Method for magnetic resonance scanner simulation

Abstract

A method for performing magnetic resonance (MR) scanner simulation in a magnetic resonance imaging (MRI) simulator is described, the method comprising: inputting data parameters into a web interface of the MRI simulator, wherein the inputted data parameters are at least pulse sequences and an anatomical model; connecting the web interface and a cloud-based simulator engine of the MRI simulator to pass the data parameters to the cloud-based simulator engine, the method comprising: importing a pulse sequence calculation model; setting the inputted data; and slice selection on the obtained images in the web interface; the method further comprising: recomputing the data parameters to provide one or more simulated MR signals, the recomputing being performed in the cloud, and wherein the method further comprises: reconstructing an MR image based on the one or more simulated MR signals, the reconstruction of the MR image being performed in the cloud; and sending the MR image to the web interface.

Description

Technical Field

[0001] This invention relates to a method for simulating magnetic resonance scanners. Summary of the Invention

[0002] This invention relates to a method for simulating a magnetic resonance (MR) scanner in a magnetic resonance imaging (MRI) simulator, the method comprising:

[0003] - Input the data parameters into the network interface of the MRI simulator, wherein the input data parameters are at least pulse sequences and anatomical models;

[0004] - A cloud-based simulator engine that connects a network interface and an MRI simulator to pass data parameters to the cloud-based simulator engine, the method comprising:

[0005] -Import the pulse sequence calculation model;

[0006] - Set the input data; and

[0007] - Select slices from the acquired image in the network interface;

[0008] The method also includes:

[0009] - Recalculate data parameters to provide one or more simulated MR signals; this recalculation is performed in the cloud.

[0010] Furthermore, the method also includes:

[0011] - Reconstructing MR images based on one or more simulated MR signals, the reconstruction of which is performed in the cloud; and

[0012] - Send MR images to the network interface.

[0013] Christos G. Xanthis and Anthony H. Aletras's paper, "CoreMRI: A high-performance, publicly available MR simulation platform on the cloud," published in *PLOS ONE*, discloses a cloud-oriented engine for advanced MRI simulation (coreMRI). The aim of this research is to develop the first advanced MR simulation platform offered as a web service through on-demand, scalable, and cloud-based, as well as GPU-based infrastructure. As described above, this online MR simulation platform can be used as a virtual MRI scanner, but it can also be used as a high-performance cloud-based engine for advanced MR simulations in simulation-based quantitative MR (qMR) methods. In the method used, slicing is also performed to implement the MRI simulation process. It should be noted that the methods proposed in this invention are not disclosed or implied herein.

[0014] This invention provides an improved method for implementing cloud-based MR simulations, for example, for implementing the processes provided herein. The improvements provided by this invention relate to the slicing process, and also, for example, to the system level, how different units interact (CPU, GPU, user interface, etc.). Detailed Implementation

[0015] The following provides and further describes some specific embodiments of the invention.

[0016] According to the present invention, the above method further includes: reconstructing an MR image based on the one or more simulated MR signals, the reconstruction of the MR image being performed in the cloud; and sending the MR image to the network interface.

[0017] As should be understood from the foregoing, this reconstruction is a step based on actual simulation according to the present invention. Reconstruction can be part of the method according to the present invention, but it should also be considered that the method according to the present invention also reflects the case where the original data is a controlled, expected output or a quantitative MR based on simulation.

[0018] According to another embodiment of the invention, the input data parameters are at least a pulse sequence and an anatomical model. Furthermore, other parameters may also be input in the method according to the invention. For example, a general configuration may be specified as such input.

[0019] Furthermore, and as can be understood from the foregoing, the method according to the invention also clearly refers to the interaction of different interfaces and units involved in the platform system according to the invention. In this context, it is noteworthy that the invention clearly refers to providing an analytical or numerical MRI simulation platform for educational purposes, wherein the platform is based on a graphics processing unit (GPU), is cloud-based, and is network-based. Other applications of interest according to the invention also exist, for example, for research and AI purposes.

[0020] Furthermore, in this regard, it can also be said that, according to a specific implementation, the cloud-based simulator engine performs the recalculation and sends the recalculated data to one or more graphics processing units (GPUs) of the MRI simulator, which return the one or more simulated MR signals.

[0021] Furthermore, according to another specific embodiment of the present invention, the MR image reconstruction step is performed by one or more central processing units (CPUs) and / or one or more graphics processing units (GPUs) of the MRI simulator in the cloud.

[0022] In this context, it's also worth noting that the characteristics / specifications of the GPU card determine how the experiment is broken down into smaller parts. Furthermore, there may be GPU resource limits that restrict GPU utilization (such as maximum number of threads, shared memory capacity, maximum registers per thread, register file size, etc.). An optimal GPU card will handle the transfer, host processing, and execution of the entire experiment in a single iteration without breaking the experiment down into smaller parts and without reducing GPU utilization. Such optimal GPU cards achieve the highest GPU utilization when the accumulated resource requirements are equal to the kernel requirements and the GPU's resource capacity. According to the invention, different types of computational tools and software can also be used, which can also be used to encode parts of the method. According to one specific embodiment, at least part of the above recalculation is performed using MATLAB.

[0023] As described above, different forms of input are provided to the system according to the invention, i.e., to enable the above-described method to be performed. One such parameter is the pulse sequence used. Consistent with this, a definition is given, according to a specific embodiment, that a pulse sequence is a sequence of events that alters the proper behavior of each point in space to generate a signal. Similarly, according to the invention, a general configuration can also be set as input. Examples show the type of coordinate system used, and whether it is based on, for example, a 3D or 4D model. Furthermore, the actual anatomical model used is also one such parameter defining the starting point for the system and method according to the invention. In this respect, it can also be noted that the anatomical model can be human or animal. Likewise, phantoms and virtually any other type of object are entirely possible. However, it should be noted that the human or animal anatomical model is the focus for the method and system according to the invention.

[0024] According to the present invention, the steps of the above process are as follows:

[0025] - Input the data parameters into the network interface of the aforementioned MRI simulator; and

[0026] - Connect the network interface and the cloud-based simulator engine of the MRI simulator to pass the data parameters to the cloud-based simulator engine;

[0027] Preferably, the method includes:

[0028] -Import the pulse sequence calculation model;

[0029] - Set the input data; and

[0030] -Slice the obtained image in this network interface.

[0031] In this regard, it should be noted that the pulse sequence calculation model may include several parameters, at least some of which are adjustable. One example of such adjustability is contrast. Furthermore, such slicing is a relevant aspect of the invention. According to one embodiment of the invention, each new slice selection serves to provide a reference for the next slice selection. Further, according to yet another embodiment, the phase encoding direction and the frequency encoding direction each represent an axis orthogonal to the slice selection direction. For example, in given X, Y, and Z coordinates, the above two parameters can be represented by X and Y, respectively.

[0032] Furthermore, according to one implementation scheme, the above-mentioned slice selection is either single slice selection in 2D acquisition or block selection in 3D acquisition.

[0033] Furthermore, continuing from the above, according to yet another specific embodiment of the present invention, the following process is performed:

[0034] - Select slices from the acquired image in the network interface;

[0035] - Obtain a new image;

[0036] - Select new slices in different directions;

[0037] - Obtain a new image; and finally:

[0038] - Perform another slice selection.

[0039] Furthermore, each obtained image is preferably a cross-section of the image from which slices were selected.

[0040] The orientation changes that may be made during slicing in the method according to the invention can, in some cases, help to obtain further improved images.

[0041] Generally, the method according to the invention is well-suited for teaching purposes in the field of MR imaging. As an example, slice planning can be trained very efficiently when using the method according to the invention. Furthermore, the method also allows users to understand the physics behind anatomical models from the perspective of MR imaging. However, it should be noted again that the invention is also suitable for several other applications, such as in research and AI fields.

[0042] Furthermore, since the method according to the invention can use different pulse sequences, computational models, and different types of slicing protocols, it can be used on almost any type of anatomical model, whether human or animal. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of a process according to one embodiment of the method of the present invention.

[0044] Figure 2 A block diagram of a system setup for interacting with the platform and method according to the present invention.

Claims

1. A method for simulating a magnetic resonance MR scanner in a magnetic resonance imaging (MRI) simulator, the method comprising: - Input data parameters into the network interface of the MRI simulator, wherein the input data parameters are at least pulse sequences and anatomical models; - A cloud-based simulator engine connecting the network interface and the MRI simulator to transmit the data parameters to the cloud-based simulator engine, the method comprising: - Import the pulse sequence calculation model to construct the pulse sequence in the simulation; - Set the input data; and -Slice the obtained image in the network interface; The method further includes: - The data parameters are recalculated to provide one or more simulated MR signals, the recalculation being performed in the cloud. And the method described therein also includes: - Reconstructing MR images based on the one or more simulated MR signals, wherein the reconstruction of the MR images is performed in the cloud; and - Send the MR image to the network interface. Wherein, the phase encoding direction and the frequency encoding direction respectively represent an axis orthogonal to the slice selection direction, and The slice selection refers to single slice selection in 2D acquisition or block selection in 3D acquisition.

2. The method according to claim 1, wherein, The cloud-based simulator engine performs the recalculation and sends the recalculated data to one or more graphics processing units (GPUs) of the MRI simulator, which then return the one or more simulated MR signals.

3. The method according to any one of claims 1 to 2, wherein, The MR image reconstruction step is performed by one or more central processing units (CPUs) and / or one or more graphics processing units (GPUs) of the MRI simulator in the cloud.

4. The method according to any one of claims 1 to 2, wherein, The recalculation was performed using at least part of MATLAB.

5. The method according to any one of claims 1 to 2, wherein, A pulse sequence is a sequence of events that alters the behavior of each point in space to generate a signal.

6. The method according to any one of claims 1 to 2, wherein, The purpose of each new slice selection is to provide a reference for the next slice selection.

7. The method according to any one of claims 1 to 2, wherein, Perform the following process: -Slice the obtained image in the network interface; - Obtain a new image; - Select new slices in different directions; - Obtain a new image; and finally: - Perform another slice selection.

8. The method according to claim 7, wherein, Each obtained image is a cross-section of the image from which the slice selection was performed.

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