Systems and methods for correcting synchronous coherent / incoherent motion imaging

Abstract

A computer-implemented method for reconstructing slow-flow velocity measurement data includes acquiring diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest from a human subject via a processing system comprising one or more processors using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses having fixed b-values ​​and fixed velocity coding values, configured to provide a velocity resolution of less than 1000 micrometers per second. The computer-implemented method further includes simultaneously determining a velocity measurement metric and a metric derived from the diffusion tensor based on the diffusion-weighted MRI data via the processing system.

Description

Background Technology

[0001] The topics disclosed in this article relate to medical imaging, and more specifically to a system and method for simultaneous coherent / incoherent motion imaging (SCIMI).

[0002] Non-invasive imaging techniques allow for the acquisition of images of the internal structures or features of a patient / subject without the need for invasive procedures. Specifically, such non-invasive imaging techniques rely on various physical principles (such as differential transmission of X-rays through a target volume, sound wave reflection within the volume, paramagnetism of different tissues and materials within the volume, and the disintegration of the target radionuclide within the body) to acquire data and construct images or otherwise represent the observed internal features of a patient / subject.

[0003] During MRI, when material such as human tissue is subjected to a uniform magnetic field (polarization field B0), the individual magnetic moments of the spins within the tissue partially align with this polarization field, but precess around it at their characteristic Larmor frequencies. If the material or tissue is subjected to a magnetic field (excitation field B1) located in the xy-plane and close to the Larmor frequency, the net aligned magnetic moment, or "longitudinal magnetization," M... z It can be rotated or "tilted" into the xy plane to produce a net transverse magnetic moment M. t After the excitation signal B1 is terminated, a signal is emitted by the excitation spin, and this signal can be received and processed to form an image.

[0004] When these signals are used to generate images, the magnetic field gradient (G) is employed. x G y and G z Typically, the area to be imaged is scanned sequentially according to a measurement cycle, during which these gradient fields vary depending on the specific localization method used. The resulting set of received nuclear magnetic resonance (NMR) signals is digitized and processed to reconstruct an image using one of many well-known reconstruction techniques.

[0005] The study of cerebrospinal fluid movement in the brain parenchyma is receiving increasing attention in neuroscience because it is closely related to the clearance of metabolic waste in the brain (lymphatic system). To date, the physiological functions of the human lymphatic system remain elusive. Currently, plain MRI is the most promising tool for non-invasive qualitative imaging of slow lymphatic flow in humans. Although studies have been conducted in mice, there is currently no method sensitive enough to map physiologically relevant fluid velocities within the human body. Summary of the Invention

[0006] The following provides an overview of some of the embodiments disclosed herein. It should be understood that these aspects are provided merely to give the reader a brief overview of these specific embodiments, and are not intended to limit the scope of this disclosure. In fact, this disclosure may cover various aspects that may not be set forth below.

[0007] In one embodiment, a computer-implemented method for reconstructing slow flow velocity measurement data is provided. The computer-implemented method includes acquiring diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest from a human subject via a processing system comprising one or more processors using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses having both a fixed b-value and a fixed velocity coding (VENC), configured to provide a velocity resolution of less than 1000 micrometers per second. The computer-implemented method also includes simultaneously determining a velocity measurement metric and a metric derived from the diffusion tensor based on the diffusion-weighted MRI data via the processing system.

[0008] In another embodiment, a system for reconstructing slow flow velocity measurement data is provided. The system includes a memory that encodes processor-executable routines. The system also includes a processing system comprising one or more processors and configured to access the memory and execute the processor-executable routines, wherein the processor-executable routines, when executed by the processing system, cause the processing system to perform actions. These actions include acquiring diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest from a human subject using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses having both a fixed b-value and a fixed velocity coding value, configured to provide a velocity resolution of less than 1000 micrometers per second. These actions also include simultaneously determining a velocity measurement metric and a metric derived from the diffusion tensor based on the diffusion-weighted MRI data.

[0009] In another embodiment, a non-transitory computer-readable medium is provided, comprising processor-executable code that, when executed by a processing system comprising one or more processors, causes the processing system to perform actions. These actions include acquiring diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest in a human subject using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses having both a fixed b-value and a fixed velocity coding value, configured to provide a velocity resolution of less than 1000 micrometers per second. These actions also include simultaneously determining a velocity measurement metric and a metric derived from the diffusion tensor based on the diffusion-weighted MRI data. Attached Figure Description

[0010] These and other features, aspects, and advantages of the invention will be better understood when reading the following detailed description with reference to the accompanying drawings, in which the same reference numerals denote the same parts throughout the drawings, wherein:

[0011] Figure 1 Embodiments of magnetic resonance imaging (MRI) systems suitable for use with the disclosed techniques according to various aspects of this disclosure are illustrated;

[0012] Figure 2 A graph illustrating the relative signal strength of a MAGNUS system in the b-VENC parameter space according to various aspects of this disclosure is shown.

[0013] Figure 3 Examples of signal attenuation for a MAGNUS system with constant VENC according to various aspects of this disclosure are provided;

[0014] Figure 4 Gradient timing in the spread / velocity encoding portion of a pulse sequence for a MAGNUS system of a specific VENC according to various aspects of this disclosure is illustrated;

[0015] Figure 5 Brain MR images under different conditions used to determine the optimal resolution and operating point using the disclosed sequences are shown according to various aspects of this disclosure.

[0016] Figure 6 An MR amplitude image obtained using the disclosed sequence according to various aspects of this disclosure is shown;

[0017] Figure 7 A flowchart illustrating a method for reconstructing slow flow velocity measurement data (SCIMI data pipeline) according to various aspects of this disclosure is shown;

[0018] Figure 8 A schematic diagram illustrating a SCIMI pipeline (e.g., a process) for phase comparison reconstruction in parallel with a diffusion pipeline, according to various aspects of this disclosure;

[0019] Figure 9 A q-space sphere is shown that has been corrected for the gradient nonlinearity of each q vector according to various aspects of this disclosure;

[0020] Figure 10 Schematic diagrams illustrating the registration of composite images according to various aspects of this disclosure are shown;

[0021] Figure 11 and Figure 12 Images of diffusion and velocity data reconstructed from a single scan using the disclosed sequence according to various aspects of this disclosure are shown;

[0022] Figure 13 Exemplary velocity measurement outputs generated using the disclosed techniques according to various aspects of this disclosure are shown; and

[0023] Figure 14 The velocity streamlines during the cardiac cycle are shown, illustrating motion in thin-walled tissue generated using the disclosed techniques according to various aspects of this disclosure. Detailed Implementation

[0024] One or more specific implementations will be described below. To provide a concise description of these implementations, not all features of an actual implementation will be described in this specification. It should be understood that in the development of any such actual implementation, as in any engineering or design project, many implementation-specific decisions must be made to achieve the developer's specific objectives, such as complying with system-related and business-related constraints that may differ from implementation to implementation. Furthermore, it should be understood that such development efforts may be complex and time-consuming, but will in any case remain routine tasks of design, fabrication, and manufacturing for those skilled in the art who benefit from this disclosure.

[0025] When describing elements of various embodiments of the subject matter of this invention, the articles “a,” “an,” “the,” and “the” are intended to indicate the presence of one or more elements among the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that additional elements may be present in addition to the listed elements. Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and therefore the additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments.

[0026] While the various aspects discussed below are provided within the context of medical imaging, it should be understood that the disclosed techniques are not limited to such medical settings. In fact, the examples and explanations provided in such medical settings are merely for illustrative purposes by offering real-world examples of implementation and application. However, the disclosed techniques can also be used in other settings, such as image reconstruction for non-destructive inspection of manufactured parts or goods (i.e., quality control or quality inspection applications) and / or non-invasive inspection of packages, boxes, luggage, etc. (i.e., security screening or screening applications). Generally, the disclosed techniques can be used in any imaging or screening setting or in the field of image processing or photography, where a set or class of acquired data undergoes a reconstruction process to generate an image or volume.

[0027] This disclosure provides systems and methods for reconstructing slow flow velocity measurement data. The velocity resolution of the slow velocity measurement data is less than 1000 micrometers per second. For example, the velocity resolution can be in the range of 0 micrometers per second to 1000 micrometers per second, between 100 micrometers per second and 900 micrometers per second, between 200 micrometers per second and 800 micrometers per second, between 300 micrometers per second and 700 micrometers per second, or between 400 micrometers per second and 600 micrometers per second. The velocity resolution can be any value between 0 micrometers per second and 1000 micrometers per second. Specifically, a phase-sensitive diffusion MRI sequence with pulse timing sequences is disclosed, capable of achieving a velocity resolution of approximately 20 micrometers (μm) per second (s) and an integrated image reconstruction and velocity map generation pipeline. Specifically, the disclosed technique provides correction for SCIMI. SCIMI reconstructs composite images using phase-contrast imaging (PCI) sequences (or diffusion tensor imaging (DTI) sequences). The composite image is processed into two parallel streams: an amplitude image for DTI processing and a phase image for unique velocity measurements. By simultaneously reconstructing amplitude and phase data, two metrics characterizing the motion of diffusing fluid (or tissue) and coherent velocity maps are non-invasively computed in human subjects (e.g., time resolution over the entire cardiac cycle).

[0028] The use of DTI pulse sequences (e.g., spin echoes) produces velocity coding (V0) of approximately less than 1000 micrometers (μm) per second (s). ENC Values ​​(e.g., 300 μm / s) (compared to gradient echo-based velocity measurements typically limited to 5 cm / s or greater). This is achieved by using "bV". ENC "Parameter space creates the encoded value V of b" ENC The disclosed pulse sequence modifies the pulse sequence (e.g., increasing the distance between encoded phases) to maximize the signal. Tissue-specific constraints (e.g., constraints on white and gray matter in the brain) are used to assess signal intensity attenuation in this space and to create pulse sequences to maximize the signal. The disclosed pulse sequence breaks with convention in achieving signal optimization via minimizing echo time (TE). Instead, the disclosed pulse sequence achieves better signal despite a longer TE time. The disclosed reconstruction algorithm also considers gradient nonlinearity, registration of composite images, background phase correction, and synchronization with periodic physiological signals (e.g., respiration or heartbeat) to map slow three-dimensional (3D) velocity vectors in tissues. The disclosed technique can be applied to different types of tissues. The disclosed technique facilitates research on the assessment, diagnosis, or intervention of traumatic brain injury, neurodegenerative diseases, and brain health by providing imaging of biomarkers previously only visible in animal studies in humans. Specifically, the disclosed technique enables the measurement of in vivo velocities at VENC levels below 1 mm / s without encountering signal attenuation.

[0029] The disclosed embodiments include systems and methods for reconstructing slow-flow velocity measurement data (e.g., SCIMI digital pipelines). The system and method include acquiring diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest in a human subject via a processing system comprising one or more processors using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence (e.g., spin-echo diffusion tensor imaging echo-plane imaging (DTI-EPI) sequence), wherein the spin-echo PCI sequence has coded pulses (e.g., sinusoidal coded pulses, trapezoidal coded pulses, etc.) having a fixed b-value and a fixed velocity coding value (VENC), configured to provide a velocity resolution of less than 1000 micrometers per second. The system and method also include simultaneously determining a velocity measurement metric and a metric derived from the diffusion tensor based on the diffusion-weighted MRI data via the processing system.

[0030] In some embodiments, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor includes generating amplitude and phase images from diffusion-weighted MRI data via a processing system. In some embodiments, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: performing rigid body registration and eddy current correction on the amplitude images via a processing system to create a forward mapping for each PCI volume, wherein the forward mapping is configured to transform the corresponding PCI volume to a common registration space, and the forward mapping represents the original location of the corresponding PCI volume; and applying the corresponding forward mapping to each corresponding PCI volume via the processing system to generate a registered amplitude volume.

[0031] In some implementations, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: creating a tissue mask from the registered amplitude volume via a processing system; generating a reverse mapping for each PCI volume via the tissue mask via the processing system; applying the corresponding reverse mapping to each PCI volume via the processing system during phase unwrapping of a composite diffusion-weighted image derived from diffusion-weighted MRI data to generate a phase image; performing background phase correction on the phase image via the processing system to generate a corrected phase image; and applying the corresponding forward mapping to each corresponding PCI volume of each corrected phase image via the processing system to generate a registered phase volume. In some implementations, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: performing gradient nonlinear correction on the registered amplitude volumes via a processing system to generate a corrected q-space vector for each PCI volume, wherein the corrected q-space vector for each PCI volume is modified in amplitude and direction to account for the actual and specified q-vectors (considering both the amplitude and direction changes of the q-vector) due to nonlinear deviations from the isocenter of the MRI gradient coils; and performing linear fitting via a processing system using the corrected q-space vectors in the corresponding q-space spheres of each PCI volume on the registered phase volumes to calculate the velocity vector for each PCI volume.

[0032] In some embodiments, the system and method include acquiring periodic physiological signals (e.g., electrocardiogram, respiratory waveform) from a human subject via a processing system while acquiring diffusion-weighted MRI data, wherein the diffusion-weighted MRI data is acquired during ungated free-running scans. In some embodiments, the system and method include: binning each PCI volume into physiological bins over time using the periodic physiological signals via a processing system, wherein each physiological bin comprises an arbitrary grouping of multi-bin q-space vectors; and calculating, via the processing system, a velocity vector for the PCI volume in each corresponding physiological bin to be generated, to generate a time-resolved velocity vector within a physiological cycle (e.g., heartbeat).

[0033] In the following disclosure, the techniques disclosed are described with respect to the brain. However, the disclosed techniques can also be used with other tissues where analysis of fluid flow or tissue movement is desired. Additionally, in the following disclosure, the disclosed techniques are described using a spin-echo DTI-EPI sequence. However, the disclosed techniques can also be used with other spin-echo PCI sequences.

[0034] Considering the above, Figure 1 The magnetic resonance imaging (MRI) system 100 is schematically illustrated as including a scanner 102, scanner control circuitry 104, and system control circuitry 106. According to the embodiments described herein, the MRI system 100 is typically configured to perform MR imaging.

[0035] System 100 further includes: a remote access and storage system or device, such as a Picture Archiving and Communication System (PACS) 108; or other devices, such as remote radiology equipment, enabling on-site or remote access to data acquired by system 100. In this way, MR data can be acquired and then processed and evaluated on-site or remotely. While MRI system 100 may include any suitable scanner or detector, in the illustrated embodiment, system 100 includes a whole-body scanner 102 with a housing 120 through which an aperture 122 is formed. An examination table 124 is movable into the aperture 122 to allow a patient 126 (e.g., a subject) to be positioned therein for imaging of selected anatomical structures within the patient's body.

[0036] Scanner 102 includes a series of associated coils for generating a controlled magnetic field used to excite gyromagnetic material within the anatomical structures of the patient being imaged. Specifically, a primary magnetic coil 128 is provided to generate a primary magnetic field B0 generally aligned with an aperture 122. A series of gradient coils 130, 132, and 134 allow the generation of a controlled gradient magnetic field during the examination sequence for positional encoding of certain gyromagnetic nuclei within the patient 126. A radio frequency (RF) coil 136 (e.g., an RF transmit coil) is configured to generate radio frequency pulses for exciting certain gyromagnetic nuclei within the patient. In addition to the coils that may be located locally within scanner 102, system 100 also includes a set of receiving coils or RF receiving coils 138 (e.g., an array of coils) configured to be placed proximal to (e.g., against) the patient 126. For example, receiving coils 138 may include cervical / thoracic / lumbar (CTL) coils, head coils, single-sided spinal coils, etc. Generally, the receiving coil 138 is placed near or above the patient 126 in order to receive weak RF signals generated by certain magnetic nuclei in the patient's body when the patient 126 returns to its relaxed state (weak in relation to the transmit pulse generated by the scanner coil).

[0037] The various coils of system 100 are controlled by external circuitry to generate desired fields and pulses and to read out emissions from the gyromagnetic material in a controlled manner. In an illustrated embodiment, a main power supply 140 powers the primary field coil 128 to generate a primary magnetic field Bo. Power inputs (e.g., power from a utility or grid), a power distribution unit (PDU), a power supply (PS), and drive circuitry 150 may together provide power to cause gradient field coils 130, 132, and 134 to generate pulses. Drive circuitry 150 may include amplification and control circuitry for supplying current to the coils according to a sequence of digitized pulses output by scanner control circuitry 104.

[0038] Another control circuit 152 is provided for regulating the operation of the RF coil 136. Circuit 152 includes a switching device for alternating between an active operating mode and a passive operating mode, wherein the RF coil 136 transmits a signal and does not transmit a signal, respectively. Circuit 152 also includes an amplifier configured to generate RF pulses. Similarly, a receiving coil 138 is connected to a switch 154 capable of switching the receiving coil 138 between a receiving mode and a non-receiving mode. Thus, in receiving mode, the receiving coils 138 resonate with the RF signal generated by the release of the magnetic nucleus within the patient 126, and in non-receiving mode, they do not resonate with the RF energy from the transmitting coil (i.e., coil 136) to prevent undesirable operation. Additionally, the receiving circuit 156 is configured to receive data detected by the receiving coil 138 and may include one or more multiplexing and / or amplification circuits.

[0039] It should be noted that although the scanner 102 and the control / amplification circuit described above are illustrated as being coupled by a single wire, in practice, many such wires may exist. For example, separate wires may be used for control, data communication, power transmission, etc. Furthermore, appropriate hardware may be provided along each type of wire for proper handling of data and current / voltage. In practice, various filters, digitizers, and processors may be provided between the scanner and either or both of the scanner control circuit 104 and system control circuit 106.

[0040] As shown in the figure, the scanner control circuit 104 includes an interface circuit 158 ​​that outputs signals for driving the gradient field coil and the RF coil, and for receiving data representing magnetic resonance signals generated in the examination sequence. The interface circuit 158 ​​is coupled to a control and analysis circuit 160. Based on a defined scheme selected via the system control circuit 106, the control and analysis circuit 160 executes commands for driving circuits 150 and 152.

[0041] The control and analysis circuit 160 is also used to receive magnetic resonance signals and perform subsequent processing before sending the data to the system control circuit 106. The scanner control circuit 104 also includes one or more memory circuits 162 that store configuration parameters, pulse sequence descriptions, inspection results, etc. during operation.

[0042] Interface circuitry 164 is coupled to control and analysis circuitry 160 for exchanging data between scanner control circuitry 104 and system control circuitry 106. In some embodiments, control and analysis circuitry 160, while exemplified as a single unit, may include one or more hardware devices. System control circuitry 106 includes interface circuitry 166 that receives data from scanner control circuitry 104 and sends data and commands back to scanner control circuitry 104. Control and analysis circuitry 168 may include a CPU in a general-purpose or special-purpose computer or workstation. Control and analysis circuitry 168 is coupled to memory circuitry 170 to store programming code for operating the MRI system 100, and to store processed image data for subsequent reconstruction, display, and transfer. The programming code may execute one or more algorithms configured to perform reconstruction of acquired data as described below when executed by a processor. In some embodiments, memory circuitry 170 may store one or more algorithms for reconstructing slow flow velocity measurement data (e.g., SCIMI digital pipeline). In some embodiments, the techniques disclosed herein may occur on a separate computing device having processing circuitry and memory circuitry.

[0043] The processing unit (e.g., a microprocessor or processing circuitry) and memory (such as those residing in scanner control circuitry 104 and / or system control circuitry 106) of the magnetic resonance imaging system 100 can be used to execute stored software code, instructions, or routines for acquiring and processing MR data. As used herein, the term "code" or "software code" refers to any instruction or set of instructions that controls the magnetic resonance imaging system 100. The code or software code can exist in the following forms: a computer-executable form, such as machine code, which is a set of instructions and data directly executed by the processing unit of scanner control circuitry 104 and / or system control circuitry 106; a human-understandable form, such as source code, which can be compiled for execution by the processing unit of scanner control circuitry 104 and / or system control circuitry 106; or an intermediate form, such as object code, which is generated by a compiler. In some embodiments, the magnetic resonance imaging system 100 may include multiple controllers.

[0044] For example, the memory may store processor-executable software code or instructions (e.g., firmware or software) tangibly stored on a non-transitory computer-readable medium. Additionally or alternatively, the memory may store data. As an example, the memory may include volatile memory (such as random access memory (RAM)) and / or non-volatile memory (such as read-only memory (ROM), flash memory, hard disk drive, or any other suitable optical, magnetic, or solid-state storage medium or combinations thereof). Furthermore, the processing unit may include multiple microprocessors, one or more "general-purpose" microprocessors, one or more application-specific microprocessors, and / or one or more application-specific integrated circuits (ASICs) or some combination thereof. For example, the processing unit may include one or more Reduced Instruction Set Computing (RISC) or Complex Instruction Set Computing (CISC) processors. The processing unit may include multiple processors and / or the memory may include multiple memory devices.

[0045] In some implementations (e.g., reconstructing slow-flow velocity measurement data), a processing component (e.g., a processing system including one or more processors) is configured to acquire diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest from a human subject using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence (e.g., a spin-echo diffusion tensor imaging echo-plane imaging (DTI-EPI) sequence), wherein the spin-echo PCI sequence has coded pulses with fixed b-values ​​and fixed velocity coding values, configured to provide a velocity resolution of less than 1000 micrometers per second. The processing component is also configured to simultaneously determine velocity measurement metrics and metrics derived from the diffusion tensor based on the diffusion-weighted MRI.

[0046] In some embodiments, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor includes generating amplitude and phase images from diffusion-weighted MRI data. In some embodiments, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: performing rigid body registration and eddy current correction on the amplitude images to create a forward mapping for each PCI volume, wherein the forward mapping is configured to transform the corresponding PCI volume to a common registration space, and the forward mapping represents the original location of the corresponding PCI volume; and applying the corresponding forward mapping to each corresponding PCI volume to generate a registered amplitude volume.

[0047] In some embodiments, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: creating a tissue mask from the registered amplitude volume; generating a reverse mapping for each PCI volume via a processing system using the tissue mask; applying the corresponding reverse mapping to each PCI volume during phase unrolling of a composite diffusion-weighted image derived from diffusion-weighted MRI data to generate a phase image; performing background phase correction on the phase image to generate a corrected phase image; and applying the corresponding forward mapping to each corresponding PCI volume of each corrected phase image to generate a registered phase volume. In some embodiments, simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: performing gradient nonlinear correction on the registered amplitude volume to generate a corrected q-space vector for each PCI volume, wherein the corrected q-space vector for each PCI volume is modified in amplitude and direction to account for the actual versus specified q-vector due to nonlinear deviations from isocenter by the MRI gradient coils; and performing linear fitting using the corrected q-space vector in the corresponding q-space sphere of each PCI volume on the registered phase volume to compute the velocity vector for each PCI volume.

[0048] In some embodiments, the processing unit is configured to acquire periodic physiological signals from a human subject simultaneously with the acquisition of diffusion-weighted MRI data, wherein the diffusion-weighted MRI data is acquired during ungated free-running scans. In some embodiments, the processing system is configured to: bin each PCI volume into a time-resolved physiological bin using the periodic physiological signals, wherein each physiological bin comprises an arbitrary grouping of multi-shell q-space vectors, and calculate the velocity vector of the PCI volume in each corresponding physiological bin to be generated, to generate a time-resolved velocity vector within the physiological cycle.

[0049] Additional interface circuitry 172 may be provided for exchanging image data, configuration parameters, etc., with external system components, such as remote access and storage device 108. Finally, system control and analysis circuitry 168 may be communicatively coupled to various peripheral devices for use in facilitating the operator interface and for generating hard copies of the reconstructed images. In the illustrated embodiment, these peripheral devices include a printer 174, a monitor 176, and a user interface 178, which includes devices such as a keyboard, mouse, and touchscreen (e.g., integrated with monitor 176).

[0050] DTI and gradient echo (GRE) PC imaging define the eigenvalues ​​(b, VENC) of the measurement results. Furthermore, DTI sequences typically acquire a large number of q-space directions distributed across the sphere to improve angular resolution, while PC imaging can be performed using at least four q-space directions to quantize three-directional flow. The similarity of the encoding schemes with additional redundant q-space information allows for the reconstruction of PC velocity images from DTI sequences. While DTI reconstruction depends on the signal amplitude, PC reconstruction depends on the phase. Therefore, if the DTI scan is reconstructed while preserving phase information, the resulting image can provide information about velocity. This allows both reconstruction paths to be used with a single scan, enabling imaging of both coherent motion (velocity) and incoherent motion (diffusion).

[0051] For trapezoidal encoded pulses, the encoding speed value VENC is determined by v. enc =π / γGδΔ is determined. Decreasing VENC requires increasing Δ or δ, which introduces practical limitations when imaging in the brain. In gradient echo-based phase-contrast imaging, VENC is limited by higher-order adjoint fields. Conversely, when using spin echo DTI-EPI (echo-plane imaging) sequences, it is not limited by such artifacts, and the redundancy from measuring multiple additional q-space directions enables in vivo velocity measurements at VENC = 300 μm / sec.

[0052] By adjusting the timing parameters of the encoding gradient, sequences with fixed b-values ​​and VENC within an appropriate range (<1000 μm / sec) for motion in thin-walled brain tissue can be designed. For the trapezoidal encoding gradient used in DTI-EPI, the shape of the encoding pulse is defined by the gradient intensity G, the ramp time ζ, the width δ of a single trapezoid (area = Gδ), and the time interval Δ between the two lobes of the trapezoid. In this configuration, the b-value and VENC are given by the following equation:

[0053] (1)

[0054] (2)

[0055] Rearrange equation 2 and add Δ=π / γGδv enc Substituting into equation 1 shows that the value δ is obtained through the roots of the equation:

[0056] (3)

[0057] Once δ is determined, the corresponding value of Δ can be calculated from Equation 2. This will be based on factors such as gradient strength G, transition rate / ramp time ζ, and the 180° RF pulse t. 180The system specifications for the required time create the b-value and the parameter space for VENC. An effective solution requires physical constraints to hold: the width of the encoded trapezoid must be longer than the ramp time (δ ≥ ζ), and Δ must be greater than the shortest time required for a trapezoid and the RF 180-degree flip pulse (Δ ≥ δ + ζ + t). 180 ). Figure 2 The total encoding time τ = δ + Δ + ζ in the parameter space of the microstructural anatomical gradient for neural imaging using the MAGNUS ultrafast imaging system is shown. This ultrafast imaging system is a head-only gradient system used for human scans at 3T with a gradient intensity of 300 mT / m and a conversion rate of 750 T / m / s. Specifically, Figure 2 The relative signal strength of the MAGNUS system with respect to a constant VENC in the b-VENC parameter space is provided. Due to gradient strength and conversion rate, the MAGNUS system allows for a larger parameter space. It should be noted that the parameter space is scanner-dependent.

[0058] Within this parameter space, we optimized the encoding pulse for a fixed VENC. When other acquisition parameters (e.g., field of view (FOV)) remain constant, the encoding time is directly related to TE, TE = τ(b, v). enc ) + t(FOV). This allows the signal attenuation in the image as a function of b to be determined according to the following formula:

[0059] (4)

[0060] Figure 3 The apparent diffusion coefficients (D = 4 × 10^(-4) mm) of white and gray matter in the brain are shown. 2 / s and 8×10^(-4) mm 2 The MAGNUS system experiences signal attenuation at VENC = 300 μm / s. For white and gray matter, T2 = 80 ms and 90 ms, respectively. In white and gray matter, lower b values ​​increase contrast, even at the cost of longer encoding times (represented by the solid line graph). Figure 3 As shown, the optimal contrast ratio for the two tissues occurs at b=1500s / mm. 2 nearby.

[0061] Figure 4A graph 180 is shown for the sample sequence at VENC = 300 μm / s. Graph 180 includes an x-axis 182 representing gradient intensity and a y-axis 184 representing time. Graph 186 represents the standard TE, and graph 188 represents the modified TE. With the modified TE (increased distance between coded pulses), the pulses are further separated and narrower. Increasing the distance between coded pulses to recover (maximize) the signal results in a decrease in the b-value. As shown, the coded pulses have a trapezoidal shape. In other embodiments, the coded pulses may have another shape (e.g., a sine wave).

[0062] Use the VENC / signal to determine the optimal resolution and operating point. Figure 5 MR images of the brain under different conditions are shown 190, 192 and 194, which are shown as a means to determine the optimal resolution and operating point using the disclosed sequence. Figure 6 MR amplitude images 196 and 198 obtained using the disclosed sequence are shown. Although image 198 has a longer TE, it has better resolution.

[0063] To improve angular resolution, DTI sequences typically use a large number of q-space orientations distributed in different arrangements (such as on a sphere or in multiple shells). Phase-contrast imaging typically uses conveniently chosen orientations (e.g., +x and -x) so that phase images can be added together or subtracted to create velocity profiles. The velocity can still be solved in the overdetermined system created using the DTI q-space sphere. Therefore, this scheme allows for the simultaneous determination of velocity and DTI metrics. The velocity vector (v) in this system... x v y v z The reconstruction of phase images extends the addition / subtraction of phase images into a series of linear equations. Represented as a matrix, the equation takes the following form.

[0064] (5)

[0065] in It is a unit vector in q-space, and This represents the phase of the T2 / b0 image as a background term. This form of the equation allows for the calculation of velocity profiles given the phase of any set of q-space vectors, including multiple b0 images or no b0 image at all.

[0066] Figure 7 A flowchart illustrating method 200 for reconstructing slow flow velocity measurements (e.g., SCIMI data pipeline) is provided. One or more steps of method 200 can be performed by... Figure 1The process is executed by the processing circuitry of the magnetic resonance imaging system 100 or a remote computing device. One or more steps of method 200 can be performed simultaneously and / or in conjunction with... Figure 7 The different execution sequences are shown.

[0067] Method 200 includes acquiring diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest (ROI) of a human subject using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses having both a fixed b-value and a fixed velocity coding value, configured to provide a velocity resolution of less than 1000 micrometers / second (box 202). The selection of the b-value and VENC determines the timing / interval of the coded pulses to achieve the desired velocity resolution. In some embodiments, the spin-echo phase PCI sequence is a spin-echo DTI-EPI sequence. In some embodiments, diffusion-weighted data is acquired during a free-run (i.e., ungated, continuous-run) scan within a dynamic, periodic physical process (e.g., heartbeat or respiration). Acquisition is performed slice-wise (e.g., two-dimensional (2D) acquisition) and volume-wise (e.g., orientation coding: q-space). Alignment of acquisitions within a physiological cycle (e.g., cardiac cycle) is pseudo-random due to natural variations (e.g., in each heartbeat). Acquisition time is slice-dependent. Each voxel (e.g., a DTI or PCI volume) has measurements from each volume and a time series of those measurements (e.g., based on normalized heart rate). This time series may differ from adjacent voxels. As described in more detail below, reconstruction can group several measurements together (e.g., via binning or sliding windows) to obtain velocity. Method 200 also includes determining a metric derived from both velocity measurement and diffusion tensor based on diffusion-weighted MRI data (box 204). In some embodiments, method 200 includes acquiring periodic physiological signals (e.g., heart rate or respiration) from a human subject while acquiring diffusion-weighted MRI data (box 201). As described above, in some embodiments, diffusion-weighted MRI data is acquired during free-run scans. Method 200 also includes binning each PCI volume into physiological bins using periodic physiological signals (box 203). Each physiological bin in a physiological bin comprises an arbitrary grouping of multi-shell q-space vectors. Method 200 also includes calculating the velocity vector of the PCI volume in each corresponding physiological bin to generate a time-resolved vector within the physiological cycle (box 205).

[0068] Figure 8A schematic diagram of a SCIMI pipeline (e.g., procedure 206) for phase contrast reconstruction running in parallel with a diffusion pipeline is illustrated. Retrospective cardiac gating is utilized in procedure 206 as depicted. Procedure 206 comprises four stages: an amplitude-based preprocessing stage 208, a phase reconstruction stage 210, a cardiac gating stage 212, and a velocity extraction stage 214. Procedure 206 includes reconstructing raw MRI data 216 (e.g., diffusion-weighted MRI data) (e.g., acquired using sequence types disclosed herein (e.g., spin-echo PCI sequences)) using a null-difference reconstruction method (as indicated by reference numeral 218) to reconstruct a complete composite image preserving both amplitude and phase information. An amplitude image 220 (e.g., amplitude DICOMS) and a composite diffusion-weighted image 222 (DWI) are generated. Images 224 and 226 are examples of amplitude images and composite diffusion-weighted images, respectively.

[0069] As depicted in amplitude-based processing stage 208, amplitude image 220 undergoes registration 228 (e.g., rigid body registration such as affine image registration) and linear eddy current distortion correction 230. For each volume (e.g., DTI volume) or voxel, the effects of registration and distortion correction are combined to create a forward mapping 232 (e.g., a transformed forward mask) indicating the original location of each voxel. When the tissue is the brain, process 206 includes performing craniotomy 233 on the registered and distortion-corrected amplitude image to generate a mask 234 (e.g., a tissue mask).

[0070] In phase reconstruction stage 210, phase unrolling 236 is performed on composite DWI 222 using a tissue mask and forward mapping 232 to generate a phase map 238 (phase image). Image 239 is an example of a phase map. Phase reconstruction stage 210 includes performing image space polynomial fitting 240 on phase map 238 to generate a background phase map 242. The background phase map 242 is utilized when performing background phase correction 243 on phase map 238 to generate a corrected phase map 244. Image 245 is an example of a corrected phase map. Phase reconstruction stage 210 includes applying a corresponding forward mapping 232 (as shown by reference numeral 246 in the attached figure) to each corresponding PCI volume of each corrected phase image to generate a registered phase map 248 (and a registered phase volume).

[0071] As described above, during free-running scans, physiological signals from the human subject are acquired simultaneously with diffusion-weighted MRI data. In some implementations, heart rate is monitored to ensure that the time interval is non-uniformly fitted to the TR, such that subsequent retrospective gating will see a q-space volume distributed across the entire RR interval, rather than clustering. The cardiac gating phase 212 includes acquiring electrocardiogram (ECG) recordings / pulse plethysmography (PPG) recordings 250 and performing peak finding as indicated by reference numeral 252 in the appended diagram. During reconstruction, the acquisition time (timing parameter 254) of each slice is mapped onto the recorded ECG / PPG signals. The difference between heart rate and TR ensures coverage of each slice throughout the entire cardiac cycle. Specifically, relative cardiac locations 258 (i.e., retrospective cardiac boxes) are generated using a cardiac phase-to-sequence reconstruction (CAPTOR) method (as indicated by reference numeral 256 in the appended diagram), each relative cardiac location consisting of a subset of the total q-space acquired. This method separately determines the cardiac systolic and diastolic phases and bins them according to the relative percentage of each, ensuring that the bins remain consistent until the end of diastole. Phase classification 260 is performed to place q-space volumes into corresponding bins to create q-space bins 262 with different values. Each q-space bin 262 comprises an arbitrary grouping of multiple q-space vectors.

[0072] (Amplitude-based processing stage 208 also includes performing gradient nonlinear correction (GRC) (as shown in reference numeral 264) on the distortion-corrected diffusion data (i.e., the registered and distortion-corrected amplitude images) to generate a corrected q-space vector 266 for each PCI volume. The amplitude and orientation are varied for the corrected q-space vector for each PCI volume to account for directional distortion. As shown by arrow 268, the distortion-corrected and gradient nonlinear corrected diffusion data are utilized in the regular diffusion pipeline. To extract synchronization contrast from these datasets, a metric derived from the synchronization diffusion tensor is further generated using the amplitude components. In addition to distortion correction, the amplitude data is denoised using generalized spherical deconvolution (which models the diffusion signal as a series of anisotropic and isotropic Gaussian partitions computed using the damped Richardson-Lucy algorithm). The coefficients are interpolated into the forward model to derive a projection of the data for tensor fitting. Tuning the model parameters is important for the denoising approximation via generalized spherical deconvolution.) For acquisitions on the MAGNUS platform, the tuning parameter space comprises three anisotropic Gaussian partitions and a series of isotropic Gaussian partitions covering a wide range of diffusion rates. The diffusion tensor is jointly fitted using nonnegative-constrained linear least squares.

[0073] GNC modulates the amplitude and direction of the q-space vector on a voxel-by-voxel basis, thus taking into account errors introduced by distortion or rotation in the q-space direction. As mentioned above, the scan can be free-running, where physiological signals are recorded for retrospective gating to classify images into different bins based on periodic signals (e.g., images can be classified into 10 bins across cardiac cycles). Utilizing the natural variations in these signals, each bin can contain any set of images and their corresponding points in q-space.

[0074] The calculation of velocity vectors has been extended from the conventional form with fixed q-space points to the ability to fit unknown classifications of q-space points using least-squares fitting. These points themselves may be affected by multi-shell acquisition (for DTI) or gradient nonlinearity, which is taken into account in the matrix design. A convex hull is created for all points, and the volume of this hull is compared with the volume of a tetrahedron created from a minimum number of desired points to ensure a well-fitted state (claimed point cluster). In some implementations, the surface areas of the hulls can be compared, or reference hulls from different point sets can be created.

[0075] Figure 9 The typical gradient nonlinearity observed using MAGNUS gradients in q-space spheres 270 and 272 is illustrated, where the imaging features furthest from the isocenter experience a significant reduction in gradient amplitude (greater than 15%). Gradient nonlinearity effects on velocity encoding are corrected by first estimating the gradient field for each logical gradient axis using a tenth-order spherical harmonic expansion. The temporal evolution of the voxel-by-voxel VENC bias is tracked (and adjusted) using the estimated field for parameter fitting. In q-space spheres 270 and 272, variations in q-space within a heart chamber are shown due to gradient nonlinearities along y (e.g., shown in q-space sphere 270) and z (e.g., shown in q-space sphere 272) from the gradient isocenter (x) to the edge of the FOV. Movement along y distorts the vector, while movement along z expands it. Each vector may experience changes and / or distortions in angle θ around the origin. Specific points within the heart chamber are related to the volume acquisition sequence, TR, and dynamic heart rate, thus creating a selection with a non-uniform distribution on the q-space sphere. For a well-fitted state, the vectors must cover as much of the q-space as the minimum number of vector sets, represented by the inner tetrahedron 274 in the q-space spheres 270, 272, to create a sufficient distribution in each direction. To evaluate the coverage, a convex hull is created from the unit vectors of each subset, and the volume of this hull is compared to the volume of the tetrahedron 274 created from the Hadamard-coded vectors representing the minimum required coverage. A subset of vectors is considered to adequately span the q-space when its volume is greater than or equal to the volume of the representative hull.

[0076] Back Figure 8The velocity extraction stage 214 includes performing a q-space linear fit (as shown in reference numeral 276) using a corrected q-vector 266, a registered phase map 248, and a q-space bin 262 to compute a 3D velocity vector (phase profile 278). Specifically, the velocity extraction stage 217 includes performing a linear fit using a corrected q-space vector in the corresponding q-space sphere of each PCI volume on the registered phase volume to compute a velocity vector for each PCI volume. Velocity encoding 280 is performed for each bin to generate a velocity profile 282. The velocity profile 282 (4D velocity profile) provides a time-resolved vector within the cycle (velocity at different points in the cardiac cycle).

[0077] Figure 10 A schematic diagram of the registered composite image is illustrated. As depicted, rigid body registration and eddy current correction are performed on amplitude image 220 to create a forward mapping for each PCI volume (after all corrections are determined), as shown in reference numeral 284. The forward mapping captures all those corrections. Registration and distortion correction (and GNC calculations) are performed for each volume. This ignores the discrete time points of each layer within the volume. Forward mapping 232 is configured to transform the corresponding PCI volume into and out of registration space 286, and the forward mapping represents the original location of the corresponding PCI volume. This forward mapping is modified to disallow inter-layer interpolation to preserve temporal information. This forward mapping is applied to each corresponding PCI volume as shown in reference numeral 290 to generate a registered amplitude volume 288. A tissue mask 234 is created from the registered amplitude volume 286 as shown in reference numeral 292. A reverse mapping is generated for each PCI volume using tissue mask 234, as shown in reference numeral 294. The corresponding inverse mapping is applied to each PCI volume (e.g., during phase unrolling and background phase correction) to generate a phase image 244. A tissue mask is brought to the native space 296 of each volume for layer-related processing 298 requiring the mask (e.g., during phase unrolling and background phase correction). The z-index of the corresponding forward mapping is adjusted (as shown in reference numeral 300) to disallow inter-layer interpolation during registration, thereby preserving the timing information specific to each layer. The corresponding forward mapping is applied to each corresponding PCI volume of each corrected phase image as shown in reference numeral 304 to generate a registered phase volume 302 and complete the registration process.

[0078] Figure 11 and Figure 12 The diffusion and velocity data acquired simultaneously during a single scan are shown. Figure 11 The top row 306 of the image shows axial, sagittal, and coronal views of the human subject’s brain reconstructed from diffusion fractional anisotropy (FA). Figure 11The bottom row 308 of the image shows axial, sagittal, and coronal views of the human subject's brain reconstructed from diffusion apparent diffusion coefficient (ADC). Figure 12 The top row 310 of the image shows axial, sagittal, and coronal views of the brain of a human subject reconstructed from right-left (RL) velocity. Figure 12 The middle row 312 of the image shows axial, sagittal, and coronal views of the human subject's brain reconstructed from anteroposterior (AP) velocity. Figure 12 The bottom row 314 of the image shows axial, sagittal, and coronal views of the human subject's brain reconstructed from vertical (SI) velocity. Although processed only in a slice-by-slice manner, the axial, sagittal, and coronal views show coherent motion in volume. Figure 11 and Figure 12 This demonstrates that diffusion can be imaged using a single scan through conventional processing pipelines, as well as whole-brain, 4D vector imaging throughout the entire cardiac cycle.

[0079] Figure 13 An example velocity measurement output generated using the disclosed technique is shown. Figure 13 The velocity in thin-walled tissue reconstructed in three dimensions is shown as a percentage of the heart cycle of a single human subject. Figure 13 The diagram shows the three components of the velocity vector throughout the cardiac cycle, shifting the significant peak of cardiac contraction to the end of the cardiac cycle (90%). During cardiac contraction, the motion moves upward and laterally outward from the ventricles. As the cardiac cycle moves into diastole, this motion is reversed, showing a pulsatile movement synchronized with the heart.

[0080] Figure 14 A velocity streamline (e.g., a snapshot thereof) is shown during the cardiac cycle, illustrating motion in thin-walled tissue generated using the disclosed technique. Each time point is approximately 0.1 seconds (meaning movement as low as 5 micrometers is captured (e.g., between 5 and 30 micrometers)). The velocity streamlines provide a view of the velocity map and highlight the changes in both direction and amplitude between phases of the cardiac cycle. The streamline color is based on the total velocity at each location, including out-of-plane velocity.

[0081] The technical advantages of the disclosed subject matter include facilitating research on the assessment, diagnosis, or intervention of traumatic brain injury, neurodegenerative diseases, and brain health by providing imaging of biomarkers previously only visible in animal studies in humans. The technical advantages of the disclosed subject matter also include enabling the measurement of in vivo velocities at VENC levels below 1 mm / s without signal attenuation.

[0082] Referring to the technology presented herein and protected by the claims, and applying it to physical objects and concrete examples of practical nature, which explicitly improves the present art, and is therefore not abstract, intangible, or purely theoretical. Furthermore, if any claim appended to the end of this specification contains one or more elements designated as “component for [performing]…the function” or “step for [performing]…the function,” such elements are intended to be interpreted according to 35 USC 112(f). However, for any claim containing elements designated in any other manner, such elements are not intended to be interpreted according to 35 USC 112(f).

[0083] This written description uses examples to disclose the subject matter of the invention, including best practices, and also enables those skilled in the art to practice the subject matter, including making and using any device or system and performing any included methods. The patent scope of this subject matter is defined by the claims and may include other examples that would occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that are not indistinguishable from the literal language of the claims, or if they include equivalent structural elements that have minor differences from the literal language of the claims.

Claims

1. A computer-implemented method for reconstructing slow-flow velocity measurement data, the computer-implemented method comprising: The diffusion-weighted magnetic resonance imaging (MRI) data of a region of interest in a human subject is acquired via a processing system comprising one or more processors using an MRI scanner that utilizes a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses having both a fixed b-value and a fixed velocity coding value, configured to provide a velocity resolution of less than 1000 micrometers per second. as well as The processing system simultaneously determines the velocity measurement metric and the diffusion tensor metric based on the diffusion-weighted MRI data.

2. The computer-implemented method of claim 1, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor comprises generating an amplitude image and a phase image from the diffusion-weighted MRI data via the processing system.

3. The computer-implemented method according to claim 2, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: Rigid body registration and eddy current correction are performed on the amplitude image via the processing system to create a forward mapping for each PCI volume, wherein the forward mapping is configured to transform the corresponding PCI volume into and out of the registration space, and the forward mapping represents the original position of the corresponding PCI volume; and The processing system applies the corresponding forward mapping to each corresponding PCI volume to generate the registered amplitude volume.

4. The computer-implemented method according to claim 3, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further includes: A tissue mask is created from the registered amplitude volume via the processing system; The processing system uses the tissue mask to generate a reverse mapping for each PCI volume; During phase unwrapping of the composite diffusion-weighted image derived from the diffusion-weighted MRI data, a corresponding inverse mapping is applied to each PCI volume via the processing system to generate the phase image; The processing system performs background phase correction on the phase image to generate a corrected phase image; and The processing system applies the corresponding forward mapping to each corresponding PCI volume of each corrected phase image to generate the registered phase volume.

5. The computer-implemented method according to claim 4, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further comprises: The processing system performs gradient nonlinear correction on the registered amplitude volumes to generate a corrected q-space vector for each PCI volume, wherein the corrected q-space vector for each PCI volume changes both amplitude and direction to account for directional distortion; and The processing system performs linear fitting using the corrected q-space vector in the corresponding q-space sphere of each PCI volume on the registered phase volume to calculate the velocity vector of each PCI volume.

6. The computer-implemented method of claim 5, further comprising acquiring periodic physiological signals from the human subject via the processing system while acquiring the diffusion-weighted MRI data, wherein the diffusion-weighted MRI data is acquired during free-running, non-gated scanning.

7. The computer-implemented method according to claim 6, further comprising: The processing system uses the periodic physiological signals to divide each PCI volume into physiological boxes, wherein each physiological box includes arbitrary grouping of multi-shell q-space vectors; as well as The processing system calculates the velocity vector of the PCI volume in each corresponding physiological chamber to generate a time-analyzed vector within the physiological cycle.

8. A system for reconstructing slow flow velocity measurement data, the system comprising: A memory that encodes processor-executable routines; and A processing system, comprising one or more processors and configured to access the memory and execute processor-executable routines, wherein the processor-executable routines, when executed by the processing system, cause the processing system to: The region of interest in human subjects was acquired using a spin-echo phase-contrast imaging (PCI) sequence MRI scanner, wherein the spin-echo PCI sequence has coded pulses having both a fixed b-value and a fixed velocity coding value, configured to provide a velocity resolution of less than 1000 micrometers per second. and The velocity measurement metric and the metric derived from the diffusion-weighted MRI data are determined simultaneously based on the diffusion tensor.

9. The system of claim 8, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor comprises generating an amplitude image and a phase image from the diffusion-weighted MRI data.

10. The system of claim 9, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further comprises: Rigid body registration and eddy current correction are performed on the amplitude image to create a forward mapping for each PCI volume, wherein the forward mapping is configured to transform the corresponding PCI volume into and out of the registration space, and the forward mapping represents the original position of the corresponding PCI volume; and The corresponding forward mapping is applied to each corresponding PCI volume to generate the registered amplitude volume.

11. The system of claim 10, wherein simultaneously determining the velocity measurement metric and the metric derived from the tensor further comprises: Create an organization mask from the registered amplitude volume; The tissue mask is used to generate an inverse mapping for each PCI volume; During phase unwrapping of the composite diffusion-weighted image derived from the diffusion-weighted MRI data, a corresponding inverse mapping is applied to each PCI volume to generate the phase image; Perform background phase correction on the phase image to generate a corrected phase image; and The corresponding forward mapping is applied to each corresponding PCI volume of each corrected phase image to generate the registered phase volume.

12. The system of claim 11, wherein simultaneously determining the velocity measurement metric and the metric derived from the diffusion tensor further comprises: Gradient nonlinear correction is performed on the registered amplitude volumes to generate a corrected q-space vector for each PCI volume, wherein the corrected q-space vector for each PCI volume is modified in both amplitude and direction to account for directional distortion; and Linear fitting is performed using the corrected q-space vector in the corresponding q-space sphere of each PCI volume on the registered phase volume to calculate the velocity vector of each PCI volume.

13. The system of claim 12, wherein the processor-executable routine, when executed by the processing system, further causes the processing system to acquire periodic physiological signals from the human subject while acquiring the diffusion-weighted MRI data, wherein the diffusion-weighted MRI data is acquired during free-running, non-gated scanning.

14. The system of claim 13, wherein the processor-executable routine, when executed by the processing system, further causes the processing system to: The periodic physiological signals are used to divide each PCI volume into physiological chambers, where each physiological chamber comprises arbitrary groupings of multi-shell q-space vectors; and The velocity vector of the PCI volume in each corresponding physiological chamber is calculated to generate a time-resolved vector within the physiological cycle.

15. A non-transitory computer-readable medium comprising processor-executable code, which, when executed by a processing system comprising one or more processors, causes the processing system to: Diffusion-weighted magnetic resonance imaging (MRI) data of regions of interest in human subjects were acquired using an MRI scanner employing a spin-echo phase-contrast imaging (PCI) sequence, wherein the spin-echo PCI sequence has coded pulses with fixed b-values ​​and fixed velocity coding values, configured to provide velocity resolution of less than 1000 micrometers per second; and The velocity measurement metric and the metric derived from the diffusion-weighted MRI data are determined simultaneously based on the diffusion tensor.

16. The non-transient computer-readable medium of claim 15, wherein the metric derived from simultaneously determining the velocity measurement metric and the diffusion tensor comprises generating an amplitude image and a phase image from the diffusion-weighted MRI data.

17. The non-transient computer-readable medium of claim 16, wherein the metric derived from simultaneously determining the velocity measurement metric and the diffusion tensor further comprises: Rigid body registration and eddy current correction are performed on the amplitude image to create a forward mapping for each PCI volume, wherein the forward mapping is configured to transform the corresponding PCI volume into and out of the registration space, and the forward mapping represents the original position of the corresponding PCI volume; and The corresponding forward mapping is applied to each corresponding PCI volume to generate the registered amplitude volume.

18. The non-transient computer-readable medium of claim 17, wherein the metric derived from simultaneously determining the velocity measurement metric and the diffusion tensor further comprises: Create an organization mask from the registered amplitude volume; The tissue mask is used to generate an inverse mapping for each PCI volume; During phase unwrapping of the composite diffusion-weighted image derived from the diffusion-weighted MRI data, a corresponding inverse mapping is applied to each PCI volume to generate the phase image; Perform background phase correction on the phase image to generate a corrected phase image; and The corresponding forward mapping is applied to each corresponding PCI volume of each corrected phase image to generate the registered phase volume.

19. The non-transient computer-readable medium of claim 18, wherein the metric derived from simultaneously determining the velocity measurement metric and the diffusion tensor further comprises: Gradient nonlinear correction is performed on the registered amplitude volumes to generate a corrected q-space vector for each PCI volume, wherein the corrected q-space vector for each PCI volume is modified in both amplitude and direction to account for directional distortion; and Linear fitting is performed using the corrected q-space vector in the corresponding q-space sphere of each PCI volume on the registered phase volume to calculate the velocity vector of each PCI volume.

20. The non-transitory computer-readable medium of claim 19, wherein the processor-executable code, when executed by a processing system, further causes the processing system to: Periodic physiological signals are acquired from the human subject while the diffusion-weighted MRI data is being acquired, wherein the diffusion-weighted MRI data is acquired during free-running, ungated scanning. The periodic physiological signals are used to divide each PCI volume into physiological chambers, where each physiological chamber comprises arbitrary groupings of multi-shell q-space vectors; and The velocity vector of the PCI volume in each corresponding physiological chamber is calculated to generate a time-resolved vector within the physiological cycle.

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