EP4713705A1 - System and method of determining white matter structure using mri - Google Patents

Abstract

A system and method of determining white matter packing in a brain of a subject by at least one processor may include utilizing a Magnetic Resonance Imaging (MRI) device to perform a multiple-echo sequence scan of a region of interest (ROI) of the subject's white matter. Based on the multiple-echo sequence scan, the at least one processor may obtain a Magnetic Resonance (MR) data element representing exponential decay of MR signal in a plurality of voxels within the ROI. The at least one processor may analyze the MR data element to extract, for at least one voxel of the ROI, a white matter (WM) packing parameter value corresponding to that at least one voxel. The WM packing parameter may be indicative of structural packing of non-water material, on a sub-voxel scale.

Description

SYSTEM AND METHOD OF DETERMINING WHITE MATTER STRUCTURE

CROSS-REFERENCE TO RELATED APPLICATIONS

[001] This application claims the benefit of priority of U.S. Patent Application No. 63/466,913, filed on May 16, 2023 and titled: “IN-VIVO MRI MAPPING AS A NEW MYELIN PACKING MARKER”, which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

[002] The present invention relates generally to the field of medical imaging. More specifically, the present invention relates to a system and method of determining white matter structure.

BACKGROUND OF THE INVENTION

[003] Brain degenerative diseases such as Multiple Sclerosis (MS) are caused by structural alternation of myelin structures, a process commonly referred to as demyelination. This process may lead to axonal transection and degeneration deficits.

[004] MS histopathology is commonly characterized by loss of membrane adhesion, swelling across the water gaps, vesiculation, and eventual disintegration of the myelin structure.

SUMMARY OF THE INVENTION

[005] Postmortem studies have shown that the water layer gaps between the membranes are compact in healthy myelin but may change in diseased undulated myelin as a result of demyelination and remyelination in MS patients, during the aging process, as a result of inflammation, and due to other neurological diseases.

[006] Currently, available diagnostic systems employ Magnetic Resonance Imaging (MRI) technology to assess or quantify the concentration or fraction of non-water material such as lipids in Regions of Interest (ROIs) of scanned subjects, in the hope of gaining insight into tissue functionality. However, such currently available systems may not provide a direct indication regarding the actual packing, or structure, of these non-water substances.

[007] Embodiments of the invention may include a biophysical model based on the water fraction and multi-compartment T2 to estimate the width of myelin water gaps. Embodiments of the invention may thus allow in-vivo measurement of myelin water gaps, thereby providing biomarkers for assessing the quality of myelination packing and assessing a subject’s brain’s white matter condition.

[008] As elaborated herein, the inventors have designed an in-vitro lipid phantom system, where multilamellar vesicles (MLV) were used to validate the accuracy of the biophysical model in predicting myelin water gaps using cryogenic transmission electron microscope (Cryo-TEM) technology and small angle x-ray scattering (SAXS).

[009] Embodiments of the invention may include a method of determining white matter packing in a brain of a subject by at least one processor.

[0010] According to some embodiments, the at least one processor may be configured to control a Magnetic Resonance Imaging (MRI) device to perform a multiple-echo sequence scan of a region of interest (ROI) of the subject’s white matter. Based on the multiple-echo sequence scan, the at least one processor may obtain a first Magnetic Resonance (MR) data element representing exponential decay of MR signal in a plurality of voxels within the ROI. The at least one processor may analyze the first MR data element to extract, for at least one voxel of the ROI, a white matter (WM) packing parameter value corresponding to that at least one voxel. The WM packing parameter may be indicative of structural packing of nonwater material, on a molecular, voxel, or sub-voxel scale.

[0011] According to some embodiments, the WM packing parameter value of a voxel may include, or may represent a ratio between myelin water gap thickness and myelin membrane thickness in at least one voxel, as elaborated herein.

[0012] According to some embodiments, the at least one processor may control the MRI device to perform a quantitative scan of the ROI of white matter. Based on the quantitative scan, the at least one processor may obtain a second MR data element that is indicative of a non-water fraction in the plurality of ROI voxels. The at least one processor may subsequently extract the WM packing parameter value further based on the second MR data element, as elaborated in the description herein.

[0013] According to some embodiments, the at least one processor may analyze the first MR data element by expressing the exponential decay of MR signal as a combined contribution of: (a) decay of MR signal in tissue water, and (b) decay of MR signal in myelin gap water; expressing the decay of MR signal in tissue water as a first function of (i) the WM packing parameter and (ii) the non-water fraction; expressing the decay of MR signal in in myelin gap water as a second function of (i) the WM packing parameter and (ii) the non-water fraction; and applying an analytical, and/or numerical analysis algorithm on the first MR data element, to extract the WM packing parameter value for at least one voxel of the ROI, based on the first function and second function.

[0014] Additionally, or alternatively, the at least one processor may integrate, or accumulate the WM packing parameter values pertaining to the plurality of voxels, and/or plurality of ROIs into a three dimensional (3D) brain map. The 3D brain map may represent distribution of the ratio between myelin water gap thickness and myelin membrane thickness in one or more ROIs.

[0015] Additionally, or alternatively, the at least one processor may present, or visualize the 3D brain map via a user interface (UI). The UI may, for example be configured to depict at least one component of the ROI, selected from: (i) a slice of the brain, (ii) a lobe in the brain, (iii) a white matter tract in the brain, (iv) a predetermined region (e.g., a region preselected by a user) in the brain, and the like. Additionally, or alternatively, the at least one processor may present (e.g., via the UI) a distribution of ratio between myelin water gap thickness and myelin membrane thickness in relation to the at least one depicted component (e.g., within a slice of white matter, within a WM tract, within a selected region, within a lobe, etc.).

[0016] Additionally, or alternatively, the at least one processor may distinguish, or segment the 3D brain map to one or more brain regions, based on the WM packing parameter values, e.g., where each brain region is characterized by different distribution of WM packing value. The at least one processor may be further configured to present the one or more segmented brain regions via the UI.

[0017] Additionally, or alternatively, the at least one processor may obtain a reference data element, comprising expected distribution of WM packing parameter values, wherein said reference data element corresponds to (i) characteristics of the subject, and (ii) specific, anatomic brain regions. For example, reference data element may indicate known distribution of WM packing values of brain regions, characteristic of a cohort, or population of similar (e.g., similar age, similar medical background, etc.) subjects. The at least one processor may subsequently determine a condition or diagnosis of the subject’s white matter based on the WM packing parameter value, in accordance with the reference data element.

[0018] For example, the condition or diagnosis may include: an identification of a white matter disorder of the subject, an indication of an extent, a stage, a progression and/or a prognosis of the white matter disorder of the subject, an indication of normal or accelerated aging of the subject’s white mater, an indication of regions or distribution of WM packing that may be statistically significant for characterizing a specific WM disorder, and the like. [0019] Embodiments of the invention may include a system for determining white matter packing in a brain of a subject. Embodiments of the system may include a non-transitory memory device, where modules of instruction code are stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code. Upon execution of the modules of instruction code, the at least one processor may be configured to control an MRI device to perform a multiple-echo sequence scan of an ROI of the subject’s white matter. Based on the multiple-echo sequence scan, the at least one processor may obtain a first MR data element representing exponential decay of MR signal in a plurality of voxels within the ROI. The at least one processor may analyze the first MR data element to extract, for at least one (e.g., each) voxel of the ROI, a WM packing parameter value corresponding to that at least one voxel.

[0020] According to some embodiments, the WM packing parameter may be indicative of structural packing of non-water material, on a molecular, sub-voxel, or voxel scale.

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention may best be understood by reference to the following detailed description when read with the accompanying drawings in which:

[0022] Fig. 1 is a block diagram, depicting a computing device that may be included in a system for determining White Matter (WM) structure according to some embodiments;

[0023] Figs. 2A-2D Depict electron microscopy images demonstrating differences between healthy and diseased myelin structures, in both in-vitro (Fig.2A and Fig. 2B) and ex-vivo (Fig. 2C and Fig. 2D) setups;

[0024] Fig. 3 is a block diagram, depicting a system for determining WM structure according to some embodiments;

[0025] Figs. 4A and 4B are schematic illustrations, depicting MEVs with different myelin water gaps that the inventors experimentally used to validate the reliability of embodiments of the system for determining WM structure; [0026] Figs. 5A-5E are Cryo-TEM images of MLVs that the inventors experimentally used to validate the reliability of embodiments of the system for determining WM structure showing different myelin water gaps for each in-vitro system used;

[0027] Fig. 6A is a graph showing the accuracy of the model prediction cross-correlation- signal to the measured signal according to some embodiments of the invention;

[0028] Fig. 6B is a graph showing the accuracy of the predicted WM packing parameter values (i.e., the ratio between myelin water gap thickness to myelin membrane thickness) to their corresponding measurement with the electron microscopy technique, according to some embodiments of the invention;

[0029] Fig. 7A is a graph showing relaxation time T2 calculated by embodiments of the invention for myelin gap water, in a variety of brain regions;

[0030] Fig. 7B is a graph showing WM packing parameter values calculated by embodiments of the invention, in a variety of brain regions; and

[0031] Fig. 8 is a flow diagram depicting a method of determining WM structure according to some embodiments.

[0032] It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

[0033] One skilled in the art will realize the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting of the invention described herein. Scope of the invention is thus indicated by the appended claims, rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

[0034] In the following detailed description, numerous specific details are outlined to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention. Some features or elements described with respect to one embodiment may be combined with features or elements described with respect to other embodiments. For the sake of clarity, discussion of the same or similar features or elements may not be repeated.

[0035] Although embodiments of the invention are not limited in this regard, discussions utilizing terms such as “for example”, “processing”, “computing”, “calculating”, “determining”, “establishing”, “analyzing”, “checking”, or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or another electronic computing device, that manipulates and/or transforms data represented as physical (e.g., electronic) quantities within the computer’s registers and/or memories into other data similarly represented as physical quantities within the computer’s registers and/or memories or other information non-transitory storage medium that may store instructions to perform operations and/or processes.

[0036] Although embodiments of the invention are not limited in this regard, the terms “plurality” and “a plurality” as used herein may include, for example, “multiple” or “two or more”. The terms “plurality” or “a plurality” may be used throughout the specification to describe two or more components, devices, elements, units, parameters, or the like. The term “set” when used herein may include one or more items.

[0037] Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Additionally, some of the described method embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently.

[0038] Reference is now made to Fig. 1, which is a block diagram depicting a computing device, which may be included within an embodiment of a system for determining WM structure, according to some embodiments.

[0039] Computing device 1 may include a processor or controller 2 that may be, for example, a central processing unit (CPU) processor, a chip or any suitable computing or computational device, an operating system 3, a memory 4, executable code 5, a storage system 6, input devices 7 and output devices 8. Processor 2 (or one or more controllers or processors, possibly across multiple units or devices) may be configured to carry out methods described herein, and/or to execute or act as the various modules, units, etc. More than one computing device 1 may be included in, and one or more computing devices 1 may act as the components of, a system according to embodiments of the invention.

[0040] Operating system 3 may be or may include any code segment (e.g., one similar to executable code 5 described herein) designed and/or configured to perform tasks involving coordination, scheduling, arbitration, supervising, controlling, or otherwise managing operation of computing device 1, for example, scheduling execution of software programs or tasks or enabling software programs or other modules or units to communicate. Operating system 3 may be a commercial operating system. It will be noted that an operating system 3 may be an optional component, e.g., in some embodiments, a system may include a computing device that does not require or include an operating system 3.

[0041] Memory 4 may be or may include, for example, a Random- Access Memory (RAM), a read-only memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a double data rate (DDR) memory chip, a Flash memory, a volatile memory, a nonvolatile memory, a cache memory, a buffer, a short term memory unit, a long term memory unit, or other suitable memory units or storage units. Memory 4 may be or may include a plurality of possibly different memory units. Memory 4 may be a computer or processor non-transitory readable medium, or a computer non-transitory storage medium, e.g., RAM memory. In one embodiment, a non-transitory storage medium such as memory 4, a hard disk drive, another storage device, etc. may store instructions or code which when executed by a processor may cause the processor to carry out methods as described herein.

[0042] Executable code 5 may be any executable code, e.g., an application, a program, a process, a task, or a script. Executable code 5 may be executed by processor or controller 2 possibly under the control of operating system 3. For example, executable code 5 may be an application that may determine WM structure as further described herein. Although, for the sake of clarity, a single item of executable code 5 is shown in Fig. 1, a system according to some embodiments of the invention may include a plurality of executable code segments similar to executable code 5 that may be loaded into memory 4 and cause processor 2 to carry out methods described herein.

[0043] Storage system 6 may be or may include, for example, a flash memory as known in the art, a memory that is internal to, or embedded in, a microcontroller or chip as known in the art, a hard disk drive, a CD-Recordable (CD-R) drive, a Blu-ray disk (BD), a universal serial bus (USB) device or other suitable removable and/or fixed storage unit. Data pertaining to MR measurements may be stored in storage system 6 and may be loaded from storage system 6 into memory 4 where it may be processed by processor or controller 2. In some embodiments, some of the components shown in Fig. 1 may be omitted. For example, memory 4 may be a non-volatile memory having the storage capacity of storage system 6. Accordingly, although shown as a separate component, storage system 6 may be embedded or included in memory 4.

[0044] Input devices 7 may be or may include any suitable input devices, components, or systems, e.g., a detachable keyboard or keypad, a mouse, and the like. Output devices 8 may include one or more (possibly detachable) displays or monitors, speakers, and/or any other suitable output devices. Any applicable input/output (VO) devices may be connected to Computing device 1 as shown by blocks 7 and 8. For example, a wired or wireless network interface card (NIC), a universal serial bus (USB) device, or an external hard drive may be included in input devices 7 and/or output devices 8. It will be recognized that any suitable number of input devices 7 and output device 8 may be operatively connected to Computing device 1 as shown by blocks 7 and 8.

[0045] A system according to some embodiments of the invention may include components such as, but not limited to, a plurality of central processing units (CPU) or any other suitable multi-purpose or specific processors or controllers (e.g., similar to element 2), a plurality of input units, a plurality of output units, a plurality of memory units, and a plurality of storage units.

[0046] Reference is now made to Figs. 2A-2D, which are electron microscopy images demonstrating differences between healthy and diseased myelin structures, in both in-vitro and ex-vivo setups.

[0047] As shown in Figs. 2A and 2C, healthy myelin has a compact and round structure in comparison to diseased undulated myelin (Figs. 2B and 2D). While the width of myelin membrane thickness (dark structures, denoted herein as d_m) remains practically constant, the thickness of water gaps (denoted herein as d_w, also referred to as “myelin water gaps”) between myelin membranes increases or becomes sporadic in a diseased state.

[0048] Embodiments of the invention may utilize this observation to identify a change in myelin water gap d_w, or a change in the ratio between myelin water gap d_w and myelin membrane thickness d_m (e.g., d_w / d_m). Embodiments of the invention may subsequently gain insight into characterizing a condition of a subject’s brain WM based on the distribution of myelin water gap thickness d_w in their brain.

[0049] As known in the art, Myelin Water Imaging (MWI) is a multiexponential T2 model that estimates a fraction of the signal that arises from (a) water trapped between myelin membranes and (b) from other tissue water.

[0050] Macromolecular and lipid Tissue Volume (MTV, defined as a complementary of Water Fraction (WF) in the scanned region), has been shown to approximate a total myelin fraction of WM tissue.

[0051] Both MWI and MTV are known to be sensitive to the reduction of myelin in MS patients. However, neither MWI nor MTV is sensitive to changes in myelin packing, or organization. For example, neither MWI nor MTV may provide an indication of the size of a myelin water gap (e.g., the water-filled gap between myelin membranes).

[0052] Embodiments of the invention may include a new, quantitative MRI (qMRI) framework, that may employ multiple echo MRI sequences such as spin-echo (SE) or gradient echo (GRE) in combination with myelin fraction estimation (e.g., macromolecular and lipid tissue volume (MTV), to facilitate in-vivo estimation of the width of a myelin water gap. As explained herein, to validate the water gap model, the inventors used in-vitro lipid phantoms and tested the model's outcome for in-vivo human data.

[0053] Reference is now made to Fig. 3, which depicts a system 10 for determining WM structure, according to some embodiments.

[0054] According to some embodiments of the invention, system 10 may be implemented as a software module, a hardware module, or any combination thereof. For example, system 10 may be, or may include a computing device such as element 1 of Fig. 1 and may be adapted to execute one or more modules of executable code (e.g., element 5 of Fig. 1) to determine WM structure, as further described herein.

[0055] As shown in Fig. 3, arrows may represent flow of one or more data elements to and from system 10 and/or among modules or elements of system 10. Some arrows have been omitted in Fig. 3 for clarity.

[0056] As shown in Fig. 3, system 10 may be associated with (e.g., communicatively connected to) an MRI device 20. In some embodiments, at least one processor 2 (Fig. 1) of system 10 may configure, and/or control MRI device 20 to perform specific MRI scans, as known in the art. [0057] For example, system 10 may utilize MRI device 20, or control MRI device 20 to perform a multiple-echo sequence scan of an ROI 20R of a subject’s brain WM.

[0058] As known in the art, an MRI multiple-echo sequence scan (also known as multi-echo imaging), is an MRI technique that acquires multiple echoes or echoes at different echo times during a single scan. In a multi-echo sequence, the MRI pulse sequence is designed to acquire multiple echoes after a single excitation pulse. Each echo represents the signal generated by the magnetic moments in the imaged tissue. By acquiring multiple echoes at different echo times, the multi-echo sequence provides multi-layered information about the relaxation properties of the tissue in each voxel of ROI 20R. Such information may pertain, for example, to T2* and T2 relaxation times, susceptibility, as well as other tissue- specific properties. For example, MRI multiple-echo sequence scans may include Spin Echo (SE) scans and/or Gradient Echo (GE) scans, as known in the art.

[0059] Based on the multiple-echo sequence scan, system 10 may obtain from MRI 20 a first Magnetic Resonance (MR) data element 20ES. MR data element 20ES may include or represent exponential decay of MR signal in a plurality of voxels pertaining to ROI 20R.

[0060] In other words, MR data element 20ES may include a plurality of member, voxelspecific data elements 20ESV. Each data element 20ESV may be dedicated to at least one respective voxel within ROI 20R. For example, Each data element 20ESV may represent a single voxel in ROI 20R. In another example, data element 20ESV may represent all the voxels in ROI 20R. Additional combinations of representation of voxels by data element 20ESV are also possible.

[0061] Each data element 20ESV may be a multiple-entry entity such as a vector, a table, a linked list, and the like. Each entry of data element 20ESV may hold an MR signal value corresponding to a specific time (i.e., echo) of the multi-echo sequence (e.g., SE, GE) scan 20ES in the at least one respective voxel of ROI 20R.

[0062] As elaborated herein, system 10 may analyze the MR data element 20ES (e.g., member data element 20ESV) to extract, for at least one (e.g., each) voxel of the ROI, a WM packing parameter HOP value corresponding to that at least one voxel. For example, WM packing parameter HOP may pertain to one or more (e.g., each) individual voxel of a multitude of voxels in ROI 20R. Additionally, or alternatively, WM packing parameter 110P may pertain to a subset (e.g., all) voxels within ROI 20R. [0063] The term “packing” may be used in this context to indicate a mechanical, physical form, or structural property of WM material on a molecular, sub-voxel, or voxel scale. In other words, WM packing parameter 110P may be indicative of the structural packing of non-water material, on such scale length. The term “packing” may not be confused with tissue characteristics provided by currently available systems of MR imaging, such as macro-structures or anatomic components (e.g., lobes, fissures, etc.) of the brain, or quantitative measurement of the concentration of substances (e.g., lipids, water, myelin fraction, axon caliber, g-ratio, etc.) in the brain.

[0064] For example, and as elaborated herein, WM packing parameter HOP may represent, or be indicative of structural packing, or arrangement of non-water material such as myelin on a nanometric scale. In another example, WM packing parameter HOP value of a voxel may be indicative of the thickness of myelin water gaps in that voxel. In yet another example, WM packing parameter HOP value of a voxel may be indicative of a ratio between myelin water gap thickness and myelin membrane gap thickness in at least one voxel.

[0065] Additionally, or alternatively, system 10 may control MRI device 20 to perform a second, quantitative scan 20Q of ROI 20R of the WM. Quantitative scan 20Q may be performed substantially simultaneous to, or intermittent with multiple-echo sequence scan 20ES. System 10 may thus obtain a second MR data element 20QV, that may include quantitative MRI data pertaining to the plurality of voxels in ROI 20R.

[0066] For example, one or more (e.g., each) voxel in ROI 20R may be associated with a respective quantitative MRI data element 20QV, that is indicative of a composition, or a fraction of non-water material in that voxel.

[0067] As elaborated herein, system 10 may subsequently calculate WM packing parameter value 110P further based on the second, quantitative MR data element 20QV.

[0068] According to some embodiments, system 10 may include a Water Fraction (WF) calculation module 130, as shown in Fig. 3. WF module 130 may be configured to calculate WF 130W for one or more (e.g., each) of the plurality of voxels of ROI 20R, based on the voxel- specific quantitative MRI data 20QV, as known in the art.

[0069] For example, the quantitative scan 20Q of ROI 20R may include (a) Spoiled gradient echo sequence with very short TE (or multi-echo), (b) MPRAGE (Magnetization-Prepared RApid Gradient Echo) sequence (c) MP2RAGE (Magnetization-Prepared 2 RApid Gradient Echo) sequence with very short TE (or multi-echo) , (d) multi-echo Spin echo or multi-echo gradient echo sequences, and the like.

[0070] Scan 20Q may include voxel- specific quantitative MRI data 20QV, as known in the art. WF calculation module 130 may subsequently calculate WF 130W based on quantitative MRI data 20QV of quantitative scan 20Q, as known in the art.

[0071] The term “Water Fraction” (WF) may be used in the description and equations herein as a generic expression of a fraction of water in the scanned tissue. However, it may be appreciated that additional, alternative types of quantitative data may also be used by embodiments of the invention (e.g., instead of, or in addition to WF 130W), to reflect the composition of the scanned tissue, with appropriate amendments.

[0072] For example, WF module 130 may be configured to calculate the WF complementary, Non-Water Fraction (NWF=(1-WF)) 130N (also referred to herein as Macromolecular Tissue Volume (MTV)) for one or more (e.g., each) of the plurality of voxels of ROI 20R, based on quantitative MRI data 20QV of quantitative scan 20Q, as known in the art.

[0073] Additional examples of quantitative measurements indicative of a non-water fraction to replace WF 130W may include Proton Density (PD), Myelin Water Fraction (MWF), Bound Pool Fraction (BPF), and the like.

[0074] As shown in Fig. 3, system 10 may include an analysis module 110. As explained herein, analysis module 110 may be configured to extract WM packing parameter value 110P based on (i) MR data element 20ES, originating from the MRI multiple-echo sequence scan, and further based on (ii) MR data element 20QV (now WF data element 130W).

[0075] According to some embodiments, analysis module 110 may express the exponential decay of MR signal as a combined contribution of: (a) decay of MR signal in tissue water, and (b) decay of MR signal in myelin gap water, as in equations Eq. 1A and Eq. IB, below: Eq. 1A

SE(TE) = Mo [ I'vw • exp(-TE/T2Mw) + frw • exp(-TE/T2Tw)]

Eq. IB

GE(TE) = Mo [fviw • exp(-TE/T2Mw) + frw • exp(-TE/T2Tw)]

[0076] In equations Eq. 1A and Eq. IB, TE (Echo Time) represents a time interval between the application of a radiofrequency (RF) pulse and the peak of the echo signal received by MRI scanner 20. [0077] SE(TE) and GE(TE) represent MR signal intensity at time TE for multiple spin echo and gradient echo scans, respectively. In other words, SE(TE) and GE(TE) represent the exponential decay of MR SE and GE signals, at time TE, in a plurality of voxels, and correspond to data element 20ESV of Fig. 3.

[0078] T2\iw and T2 rw represent relaxation times (referred to in the art as T2) of water myelin water gaps (MW) and Tissue Water (TW), respectively.

[0079] IMW and frw represent a fraction of myelin water gaps and tissue water, respectively. The sum of f iw and frw may be water fraction WF 130W, as calculated by WF calculation module 130 of Fig. 3.

[0080] As explained herein, MR signal intensity SE(TE) may be expressed as a combination SEi(TE) + SE2(TE), where SEi(TE) represents the contribution of myelinated water, and SE2(TE) represents the contribution of tissue water.

[0081] In a similar manner, MR signal intensity GE(TE) may be expressed as a combination GEi(TE) + GE2(TE), where GEi(TE) represents the contribution of myelinated water, and GE2(TE) represents the contribution of tissue water.

[0082] IMW may be approximated as a product of the water gap membrane fraction (MTF/WF) by the ratio between the thicknesses of myelin water gap (d_w) to the thicknesses of myelin membrane (d_m) (e.g., by WM packing parameter HOP), as in equation Eq. 2A below:

Eq.2A f_MW= (MTV/WF )• (d_w / d_m) = [(l-WF)AVF] • (d_w / d_m) = [(l-WF)AVF] • HOP where WF is water fraction 130W.

[0083] Analysis module 110 may therefore define, or express the contribution of decayed MR signal (SE(TE) of Eq. 1A or GE(TE) of Eq. IB) in myelin gap water as a function (SEi) of (i) the WM packing parameter and (ii) the water fraction, or non-water fraction, as in equations Eq. 3A-1 and Eq. 3A-2 below:

Eq. 3A-1 (SE)

SEi (TE) = Mo • [(l-WF)AVF) • HOP • exp(-TEZT2_MW)]

(For example, SEi (TE) = Mo • [(l-WF)AVF • (d_w/d_m) • cxp(-TE/T2 _W)J

Eq. 3A-2 (GE)

GEi = Mo • [(l-WF)AVF • HOP • exp(-TE/T2_MW)]

(For example, GEi (TE) = Mo • [(l-WF)AVF • (d_w/d_m) • exp(-TE/T2_MW)] [0084] As explained above, the sum of fww and I'rw may be equivalent to water fraction WF 130W. Therefore, based on Eq. 2A, frw may be approximated as in Eq. 2B below:

Eq.2B frw = WF-[(1-WF)/WF • d_w/d_m] = [(WF²+WF-1)/WF] • HOP

[0085] Analysis module 110 may therefore define or express the contribution of decayed MR signal (SE(TE) of Eq. 1A or GE(TE) of Eq. IB) in tissue water as a second function (SE2 or GE2) of (i) the WM packing parameter and (ii) the water fraction, or non-water fraction, as in equations Eq. 3B-1 and 3B-2 below:

Eq. 3B-1(SE)

SE₂ (TE) = Mo • [(WF²+WF-1)/WF • HOP • cxp(-TE/T2i w)J

(For example, SE₂ (TE) = Mo • [(WF²+WF-1)/WF • (d_w/d_m) • exp(-TE/T2_Tw)]

Where SE(TE) = SEi(TE) + SE₂(TE).

Eq. 3B-2(GE)

GE₂ (TE) = Mo • [((WF²+WF-1)/WF) • HOP • exp(-TE/T2_Tw)]

(For example, GE₂ (TE)= Mo • [((WF²+WF-1)/WF) • (d_w/d_m)- cxp(-TE/T2i w)J

Where GE(TE) = GEi(TE) + GE₂(TE).

[0086] It is appreciated that Eqs. 3A (3A-1 for SE and 3A-2 for GE) and 3B (3B-1 for SE and 3B-2 for GE) are equivalent to the MRI signal decay equations in a split form, e.g., where the contribution of myelin gap water and tissue water is provided separately (hence the term “split”), as functions of water fraction WF, for each voxel of ROI 20R.

[0087] Analysis module 110 may therefore have three unknown parameters that need to be assessed or fitted to solve Eqs. 1A or IB. These parameters include: (a) myelin water gap exponential decay parameter T2MW, corresponding to element 110MW of Fig. 3, (b) tissue water exponential decay parameter T2TW, corresponding to 110TW of Fig. 3, and packing parameter (e.g., the ratio between myelin water gap thickness to myelin membrane thickness, d_w / d_m) corresponding to element 100P of Fig. 3.

[0088] Analysis module 110 may subsequently apply a numerical analysis algorithm on MR data element 20ESV (e.g., SE(TE) of Eq. 1A or GE(TE) of Eq. IB), to solve the MRI signal decay equations (e.g., Eqs. 1A and/or IB). Additionally, or alternatively, analysis module 110 may solve the MRI signal decay equations in their split form (EQs. 3A and 3B). [0089] In other words, analysis module 110 may employ a numerical analysis algorithm as known in the art, to fit the three unknown parameters (e.g., T2MW, T2TW, d_w /d_m) to the measured SE(TE) or GE(TE) values provided by MRI scanner 20, according to Eq. 1A (for SE(TE)) or Eq. IB (for GE(TE)). Analysis module 110 may thereby extract the WM packing parameter value 100P (e.g., ratio d_w /d_m) for at least one (e.g., for each, or for a plurality of) voxel(s) of ROI 20R, based on the first function (e.g., SEi or GEi) and second function (e.g., SE2 or GE2).

[0090] In some embodiments, analysis module 110 may (e.g., as a first approximation) assign the same values to all voxels in ROI 20R, for each of the three unknown parameters (e.g., T2MW, T2TW, d_w /d_m).

[0091] Additionally, or alternatively, analysis module 110 may apply the numerical analysis algorithm on a plurality of ROIs 20R, to extract the WM packing parameter value 100P for each ROI 20R. Mapping module 140 may thereby create a map MOM of WM packing parameter value 100P throughout a selected region of the brain.

[0092] According to some embodiments, analysis module 110 may apply some assumptions to fit parameters T2MW, T2TW, and d_w /d_m (100P). For example, analysis module 110 may assume that biophysical parameters T2MW and T2TW are constant across ROI 20R, while the water fraction WF is allowed to vary. As elaborated herein, experimental results have shown the validity of these assumptions. Analysis module 110 may thus have an overdetermined equation system, with three fitted parameters for all the voxels in ROI 20R.

[0093] Additionally, or alternatively, analysis module 110 may employ various physical considerations and/or restrictions on variables of EQs. 1A and/or IB, and apply an analytical algorithm on EQs. 1A and/or IB, to determine value of parameters T2MW, T2TW and/or dw/dm). For example, system 10 may perform a long scan sequence, in which one of the pools (e.g., f iwfor myelin water or frw for tissue water) does not contribute to the SE(TE) or GE(TE) signal (leaving EQs. 1A and/or IB with only one exponent). Analysis module 110 may subsequently employ the analytic algorithm on the simplified EQs. 1 A and/or IB to determine a value of packing parameter (e.g., d_w/d_m) in at least one (e.g., each) voxel of ROI 20R.

[0094] As shown in Fig. 3, system 10 may further include a mapping module 140, configured to produce a map data structure MOM. Map data structure MOM may, for example, include a 3D array, where each entry of the array may represent a value of at least one property of respective voxels in ROI 20R. For example, mapping module 140 may integrate the WM packing parameter 100P (e.g., ratio d_w/d_m) values pertaining to the plurality of voxels of ROI 20R into a three-dimensional (3D) brain map MOM, representing distribution of packing parameter 100P among one or more ROIs 20R. In other words, mapping module 140 may generate a map data structure MOM that indicates the ratio between myelin water gap thickness d_w and myelin membrane thickness d_m in voxels of one or more ROIs 20R.

[0095] As shown in Fig. 3, system 10 may include or may be associated with a user interface (UI) 150, allowing presentation, or visualization of 3D brain map data structure MOM. UI 150 may be configured to receive (e.g., from a user) a selection of a region in the brain, and present WM packing parameter 100P at, or in relation to the selected region.

[0096] For example, UI 150 may be configured to depict at least one component or segment of ROI 20R, such as a slice of the brain or a region or lobe in the brain. UI 150 may present WM packing parameter 100P (e.g., the distribution of ratio between myelin membrane thickness and myelin water gap thickness) in relation to the at least one depicted component. [0097] Additionally, or alternatively, system 10 may include, or may be integrated with a tractography module 170, configured to identify and present at least one WM tract 170T in the brain, as known in the art. UI 150 may present WM packing parameter 100P (e.g., ratio d_w /dm) in relation to the at least one WM tract 170T.

[0098] It may be appreciated that the combination of presenting physiological, and anatomic data adjoint with WM packing parameter 100P may provide various benefits in diagnosing and treating health conditions. For example, by integrating tractography data, system 10 may allow physicians to pinpoint the location of specific defects in myelination in WM tracts, that may lead to “bottlenecks” or failure in signal transfer through specific junctions and tracts of WM.

[0099] In another example, system 10 may include a segmentation module 120, configured to segment 3D brain map MOM to one or more brain regions 1208, based on the WM packing parameter values 100P, e.g., based on the condition of myelination, as indicated by the d_w /d_m ratio. System 10 may thus provide a novel, refined form of segmentation-based 1208 insight into WM brain regions in relation to currently available brain segmentation technology, which typically provides segmentation merely based on morphologic features. UI 150 may subsequently present the one or more segmented brain regions 120S, to provide a professional user with insight regarding myelination in different brain ROIs 20R.

[00100] As shown in Fig. 3, system 10 may include a diagnosis module 160, configured to obtain (e.g., via input 7 of Fig. 1) a reference data element 160RF. Reference data element 160RF may, for example, include a matrix (e.g., a 3D array) that represents the expected distribution (e.g., mean and standard deviation) of WM packing parameter values 100P. Reference data element 160 may be personalized in the sense that it may provide reference values of WM packing 100P, which correspond to specific characteristics of the examined subject.

[00101] For example, reference data element 160RF may correspond to the subject’s age, gender, a diagnosed or suspected medical condition such as a white matter disorder (e.g., MS), a stage or progression of the white matter disorder (e.g., stage of MS), and the like. Additionally, or alternatively, reference data element 160RF may correspond to specific, anatomic brain regions, e.g., providing reference values of WM packing 100P that are characteristic to specific lobes and regions of the brain.

[00102] Diagnosis module 160 may thereby determine a condition or diagnosis 160D of the subject’s WM based on the WM packing parameter values 100P of map MOM, in accordance with reference data element 160RF.

[00103] For example, diagnosis module 160 may compare values of reference data element 160RF with anatomically corresponding values of WM packing parameter values 100P in map MOM, to determine a diagnosis 160D of the subject’s condition. Diagnosis 160D may include for example, aging (e.g., normal aging or accelerated aging) of a subject. [00104] In another example, diagnosis 160D may include identification of a WM degenerative, or myelin-related disorder such as MS, Schizophrenia, Parkinson, small vessel disease, Leukodystrophies, and the like.

[00105] In another example, diagnosis 160D may include an extent, a stage, a progression, a prognosis, etc. of the subject’s condition or disease.

[00106] In another example, diagnosis 160D may be, or may include indication of a difference in WM packing parameter values 100P between different conditions, e.g., highlighting specific regions or distribution of WM packing parameter values 100P that are statistically significant for characterizing one WM disorder or another.

[00107] Other such combinations and configurations of diagnosis 160D are also possible. [00108] Reference is now made to Figs. 4A and 4B which are schematic diagrams, depicting multilamellar vesicles (MLVs) that were developed by the inventors as a phantom system, and experimentally used to validate the reliability of embodiments of the system for determining WM structure.

[00109] As shown in Figs. 4 A and 4B, the MLVs included charged phosphatidylserine and phosphatidylcholine lipids that mimic the assembly of biological membranes. The MLVs were composed of repeating layers of lipid bilayers with a water gap between them. The membrane size is defined as d_m, while d_w defines the water gap size between lipid membranes. The ratio between the water gap and the membrane thicknesses is defined as dw/dm.

[00110] The inventors have suspended the MLVs in salt (NaCI) solutions at different concentrations (e.g., 0-500 mM). The inventors have shown that addition of salt results in a decrease in the water gap d_w as a result of electrostatic screening of the membrane charge. Membrane thickness d_m does not change. Therefore, the ratio d_w/d_m (corresponding to WM parameter value 100P of Fig. 3) decreases as a function of suspension salinity.

[00111] Reference is also made to Figs. 5A-5E which are Cryogenic Transmission Electron Microscopy (Cryo-TEM) images of MLVs that were experimentally used by the inventors to validate the reliability of embodiments of the system for determining WM structure.

[00112] The inventors have imaged the MLVs (also referred to herein as “phantoms”) using cryo-TEM microscopy and measured the membrane and water gap thickness. As shown in Figs. 5A-5E, each MLV is formed as an onion, having multiple concentric lipid membranes (black circle lines). Between the membranes, there are water gap layers (grey areas between the black lines). All scale bars are 100 nm in length.

[00113] The MLV lipids membrane thickness was measured to be 4.5-5 nm, which is similar to myelin membrane thickness. As shown in Figs. 5A-5E the water gap thickness (e.g., the gap between the membranes) varied between 15 nm and 4 nm while the salt concentration increased between 0 mM to 500 mM. Since d_m remained relatively consistent across the myelin membrane, the ratio of d_w/d_m was identified as a metric, or indicator of membrane packing.

[00114] Reference is now made to Figs. 6A and 6B. Fig. 6A is a graph showing cross- correlation-based accuracy in predicting magnetic resonance signal strength (e.g., SE(TE) of Eq. 1 A or GE(TE) of Eq. IB), according to some embodiments of the invention. As shown in Fig. 6 A, the signal (SE(TE) or GE(TE)) predicted from the multi-exponential model using cross-validation (y-axis) correlates well (r=0.99, P<10“²) with the measured signal (x-axis). Different colors represent different salt concentration samples.

[00115] Fig. 6B is a graph showing the accuracy of predicting WM packing parameter values 100P (e.g., ratio between myelin membrane thickness and myelin water gap thickness) according to some embodiments of the invention. As shown in Fig. 6B, the membrane packing ratio (e.g., 100P, d_w/d_m) extracted from the biophysical model highly correlates with the membrane packing ratio estimated from cryo-TEM measurements (r=0.97, p<10’²).

[00116] Reference is now made to Figs. 7A and 7B . Fig. 7A is a graph showing relaxation time T2 calculated by embodiments of the invention for myelin gap water (e.g., T2MW of equations 1A, IB, and 3A (3A-1, 3A-2)), in a variety of brain regions (e.g., lateral-occipital, superior-temporal, superior-frontal, and superior-parietal). Fig. 7B is a graph showing WM packing parameter values calculated by embodiments of the invention, in these brain regions.

[00117] As shown in Fig. 7A, the inventors have estimated the T2 tissue water (T2TW) as approximately 90 msec, and the myelin water T2 values (T2MW) ranged between 20-40 msec across subjects and ROIs 20R, using cross-validation. Importantly, these values agreed with myelin water gap T2 values previously estimated using MWI techniques.

[00118] As shown in Fig. 7B, all WM areas yielded similar membrane packing ratios between 0.7-1 across all subjects and ROIs. Assuming a constant membrane thickness of 4.5 nm, the estimated water gap thickness was calculated to be between 3 and 4.5 nm. This result agrees with ex-vivo and animal model estimations.

[00119] It may therefore be appreciated that both in-vitro and in-vivo data suggest that myelin water gap mapping by embodiments of system 10 is feasible, consistent, and physically sound.

[00120] Fig. 8 is a flow diagram depicting a method of determining WM structure by at least one processor or controller (e.g., processor or controller 2 of Fig. 1), according to some embodiments.

[00121] As shown in step S1005, the at least one processor 2 may utilize or control a Magnetic Resonance Imaging (MRI) device (e.g., MRI scanner 20 of Fig. 3) to perform a multiple-echo sequence scan (e.g., 20ES of Fig. 3) of a region of interest (ROI 20R) of the subject’s brain’s WM.

[00122] As elaborated herein and shown in step S1010, based on multiple-echo sequence scan 20ES, the at least one processor 2 may obtain a first Magnetic Resonance (MR) data element (e.g., SE(TE) of Eq. 1A, GE(TE) of Eq. IB, 20ESV of Fig. 3). The first MR data element may represent exponential decay of MR signal in a plurality of voxels pertaining to ROI 20R.

[00123] As elaborated herein (e.g., in relation to Fig. 3) and shown in step S1015, the at least one processor may analyze the first MR data element (e.g., 20ESV) to extract, for each voxel of ROI 20R, a WM packing parameter value (e.g., 100P of Fig. 3), corresponding to that voxel. The WM packing parameter 100P may be indicative of structural packing of nonwater material such as myelin, on a sub-voxel scale.

[00124] The iterative process of analyzing data element 20ESV and extracting WM packing parameter 100P for each voxel is depicted in steps S 1020-S 1035.

[00125] As shown in step S1020, the at least one processor may express the exponential decay of MR signal as a combined contribution of: (a) decay of MR signal in tissue water, and (b) decay of MR signal in myelin gap water as elaborated herein, in equations Eq. 1A and Eq. IB.

[00126] As shown in step S1025, the at least one processor may define the decay of MR signal in tissue water as a first function of (i) the WM packing parameter 100P and (ii) the water fraction or non-water fraction, as in Eqs. 3B(3B-1 and 3B-2).

[00127] As shown in step S 1030, the at least one processor may define the decay of MR signal in in myelin gap water as a second function of (i) the WM packing parameter 100P and (ii) the water fraction or non-water fraction, as in Eqs. 3A(3A-1 and 3A-2).

[00128] As shown in step S1035, the at least one processor may subsequently apply an analytical, and/or numerical analysis algorithm (e.g., 110 of Fig. 3) on the first MR data element (e.g., SE(TE), GE(TE) or 20ESV of Fig. 3), to extract the WM packing parameter value 100P (e.g., d_w/d_m) for each voxel of ROI 20R, based on the first function (Eq. 3A(3A- 1 and 3A-2)) and second function (Eq. 3B(3B-1 and 3B-2)).

[00129] As elaborated herein, embodiments of the invention may provide a practical application, improving the technological field of medical imaging. For example, system 10 may provide previously unobtainable information representing myelin membrane structure on a molecular, sub-voxel or voxel scale. Additionally, embodiments of the invention may facilitate mapping of a subject’s brain according to such myelin packing data, e.g., to pinpoint regions that are suspected to have abnormal WM structure and/or function.

[00130] Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Furthermore, all formulas described herein are intended as examples only and other or different formulas may be used. Additionally, some of the described method embodiments or elements thereof may occur or be performed at the same point in time.

[00131] While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

[00132] Various embodiments have been presented. Each of these embodiments may of course include features from other embodiments presented, and embodiments not specifically described may include various features described herein.

Claims

1. A method of determining white matter packing in a brain of a subject by at least one processor, the method comprising: controlling a Magnetic Resonance Imaging (MRI) device to perform a multiple-echo sequence scan of a region of interest (ROI) of the subject’s white matter; based on the multiple-echo sequence scan, obtaining a first Magnetic Resonance (MR) data element representing exponential decay of MR signal in a plurality of voxels within the ROI; and analyzing the first MR data element to extract, for at least one voxel of the ROI, a white matter (WM) packing parameter value corresponding to that at least one voxel, wherein the WM packing parameter is indicative of structural packing of non-water material, on a molecular, voxel, or sub-voxel scale.

2. The method of claim 1, wherein the WM packing parameter value of a voxel represents a ratio between myelin water gap thickness and myelin membrane thickness in at least one voxel.

3. The method according to any one of claims 1-2 further comprising: controlling the MRI device to perform a quantitative scan of the ROI of white matter; based on the quantitative scan, obtaining a second MR data element indicative of a non-water fraction in the plurality of ROI voxels; and extracting the WM packing parameter value further based on the second MR data element.

4. The method of claim 3, wherein analyzing the first MR data element comprises: expressing the exponential decay of MR signal as a combined contribution of: (a) decay of MR signal in tissue water, and (b) decay of MR signal in myelin gap water; expressing the decay of MR signal in tissue water as a first function of (i) the WM packing parameter and (ii) the non-water fraction; expressing the decay of MR signal in in myelin gap water as a second function of (i) the WM packing parameter and (ii) the non-water fraction; and applying a numerical analysis algorithm on the first MR data element, to extract the WM packing parameter value for at least one voxel of the ROI, based on the first function and second function.

5. The method according to any one of claims 2-4, further comprising integrating the WM packing parameter values pertaining to the plurality of voxels into a three dimensional (3D) brain map, representing distribution of the ratio between myelin water gap thickness and myelin membrane thickness in one or more ROIs.

6. The method of claim 5, further comprising visualizing the 3D brain map via a user interface (UI), wherein the UI is configured to: depict at least one component of the ROI, selected from: (i) a slice of the brain, (ii) a lobe in the brain, (iii) a white matter tract in the brain, and (iv) a predetermined region in the brain; and present the distribution of ratio between myelin water gap thickness and myelin membrane thickness in relation to the at least one depicted component.

7. The method of claim 6, further comprising segmenting the 3D brain map to one or more brain regions, based on the WM packing parameter values, wherein the UI is further configured to present the one or more segmented brain regions.

8. The method according to any one of claims 1-7, further comprising: obtaining a reference data element, comprising expected distribution of WM packing parameter values, wherein said reference data element corresponds to (i) characteristics of the subject, and (ii) specific, anatomic brain regions; and determining a condition or diagnosis of the subject’s white matter based on the WM packing parameter value, in accordance with the reference data element.

9. The method of claim 8, wherein said condition or diagnosis is selected from a list consisting of: an identification of a white matter disorder of the subject, indication of an extent, a stage, a progression or prognosis of the white matter disorder of the subject, an indication of normal or accelerated aging of the subject’s white mater, and indication of regions or distribution of WM packing that is statistically significant for characterizing a specific WM disorder.

10. A system for determining white matter packing in a brain of a subject, the system comprising: a non-transitory memory device, wherein modules of instruction code are stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the at least one processor is configured to: control a Magnetic Resonance Imaging (MRI) device to perform a multiple-echo sequence scan of a region of interest (ROI) of the subject’s white matter; based on the multiple-echo sequence scan, obtain a first Magnetic Resonance (MR) data element representing exponential decay of MR signal in a plurality of voxels within the ROI; and analyze the first MR data element to extract, for at least one voxel of the ROI, a white matter (WM) packing parameter value corresponding to that at least one voxel, wherein the WM packing parameter is indicative of structural packing of non-water material, on a molecular, sub-voxel, or voxel scale.

11. The system of claim 10, wherein the WM packing parameter value of a voxel represents a ratio between myelin water gap thickness and myelin membrane thickness in at least one voxel.

12. The system according to any one of claims 10-11, wherein the at least one processor is further configured to: control the MRI device to perform a quantitative scan of the ROI of white matter; based on the quantitative scan, obtain a second MR data element indicative of a nonwater fraction in the plurality of ROI voxels; and extract the WM packing parameter value further based on the second MR data element.

13. The system of claim 12, wherein the at least one processor is further configured to analyze the first MR data element by: expressing the exponential decay of MR signal as a combined contribution of: (a) decay of MR signal in tissue water, and (b) decay of MR signal in myelin gap water; expressing the decay of MR signal in tissue water as a first function of (i) the WM packing parameter and (ii) the non-water fraction; expressing the decay of MR signal in in myelin gap water as a second function of (i) the WM packing parameter and (ii) the non-water fraction; and applying a numerical analysis algorithm on the first MR data element, to extract the WM packing parameter value for at least one voxel of the ROI, based on the first function and second function.

14. The system according to any one of claims 11-13, wherein the at least one processor is further configured to integrate the WM packing parameter values pertaining to the plurality of voxels into a 3D brain map, representing distribution of the ratio between myelin water gap thickness and myelin membrane thickness in one or more ROIs.

15. The system of claim 14, wherein the at least one processor is further configured to visualize the 3D brain map via a UI, and wherein the UI is configured to: depict at least one component of the ROI, selected from: (i) a slice of the brain, (ii) a lobe in the brain, (iii) a white matter tract in the brain, and (iv) a predetermined region in the brain; and present the distribution of ratio between myelin water gap thickness and myelin membrane thickness in relation to the at least one depicted component.

16. The system of claim 15, wherein the at least one processor is further configured to segment the 3D brain map to one or more brain regions, based on the WM packing parameter values, wherein the UI is further configured to present the one or more segmented brain regions.

17. The system according to any one of claims 10-16, wherein the at least one processor is further configured to: obtain a reference data element, comprising expected distribution of WM packing parameter values, wherein said reference data element corresponds to (i) characteristics of the subject, and (ii) specific, anatomic brain regions; and determine a condition of the subject’s white matter based on the WM packing parameter value, in accordance with the reference data element.

18. The system according to any one of claims 10- 17, wherein said condition or diagnosis is selected from a list consisting of: an identification of a white matter disorder of the subject, indication of an extent, a stage, a progression or prognosis of the white matter disorder of the subject, an indication of normal or accelerated aging of the subject’s white mater, and indication of regions or distribution of WM packing that is statistically significant for characterizing a specific WM disorder.