzed and used to generate images of anatomical regions; one example MRI image 200 is shown in FIG. 2A (showing a patient's pelvic region). Certain anatomical regions in the typical image 200 are labeled, such as the hard bone of the thigh (black), softer bone (light gray), urine (light gray), muscle (dark gray), adipose tissue (white), and gas (black).

[0022] The appearance of a composition shown in an MRI image may depend on the actual composition itself and the imaging technique used. Depending on the composition being imaged, different tissues, air, fluids, etc., may have different relaxation times, i.e., T1 and T2. T1 (longitudinal relaxation time) is the time constant that determines how quickly excited protons return to their initial position aligned by the magnetic field. T2 (transverse relaxation time) is the time constant that determines how quickly excited protons reach equilibrium or become out of phase with each other. Various types of images may be generated based on the sequence of RF pulses. In various imaging techniques, key parameters may include the repetition time (TR), which is the amount of time between successive pulse sequences applied to the same slice. Another parameter is the echo time (TE), which is the time between the application of an RF pulse and the receipt of an echo signal.

[0023] From the foregoing, MRI sequences may be T1-weighted or T2-weighted. T1-weighted images are produced by using short TE and TR times; the contrast and brightness of the image are primarily determined by the T1 characteristics. T2-weighted images are produced by using longer TE and TR times; the contrast and brightness are primarily determined by the T2 characteristics.

[0024] FIG. 2B is a table listing typical anatomical regions of a patient, how they may appear on an MRI, and their approximate relative electron densities (important for typical applications of radiation therapy). During radiation therapy, high-energy photons are scattered by electrons within the patient, depositing energy along their path. The degree of photon scattering depends on the patient's composition (e.g., water, bone, fatty tissue, etc.) that the photons strike, which may have different electron densities. Because scattered photons deliver a radiation dose, radiation treatment plans must accurately account for this scattering. Thus, dose deposition may be determined based in part on the relative electron densities (REDs) of the struck compositions. As described herein, REDs may be mapped or assigned to regions within the patient to form RED maps that can be used in radiation treatment planning and / or treatment software.

[0025] When this disclosure refers to a RED map, the term RED map refers to an approximation of the RED commonly used in radiation therapy dose calculations (e.g., the RED of urine is 1.03, although actual mass densities may also be used in planning calculations, typically between 1.005 and 1.03 g / cm). 3 an additional map of the patient's density metrics, e.g., a map of density (g / cm 3 ), Hounsfield numbers, etc. Furthermore, when this disclosure uses the term map, it refers to any data structure detailing the REDs in one or more regions of a patient.

[0026] As shown in Figure 2B, regions with different compositions (e.g., air vs. solid bone) may have very different REDs (e.g., 0.0 vs. 1.6), but at the same time have similar intensities (i.e., both are black) in the MRI image. Therefore, RED maps cannot be generated simply based on correlation with MRI image intensity.

[0027] 3 is a flow diagram illustrating an exemplary process for generating a relative electron density map according to certain aspects of the present disclosure. The exemplary process may utilize segmentation (or contouring) of regions of a patient, assignment of REDs to the segmented regions based on identifying the composition of the segmented regions, and assignment of REDs based on MRI intensity for unsegmented regions.

[0028] As shown at 310 in process 300 of FIG. 3, some embodiments may include obtaining a magnetic resonance imaging (MRI) scan of a portion of a patient, such as depicted in FIG. 2A. At 320, a first region in the MRI scan may be segmented. The first region may be a portion of the patient consisting of cortical bone, spinal tissue, bladder, muscle, brain tissue, etc. At 330, a second region in the MRI scan may be segmented. The second region may be an additional region, such as adipose tissue, ocular fluid, cerebrospinal fluid, air bubbles, etc. (Although some embodiments are described herein as segmenting multiple regions, the present disclosure contemplates that only a single region (e.g., only the first region) may be segmented.)

[0029] Once regions of a particular patient have been segmented, a RED map may be generated from the MRI scan at 340 in FIG. 3 . In some embodiments, generating the RED map may include assigning a first RED to a first region at 350 and assigning a second RED to a second region at 360. In some embodiments, the assignment of the first RED and / or second RED may be performed by software that automatically determines the composition of a region and assigns a RED to the region. In other embodiments, the assignment may be performed by software that receives manual input of a RED from a user and assigns the RED to the region. Once a RED has been assigned to the first and / or second region, some embodiments for creating a RED map may also include assigning a RED to unsegmented regions in the MRI scan based on intensity in the MRI scan at 370. In embodiments described in more detail herein, the assignment of a RED to unsegmented regions may be performed automatically by computer software based on intensity in the MRI scan in the unsegmented regions.

[0030] As used herein, "segmentation" may include manual segmentation, where the software receives manual contouring commands, such as provided by a user who draws a contour using a computer interface. Segmentation may also include automatic contouring, where the software automatically determines a contour, for example, using edge detection or other such methods. Segmentation may also include software that determines a region but does not explicitly create a contour, for example, by identifying a region based on known locations or intensity of pixel values ​​near the region to effectively determine a boundary.

[0031] FIG. 4A illustrates the segmentation of regions in an MRI scan according to certain embodiments of the present disclosure. In the exemplary MRI scan 400 of FIG. 4A , the first region may be (hard) cortical bone, and the second region may be gas. The first and second regions may have substantially similar intensities in the MRI scan (e.g., black), but may also have significantly different relative electron densities (e.g., 1.6 and 0.0, respectively). Such regions may be referred to herein as “confusing structures” because similar intensities in the MRI scan may confuse the assignment of RED if the assignment is based solely on MRI scan intensity. While the example of FIG. 4A depicts the segmentation of cortical bone and gas, other MRI scans may contain other “confusing structures.” In another example, if the region being imaged is a patient's head, the first region may be ocular fluid and the second region may be fat, and the first and second regions again have substantially similar intensities in the MRI scan (e.g., white) but significantly different relative electron densities (e.g., 1.03 and 0.9, respectively). Thus, in various embodiments, the first and second regions may correspond to, for example, ocular fluid, urine, cerebrospinal fluid, adipose tissue, hard cortical bone, air bubbles, etc. The present disclosure also contemplates embodiments with division into more than first and second regions. For example, the other thigh in FIG. 4A may be outlined as a third region, as may any other air bubbles or other confounding structures.

[0032] FIG. 4B illustrates an exemplary partial RED map according to certain aspects of the present disclosure. The segmented regions may be added to the RED map 450 and assigned appropriate RED values. The present disclosure contemplates numerous methods of assigning RED to regions, for example, assigning a first RED to a first region and a second RED to a second region may include assigning known average values ​​corresponding to the composition of the first and second regions. Such known average values ​​may be obtained from a database, lookup table, etc., or may be provided through manual input or software determination. In the example of FIG. 4B, a RED of 1.6 may be assigned to solid bone, and a RED of 0.0 may be assigned to the gas region. In other embodiments, assigning a first RED to a first region and a second RED to a second region may include assigning measured values ​​corresponding to the composition of the first and second regions. Such measured values ​​may be obtained, for example, from a CT scan of the patient, or may be assigned manually or automatically through software determination.

[0033] After assigning REDs to the various segmented regions as described above (e.g., with respect to confusing structures), REDs may be further determined and assigned to unsegmented regions. This may include identifying intensity ranges in the MRI scan and assigning REDs based on the intensity ranges. For example, an MRI scan 400 may have intensities ranging from 0 (black) to 255 (white), and intensities ranging from 20 to 40 (dark gray) may be identified as muscle and assigned a RED of 1.03. In this manner, composition types may be associated with intensity ranges, and then RED values ​​are obtained (e.g., using a lookup table or other such data store) and assigned to regions corresponding to the intensity ranges.

[0034] In some embodiments, the assignment of REDs may also include utilizing REDs for compositions expected to be found at a particular MRI scan location (within a patient). If the region being scanned is, for example, the pelvic region, the information may be used to assign an appropriate RED for compositions expected to be found in the pelvic region. In contrast, if the MRI scan location is the brain, an area of ​​brain tissue that is dark gray may be assigned a RED of 1.05 (instead of a RED of 1.03 for muscle, which is assigned the same dark gray intensity found in a scan of the pelvic region).

[0035] As such, various embodiments may include a system that utilizes a specific table for RED values ​​for certain MRI scan locations, where the RED values ​​are based on compositions expected to be found at those MRI scan locations.

[0036] 5A and 5B are diagrams illustrating an exemplary process for the assignment of RED to bone structures.

[0037] FIG. 5A illustrates an exemplary process for assigning REDs using segmentation according to certain aspects of the present disclosure. A simplified cross-section of bone 510 is shown. Some embodiments may include segmenting a first region of bone 510 by segmenting cortical bone 520. A first RED may then be assigned to this first region based on a known average value corresponding to the cortical bone composition. A second RED may be assigned to a second region, such as gas bubbles. Then, in some embodiments, a third RED may be assigned to an interior region of the segmented cortical bone (i.e., region 530) based on a known average value for the soft bone composition. In this example, the first region of cortical bone may be assigned a RED of 1.6, the second region of gas may be assigned a RED of 0.0, and the interior region of cortical bone may be assigned a RED of 1.1 for soft bone. The system may then proceed to assign REDs to unsegmented regions based on their intensity in the MRI scan.

[0038] FIG. 5B illustrates an exemplary process for assigning REDs based on intensity in an MRI scan within a range, according to certain aspects of the present disclosure. In this example, segmenting a first region includes segmenting the outer boundary of the bone 510. The system may then determine a first sub-region 540 within the outer boundary of the bone 510 whose intensity is within a first range 542, and then a second sub-region 550 within the bone 510 whose intensity is within a second range 552. A first RED (e.g., 1.6) corresponding to cortical bone may be assigned to the first sub-region. A second RED may be assigned to a second region, such as a porous region. And a third RED (e.g., 1.1) corresponding to soft bone may be assigned to the second sub-region. As discussed above, REDs may be based on known average values ​​of composition, CT / MR intensity correlations, measurements from CT scans, etc. In some embodiments, determining the first and second sub-regions may utilize thresholding techniques.

[0039] As a general example for patient composition (as opposed to the bone example above), embodiments of the present disclosure may include a system for determining a first sub-region within a first region having intensities within a first range. A second sub-region within the first region having intensities within a second range may be determined. A first RED may be assigned to the first sub-region, a second RED may be assigned to the second region, and a third RED may be assigned to the second sub-region. As with the bone structure example above, determining the first and second sub-regions may utilize thresholding techniques. Additionally, the REDs may be based on known average values ​​of composition, CT / MR intensity correlations, measurements from CT scans, etc.

[0040] FIG. 6 is a process flow diagram illustrating an exemplary process for adaptive radiation therapy utilizing the creation of a RED map according to certain aspects of the present disclosure. Any of the embodiments may be utilized in conjunction with performing adaptive radiation therapy by generating a RED map while the patient is within the MRgRT system for treatment (e.g., without requiring a separate CT scan). Such embodiments may include embodiments in which an MRI scan is acquired of the patient within the MRI-guided radiation therapy system, and operations (e.g., as described with reference to FIG. 3 and reproduced in FIG. 6) further include, at 610, determining a radiation treatment plan utilizing the relative electron density map while the patient is within the MRI-guided radiation therapy system. Additionally, at 620, the system may control the MRI-guided radiation therapy system to deliver treatment to the patient while the patient is within the MRI-guided radiation therapy system. As such, in some embodiments, the RED map may be generated without a CT scan, or may be generated from a single MRI scan.

[0041] While many different types of MRI pulse sequences may be performed, in one embodiment, the MRI scan may be a T2-weighted scan. In another embodiment, the MRI scan may be a balanced rapid imaging MRI scan with steady-state free precession, in which the pulse sequence may be balanced so that the gradient fields return the nuclei to the same phase as before the gradient fields were applied. For example, these embodiments may be utilized to clearly depict fluids such as cerebrospinal fluid.

[0042] In the following, further features, characteristics and exemplary technical solutions of the present disclosure will be described in terms of items that can be selected and claimed in any combination.

[0043] Item 1 is a system comprising at least one programmable processor and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising acquiring a magnetic resonance imaging (MRI) scan of a portion of a patient, segmenting a first region in the MRI scan, segmenting a second region in the MRI scan, and generating a relative electron density (RED) map from the MRI scan, wherein the generation comprises assigning a first RED to the first region, assigning a second RED to the second region, and assigning REDs to unsegmented regions in the MRI scan based on intensity in the MRI scan.

[0044] Item 2 is the system of item 1, wherein the first region is cortical bone and the second region is gas, and the first and second regions have substantially similar intensities in an MRI scan and significantly different relative electron densities.

[0045] Item 3 is a system such as any one of the preceding items, wherein the first region is ocular fluid and the second region is adipose tissue, and the first and second regions have substantially similar intensities in an MRI scan and significantly different relative electron densities.

[0046] Item 4 is a system such as any one of the preceding items, wherein the first and second regions correspond to ocular fluid, urine, cerebrospinal fluid, adipose tissue, hard cortical bone, or an air bubble.

[0047] Item 5 is a system as in any one of the preceding items, wherein the assignment of the first RED to the first region and the assignment of the second RED to the second region further comprises assignment of known average values ​​corresponding to the compositions of the first and second regions.

[0048] Item 6 is a system as in any one of the preceding items, wherein the assignment of the first RED to the first region and the assignment of the second RED to the second region further comprises an assignment of measurements corresponding to compositions of the first region and the second region.

[0049] Item 7 is a system such as any one of the preceding items, wherein assigning REDs to unsegmented regions further comprises identifying intensity ranges in the MRI scan and assigning REDs based on the intensity ranges.

[0050] Item 8 is a system as in any one of the preceding items, wherein the assignment of RED further comprises associating the composition with the identified intensity range and assigning a known average value of RED corresponding to the composition.

[0051] Item 9 is a system as in any one of the preceding items, wherein the assignment of a RED further comprises utilizing a RED for compositions expected to be seen in an MRI scan.

[0052] Item 10 is a system as in any one of the preceding items, wherein segmenting the first region comprises segmenting the cortical bone, and assigning the first RED to the first region comprises assigning the first RED based on a known average value corresponding to the composition of the cortical bone, and the calculation further comprises assigning a third RED to the region within the segmented cortical bone based on a known average value for the composition of soft bone.

[0053] Item 11 is a system as in any one of the preceding items, wherein segmenting the first region comprises segmenting an outer boundary of the bone, and wherein the operation further comprises determining a first sub-region within the outer boundary of the bone having an intensity within a first range, determining a second sub-region within the outer boundary of the bone having an intensity within a second range, and assigning a first RED corresponding to cortical bone in the first sub-region and assigning a third RED corresponding to soft bone in the second sub-region.

[0054] Item 12 is a system as in any one of the preceding items, wherein the first RED and the third RED are based on known average values ​​of composition, CT / MR intensity correlation, or measurements from a CT scan.

[0055] Item 13 is a system such as any one of the preceding items, wherein the determination of the first and second subregions utilizes a thresholding technique.

[0056] Item 14 is a system as in any one of the preceding items, wherein the operations further include determining a first sub-region within the first region having an intensity within a first range, determining a second sub-region within the first region having an intensity within a second range, assigning a first RED corresponding to a composition of the first sub-region, and assigning a third RED corresponding to a composition of the second sub-region.

[0057] Item 15 is a system as in any one of the preceding items, wherein the first RED and third RED are based on known average values ​​of composition, CT / MR intensity correlation, or measurements from a CT scan.

[0058] Item 16 is a system such as any one of the preceding items, wherein the determination of the first and second subregions utilizes a thresholding technique.

[0059] Item 17 is a system as in any one of the preceding items, wherein an MRI scan is obtained of a patient within the MRI-guided radiation therapy system, and the computing further comprises determining a radiation treatment plan utilizing the relative electron density map while the patient is within the MRI-guided radiation therapy system, and controlling the MRI-guided radiation therapy system to deliver treatment to the patient while the patient is within the MRI-guided radiation therapy system.

[0060] Item 18 is a system like any one of the preceding items in which the relative electron density map is generated without a CT scan.

[0061] Item 19 is a system such as any one of the preceding items, in which the relative electron density map is generated from a single MRI scan.

[0062] Item 20 is a system as in any one of the preceding items, wherein the MRI scan is a balanced fast imaging MRI scan with steady-state free precession.

[0063] Item 21 is a system like any one of the preceding items, wherein the MRI scan is a T2-weighted scan.

[0064] This disclosure contemplates that the computations disclosed in the embodiments may be performed in many ways that apply the same concepts taught herein, and that such computations are equivalent to the disclosed embodiments.

[0065] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuitry, integrated circuits, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. Various aspects or features may include implementation in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which may be special purpose or general purpose, coupled to receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0066] These computer programs, which may also be referred to as programs, software, software applications, applications, components, or code, may include machine instructions for a programmable processor and may be implemented in a high-level procedural language, an object-oriented programming language, a functional programming language, a logic programming language, and / or an assembly / machine language. As used herein, the term "machine-readable medium" (or computer-readable medium) refers to any computer program product, apparatus, and / or device used to provide machine instructions and / or data to a programmable processor, such as, for example, magnetic disks, optical disks, memories, and programmable logic devices (PLDs), and includes a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" (or computer-readable signal) refers to any signal used to provide machine instructions and / or data to a programmable processor. A machine-readable medium may store such machine instructions non-transitoryly, such as, for example, a non-transitory solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium may alternatively or additionally store such machine instructions in a transitory manner, such as a processor cache or other random access memory associated with one or more physical processor cores.

[0067] To provide for user interaction, one or more aspects or features of the subject matter described herein may be implemented on a computer having a display device, such as a cathode ray tube (CRT), liquid crystal display (LCD), or light emitting diode (LED) monitor, for displaying information to a user, and a keyboard and pointing device, such as a mouse or trackball, by which the user may provide input to the computer. Similarly, other types of devices may be used to provide for user interaction. For example, feedback provided to the user may be in any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user may be received in any form, including, but not limited to, acoustic input, voice input, or tactile input. Other possible input devices include, but are not limited to, touchscreens or other contact-sensitive devices, such as single or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, etc.

[0068] In the description above and in the claims, phrases such as "at least one" or "one or more" may follow a conjunctive list of elements or features. The term "and / or" may also follow a list of more than one element or feature. Unless otherwise expressly or explicitly contradicted by the context, such phrases are intended to mean either of the elements or features on the individual list, or any of the listed elements or features in combination with any of the other listed elements or features. For example, the phrases "at least one of A and B," "one or more of A and B," and "A and / or B" are intended to mean "A only, B only, or A and B together," respectively. A similar interpretation is intended for lists containing more than two items. For example, the phrases "at least one of A, B, and C," "one or more of A, B, and C," and "A, B, and / or C" are intended to mean "A only, B only, C only, A and B together, A and C together, B and C together, or A, B, and C together," respectively. Use of the term "based on" above and in the claims is intended to mean "based at least in part on," allowing for unrecited features or elements.

[0069] The subject matter described herein may be embodied in systems, apparatus, methods, computer programs, and / or articles in any desired configuration. Any method or logic flow depicted in the accompanying figures and / or described herein does not necessarily require the particular order shown or order for achieving desired results. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. While a few variations have been described in detail above, other modifications or additions are possible. In particular, additional features and / or variations may be provided in addition to those set forth herein. The above-described implementations may be directed to various combinations and subcombinations of the disclosed features and / or combinations and subcombinations of the additional features described above. Furthermore, the above-described advantages are not intended to limit the application of any issued claims to processes and structures that achieve any or all advantages.

[0070] Additionally, section headings should not limit or characterize the invention(s) presented in any claim(s) that may issue from this disclosure. Moreover, descriptions of technology in the "Background" section should not be construed as admissions that they are prior art to any invention(s) of this disclosure. The "Summary" section should also not be considered as a characterization of the invention(s) set forth in the issued claims. Furthermore, any reference to this disclosure generally or use of the word "invention" in the singular is not intended to imply any limitations on the claims set forth below. Multiple inventions may be set forth by the limitations of the multiple claims issuing from this disclosure, and such claims accordingly define the invention(s) and their equivalents protected thereby.

Claims

1. at least one programmable processor; a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform an operation, the operation comprising: obtaining a magnetic resonance imaging (MRI) scan of a portion of the patient; Segmenting a first region in the MRI scan; Segmenting a second region in the MRI scan; generating a relative electron density (RED) map from the MRI scan, allocating a first RED to the first region; allocating a second RED to the second region; and assigning REDs to unsegmented regions in the MRI scan based on their intensities in the MRI scan. a non-transitory machine-readable medium comprising: A system comprising:

2. 2. The system of claim 1, wherein the first region is cortical bone and the second region is gas, and the first region and the second region have substantially similar intensities in the MRI scan and significantly different relative electron densities.

3. 2. The system of claim 1, wherein the first region is ocular fluid and the second region is fatty tissue, and the first region and the second region have substantially similar intensities in the MRI scan and significantly different relative electron densities.

4. The system of claim 1 , wherein the first region and the second region correspond to ocular fluid, urine, cerebrospinal fluid, adipose tissue, hard cortical bone, or an air bubble.

5. 2. The system of claim 1, wherein the allocation of the first RED to the first region and the allocation of the second RED to the second region further comprises an allocation of known average values ​​corresponding to compositions of the first region and the second region.

6. 2. The system of claim 1, wherein the allocation of the first RED to the first region and the allocation of the second RED to the second region further comprises an allocation of measurements corresponding to compositions of the first region and the second region.

7. The allocation of RED to the undivided area is Identifying intensity ranges in the MRI scan; and allocating the REDs based on the intensity range.

8. The allocation of the REDs is Associating compositions with the identified intensity ranges; and and assigning a known average value of RED corresponding to said composition.

9. The allocation of the REDs is The system of claim 7 further comprising utilizing RED for compositions expected to be seen in the MRI scan.

10. segmenting the first region comprises segmenting cortical bone, and assigning the first RED to the first region comprises assigning the first RED based on a known average value corresponding to a composition of cortical bone; The system of claim 1 , wherein the calculation further comprises assigning a third RED to the region within the segmented cortical bone based on a known average value for soft bone composition.

11. The segmentation of the first region comprises segmenting an outer boundary of a bone, the operation comprising: determining a first sub-region within the outer boundary of the bone in which the intensity is within a first range; determining a second sub-region within the outer boundary of the bone in which the intensity is within a second range; an assignment of the first RED corresponding to the cortical bone of the first sub-region; and assigning a third RED corresponding to the soft bone of the second sub-region.

12. 12. The system of claim 11, wherein the first RED and the third RED are based on known average values ​​of the composition, CT / MR intensity correlation, or measurements from a CT scan.

13. The system of claim 11 , wherein the determination of the first and second sub-regions utilizes a thresholding technique.

14. The calculation is determining a first sub-region within the first region where the intensity is within a first range; determining a second sub-region within the first region where the intensity is within a second range; an allocation of the first RED corresponding to the composition of the first sub-region; and a third RED allocation corresponding to the composition of the second sub-region.

15. 15. The system of claim 14, wherein the first RED and the third RED are based on known average values ​​of the composition, CT / MR intensity correlation, or measurements from a CT scan.

16. The system of claim 14 , wherein the determination of the first and second sub-regions utilizes a thresholding technique.

17. The MRI scan is acquired of a patient in an MRI guided radiation therapy system, and the calculation comprises: determining a radiation treatment plan utilizing the relative electron density map while the patient is within the MRI-guided radiation treatment system; 10. The system of claim 1, further comprising: controlling the MRI-guided radiation therapy system to deliver treatment to the patient while the patient is within the MRI-guided radiation therapy system.

18. The system of claim 1 , wherein the relative electron density map is generated without a CT scan.

19. The system of claim 1 , wherein the relative electron density map is generated from a single MRI scan.

20. The system of claim 1 , wherein the MRI scan is a balanced high-speed imaging MRI scan with steady-state free precession.

21. The system of claim 1 , wherein the MRI scan is a T2-weighted scan.

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