System and method of simultaneous mapping of slow flow, functional, and diffusion contrasts using MRI

Abstract

Systems and methods are described for investigating brain-wide CSF flow dynamics and understanding the role of neural activity, including an Echo-Planar Time-resolved Imaging (EPT)I CSF flowmetry technique that can simultaneously acquire i) phase-contrast 4D CSF flow, ii) T2*-BOLD fMRI, and iii) T2- and S0-contrast changes. EPTI eliminates the distortion and blurring artifacts in Echo-Planar Imaging (EPI), as well as resolving multi-echo images within the readout, allowing for the separation of various contrasts including T2*, T2, and flow-encoded S0. This separation is particularly useful for obtaining clean BOLD-fMRI signals.

Description

MGH 2024-177-02125141.04930System and Method of Simultaneous Mapping of Slow Flow, Functional, and Diffusion Contrasts Using MRICross Reference to Related Applications

[0001] The present application is based on, claims priority to, and incorporates herein by reference in its entirety for all purposes, US Provisional Application Serial No. 63 / 723,268, filed November 21, 2024.Statement of Government Support

[0002] This invention was made with government support under award numbers 5K99AG083056-02 and 5U24NS129893-02 (agreements 2022A012377 and 2022A001934) from the National Institutes of Health. The government has certain rights in the invention.Background

[0003] Cerebrospinal fluid (CSF) flow is a key component of the brain’s waste clearance system which is impaired in many neurological disorders, such as Alzheimer's disease. However, both CSF flow dynamics and how they link to brain function / physiology are not well understood, especially in the subarachnoid space (SAS) surrounding the cerebrum and in perivascular space (PVS) surrounding blood vessels within the tissue. Magnetic resonance imaging (MRI) is a useful non- invasive tool for CSF flow measurement, and different contrast mechanisms have been investigated to provide complementary information. Among them, velocity -encoded phase-contrast (PC) methods can directly measure coherent bulk flow of the CSF and can provide comprehensive information of the flow dynamics, including its velocity, direction, and net flow. However, most studies using PC methods applied to measuring CSF flow are focused on brain regions with relatively fast CSF flow such as ventricles and aqueducts, due to the challenges to map the slow flow in the SAS and PV S regions, such as low sensitivity, physiological noise in the phase-valued image data, and signal dephasing. The lack of an imaging method to study slow CSF flow in SAS and PVS limits our understanding of the brain’s clearance pathway and the underlying mechanism of many neurological diseases

[0004] To map brain-wide CSF flow, an acquisition with high sensitivity to slow flow is needed.1QB\125141.04930\99553893.4MGH 2024-177-02125141.04930Summary

[0005] The present disclosure addresses the aforementioned drawbacks by providing systems and methods for investigating brain-wide CSF flow dynamics and understanding the role of neural activity, including an Echo-Planar Time-resolved Imaging (EPTI) CSF flowmetry technique that can simultaneously acquire i) phase-contrast 4D CSF flow, ii) T2*-blood- oxygen-level-dependent (BOLD) functional MRI (fMRI), and iii) T2- and SO-contrast changes. EPTI eliminates the distortion / blurring artifacts in EPI, resulting in improved anatomical integrity to study the intricate SAS areas. In addition, it resolves multi-echo images within the readout, allowing for the separation of various contrasts including T2*, T2, and now -encoded SO. This separation is particularly useful for obtaining clean BOLD-fMRI signals, providing additional improvements over EPI in which different contrasts are intertwined and are challenging to distinguish / interpret.

[0006] According to an aspect of the present disclosure, a method for mapping brain-wide cerebrospinal fluid (CSF) flow- imaging with a magnetic resonance imaging (MRI) system is provided. The method includes acquiring multi-echo magnetic resonance data from a subject with an MRI system, where the magnetic resonance data are acquired using a pulse sequence (i) having velocity-encoding gradients sufficient to encode slow flow of the CSF and (ii) that samples a hybrid space along a sampling trajectory, where the hybrid space includes a first axis along a temporal dimension and a second axis along a phase-encoding k-space dimension. The method includes reconstructing multi-echo images from the multi-echo magnetic resonance data using a computer system. The method includes separating the multiecho images into separate T2* contrast, T2 contrast, and SO contrast. The method includes reconstructing phase-contrast images from the multi-echo magnetic resonance data using a computer system. The method includes generating velocity images from the phase-contrast images using the computer system, where the velocity images are indicative of a slow flow of CSF in the subject. The method includes generating four-dimensional (4D) CSF flow data from the velocity images using the computer system, where the 4D CSF flow data are indicative of at least one of flow velocity7magnitude, flow' velocity direction, net flow', or flow volume of the CSF in the subject. The method includes generating T2* -blood-oxygen- level dependent (BOLD) functional contrast images by combining the T2* contrast and phase-contrast images. The method includes outputting the 4D CSF flow data, T2*-BOLD functional contrast images, and diffusion-weighted contrast images using the computer system.2QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0007] According to another aspect of the present disclosure, a method for non-invasive assessment of brain waste clearance function using magnetic resonance imaging (MRI) is provided. The method includes acquiring velocity-encoded magnetic resonance data from a subject using a pulse sequence configured with low velocity encoding to detect cerebrospinal fluid (CSF) flow in subarachnoid space and perivascular space, where the velocity-encoded magnetic resonance data are acquired by sampling a hybrid space along a sampling trajectory, where the hybrid space includes a first axis along a temporal dimension and a second axis along a phase-encoding k-space dimension. The method includes reconstructing flowsensitive images from the velocity -encoded magnetic resonance data. The method includes generating CSF flow maps indicating flow dynamics in the subarachnoid space and perivascular space. The method includes acquiring functional magnetic resonance data from the subject during a physiological state change. The method includes analyzing changes in the CSF flow maps corresponding to the physiological state change. The method includes determining brain waste clearance function based on the analyzed changes in CSF flow dynamics.

[0008] These aspects are nonlimiting. Other aspects and features of the systems and methods described herein will be provided below.Brief Description of the Drawings

[0009] The foregoing features of embodiments will be more readily understood by reference to the following detailed description, taken with reference to the accompanying drawings, in which:

[0010] FIG. 1A is a diagram of Pulsed-Gradient Spin-Echo Echo-Planar Time-resolved Imaging (PGSE-EPTI) sequence, according to aspects of the present disclosure.

[0011] FIG. IB is a k-space diagram for the EPTI frequency encoding, according to aspects of the present disclosure.

[0012] FIG. 2 is a flowchart setting forth the steps of an example method for neural activity invoked slow cerebrospinal fluid (CSF) flow imaging in accordance with some embodiments described in the present disclosure.

[0013] FIG. 3A shows resolved multi-echo distortion-free images based on the EPTI readout of FIG. 1A.

[0014] FIG. 3B shows distorted images based on echo planar imaging (EPI, left) and distortion-free images based on EPTI (right). EPTI eliminates the severe distortion artifacts in EPI at 7T.3QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0015] FIG. 3C is a signal plot illustrating the mixed contrast images (T2, T2*, and flow encoded SO) acquired using EPI.

[0016] FIG. 3D shows images resulting from the separation of multi-echo images into separate contrasts to obtain simultaneous phase-contrast flow maps and T2* BOLD fMRI maps using EPTI.

[0017] FIG. 4A shows CSF flow velocity maps in a cardiac cycle acquired by EPTI CSF flowmetry. The velocity magnitude increases during systole compared to diastole, and the flow direction (along both F-H and A-P) changes rapidly from diastole to systole with distinct flow patterns (e.g., flow up into the brain during diastole and down out of the brain in ventricles as indicated).

[0018] FIG. 4B shows cardiac-cycle locked CSF flow changes in 3 example region of interest (ROIs) along the F-H direction.

[0019] FIG. 5 shows examples the spatiotemporal dynamics of CSF flow- directions and velocities across a cardiac cycle (12 retrospectively gated cardiac phases) in the midsagittal view. The flow directions are indicated by color-coded vectors (top left) with uniform length and a threshold over 0.2 mm / s. The slow CSF at Imm / s level was mapped and a rapid systole-driven CSF flow change can be observed from the dynamic movie.

[0020] FIG. 6A show s activation maps calculated from the separated image contrasts. T2* (BOLD contrast) shows strong activation around the visual cortex, while T2 and SO show much less and sparse activation.

[0021] FIG. 6B shows the time series of T2* averaged over voxels with a t-score > 5, and the task-locked T2* BOLD response.

[0022] FIG. 6C show s a zoomed-in view' of the SO activation area from FIG. 6A, w hich is at a subarachnoid CSF region. The SO activation region shows a high percent signal change -10%, which may be related to CSF / blood volume change or flow-related diffusion contrast change during visual stimuli.

[0023] FIG. 7A shows visual task evoked CSF flow changes in 3 example ROIs: 4th ventricle. SAS CSF around the left & right visual cortex (crosses). The velocities are calculated using diastole phase data to minimize cardiac effects. Clear global and local CSF responses are observed in all 3 ROIs. The upward flow in CSF reduces during "ON’ in the ventricle as expected (blood volume increase leads to CSF outflow). The task-evoked CSF flows are also stronger along the CSF route and much weaker along the perpendicular direction as expected.

[0024] FIG. 7B shows simultaneously acquired T2* BOLD signal.4QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0025] FIG. 7C shows the timing of CSF flow responses synchronized with T2* BOLD.

[0026] FIG. 8 is a block diagram of an example magnetic resonance imaging (“MRI”) system that can implement some embodiments described in the present disclosure.

[0027] FIG. 9 is a block diagram of an example system for imaging neural-activity-evoked CSF flow in accordance with some embodiments described in the present disclosure.

[0028] FIG. 10 is a block diagram of example components that can implement the system of FIG. 9.

[0029] FIG. 11A illustrates a slow-flow sensitized PGSE-EPTI sequence. Long velocity encoding time is used to increase the sensitivity7to slow flow (phase-contrast), and the asymmetric spin-echoes with more T2*-weightings enhance BOLD sensitivity (magnitude image).

[0030] FIG. 1 IB illustrates phase-contrast processing that utilizes central echoes near spinecho with further background phase correction to reduce physiological noise. By acquiring three flow encoding directions (x, y, z), it can quantitatively measure 4D CSF velocity and flow directions with concurrent BOLD fMRI.

[0031] FIG. 12A shows a model for neural-activity -related hemodynamic response-induced subarachnoid CSF flow, driven by CBV changes.

[0032] FIG. 12B shows BOLD fMRI activation and CSF flow response maps (using dBOLD / dt as regressor), showing strong hemodynamic and CSF flow response around the visual cortex area (left) and he time-series of BOLD signals, the measured subarachnoid CSF flow of an example voxel (indicated by blue arrow), and the predicted CSF flow response (dBOLD / dt) based on the hypothesized model (right). The temporal response of subarachnoid CSF flow is highly correlated to dBOLD / dt.

[0033] FIG. 13A illustrates a SOPHI acquisition. A pulsed-gradient spin-echo sequence with long velocity encoding and single-shot EPTI readout is employed to provide high sensitivity and specificity to slow CSF flow.

[0034] FIG. 13B shows a processing pipeline of SOPHI’s phase-contrast data, from raw phase images to cardiac-gated or net-flow (mean) velocity maps.

[0035] FIG. 13C shows group-averaged CSF flow analysis in MNI space with vector orientation correction. Finally, the 3-directional flow maps are combined to generate Flow Vector Field.Detailed Description

[0036] Described here are systems and methods for investigating slow CSF flow dynamics and the role of neural activity thereon using magnetic resonance imaging (MRI). The5QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 disclosed systems and methods are capable of imaging the slow flow of CSF and simultaneously-acquired T2*-BOLD-fMRI signal in subarachnoid and perivascular spaces. Specifically, an EPTI CSF flowmetry technique is provided that can simultaneously acquire phase-contrast 4D CSF flow, T2*-BOLD fMRI, and T2- and SO-contrast changes. In some aspects, the disclosed methods implement a slow-flow sensitized phase-contrast imaging (SOPHI) technique to provide high sensitivity to measure slow flow. The SOPHI technique may be capable of measuring ultra-slow flow velocities as low as 10-100 pm / s using specific VENC values such as 1.6 rnm / s optimized for ultra-slow flow detection.

[0037] EPTI eliminates the distortion and blurring artifacts in echo planar imaging (EPI), as described in PCT / US2023 / 076679, which is incorporated herein by reference in its entirety. The use of EPTI result is in improved anatomical integrity to study the intricate subarachnoid space (SAS) areas, as described in US 16 / 430,349 and PCT / US2023 / 076677, which are incorporated herein by reference in their entirety. In addition, it resolves multi-echo images within the readout, allowing for the separation of various contrasts including T2*, T2, and flow-encoded SO. This separation is particularly useful for obtaining clean BOLD-fMRI signals, providing additional improvements over EPI in which different contrasts are intertwined and are challenging to distinguish and interpret.

[0038] In general, the slow CSF flow imaging can be provided by using low velocity encoding (VENC) and an efficient data acquisition. Additionally or alternatively, tailored image processing can be used to further improve the accuracy, specificity, and visualization of CSF flow dynamics. The slow CSF flow imaging framework may include quantitative validation through slow-flow phantom experiments with controlled flow rates ranging from 100-500 pm / s to validate measurement accuracy. Laminar flow characterization within measurement regions may be performed, achieving correlation coefficients exceeding 0.99 between measured and applied flow velocities. Standard deviation analysis may account for laminar flow effects to ensure measurement precision.

[0039] As one advantage, the disclosed systems and methods for slow CSF flow imaging can provide a useful tool to study the flow dynamics and patterns in the subarachnoid and perivascular spaces. By analyzing these flow dynamics and patterns their association with brain function and physiology can be assessed. As another advantage, imaging the slow CSF flow can provide for monitoring and otherwise assessing dysfunction of the waste clearance system of the human brain. As a result, the disclosed systems and methods can be used to find new biomarkers for many neurological disorders, such as Alzheimer's disease.6QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0040] In general, the disclosed systems and methods implement a CSF flowmetry technique that is based on phase-contrast MRI. The disclosed imaging framework allows for measurements of the slow flow in neuroanatomical regions (e.g., the subarachnoid space, perivascular spaces) with high sensitivity, spatiotemporal resolution, and wide spatial coverage. In this way, the disclosed systems and methods allow for the investigation of flow dynamics across the whole brain and can provide further insight into regions with slow flow. The CSF flowmetry technique may include analysis of specific correlations between subarachnoid CSF flow and the derivative of BOLD signals (dBOLD / dt), based on hemodynamic models that link cerebral blood volume (CBV) changes to CSF flow generation. Temporal synchronization analysis between neural activation and CSF flow responses may be performed to characterize the coupling between neural activity and fluid dynamics.

[0041] Using the slow CSF flow imaging techniques described herein, CSF flow data can be obtained from magnetic resonance image data. The flow information in the obtained CSF flow data can be four-dimensional (4D). including three spatial dimensions and one temporal dimension. The magnitude of the flow velocity, direction, and net flow can be obtained from the acquired image data and stored as part of the CSF flow data.

[0042] The general slow CSF flow imaging framework can include some or all of the following components. In one aspect, a pulse sequence that can achieve low VENC with minimized signal loss and high acquisition efficiency (e.g., an EPTI readout, an EPI readout, a multi-shot EPI readout) can be used to acquire magnetic resonance data, from which images are reconstructed. Postprocessing can be used to remove background phase variations (e.g., related to noise and artifacts) from the acquired data. The framework may include group-level analysis capabilities with spatial alignment and averaging in standardized coordinate spaces such as MNI space at 1-mm resolution. Vector orientation correction may be performed using rotational components of Jacobian matrices derived from deformation fields, and ANTs registration methodology' may be employed for group analysis. The postprocessing may also include use of central EPTI echoes near spin-echo to reduce physiological Bo phase variations and background phase correction techniques for phase-contrast processing. Multi-directional encoding analysis along x, y, and z directions may provide comprehensive flow characterization. Application-dependent processing can then be used to generate CSF flow maps (e.g., magnitude flow maps, direction flow maps, flow volume maps) and / or its dynamic change with specific brain physiological or functional activities.7QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0043] The disclosed systems and methods enable imaging of the slow CSF flow in the human brain non-invasively. with high sensitivity, efficiency, spatiotemporal resolution, and coverage. As noted above, the developed slow CSF flow imaging method can provide a useful tool to study the flow dynamics and patterns in the subarachnoid and perivascular spaces, and their association with brain function and physiology (e.g., cardiac pulsation, respiration), which have been previously inaccessible. For example, the response and flow dynamics under different functional activations (e.g., generated by presenting visual or auditory stimuli), interventions (exercise), and arousal states (awake, sleep) can be investigated using the disclosed systems and methods. The systems and methods may include systematic repeatability experiments across multiple subjects and runs to assess measurement reliability. Spatial pattern reproducibility assessment for subarachnoid CSF flow responses may be performed, including coherent spatial pathway identification around specific brain regions such as the visual cortex.

[0044] Advantageously, the disclosed systems and methods can also be applied to measuring slow flow in other parts of the brain, including the slow blood flow in the small blood vessels such as parenchymal or penetrating arteries and veins. Moreover, imaging the slow CSF flow in the human brain can enable monitoring and / or assessing dysfunction of the brain’s waste clearance system; to facilitate the search of new biomarkers (e.g., global or regional flow change between health and disease population) for many neurological disorders, such as Alzheimer’s disease: and to evaluate the effectiveness or efficacy of treatments.

[0045] As one non-limiting example, a pulsed-gradient-spin-echo (PGSE) pulse sequence can be used to achieve an efficient low-VENC acquisition. The PGSE pulse sequence provides several advantages for slow CSF flow imaging. As one example, the PGSE pulse sequence enables fast sampling provided by a single-shot EPTI readout used when acquiring k-space data, capable of resolving multi-echo, distortion-free images, while providing fast sampling with high robustness to motion and physiological noise. For example, repetition time (TR) may be 3 seconds for the whole brain with 108 echoes. As another example, snapshot velocity encoding can be implemented by using a single-shot image acquisition. By using a single-shot acquisition, shot-to-shot variations can be avoided. In another example, the PGSE pulse sequence is more robust to physiological noise in the phase-valued image data (e.g., breathing-related B0 field variation) when using a spin-echo over gradient-echo acquisition. The PGSE pulse sequence also has the flexibility to achieve low VENC (e.g., 1 cm / s or lower), which can be used to measure slow flow, because the PGSE pulse sequence allows for long velocity encoding time (or pulse time interval) and therefore smaller gradient8QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 amplitude, thereby reducing undesirable strong diffusion weighting and associated signal loss. Additionally, the strong T2-weighting from long echo time (TE) values in the PGSE pulse sequence reduces the signal from blood or parenchyma, which can reduce partial volume effects and improve specificity to CSF.

[0046] An example of a PGSE pulse sequence that can be used in some embodiments described in the present disclosure is shown in FIG. 1 A. The pulse sequence includes a radio frequency (RF) excitation pulse 102 (i. e. , 90°) that is played out in the presence of a sliceselect gradient 104 in order to produce transverse magnetization in a prescribed imaging slice. The slice-select gradient 104 includes a rephasing lobe 106 that acts to rephase unwanted phase dispersions introduced by the slice-select gradient 104, such that signal losses resultant from these phase dispersions are mitigated. Next, a refocusing RF pulse 108 (i. e. , 180°) is applied in the presence of another slice select gradient 110 in order to refocus transverse spin magnetization.

[0047] The slice select gradient 110 is bridged by first and second velocity-encoding gradients, 116 and 118, respectively. These velocity-encoding gradients 116 and 118 are equal in size, that is, their areas are equal. The velocity-encoding gradients 116 and 118, are produced through the application of velocity-encoding gradient lobes along one or more of the slice-encoding, phase-encoding, and frequency-encoding (e.g., readout) gradient directions. By changing the amplitudes and other characteristics of the velocity-encoding gradient lobes, the acquired echo signals can be weighted for flow velocities along any arbitrary direction. For example, when the velocity-encoding weighting gradients 116 and 118 are composed solely of gradient lobes applied along the slice encoding (Gz) gradient axis, the acquired echo signals will be weighted for flow velocities occurring along the z-direction. As another example, if the diffusion weighting gradients 116 and 118 are composed of gradient lobes applied along both the frequency encoding (Gx) and phase encoding (Gy) gradient axes, then the echo signals will be weighted for flow velocities occurring in the x-y plane along a direction defined by the relative amplitudes of the gradient lobes.

[0048] Velocity encoding of the acquired echo signals is provided when spins move along the direction of the velocity-encoding direction during the time interval, A , spanned between the application of the first and second velocity -encoding gradients 116 and 118, respectively. The first velocity-encoding gradient 116 dephases the spins in the imaging volume, whereas the second velocity-encoding gradient 118 acts to rephase the spins by an equal amount. Spins that are stationary during the time interval, A. do not receive a net phase accumulation,9QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 while spins that are moving along the velocity-encoding direction will receive a net phase accumulation that is proportional to the velocity of the spins.

[0049] Following excitation of the nuclear spins in the prescribed imaging volume, data are acquired by sampling a series of velocity-encoded echo signals in the presence of an alternating readout gradient 120. The alternating readout gradient 120 may be preceded by the application of a pre-winding gradient (not shown) that acts to move the first sampling point along the frequency -encoding, or readout, direction by a prescribed distance in k-space. Spatial encoding of the echo signals along a phase-encoding direction is performed by a series of phase encoding gradient “blips” 124, which are each played out in between the successive signal readouts such that each echo signal is separately phase-encoded. The phaseencoding gradient blips 124 may preceded by the application of a pre- winding gradient (not shown) that acts to move the first sampling point along the phase-encoding direction by a prescribed distance in k-space.

[0050] As is known in the art, the foregoing pulse sequence can be repeated a plurality of times while applying a different slice select gradients 104 and 110 during each repetition such that a plurality of slice locations are imaged.

[0051] Furthermore, the phase-encoding gradient blips 124 produce the EPTI sampling pattern of FIG. IB. Specifically, FIG. IB illustrates example sampling patterns 150 in k-t (i.e., k-TE) space. As one example, the k-t space is spanned by the kyk-space dimension and the temporal dimension, t. In general, the sampling pattern 150 may take the form of a zigzag pattern. The sampling pattern 150 is created by selectively sampling data samples 160, which are phase encoding lines in k-space.

[0052] A k-space trajectory can be designed to continuously transverse through the k-t space up and down (e.g., along the ky-direction) with smaller gradient blips, thereby reducing the dead time and achieving much shorter temporal spacing 170 (e.g., echo spacing), as illustrated in FIG. IB. This approach significantly improves the sampling efficiency and increases the temporal correlation for improved reconstruction. For instance, the g-factor (noise amplification factor) of the spatiotemporal reconstruction using this blip-size minimized encoding is significantly reduced by reducing the echo spacing.

[0053] Using the systems and methods described in the present disclosure to generate higher temporal correlation in the sampled data, improved reconstruction performance can be achieved. This improved reconstruction performance can allow for reliable reconstruction of distortion / blurring-free multi-echo data acquired in significantly fewer number of shots, including in a single-shot acquisition.10QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0054] Referring now to FIG. 2, a flowchart is illustrated as setting forth the steps of an example method 200 for simultaneous slow CSF flow imaging and T2*-BOLD fMRI with an MRI system. At step 202, the method includes acquiring multi-echo magnetic resonance (MR) data with an MRI system, wherein the MR data are acquired using a pulse sequence (i) having velocity-encoding (or flow-encoding) gradients sufficient to encode slow flow of the CSF and (ii) that samples a hybrid space along a sampling trajectory. As described above, in one non-limiting example a PGSE pulse sequence, such as the one illustrated in FIG. 1, can be used. Furthermore, the hybrid space hybrid space comprises a first axis along a temporal dimension and a second axis along a phase-encoding k-space dimension, as described with reference to FIG. IB above.

[0055] In some implementations, additional magnetic resonance data can be acquired using different imaging techniques. For example, in addition to slow flow imaging using phasecontrast MRI, data can be acquired with other imaging contrasts. For example, diffusion contrast, derived from the magnitude-valued data, can be used to map the level of incoherence of the flow (diffusion contrast, intravoxel dephasing) concurrently with or alongside the phase valued data used to map the coherent bulk flow (using phase contrast as described above), which can provide complementary and thus more comprehensive information of the slow CSF flow dynamics.

[0056] Additionally, in some implementations the magnetic resonance data are acquired while the subject is performing a functional task, or otherwise engaging in a functional activation task. For example, the magnetic resonance data may be acquired while the subject is being presented with a visual and / or auditory stimulus. Alternatively, the magnetic resonance data can be acquired while the subject is performing an intervention (e.g., exercise) or in a particular arousal state (e g., awake, asleep). In these instances, the response and flow dynamics under these different functional activations may be analyzed, as noted below.

[0057] Background phase offset may be removed from the MR data to minimize the effect of phase variations caused by systemic physiology and other nuisance factors, background phase removal can be performed by removing the spatially low-polynomial-order phase of each dynamic independently. The background phase removal may specifically utilize central EPTI echoes near spin-echo to reduce physiological Bo phase variations, followed by background phase correction techniques optimized for phase-contrast processing. As one non-limiting example, an optimized coil combination for the phase value image can be used to ensure the quality of the phase images and avoid artifacts.11QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0058] In some implementations, motion correction may be performed on the magnetic resonance data. For instance, bulk motion of the subject (e.g.. subject movement during scanning, respiration, cardiac motion) can be measured or otherwise estimated and the effects of these motions can be corrected for in the magnetic resonance data. As an example, motion measurements can be obtained from external devices, such as respiratory belts, pulse oximeters, ECG leads, and so on.

[0059] At step 204, multi-echo images are reconstructed from the multi-echo MR data and subsequently separated into T2*, T2, and SO contrasts at step 206. Specifically, the multiecho images reconstructed in step 204 include the mixed contrasts, which are then separated at step 206.

[0060] Phase-contrast images are then reconstructed from the magnetic resonance data, as indicated at step 208. Velocity images are then generated from the reconstructed phasecontrast images, as indicated at step 210. For instance, the phase information in the phasecontrast images may be converted to velocity7image data.

[0061] 4D flow data are then generated based on the velocity images, as indicated at step 212. As noted above, the CSF flow data may include slow CSF flow data that indicate slow flow dynamic of CSF in the brain or other neuroanatomi cal locations. The CSF flow data may include flow maps, which may include velocity7magnitude maps, velocity7direction maps, flow volume maps, net flow-velocity maps, net flow volume maps, and the like. The 4D flow data generation may include comprehensive 4D CSF Flow-Vector-Field mapping using vector arrows indicating flow direction, with arrow length proportional to velocity magnitude and color-coding systems for head-foot (H-F) and anterior-posterior (A-P) flow directions. The mapping may provide spatiotemporal dynamics visualization across cardiac phases to characterize both transient and cumulative flow patterns.

[0062] As an example, the net flow-velocity maps may indicate both net flow direction and magnitude of the CSF. The net flow velocity maps may7represent cumulative CSF flow movements and transport patterns by averaging flow velocities across temporal measurements, thereby providing quantitative information about the predominant flow direction and the magnitude of net CSF movement in each voxel. The net flow velocity7maps may distinguish cumulative flow patterns from transient cardiac-driven or respiratory-driven flow variations, enabling characterization of underlying CSF circulation pathways and waste clearance transport mechanisms.

[0063] As another example, velocity7direction mapping can be performed to generate velocity direction maps. In this instance, the velocity images may be processed using a12QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 velocity direction analysis with multi-directional encoding to perform the velocity direction mapping. The multi-directional encoding analysis may be performed along x, y, and z directions to provide comprehensive flow characterization and enable quantitative measurement of 4D CSF velocity and flow directions. The direction of the CSF flow of each voxel can also be obtained by acquiring multiple encoding directions (e.g., x, y, z). The velocity’ direction mapping may distinguish between transient cardiac-driven flow and cumulative net-flow movements, and may identify large-scale flow patterns in specific anatomical regions including the sylvian fissure, interhemispheric fissure, and convexity subarachnoid space. Temporal delay analysis of cardiac-driven responses across brain regions may be performed to characterize regional flow dynamics. In addition, to improve the visualization of the flow information, especially the flow direction information, vector fields or streamline maps can be calculated and generated from multi-directional CSF flow datasets.

[0064] The CSF flow data provide quantitative estimation of the CSF flow, which can be either absolute or relative velocities depending on the acquisition schemes and estimation method. The estimation of absolution velocity utilizes reference data without velocity encoding, which can be acquired in step 202. As a non-limiting example, the quantitative analysis may include net-flow velocity' distribution analysis reflecting cumulative CSF transport, cardiac-gated spatiotemporal flow pattern analysis, and regional flow pattern characterization distinguishing anterior versus posterior and upward versus downward flows across different brain regions. As described above, in some cases the 4D CSF flow data may include net flow velocity maps that quantify both the direction and magnitude of cumulative CSF movement by temporally averaging flow' measurements to remove transient variations. These net flow velocity' maps may provide directional vectors indicating the predominant flow pathway and scalar values representing the magnitude of net CSF transport, enabling assessment of long-term circulation patterns and waste clearance efficiency across different brain regions.

[0065] When data are acquired under different functional activations, as described above, the CSF flow data will be indicative of the response and / or flow dynamics associated with the functional activation performed (e.g., generated by presenting visual or auditory stimuli), interventions (exercise), or arousal states (awake, sleep). In these instances, the CSF flow data can indicate, for example, changes in flow dynamics that are associated with the functional activation, intervention, or arousal state. The analysis may include examination of specific correlations between subarachnoid CSF flow and the derivative of BOLD signals (dBOLD / dt), based on hemodynamic models linking cerebral blood volume changes to CSF13QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 flow generation. Temporal synchronization analysis between neural activation and CSF flow responses may characterize the coupling mechanisms. By monitoring these changes in CSF flow dynamics under different conditions, further insight into functional neuroanatomy can be realized.

[0066] In some aspects, the disclosed systems and methods may include comprehensive test- retest repeatability analysis to ensure measurement reliability and reproducibility. Systematic repeatability experiments may be conducted across multiple subjects and runs, with spatial pattern reproducibility assessment for subarachnoid CSF flow responses. The repeatability analysis may include coherent spatial pathway identification around specific brain regions, such as the visual cortex, demonstrating consistent flow patterns across different scanning sessions. The test-retest analysis may evaluate both velocity range measurements and net- flow velocity measurements, with correlation analysis providing quantitative metrics of measurement stability across repeated acquisitions.

[0067] In some cases, the disclosed systems and methods may provide advanced flow analysis capabilities including comprehensive repeatability metrics. Velocity range analysis may be performed to assess the maximum-minimum velocity across cardiac cycles, with scan-rescan correlation analysis providing specific correlation coefficients for measurement reliability assessment. ROI-based repeatability assessment may be conducted across multiple brain regions to evaluate spatial consistency of flow measurements. The advanced analysis may characterize circulation patterns by distinguishing between transient cardiac-driven flow dynamics and cumulative net-flow movements, enabling identification of large-scale flow patterns across anatomical regions such as the sylvian fissure, interhemispheric fissure, and convexity subarachnoid space.

[0068] As noted above, the CSF flow data can be indicative of slow CSF flow in the human brain. As a result, the analysis of the CSF flow data can enable monitoring and / or assessing dysfunction of the brain’s waste clearance system; can facilitate searching for biomarkers (e.g., global or regional flow change between health and disease population) for many neurological disorders, such as Alzheimer’s disease; and can be used to evaluate the effectiveness or efficacy of treatments.

[0069] It is noted that steps 204-206 may occur substantially simultaneously with steps 208- 212. Alternatively, steps 204-206 may precede or follow steps 208-212. This flexibility in processing order allows for optimized computational efficiency and resource allocation during the image reconstruction and analysis pipeline. The parallel processing capability is particularly advantageous when dealing with large datasets typical of whole-brain 4D flow14QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 imaging, as it can significantly reduce overall processing time while maintaining data integrity across the multiple contrast mechanisms.

[0070] At step 214, T2* blood-oxygen-level dependent (BOLD) functional contrast images are generated by combining the T2* contrast from step 206 and the phase-contrast images from step 208. This combination leverages the complementary information provided by both contrast mechanisms to create enhanced functional images that can simultaneously capture neural activity-related hemodynamic changes and CSF flow dynamics. The T2* contrast provides sensitivity to blood oxygenation changes associated with neural activation, while the phase contrast information contributes flow-related signal changes that may be correlated with functional activity. The resulting T2*-BOLD functional contrast images thus provide a more comprehensive view of brain function by incorporating both vascular and CSF compartment responses to neural stimulation. This integrated approach is particularly valuable for understanding the coupling between neural activity7, hemodynamic responses, and CSF flow changes, which has important implications for studying brain waste clearance mechanisms and their relationship to functional activation.

[0071] At step 216, the 4D CSF flow data, T2*-BOLD functional contrast images, and diffusion-weighted contrast images may be output, such as to display. The output data can be presented in various formats optimized for different analytical purposes, including velocity' magnitude maps, directional flow maps, streamline visualizations, and time-series plots showing temporal dynamics of flow patterns. The 4D CSF flow data provides comprehensive spatiotemporal information about cerebrospinal fluid movement throughout the brain, including both bulk flow patterns and localized flow changes associated with cardiac pulsation, respiratory cycles, and functional activation. The T2*-BOLD functional contrast images reveal regions of neural activation and their associated hemodynamic responses, while the diffusion-weighted contrast images provide information about tissue microstructure. Together, these multi-contrast outputs enable comprehensive analysis of brain physiology, including the investigation of neurovascular coupling, assessment of waste clearance pathways, and evaluation of potential biomarkers for neurological disorders. The integrated display of these complementary datasets facilitates correlation analysis between different physiological processes and supports both clinical assessment and research applications in understanding brain function and pathology.

[0072] The following examples include in-vivo experiments with visual tasks conducted on 7T Siemens-Terra using a custom-built 64-channel-coil. Whole-brain 2-mm-isotropic15QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 resolution PGSE-EPTI data were acquired with different VENCs (Icm / s, 2cm / s) and velocity-encoding directions.

[0073] FIGS. 3A-3B illustrate the resolved multi-echo multi-contrast images within the EPTI readout at a short TE interval, which removes distortion artifacts while providing fast sampling with high robustness to motion and physiological noise. The EPTI readout achieves superior image quality by utilizing a continuous k-space trajectory that minimizes gradient switching and reduces susceptibility-induced distortions that commonly plague conventional echo planar imaging sequences. FIG. 3B further illustrates the difference in distortion between an EPTI read out (right column) compared to an EPI read out (left column), demonstrating the improvement in anatomical fidelity particularly in regions prone to susceptibility artifacts such as the frontal and temporal lobes near air-tissue interfaces.

[0074] FIGS. 3C-3D demonstrate that the multi-echo imaging of the proposed method enables the separation of mixed contrasts within the magnitude images, obtaining clean T2* BOLD, T2, and diffusion-weighted contrasts with improved specificity and interpretability . The multi-echo acquisition strategy allows for the separation of overlapping signal contributions that would otherwise confound interpretation in single-echo approaches. For contrast separation, a simple signal model fitting can be used to measure T2*, SO, and T2 parameters, corresponding to the T2* BOLD, diffusion-weighted, and T2 BOLD contrasts. The signal model described above employs a mono-exponential decay function to characterize T2* relaxation, while SO represents the signal intensity at zero echo time, which may be sensitive to proton density and diffusion effects. Other methods can also be used for contrast separation such as model-based reconstruction and deep learning methods, including machine learning approaches that can leverage the temporal correlations across echo times to improve separation accuracy and noise robustness. The obtained multi -contrast images can then be used to analyze the flow, functional activity, and diffusion contrast changes related to water exchange or flow. For the velocity / dilTusion encoding, different directions can be applied to obtain multi-directional flow and diffusion information, enabling comprehensive characterization of higher-dimensional CSF flow patterns and their directional dependencies.

[0075] Example results of the simultaneously obtained flow maps are presented in FIG. 4A. Retrospective gating was applied to the phase- contrast flow signals to obtain flow velocity and direction changes in a cardiac cycle as shown in FIG. 4A. The retrospective cardiac gating technique utilizes physiological monitoring signals to bin the acquired data into discrete cardiac phases, allowing for the reconstruction of time-resolved flow maps that capture the pulsatile nature of CSF movement driven by arterial pulsation and ventricular16QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 volume changes. FIG. 4B provides example results of cardiac-cycle locked CSF flow changes in three regions of interest (ROIs) along the F-H direction, showing a strong and sharp velocity change in all three ROIs during systole. The observed velocity changes demonstrate peak systolic flow velocities reaching approximately 2-3 mm / s, which represents a significant increase from the baseline diastolic flow of less than 1 mm / s, indicating the substantial influence of cardiac pulsation on CSF dynamics even in regions with relatively slow baseline flow.

[0076] FIG. 5 shows detailed patterns of the cardiac-related CSF flow, revealing comprehensive spatiotemporal dynamics of CSF flow across the entire brain. The visualization demonstrates the complex interplay between different CSF compartments, including the ventricular system, subarachnoid spaces, and perivascular regions, with flow patterns that exhibit both regional variations and temporal synchronization with the cardiac cycle. The spatiotemporal maps reveal that CSF flow exhibits a coordinated response across multiple brain regions, with flow direction reversals occurring systematically during different cardiac phases, supporting the concept of a brain-wide CSF circulation system.

[0077] FIGS. 6A-6C show the activation maps from the simultaneously acquired T2*-BOLD, T2 and SO. Strong visual cortex activation was observed in T2*, and much less in T2 and SO. The differential activation patterns across contrast mechanisms provide complementary information about the underlying physiological processes, with T2*-BOLD primarily reflecting blood oxygenation changes associated with neural activity, while T2* and SO contrasts capture different aspects of tissue and fluid dynamics. SO shows a focal activation in SAS CSF with a high percent change of 10%, likely related to CSF volume change or flow- related diffusion contrast. This significant signal change in the SO contrast within the subarachnoid space suggests that functional activation induces measurable alterations in CSF properties, potentially reflecting changes in CSF flow velocity, volume displacement, or microscopic motion that affects the diffusion-weighted signal component.

[0078] FIGS. 7A-7C show both global (4th-ventricle) and local (SAS around both sides of the visual cortex) visual-task-evoked CSF flow changes. The observed visual-task evoked CSF flow changes align with the structure of the CSF routes: stronger flow change along the direction of the structural routes of CSF (F-H in 4th -ventricle, A-P in SAS ROIs), and less in the perpendicular direction as expected. The timing of flow changes synchronized with the T2*-BOLD response (FIG. 7C) in accordance with the Monro-Kellie doctrine.

[0079] In an example study, the disclosed SOPHI technique was used to evaluate the subarachnoid-CSF flow responses to neural activity and its relationship to hemodynamic17QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 response. Specifically, the quantitative slow-flow measurements of the sequence were first validated on a slow-flow phantom within 100-500um / s to ensure its accuracy. Then, by investigating the relationship between the task-induced BOLD and CSF-flow signals, subarachnoid-CSF flow is found to be mainly driven by the change of hemodynamic response or cerebral-vascular-volume (CBV) changes. Finally, test-retest repeatability experiments are conducted on multiple subjects, and coherent subarachnoid-CSF flow changes around visual cortex are observed, with highly reproducible 3-directional spatial flow patterns, suggesting neural activity can drive strong subarachnoid-CSF flow with coherent spatial pathways.

[0080] As shown in FIG. 11 A, a pulsed-gradi ent-spin-echo EPTI (PGSE-EPTI) sequence was developed to provide high sensitivity and specificity for slow CSF-flow imaging. The long velocity-encoding time enables ultra-low-VENC values (e.g.,1.6mm / s), and together with the long TE, minimizes the confounding blood-flow signals. The single-shot EPTI readout9resolves multi-echo distortion-free images, providing additional T2*-weighting from asymmetric spin-echoes to enhance BOLD sensitivity. For phase-contrast processing (FIG.1 IB), only central EPTI echoes near spin-echo are used to reduce physiological BO-phase variations, and are further cleaned by background phase correction. By applying three flowencoding directions (x,y,z), quantitative velocity7and flow directions are obtained. With these developments, PGSE-EPTI provides simultaneous mapping of BOLD fMRI and slow CSF flow with high sensitivity.

[0081] PGSE-EPTI data w ere acquired with VENC=1 6mm / s and 3 velocity-encoding directions, TR / TE=3000 / 50ms, w hole-brain coverage. To evaluate the accuracy of the slow - flow quantification, a slow-flow phantom experiment was first performed with flow rates of 100-500um / s. Healthy volunteers were also scanned, each with 2 repeats of 3-directional CSF-flow measurements (24mins for each repeat including 2 runs / direction). For each run, a block-design visual-stimulus paradigm consisting of 30s flickering checkerboard followed by 30s gray field was presented.

[0082] The measured CSF flow velocity agreed well with the true flow rates (100, 250, 500um / s) on the slow-flow7phantom, demonstrating the accuracy of the quantitative slow- flow measurement. The relatively large standard deviation was due to the laminar flow7within each tube.

[0083] For subarachnoid-CSF flow, it was hypothesized that neural-activity -induced hemodynamic response would lead to a CBV increase (FIG. 12A) and reduce the CSF volume and generate directional CSF flow7along the subarachnoid space. Using EPTI CSF-18QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 flowmetry, BOLD and CSF-flow signals were concurrently measured, and strong BOLD activation and subarachnoid-CSF flow changes around the visual cortex were observed (FIG. 12B). Importantly, the time-series of the subarachnoid-CSF flow was highly correlated to the derivative of BOLD (dBOLD / dt), which aligns with the hypothesis, and suggests that the stimulus -induced change in CBV produces stronger subarachnoid-CSF flow changes compared to steady-state.

[0084] The dBOLD / dt was used as a regressor and a GLM analysis was performed on the 3- directional CSF flow data. Significant subarachnoid-CSF responses around the visual cortex were observed, and the flow direction aligned well with the anatomical curvature of the subarachnoid space.

[0085] In test-retest experiments, the spatial patterns of subarachnoid-CSF responses were highly reproducible across runs on all tested subjects (Fig.4B), flowing posterior-to-anterior along subarachnoid space.

[0086] It is demonstrated that neural-activation-related hemodynamic responses induce significant subarachnoid-CSF flow changes. The spatial patterns and flow pathways are reproducible across subjects. These findings suggest that brain activity can effectively modulate subarachnoid-CSF flow. Future work will further elucidate the complex subarachnoid-CSF flow pathways.

[0087] In another example study, the SOPHI technique described in the present disclosure was evaluated for quantifying velocity and direction of ultra-slow CSF flow in the subarachnoid space (e.g., ~10-1 OOpm / s). An example workflow of the implemented method is shown in FIG. 13 A. The SOPHI technique implements a distortion-free EPTI readout to improve image quality and stability by eliminating geometric distortions and its temporal fluctuations, which can otherwise bias the subarachnoid-flow measurements due to the narrow geometry of SAS near brain peripheries. Using SOPHI, it was identified that cardiac pulsation, respiration, and neural activity all contribute to subarachnoid-CSF flow dynamics.

[0088] In this example study, the SOPHI technique was used to elucidate the circulation pathway of brain-wide subarachnoid-CSF flow, including both transient and cumulative flow behaviors. At the group-level, cardiac-driven spatiotemporal flow patterns, reflecting transient flow velocity / directions; and net-flow velocity distribution, reflecting the cumulative CSF flow' movement were investigated. A 4D CSF Flow-Vector-Field mapping was employed to comprehensively visualize directional flow architecture spanning large- scale and local CSF pathways.19QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0089] SOPHI (FIG. 13 A) acquires phase-contrast data with high sensitivity to slow flow using long velocity encoding in a pulsed-gradient spin-echo (PGSE) sequence, and achieves high specificity through long-TE acquisition and a single-shot EPTI readout that reduces dynamic distortions and Bo-related physiological noise. 2-mm-iso SOPHI-EPTI data were acquired on healthy volunteers with VENC=1.6mm / s, 3 velocity-encoding directions (x,y,z), TR / TE=3000 / 50ms, whole-brain, 80 dynamics / run (4-minutes).

[0090] FIG. 13B overviews the processing of SOPHI data. To reduce physiological Bo-phase variations, only central EPTI echoes near spin-echo were used, followed by background phase correction for further refinement. Both the cardiac-gated velocity and the net-flow velocity along 3 directions were calculated. In this study, as the general patterns of CSF flow were analyzed, group-level analysis (FIG. 13C) was performed, where individual CSF flow maps were spatially aligned and averaged in MNI space (1-mm). Registration was performed using ANTs. The orientation of flow vectors was then corrected using the rotational component of Jacobian matrix derived from estimated deformation fields. Finally, group- averaged 3-directional flow data were combined to generate the 4D CSF Flow-Vector-Field maps for visualization.

[0091] The example Flow-Vector-Field of the group-averaged cardiac-gated 4D CSF flow acquired by SOPHI demonstrates flow-vector at each voxel displayed as arrows indicating flow direction, with arrow length proportional to ^velocity, and color-coding representing H-F and A-P flow directions (L-R is not color-coded). Examples of these cardiac-driven flow pathways show large-scale upward / downward flow changes in lateral sylvian fissure, which are opposite to the flow in interhemispheric-fissure and outer convexity subarachnoid space. This pattern reflects the temporal delays of the cardiac-driven responses across brain regions. The directional changes of the large-scale convexity -subarachnoid flow and lateral- ventricular flow across cardiac phases can be visualized to better understand the flow-vector changes.

[0092] The Flow-Vector-Field of net-flow (mean) velocity reflects cumulative CSF flow movements / transport. Several preliminary observations include: i) In interhemispheric fissure, the anterior portion exhibits upward net-flow, whereas posterior portion shows downward / backward flow; ii) CSF moves upward in anterior regions of the left-right convexity subarachnoid spaces, but moves down across much of the upper sulcal subarachnoid-space and also the lateral sylvian fissure; iii) Localized and distinct upward net- flow round superior sagittal sinus.20QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0093] The ROI-based repeatability analysis of the cardiac-driven flow velocity range and net-flow velocity both exhibit clear correlation between test-retest scans, while the velocityrange shows higher repeatability than net-flow velocity, which is expected given the much lower magnitude of net-flow (e.g., 50pm / s vs. 250pm / s).

[0094] Referring particularly now to FIG. 8, an example of a magnetic resonance imaging (“MRI ”) system 800 that can implement the methods described here is illustrated. The MRI system 800 includes an operator workstation 802 that may include a display 804, one or more input devices 806 (e.g., a keyboard, a mouse), and a processor 808. The processor 808 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 802 provides an operator interface that facilitates entering scan parameters into the MRI system 800. The operator workstation 802 may be coupled to different servers, including, for example, a pulse sequence server 810, a data acquisition server 812, a data processing server 814, and a data store server 816. The operator workstation 802 and the servers 810, 812, 814, and 816 may be connected via a communication system 840, which may include wired or wireless network connections.

[0095] The pulse sequence server 810 functions in response to instructions provided by the operator workstation 802 to operate a gradient system 818 and a radiofrequency (“RF”) system 820. Gradient waveforms for performing a prescribed scan are produced and applied to the gradient system 818, which then excites gradient coils in an assembly 822 to produce the magnetic field gradients Gx, Gy, and Gzthat are used for spatially encoding magnetic resonance signals. The gradient coil assembly 822 forms part of a magnet assembly 824 that includes a polarizing magnet 826 and a whole-body RF coil 828.

[0096] RF waveforms are applied by the RF system 820 to the RF coil 828, or a separate local coil to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil 828, or a separate local coil, are received by the RF system 820. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server 810. The RF system 820 includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server 810 to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the wholebody RF coil 828 or to one or more local coils or coil arrays.

[0097] The RF system 820 also includes one or more RF receiver channels. An RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signal received by21QB\125141.04930\99553893.4MGH 2024-177-02 125141.04930 the coil 828 to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at a sampled point by the square root of the sum of the squares of the I and Q components:M = V / 2+ Q2; and the phase of the received magnetic resonance signal may also be determined according to the following relationship:<P = tan’1( ).

[0098] The pulse sequence server 810 may receive patient data from a physiological acquisition controller 830. By way of example, the physiological acquisition controller 830 may receive signals from a number of different sensors connected to the patient, including electrocardiogram (“ECG”) signals from electrodes, or respiratory signals from a respiratory bellows or other respiratory monitoring devices. These signals may be used by the pulse sequence server 810 to synchronize, or “gate,” the performance of the scan with the subject's heart beat or respiration.

[0099] The pulse sequence server 810 may also connect to a scan room interface circuit 832 that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit 832, a patient positioning system 834 can receive commands to move the patient to desired positions during the scan.

[0100] The digitized magnetic resonance signal samples produced by the RF system 820 are received by the data acquisition server 812. The data acquisition server 812 operates in response to instructions downloaded from the operator workstation 802 to receive the realtime magnetic resonance data and provide buffer storage, so that data is not lost by data overrun. In some scans, the data acquisition server 812 passes the acquired magnetic resonance data to the data processor server 814. In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server 812 may be programmed to produce such information and convey it to the pulse sequence server 810. For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 810. As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system 820 or the gradient system 818, or to control the view order in which k-space is sampled. For example, the data acquisition server 812 may acquire magnetic22QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 resonance data and processes it in real-time to produce information that is used to control the scan.

[0101] The data processing server 814 receives magnetic resonance data from the data acquisition server 812 and processes the magnetic resonance data in accordance with instructions provided by the operator workstation 802. Such processing may include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., iterative or backproj ection reconstruction algorithms), applying filters to raw k-space data or to reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images. For example, the data processing server 814 may implement the motion estimation and / or motion correction techniques described in the present disclosure.

[0102] Images reconstructed by the data processing server 814 are conveyed back to the operator workstation 802 for storage. Real-time images may be stored in a data base memory cache, from which they may be output to operator display 802 or a display 836. Batch mode images or selected real time images may be stored in a host database on disc storage 838. When such images have been reconstructed and transferred to storage, the data processing server 814 may notify the data store server 816 on the operator workstation 802. The operator workstation 802 may be used by an operator to archive the images, produce films, or send the images via a network to other facilities.

[0103] The MRI system 800 may also include one or more networked workstations 842. For example, a networked workstation 842 may include a display 844, one or more input devices 846 (e.g., a keyboard, a mouse), and a processor 848. The networked workstation 842 may be located within the same facility as the operator workstation 802, or in a different facility, such as a different healthcare institution or clinic.

[0104] The networked workstation 842 may gain remote access to the data processing server 814 or data store sen' er 816 via the communication system 840. Accordingly, multiple networked workstations 842 may have access to the data processing server 814 and the data store server 816. In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing sen- er 814 or the data store server 816 and the networked workstations 842, such that the data or images may be remotely processed by a networked workstation 842.

[0105] Referring now to FIG. 9. an example of a system 900 for neural-activity-evoked cerebrospinal fluid (CSF) flow imaging in accordance with some embodiments of the systems23QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 and methods described in the present disclosure is shown. As shown in FIG. 9 , a computing device 950 can receive one or more types of data (e.g., k-space data, k-t space data) from data source 902, which may be a magnetic resonance imaging data source. In some embodiments, computing device 950 can execute at least a portion of a neural-activity-evoked cerebrospinal fluid (CSF) flow imaging system 904 to estimate and retrospectively correct motion from data received from the data source 902.

[0106] Additionally or alternatively, in some embodiments, the computing device 950 can communicate information about data received from the data source 902 to a server 952 over a communication network 954, which can execute at least a portion of the neural-activity- evoked cerebrospinal fluid (CSF) flow imaging system 904. In such embodiments, the server 952 can return information to the computing device 950 (and / or any other suitable computing device) indicative of an output of the neural-activity-evoked cerebrospinal fluid (CSF) flow imaging system 904.

[0107] In some embodiments, computing device 950 and / or server 952 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 950 and / or server 952 can also reconstruct images from the data. For example, the computing device 950 and / or server 952 can reconstruct motion-corrected images from k-space and / or k-t space data received from the data source 902.

[0108] In some embodiments, data source 902 can be any suitable source of data (e.g., k- space data, k-t space data, images reconstructed from k-space and / or k-t space data), such as an MRI system, another computing device (e.g., a server storing k-space and / or k-t space data), and so on. In some embodiments, data source 902 can be local to computing device 950. For example, data source 902 can be incorporated with computing device 950 (e.g., computing device 950 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data). As another example, data source 902 can be connected to computing device 950 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 902 can be located locally and / or remotely from computing device 950, and can communicate data to computing device 950 (and / or server 952) via a communication network (e.g., communication network 954).

[0109] In some embodiments, communication network 954 can be any suitable communication network or combination of communication networks. For example.24QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 communication network 954 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other types of wireless netw ork, a wired netw ork, and so on. In some embodiments, communication network 954 can be a local area network, a wide area network, a public network (e g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of netw ork, or any suitable combination of netw orks. Communications links shown in FIG. 6 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.

[0110] Referring now to FIG. 10, an example of hardware 1000 that can be used to implement data source 902, computing device 950, and server 952 in accordance with some embodiments of the systems and methods described in the present disclosure is shown.[OHl] As show n in FIG. 10, in some embodiments, computing device 950 can include a processor 1002, a display 1004, one or more inputs 1006, one or more communication systems 1008, and / or memory 1010. In some embodiments, processor 1002 can be any suitable hardw are processor or combination of processors, such as a central processing unit (“CPU"’), a graphics processing unit (‘‘GPU'’), and so on. In some embodiments, display 1004 can include any suitable display devices, such as a liquid crystal display (“LCD”) screen, a light-emitting diode (“LED”) display, an organic LED (“OLED”) display, an electrophoretic display (e.g., an “e-ink” display), a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 1006 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0112] In some embodiments, communications systems 1008 can include any suitable hardw are, firmware, and / or software for communicating information over communication network 954 and / or any other suitable communication networks. For example, communications systems 1008 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1008 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.25QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0113] In some embodiments, memory' 1010 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 1002 to present content using display 1004, to communicate with server 952 via communications system(s) 1008, and so on. Memory' 1010 can include any suitable volatile memory, non-volatile memory7, storage, or any suitable combination thereof. For example, memory 1010 can include random-access memory7(“RAM”), read-only memory (“ROM”), electrically programmable ROM (“EPROM”), electrically erasable ROM (“EEPROM”), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semi-volatile memory7, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 1010 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 950. In such embodiments, processor 1002 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 952, transmit information to server 952, and so on. For example, the processor 1002 and the memory 1010 can be configured to perform the methods described herein (e.g., the method of FIG. 2).

[0114] In some embodiments, server 952 can include a processor 1012, a display 1014, one or more inputs 1016, one or more communications systems 1018, and / or memory71020. In some embodiments, processor 1012 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display 1014 can include any suitable display devices, such as an LCD screen, LED display, OLED display, electrophoretic display, a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 1016 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0115] In some embodiments, communications systems 1018 can include any suitable hardware, firmware, and / or software for communicating information over communication network 954 and / or any other suitable communication networks. For example, communications systems 1018 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1018 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.26QB\125141.04930\99553893.4MGH 2024-177-02125141.04930

[0116] In some embodiments, memory' 1020 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 1012 to present content using display 1014, to communicate with one or more computing devices 950, and so on. Memory' 1020 can include any suitable volatile memory7, non-volatile memory , storage, or any suitable combination thereof. For example, memory 1020 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory' 1020 can have encoded thereon a server program for controlling operation of server 952. In such embodiments, processor 1012 can execute at least a portion of the server program to transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 950, receive information and / or content from one or more computing devices 950, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.

[0117] In some embodiments, the server 952 is configured to perform the methods described in the present disclosure. For example, the processor 1012 and memory 1020 can be configured to perform the methods described herein (e.g., the method of FIG. 2).

[0118] In some embodiments, data source 902 can include a processor 1022, one or more data acquisition systems 1024. one or more communications systems 1026. and / or memory 1028. In some embodiments, processor 1 22 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more data acquisition systems 1024 are generally configured to acquire data, images, or both, and can include an MRI system. Additionally or alternatively, in some embodiments, the one or more data acquisition systems 1024 can include any suitable hardware, firmware, and / or software for coupling to and / or controlling operations of an MRI system. In some embodiments, one or more portions of the data acquisition system(s) 1024 can be removable and / or replaceable.

[0119] Note that, although not shown, data source 902 can include any suitable inputs and / or outputs. For example, data source 902 can include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, data source 902 can include any suitable display devices, such as an LCD screen, an LED display, an OLED display, an27QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 electrophoretic display, a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.

[0120] In some embodiments, communications systems 1026 can include any suitable hardware, firmware, and / or software for communicating information to computing device 950 (and, in some embodiments, over communication network 954 and / or any other suitable communication networks). For example, communications systems 1026 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1026 can include hardware, firmware, and / or software that can be used to establish a wired connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0121] In some embodiments, memory 1028 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 1022 to control the one or more data acquisition systems 1024, and / or receive data from the one or more data acquisition systems 1024; to generate images from data; present content (e.g., images, a user interface) using a display; communicate with one or more computing devices 950; and so on. Memory 1028 can include any suitable volatile memory', non-volatile memory', storage, or any suitable combination thereof. For example, memory 1028 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 1028 can have encoded thereon, or otherwise stored therein, a program for controlling operation of data source 902. In such embodiments, processor 1022 can execute at least a portion of the program to generate images, transmit information and / or content (e.g., data, images) to one or more computing devices 950, receive information and / or content from one or more computing devices 950, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc ), and so on.

[0122] In some embodiments, any suitable computer-readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer-readable media can be transit ory or non-transitory. For example, non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., RAM, flash memory, EPROM, EEPROM), any suitable28QB\125141.04930\99553893.4MGH 2024-177-02125141.04930 media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer-readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0123] The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

[0124] As used in this specification and the claims, the singular forms “a,” “an,” and “the” include plural forms unless the context clearly dictates otherw ise.

[0125] As used herein, “about”, “approximately,” “substantially,” and “significantly” will be understood by persons of ordinary skill in the art and will vary to some extent on the context in which they are used. If there are uses of the term which are not clear to persons of ordinary skill in the art given the context in which it is used, “about” and “approximately” will mean up to plus or minus 10% of the particular term and “substantially” and “significantly” will mean more than plus or minus 10% of the particular term.

[0126] As used herein, the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising.” The terms “comprise” and “comprising” should be interpreted as being “open” transitional terms that permit the inclusion of additional components further to those components recited in the claims. The terms “consist” and “consisting of’ should be interpreted as being “closed” transitional terms that do not permit the inclusion of additional components other than the components recited in the claims. The term “consisting essentially of’ should be interpreted to be partially closed and allowing the inclusion only of additional components that do not fundamentally alter the nature of the claimed subject matter.

[0127] The phrase “such as” should be interpreted as “for example, including.” Moreover, the use of any and all exemplary language, including but not limited to “such as”, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.

[0128] Furthermore, in those instances where a convention analogous to “at least one of A, B and C, etc.” is used, in general such a construction is intended in the sense of one having ordinary skill in the art w ould understand the convention (e g., “a system having at least one of A. B and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together.). It29QB\125141.04930\99553893.4MGH 2024-177-02 125141.04930 will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description or figures, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

[0129] All language such as “up to,” “at least.” “greater than.” “less than,” and the like, include the number recited and refer to ranges which can subsequently be broken down into ranges and subranges. A range includes each individual member. Thus, for example, a group having 1-3 members refers to groups having 1, 2, or 3 members. Similarly, a group having 6 members refers to groups having 1, 2, 3. 4, or 6 members, and so forth.

[0130] The modal verb “may” refers to the preferred use or selection of one or more options or choices among the several described embodiments or features contained within the same. Where no options or choices are disclosed regarding a particular embodiment or feature contained in the same, the modal verb “may” refers to an affirmative act regarding how to make or use an aspect of a described embodiment or feature contained in the same, or a definitive decision to use a specific skill regarding a described embodiment or feature contained in the same. In this latter context, the modal verb “may” has the same meaning and connotation as the auxiliary verb “can.”30QBU25141.04930199553893.4

Claims

MGH 2024-177-02 125141.04930Claims1. A method for mapping brain-wide cerebrospinal fluid (CSF) flow imaging with a magnetic resonance imaging (MRI) system, the method comprising:(a) acquiring multi-echo magnetic resonance data from a subject with an MRI system, wherein the magnetic resonance data are acquired using a pulse sequence (i) having velocity -encoding gradients sufficient to encode slow flow of the CSF and (ii) that samples a hybrid space along a sampling trajectory, wherein the hybrid space comprises a first axis along a temporal dimension and a second axis along a phaseencoding k-space dimension;(b) reconstructing multi-echo images from the multi-echo magnetic resonance data using a computer system;(c) separating the multi-echo images into separate T2* contrast, T2 contrast, and SO contrast;(d) reconstructing phase-contrast images from the multi-echo magnetic resonance data using a computer system;(e) generating velocity images from the phase-contrast images using the computer system, wherein the velocity7images are indicative of a slow flow of CSF in the subj ect(1) generating four-dimensional (4D) CSF flow data from the velocity images using the computer system, wherein the 4D CSF flow data are indicative of at least one of flow velocity magnitude, flow velocity direction, net flow, or flow volume of the CSF in the subject;(g) generating T2* -blood-oxygen-level dependent (BOLD) functional contrast images by combining the T2* contrast and phase-contrast images; and(h) outputting the 4D CSF flow data, T2*-BOLD functional contrast images, and diffusion-weighted contrast images using the computer system.31QB\125141.04930\99553893.4MGH 2024-177-02 125141.049302. The method of claim 1, wherein the pulse sequence comprises a pulsed gradient spin-echo pulse (PGSE) sequence with one of a single shot echo-planar time- resolved imaging (EPTI) readout or an echo planar imaging (EPI) readout.

3. The method of claim 1, wherein the 4D CSF flow data includes a flow velocity7direction map that indicates the flow velocity direction of the CSF in the subject.

4. The method of claim 1. wherein the 4D CSF flow data includes a flow velocity magnitude map that indicates the flow velocity magnitude of the CSF in the subject.

5. The method of claim 4, wherein the flow velocity magnitude depicted in the flow velocity' magnitude map is an absolute flow velocity magnitude.

6. The method of claim 1 , further comprising performing motion correction on the multi-echo magnetic resonance data before reconstructing the phase-contrast images from the multi-echo magnetic resonance data.

7. The method of claim 1, wherein the pulse sequence is a multi-velocity encoding (VENC) pulse sequence and the multi-echo magnetic resonance data are indicative of multiple different velocity encodings corresponding to multiple different flow velocities.

8. The method of claim 1. wherein the velocity encoding gradients encode slow flow of the CSF of 2 cm / s or less.

9. The method of claim 8, wherein the velocity encoding gradients encode slow flow of the CSF of 1 cm / s or less.32QB\125141.04930\99553893.4MGH 2024-177-02 125141.0493010. The method of claim 1, wherein the magnetic resonance data are acquired from a whole brain of the subject and the 4D CSF flow data are indicative of slow CSF flow in a subarachnoid space.

11. The method of claim 1 , further comprising retrospectively gating at least one of the phase-contrast images or the velocity images based on the 4D CSF flow7data.

12. The method of claim 11, wherein retrospectively gating the at least one of the phase-contrast images or the velocity images based on the 4D CSF flow data comprises grouping the at least one of the phase-contrast images or the velocity images into groups associated with a physiological status.

13. The method of claim 12, further comprising generating velocity7maps for each group.

14. The method of claim 13, wherein the physiological status comprises at least one of a cardiac cycle or a respiratory cycle.

15. The method of claim 1, wherein the acquired multi-echo magnetic resonance data undersample the hybrid space and step b) includes synthesizing additional data in the hybrid space using a reconstruction kernel that spans the phase-encoding k-space dimension and the temporal dimension, wherein the multi-echo images are reconstructed from the acquired multi-echo magnetic resonance data and the additional data.

16. The method of claim 15 wherein the sampling trajectory7comprises a zigzag trajectory and the reconstruction kernel is a tilted reconstruction kernel that is oriented at an angle with respect to the phase-encoding k-space dimension in the hybrid space.33QB\125141.04930\99553893.4MGH 2024-177-02 125141.0493017. The method of claim 16 wherein the zigzag trajectory comprises a plurality of temporally adjacent linear sections each comprising a plurality of temporally adjacent data samples that are spaced apart along the phase-encoding k-space dimension, and wherein the reconstruction kernel spans at least two of the plurality of temporally adjacent linear sections.

18. The method of claim 1 wherein the sampling trajectory7comprises a zigzag trajectory comprising a plurality of temporally adjacent linear sections each comprising a plurality of temporally adjacent data samples that are spaced apart along the phase-encoding k-space dimension by a phase encoding spacing and are spaced apart along the temporal dimension by a temporal spacing.

19. The method of claim 18 wherein the temporal spacing is selected to minimize Bo-inhomogeneity induced phase and T2* decay.

20. The method of claim 19 wherein the temporal spacing is less than a few milliseconds.

21. The method of claim 18 wherein the phase encoding spacing is selected such that data samples in the plurality of temporally adjacent linear sections are interleaved in the phase encoding dimension.

22. The method of claim 1, wherein the pulse sequence implement simultaneous multislice imaging for acceleration.

23. The method of claim 1, wherein the 4D CSF flow data includes a net flow velocity7map that indicates a net flow direction and magnitude of the CSF.34QB\125141.04930\99553893.4MGH 2024-177-02125141.0493024. A method for non-invasive assessment of brain waste clearance function using magnetic resonance imaging (MRI), comprising: acquiring velocity-encoded magnetic resonance data from a subject using a pulse sequence configured with low velocity encoding to detect cerebrospinal fluid (CSF) flow in subarachnoid space and perivascular space, wherein the velocity- encoded magnetic resonance data are acquired by sampling a hybrid space along a sampling trajectory, wherein the hybrid space comprises a first axis along a temporal dimension and a second axis along a phase-encoding k-space dimension; reconstructing flow-sensitive images from the velocity-encoded magnetic resonance data; generating CSF flow maps indicating flow dynamics in the subarachnoid space and perivascular space; acquiring functional magnetic resonance data from the subject during a physiological state change; analyzing changes in the CSF flow maps corresponding to the physiological state change; and determining brain waste clearance function based on the analyzed changes in CSF flow dynamics.

25. The method of claim 24, wherein the low velocity encoding comprises a velocity encoding (VENC) of 2 cm / s or lower.

26. The method of claim 24, wherein the physiological state change comprises presentation of visual or auditory stimuli to the subject.

27. The method of claim 24, wherein determining brain waste clearance function comprises comparing the analyzed changes in CSF flow dynamics to reference values from healthy subjects.35QB\125141.04930\99553893.4MGH 2024-177-02 125141.0493028. The method of claim 24, wherein the pulse sequence comprises at least one of a pulsed-gradient spin-echo (PGSE) echo-planar time-resolved imaging (EPTI) pulse sequence or a PGSE echo-planar imaging (EPI) pulse sequence.

29. The method of claim 24, further comprising identifying biomarkers for neurological disorders based on the determined brain waste clearance function.36QB\125141.04930\99553893.4

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