WO2023093842A1 - 一种肝脏多参数定量成像方法 - Google Patents

一种肝脏多参数定量成像方法 Download PDF

Info

Publication number
WO2023093842A1
WO2023093842A1 PCT/CN2022/134310 CN2022134310W WO2023093842A1 WO 2023093842 A1 WO2023093842 A1 WO 2023093842A1 CN 2022134310 W CN2022134310 W CN 2022134310W WO 2023093842 A1 WO2023093842 A1 WO 2023093842A1
Authority
WO
WIPO (PCT)
Prior art keywords
echo
liver
parameter
quantitative
data
Prior art date
Application number
PCT/CN2022/134310
Other languages
English (en)
French (fr)
Inventor
叶慧慧
张子敬
刘华锋
Original Assignee
浙江大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 浙江大学 filed Critical 浙江大学
Priority to US18/120,988 priority Critical patent/US20230210446A1/en
Publication of WO2023093842A1 publication Critical patent/WO2023093842A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/4244—Evaluating particular parts, e.g. particular organs liver
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/50—NMR imaging systems based on the determination of relaxation times, e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/561—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
    • G01R33/5611—Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/561—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
    • G01R33/5615—Echo train techniques involving acquiring plural, differently encoded, echo signals after one RF excitation, e.g. using gradient refocusing in echo planar imaging [EPI], RF refocusing in rapid acquisition with relaxation enhancement [RARE] or using both RF and gradient refocusing in gradient and spin echo imaging [GRASE]
    • G01R33/5616—Echo train techniques involving acquiring plural, differently encoded, echo signals after one RF excitation, e.g. using gradient refocusing in echo planar imaging [EPI], RF refocusing in rapid acquisition with relaxation enhancement [RARE] or using both RF and gradient refocusing in gradient and spin echo imaging [GRASE] using gradient refocusing, e.g. EPI
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/561—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
    • G01R33/5615—Echo train techniques involving acquiring plural, differently encoded, echo signals after one RF excitation, e.g. using gradient refocusing in echo planar imaging [EPI], RF refocusing in rapid acquisition with relaxation enhancement [RARE] or using both RF and gradient refocusing in gradient and spin echo imaging [GRASE]
    • G01R33/5618—Echo train techniques involving acquiring plural, differently encoded, echo signals after one RF excitation, e.g. using gradient refocusing in echo planar imaging [EPI], RF refocusing in rapid acquisition with relaxation enhancement [RARE] or using both RF and gradient refocusing in gradient and spin echo imaging [GRASE] using both RF and gradient refocusing, e.g. GRASE

Definitions

  • the invention belongs to the technical field of magnetic resonance imaging (MRI) imaging, and in particular relates to a liver multi-parameter quantitative imaging method.
  • MRI magnetic resonance imaging
  • Quantitative magnetic resonance imaging is a rapidly emerging non-invasive imaging method for the diagnosis of multiple liver diseases. For example, it can use T2 quantitative maps to diagnose liver damage characteristics, and use T2 and T2 * quantitative maps to monitor iron content load levels. assessment etc. Multiparametric quantitative magnetic resonance imaging has shown great potential in providing valuable diagnostic and prognostic information by jointly monitoring different pathophysiological properties of diseases.
  • Quantitative mapping imaging of multiple parameters usually involves multiple weighted imaging with different contrasts to capture the signal evolution over time, then the corresponding data scan acquisition time will be very long, which greatly limits its clinical development and research practical value. limits.
  • traditional quantitative imaging methods one typically uses multiple separate data acquisition sequences to obtain quantitative maps of multiple parameters. For example, use the classic spin single echo sequence to acquire spin echo signals at different TEs multiple times to estimate the T2 quantitative map, and use the classic gradient multi-echo sequence to acquire gradient echo signals at different TEs to estimate T2 * Quantitative graph.
  • These multiple distinct scans are often performed sequentially during separate breath-hold sessions, resulting in long scan times in liver quantification experiments, patient fatigue, and potentially misregistered multiparameter quantitation maps.
  • Magnetic resonance fingerprinting is a method that can perform multi-parameter quantitative liver tissue imaging on a single slice with a single breath-hold, but the low coverage of the whole liver in these studies is not efficient, which hinders their practical clinical application. Applications.
  • the present invention proposes a method based on plane echo Liver top-down, bottom-up multiple-shot multi-echo planar imaging (liver-msBUDA-SAGE) for EPI imaging (liver-msBUDA-SAGE).
  • the present invention provides a liver multi-parameter quantitative imaging method, which designs a new magnetic resonance sequence, that is, top-down, bottom-up multiple excitation acquisition and gradient echo, spin echo As well as the multi-echo acquisition method of mixed echoes, the estimated main magnetic field information is merged into the coding matrix at the same time, and the low-rank constraint is used to perform multi-parameter quantitative imaging without distortion.
  • a liver multi-parameter quantitative imaging method comprising the steps of:
  • the tissue parameters include the transverse relaxation time T2 and the effective transverse relaxation time
  • the multi-parameter magnetic resonance data acquisition sequence designed in the step (1) is composed of pulses P1 and P2 of two excitation angles repeatedly interleaved, and the interval between two adjacent pulses P1 is a repetition time (TR), and a TR
  • TR repetition time
  • echoes in which are gradient echo, hybrid echo and spin echo in sequence.
  • the pulse P2 is located between the gradient echo and the hybrid echo; for two adjacent TRs, the echo in one TR adopts A top-down phase-encoding direction, and an echo in the other TR adopts a bottom-up phase-encoding direction.
  • the step (1) generates a multi-parameter magnetic resonance data acquisition sequence using msBUDA and SAGE joint acquisition, wherein the msBUDA method uses the opposite phase encoding directions for the odd-numbered and even-numbered excitations in the multiple excitations to acquire data , so as to obtain the opposite geometrically distorted image, and then estimate the non-uniform main magnetic field B 0 intensity distribution that causes the distortion;
  • the SAGE acquisition method is to design and add multiple echo data with different weights in the planar echo sequence. , including spin echo, gradient echo, and spin-gradient mixed echo, so that three echo signals with different echo times can be obtained in one TR.
  • the image pairs corresponding to two adjacent echoes of the same type are input into the topup software of the nuclear magnetic data processing software FSL, and the non-uniform main magnetic field B that causes image distortion can be estimated . .
  • F s is the undersampled Fourier transform operation of the sth excitation
  • W s is the distortion operation based on the main magnetic field B 0 of the sth excitation
  • N s is the total number of self-defined excitations
  • C is never distorted
  • x s is the reconstructed image corresponding to the s-th excitation
  • d s is the data obtained by scanning and collecting corresponding to the s-th excitation in the k-space data
  • ⁇ ⁇ 2 means 2 Norm
  • ⁇ ⁇ * represents the nuclear norm
  • ⁇ is the weight coefficient
  • x is the set of all xs
  • H(x) is the result of low-rank prior constraints on x.
  • the convex set projection method (POCS) is used to iteratively optimize and solve the image reconstruction model, and the liver images of each cross-section at each echo time are reconstructed one by one, and the data consistency item and the iterative Alternating between low-rank constrained terms, the stopping condition was when the mean squared error reached 0.01% between two consecutive iterations.
  • POCS convex set projection method
  • S(t) is the MRI signal collected at the echo time t
  • T is the echo time of the spin echo
  • S 0 I is the initial signal after 90° pulse excitation
  • S 0 II is the 90° pulse
  • R 2 1/T 2
  • R 2 * 1/T 2 *
  • ⁇ S
  • the numerical range of 0 I /S 0 II is 1 ⁇ 1.82.
  • FIG. 1A is a schematic diagram of a magnetic resonance data acquisition sequence (liver-msBUDA-SAGE) designed in the present invention, including liver msBUDA and SAGE acquisition methods.
  • FIG. 1B is a schematic diagram of a distortion-free multi-contrast echo image reconstruction algorithm based on low-rank constraints designed in the present invention.
  • FIG 2A is a schematic diagram of the comparison results of the T2 and T2 * quantitative mapping parameter maps obtained by the experimental verification on the phantom, and the methods are respectively the traditional acquisition and quantitative imaging method (CMM), and the first experimental verification method of the present invention (BUDA-SAGE 8-shot, Protocol1), the second experimental verification method of the present invention (BUDA-SAGE 4-shot, Protocol2).
  • CMS traditional acquisition and quantitative imaging method
  • BUDA-SAGE 8-shot, Protocol1 the first experimental verification method of the present invention
  • BUDA-SAGE 4-shot, Protocol2 the second experimental verification method of the present invention
  • Fig. 2B is the Bland-Altman statistic chart that the experimental result of the present invention method and the experimental result of traditional method are done consistency analysis and draws, and wherein the first line is Protocol1 and CMM T 2 and T 2 * result consistency analysis figure, the second The second line is the consistency analysis chart of T 2 and T 2 * results between Protocol2 and CMM.
  • Fig. 3A is the undistorted multi-contrast echo image reconstruction result obtained by Protocol1 in the in-vivo experiment.
  • Figure 3B shows the undistorted multi-contrast echo image reconstruction results obtained by Protocol2 in the in-vivo experiment.
  • Figure 4A is a comparison chart of T 2 * quantitative results (CCM vs. Protocol1 vs. Protocol2) of the 3rd, 7th, and 13th slices of the liver, in which 6 regions of interest (ROI) were selected for statistical analysis.
  • Figure 4B is a comparison chart of the quantitative results of T2 in the third, seventh and thirteenth slices of liver (Protocol1 vs. Protocol2).
  • the liver multi-parameter quantitative imaging method of the present invention comprises the following steps:
  • the phase encoding direction in the magnetic resonance data acquisition is designed as top-down and bottom-up alternate excitation acquisition, the magnetic resonance data with the opposite phase encoding direction can be obtained, thus obtaining Images with opposite geometric distortions provide information for subsequent estimation of the intensity distribution of the inhomogeneous main magnetic field that caused the image distortions.
  • liver-msBUDA-SAGE sequence Using the designed liver-msBUDA-SAGE sequence, perform magnetic resonance scanning on the subject.
  • the liver-msBUDA-SAGE data of 4-shots is collected, and the phase encoding directions between different shots are alternately top-down and bottom-up, and the total time of data collection is 19.8s.
  • Magnetic resonance data scans were performed on a healthy volunteer with the consent of the Institutional Review Board.
  • the in-vivo scanning experimental parameter design of the sequence liver-msBUDA-SAGE of the present invention is the same as the above two experimental designs of the phantom (Protocol1 and 2); it should be noted that the volunteers need to hold their breath during the scanning process.
  • a two-dimensional low-resolution parameter-matched gradient echo sequence was also scanned to obtain undistorted coil susceptibility maps for image reconstruction of the present sequence liver-msBUDA-SAGE.
  • F s is the under-sampled Fourier transform operation of the sth excitation
  • W s is the distortion operation of the sth excitation (the non-uniform main magnetic field B 0 can be estimated by FSL topup software)
  • C is the distortion-free
  • the coil susceptibility map estimated from the gradient echo data of , d s is the k-space data of each excitation obtained by scanning acquisition,
  • 2 represents the 2-norm,
  • the model was solved by iterative optimization using the Projection on Convex Sets (POCS) method, which alternated between data consistent items and low-rank constrained items, and the stopping condition was that the mean square error (RMSE) between two consecutive iterations was 0.01% .
  • POCS Projection on Convex Sets
  • Quantitative imaging is performed based on the undistorted multi-contrast image reconstruction results and the dictionary matching method.
  • the magnetic resonance signal dictionary is established by discretizing each parameter value in the equation:
  • S(t) is the MRI signal collected at echo time t
  • T is the echo time of spin echo
  • S 0 I is the initial signal after 90 0 pulse excitation
  • S 0 II is 90 0 pulse
  • the numerical range of ⁇ is 1.00 ⁇ 1.82, and within this range, it is discretized into 100 numerical values for dictionary establishment; for each ⁇ value, the discrete numerical value of T2 is set to [1:1: 50 52:2:150 155:5:250], the discrete value of T 2 * is [1:1:50 52:2:150], based on these discrete values and the above-mentioned Bloch equation, the method applicable to the present invention can be established The magnetic resonance signal dictionary.
  • FIG. 2A shows the comparison results of the T2 and T2 * quantitative mapping parameter maps obtained by the experimental verification on the phantom, the methods are respectively: the traditional acquisition and quantitative imaging method (CMM), the first experiment of the present invention Verification method (BUDA-SAGE 8-shot, Protocol1), the second experimental verification method of the present invention (BUDA-SAGE 4-shot, Protocol2);
  • CMS traditional acquisition and quantitative imaging method
  • BUDA-SAGE 8-shot, Protocol1 the second experimental verification method of the present invention
  • BUDA-SAGE 4-shot, Protocol2 The Bland-Altman statistical chart drawn by the consistency analysis, the first line is the consistency analysis chart of T 2 and T 2 * results between Protocol1 and CMM, and the second line is the consistency analysis chart of T 2 and T 2 * results between Protocol2 and CMM.
  • Figure 3A and Figure 3B respectively show the undistorted multi-contrast echo image reconstruction results obtained by the two experimental schemes Protocol1 and Protocol2 in the in-vivo experiment.
  • the good consistency verifies that the method of the present invention has high robustness for in-vivo data.
  • Figure 4A and Figure 4B are the comparison charts of the quantitative results of T 2 * and T 2 of the 3rd, 7th and 13th slices of liver respectively (CCM vs. Protocol1 vs. Protocol2). Selected 6 regions of interest (ROI) to do statistical analysis, as shown in table 1, this numerical result proves that the method result of the method of the present invention and listed in the literature has good consistency, further from the aspect of quantification The accuracy and robustness of the method of the present invention in in-vivo data are verified.
  • ROI regions of interest
  • T 2 Transverse relaxation time, which refers to the time required for the transverse magnetization vector to decay from 100% to 37%; according to the length of the transverse relaxation time, tissues can be divided into short T 2 tissues (1ms ⁇ T2 ⁇ 10ms), and Long T 2 tissue (10ms ⁇ T 2 ).
  • T 2 * Effective transverse relaxation time, which refers to the time required for the transverse magnetization vector to decay from 100% to 37% in the presence of magnetic field inhomogeneity.
  • the B 0 diagram usually only shows the difference of the magnetic field distribution relative to the main magnetic field.
  • TE echo time (echo time), refers to the time interval between the signal excitation center and the echo center.
  • TR repetition time, which refers to the time interval between two adjacent excitations of the sequence.

Landscapes

  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Medical Informatics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • General Physics & Mathematics (AREA)
  • Gastroenterology & Hepatology (AREA)
  • Signal Processing (AREA)
  • Physiology (AREA)
  • Endocrinology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

一种快速的肝脏多参数定量成像方法，设计了一种新的磁共振序列，即自上而下、自下而上的多次激发采集和梯度回波、自旋回波以及混合回波的多回波采集方式，同时将估计出的主磁场信息合并到编码矩阵中，利用低秩约束来联合重建求解出多个无畸变的回波图像，利用这些回波图像进行字典匹配从而得到定量的T 2和T 2 *参数映射图。结合该方法在水膜和人体数据实验中的表现，通过与传统定量成像方法相比较，证明了该方法能够在较快的时间内实现与传统金标准方法一致的定量成像结果，这对于肝脏损伤特性的诊断、铁成分负荷水平的评估、癌症病灶的检测等具有重要的实际应用价值。

Description

一种肝脏多参数定量成像方法 技术领域
本发明属于磁共振成像(MRI)成像技术领域，具体涉及一种肝脏多参数定量成像方法。
背景技术
定量磁共振成像是一种快速兴起的诊断多发性肝脏疾病的无创的成像方式，例如它可利用T 2定量图进行肝脏损伤特性诊断、利用T 2和T 2 *定量图进行铁成分负荷水平的评估等。多参数定量磁共振成像通过联合监测疾病的不同病理生理特性，已在提供有价值的诊断和预后信息方面表现出巨大的潜力。
多个参数的定量映射成像通常涉及多个不同对比度的加权成像来捕获信号随时间的演变，那么相应的数据扫描采集时间就会很长，这使得它的临床发展和研究实用价值受到了极大的限制。在传统的定量成像方法中，人们通常使用多个分开的数据采集序列来获得多个参数的定量图。例如，使用经典自旋单回波序列来多次采集不同TE下的自旋回波信号来估计T 2定量图、使用经典梯度多回波序列来采集不同TE下的梯度回波信号来估计T 2 *定量图。这些多个不同的扫描通常在分开的屏气过程中按顺序进行，故而导致肝脏定量实验中的扫描时间过长，患者疲劳，并且可能得到错误配准的多参数定量图。
基于此，快速的同时多参数肝脏定量成像是一个亟需解决的问题。磁共振指纹成像是一种可以在单次屏息的情况下对单个层面进行多参数的肝脏组织定量成像的方法，但是这些研究的整个肝脏覆盖率低扫描效率不高，阻碍了它们在实际临床上的应用。
发明内容
为了能够在单次屏气的情况下实现对全肝同时进行快速的多参数T 2和T 2 *定量成像，并得到适用于临床应用，发挥实用研究价值的解决方案，本发明提出 了基于平面回波成像(EPI)的肝脏自上而下、自下而上的多次激发的多回波平面成像技术(liver-msBUDA-SAGE)。
具体地，本发明提供了一种肝脏多参数定量成像方法，其设计了一种新的磁共振序列，即自上而下、自下而上的多次激发采集和梯度回波、自旋回波以及混合回波的多回波采集方式，同时将估计出的主磁场信息合并到编码矩阵中，利用低秩约束来进行无畸变的多参数定量成像。
一种肝脏多参数定量成像方法，包括如下步骤：
(1)针对所需定量的组织参数设计并生成基于平面回波的多参数磁共振数据采集序列，所述组织参数包括横向驰豫时间T 2和有效横向驰豫时间
Figure PCTCN2022134310-appb-000001
(2)将上述多参数磁共振数据采集序列导入磁共振扫描仪，在一次屏气过程中利用扫描仪对被试者肝脏部位进行扫描，获得每一回波时间下的多横切面原始肝脏k空间数据；
(3)利用经典并行成像算法(SENSE或MUSSELS)对多横切面原始肝脏k空间数据进行重建，得到每一回波时间下对应的多横切面肝脏图像；
(4)利用具有相反相位编码方向的回波对应的所有图像对估计出引起图像畸变的非均匀的主磁场B 0，将B 0合并到编码矩阵中并加入低秩矩阵约束以构建图像重建模型，通过对模型优化求解重建出各回波时间下对应的多横切面肝脏图像；
(5)给定所需定量的组织参数的动态变化范围以及离散化步长，基于Bloch方程建立反映回波信号时序变化的字典；
(6)将步骤(4)重建得到的多横切面肝脏图像的每一像素点的时序变化信号与字典中的回波信号逐一进行匹配，从而为每一像素点索引得到具体的组织参数值，进而得到所需组织参数的定量图像。
进一步地，所述步骤(1)设计的多参数磁共振数据采集序列由两种激发角度的脉冲P1和P2重复交错组成，相邻两个脉冲P1的间隔为一个重复时间(TR)，一个TR中包含有三个回波，依次为梯度回波、混合回波以及自旋回波，脉冲P2位于梯度回波与混合回波之间；对于相邻两个TR，其中一个TR中的回波采用自上而下的相位编码方向，另一个TR中的回波采用自下而上的相位编码方向。
进一步地，所述步骤(1)生成多参数磁共振数据采集序列采用msBUDA和 SAGE联合采集的方式，其中msBUDA方法将多次激发中的奇数次和偶数次激发用相反的相位编码方向来采集数据，从而得到相反的几何畸变图像，进而估计出引起其发生畸变的非均匀的主磁场B 0强度分布；SAGE采集方法则是在平面回波序列中设计增加多个不同加权的回波数据读出，包括自旋回波、梯度回波以及自旋-梯度混合回波，由此在一个TR中获得三个不同回波时间的回波信号。
进一步地，所述步骤(4)中将两个相邻同类型回波对应的图像对输入到核磁数据处理软件FSL的topup软件中，即可估计出引起图像畸变的非均匀的主磁场B 0。
进一步地，所述图像重建模型的表达式如下：
Figure PCTCN2022134310-appb-000002
其中：F s为第s次激发的欠采样傅里叶变换操作，W s为基于主磁场B 0的第s次激发的畸变操作，N s为自定义的总激发次数，C为从无畸变的梯度回波数据中估计得到的线圈磁敏感图，x s为对应第s次激发的重建图像，d s为k空间数据中对应第s次激发所扫描采集得到的数据，‖ ‖ 2表示2范数，‖ ‖ *表示核范数，λ为权重系数，x为所有xs组成的集合，H(x)为对x进行低秩先验约束得到的结果。
进一步地，所述步骤(4)中采用凸集投影方法(POCS)对图像重建模型进行迭代优化求解，逐一重建出每一回波时间下每一横切面的肝脏图像，迭代在数据一致项和低秩约束项之间交替，停止条件为两次连续迭代之间的均方误差达到0.01％。
进一步地，所述Bloch方程的表达式如下：
Figure PCTCN2022134310-appb-000003
其中：S(t)为在回波时间t下采集到的MRI信号，T为自旋回波的回波时间，S 0 I为90°脉冲激发后的初始信号，S 0 II为90°脉冲和180°脉冲激发后的叠加初始信号，R 2＝1/T 2，R 2 *＝1/T 2 *；建立字典过程中，在给定组织参数动态变化范围的同时，需给定δ＝S 0 I/S 0 II的数字范围为1～1.82。
本发明设计了一种基于EPI的自上而下、自下而上的多次激发采集和同时多回波采集的序列，将所得到的blip up/down图像对输入到FSL的topup软件中，从而估计出引起图像畸变的主磁场B 0，并将其合并到编码矩阵中利用低秩约束来联合重建求解出多个无畸变的回波图像，利用这些回波图像进行字典匹配从而得到定量的T 2(＝1/R 2)和T 2 *(＝1/R 2 *)参数映射图。结合本发明在水膜和人体数据实验中的表现，通过与传统定量成像方法相比较，证明了本发明能够在较快的时间内(约20秒，一次屏气)实现与传统金标准方法一致的定量成像结果，这对于肝脏损伤特性的诊断、铁成分负荷水平的评估、癌症病灶的检测等具有重要的实际应用价值。
附图说明
图1A为本发明所设计的磁共振数据采集序列(liver-msBUDA-SAGE)示意图，包括肝脏的msBUDA和SAGE采集方法。
图1B为本发明所设计的基于低秩约束的无畸变的多对比度回波图像重建算法示意图。
图2A为在phantom上实验验证所得到的T 2和T 2 *定量映射参数图的对比结果示意图，其方法分别为传统的采集和定量成像方法(CMM)、本发明的第一种实验验证方法(BUDA-SAGE 8-shot,Protocol1)、本发明的第二种实验验证方法(BUDA-SAGE 4-shot,Protocol2)。
图2B为将本发明方法的实验结果与传统方法的实验结果做一致性分析所绘制的Bland-Altman统计图，其中第一行为Protocol1与CMM的T 2和T 2 *结果一致性分析图，第二行为Protocol2与CMM的T 2和T 2 *结果一致性分析图。
图3A为Protocol1在in-vivo实验中所得到的无畸变的多对比度的回波图像重建结果。
图3B为Protocol2在in-vivo实验中所得到的无畸变的多对比度的回波图像重建结果。
图4A为肝脏第3片、第7片、第13片的T 2*的定量结果对比图(CCM vs.Protocol1 vs.Protocol2)，其中选取了6个感兴趣区域(ROI)来做统计分析。
图4B为肝脏第3片、第7片、第13片的T 2的定量结果对比图(Protocol1 vs. Protocol2)。
具体实施方式
为了更为具体地描述本发明，下面结合附图及具体实施方式对本发明的技术方案进行详细说明。
本发明肝脏多参数定量成像方法，包括如下步骤：
S1.msBUDA采集方法的序列设计。
如图1A所示，将磁共振数据采集中的相位编码方向设计为自上而下和自下而上这种交替激发采集的方式，可以得到具有相反的相位编码方向的磁共振数据，从而得到具有相反的几何畸变的图像，为后续估计引发图像畸变的非均匀的主磁场的强度分布提供信息。
S2.SAGE采集方法的序列设计。
如图1A所示，在基于EPI快速成像的序列中，设计添加多个不同加权的回波数据读出，从而得到具有不同对比度的图像信息，其中包括自旋回波、梯度回波以及自旋-梯度混合回波，为后续进行多参数定量成像提供信息。
S3.利用所设计的liver-msBUDA-SAGE序列，对被试进行磁共振扫描。
在该过程中，要求人体被试屏住呼吸，然后快速采集在不同TE下的多个不同加权的多对比度全肝图像的原始k空间数据。在扫描实验中，设计了两种不同的实验方案(Protocol1和Protocol2)来验证本发明的结果鲁棒性，并且实现了传统的单个参数的定量成像扫描以用作结果准确性的验证。
3.1 Phantom实验验证：
Protocol1：平面内分辨率＝1.5×1.5mm 2，层厚＝5mm，重建矩阵＝330×220×80mm 3，16片(层)，欠采样因子＝4，局部傅里叶因子＝75％，TR＝2.2s，TE时间为：12，28，48，70，80，102ms。采集4-shots的liver-msBUDA-SAGE数据，不同shot之间的相位编码方向交替自上而下和自下而上，数据采集的总时间为19.8s。
Protocol2：其具有与Protocol1相同的参数设置，除了欠采样因子＝8，局部傅里叶因子＝100％，TR＝1.8s，TE时间为：12，28，40，56，72，88ms，数据采集时间为30.6s。
CMM(传统采集和定量成像方法)：(1)为了估计T 2 *定量参数图，一个二维的多回波的梯度回波序列被扫描，具体参数设置为：分辨率＝1.5×1.5×5mm 3，10片，重建矩阵＝192×192×50mm 3，TR＝2.5s，TE时间为：3.55、8.30、13.05、17.80、22.55、27.30、32.05、36.80、41.55、46.30ms，数据采集时间为2分钟40s。(2)为了估计T 2定量参数图，6个多层单回波的自旋回波序列被扫描，具体参数设置为：分辨率＝1.5×1.5×5mm 3，10片，重建矩阵＝192×192×50mm 3，TR＝2.5s，TE时间为：10、30、50、70、90、110ms，数据采集时间为10.5分钟。
3.2 In-vivo实验验证：
在Institutional Review Board的同意批准下，对一名健康的志愿者进行了磁共振数据扫描。
本发明序列liver-msBUDA-SAGE的in-vivo扫描实验参数设计与上述phantom的两种实验设计相同(Protocol1和2)；需要注意的是，在志愿者被扫描的过程中，需要屏住呼吸。
In-vivo的CMM扫描方法设计为：为了获得T 2 *的定量参数估计图，一个一个二维的多回波的梯度回波序列被扫描，具体参数设置为：分辨率＝1.5×1.5×5mm 3，16片，重建矩阵＝336×216×80mm 3，TR＝286ms，TE时间为：3.02、6.67、10.32、13.97、17.62、21.27、24.92、28.57、32.22ms，数据总的采集时间为32.6s。需要注意的是分两次进行的数据扫描，每次时间为16.3s，并且需要被试者屏住呼吸。
一个二维的低分辨率的参数匹配的梯度回波序列也被扫描以获取无畸变的线圈磁敏感图用于本发明序列liver-msBUDA-SAGE的图像重建中。
S4.利用所获取的k空间数据，进行无畸变图像重建。
如图1B所示，基于S3的实验设计中所获得的原始k空间数据，首先利用经典的并行成像算法(MUSSELS)进行blip up/down(自上而下/自下而上)图像对的分别重建；然后将所有图像对输入到FSL topup软件中来估计引起图像畸变的非均匀的主磁场B 0，最后将B 0的信息合并到系统编码矩阵中，并加入低秩矩阵的正则化约束进行联合重建求解。本发明所设计对于肝脏的联合重建模型如下：
Figure PCTCN2022134310-appb-000004
其中：F s为第s次激发的欠采样傅里叶变换操作，W s是第s次激发的畸变操作(可通过FSL topup软件估计得到非均匀的主磁场B 0)，C是从无畸变的梯度回波数据中估计得到的线圈磁敏感图，d s是扫描采集得到的每次激发的k空间数据，|| || 2表示2范数，|| || *表示核范数，λ为权重系数，||H(x)|| *是对blip up/down数据进行低秩先验约束。
采用凸集投影(POCS)方法对该模型进行迭代优化求解，该迭代在数据一致项和低秩约束项之间交替，停止条件为两次连续迭代之间的均方误差(RMSE)为0.01％。
S5.基于无畸变的多对比度图像重建结果和字典匹配方法，进行定量成像。
首先，基于如下Bloch方程，通过离散化方程中的各个参数值，建立磁共振信号字典：
Figure PCTCN2022134310-appb-000005
其中：S(t)为在回波时间t下采集到的MRI信号，T是自旋回波的回波时间，S 0 I为90 0脉冲激发后的初始信号，S 0 II为90 0脉冲和180 0脉冲激发后的叠加初始信号，R 2(＝1/T 2)为横向弛豫时间的倒数，R 2 *(＝1/T 2 *)为有效横向弛豫时间的倒数。三个回波的t的大小分别为自旋回波的t＝T、梯度回波的t<T以及自旋-梯度混合回波的t>T。
字典的具体建立方式为：首先基于上述Bloch方程，将在S4中不同TE下的回波图像结果输入方程中，进行最小二乘求解，从而得到S 0 I，S 0 II，R 2(＝1/T 2)和R 2 *(＝1/T 2 *)的估计值；然后可以得到常量比值参数δ＝S 0 I/S 0 II，接下来可基于这三个参数δ，R 2和R 2 *进行字典建立。基于三参数迭代求解结果，δ的数值范围为1.00～1.82，在该范围内将其离散为100个数值用于字典建立；对于每一个δ值，设置T 2的离散数值为[1:1:50 52:2:150 155:5:250]，T 2 *的离散数值为[1:1:50 52:2:150]，基于这些离散数值和上述Bloch方程，即可建立适用于本发明方法的磁共振信号字典。
将上述S4中所重建得到的无畸变的各个磁共振回波图像与所建字典进行模式匹配，从而可得到T 2(＝1/R 2)和T 2 *(＝1/R 2 *)定量参数映射图。
以下是对phantom和in-vivo进行实验从而验证本发明方法结果的准确性和鲁棒性。图2A所示为在phantom上实验验证所得到的T 2和T 2 *定量映射参数图的对比结果，其方法分别为：传统的采集和定量成像方法(CMM)、本发明的第一种实验验证方法(BUDA-SAGE 8-shot,Protocol1)、本发明的第二种实验验证方法(BUDA-SAGE 4-shot,Protocol2)；图2B为将本发明方法的实验结果与传统方法的实验结果做一致性分析所绘制的Bland-Altman统计图，第一行为Protocol1与CMM的T 2和T 2 *结果一致性分析图，第二行为Protocol2与CMM的T 2和T 2 *结果一致性分析图。综合定性和定量评价总体来说，本发明方法的两种实验设计结果均与传统方法的结果在一致性区间内，同时验证了对于phantom实验结果，本发明方法具有良好的准确性和鲁棒性。
图3A和图3B分别为两中实验方案Protocol1和Protocol2在in-vivo实验中所得到的无畸变的多对比度的回波图像重建结果，这两种实验设计在in-vivo数据结果中表现出了良好的一致性，验证了本发明方法针对于in-vivo数据具有较高的鲁棒性。图4A和图4B分别为肝脏第3片、第7片、第13片的T 2 *和T 2的定量结果对比图(CCM vs.Protocol1 vs.Protocol2)。选取了6个感兴趣区域(ROI)来做统计分析，如表1所示，该数值结果证明了本发明的方法与文献中所列出的方法结果具有良好的一致性，进一步从量化的方面验证了本发明方法在in-vivo数据中的准确性和鲁棒性。
上述实验均在西门子Prisma扫描仪上完成，实施例中参数定义如下：
T 2：横向弛豫时间，指横向磁化矢量从100％衰减至37％所需的时间；根据横向弛豫时间的长短，可以将组织划分为短T 2组织(1ms<T2≤10ms)，和长T 2组织(10ms<T 2)。
T 2 *：有效横向弛豫时间，指存在磁场不均匀性的情况下，横向磁化矢量从100％衰减至37％所需的时间。
B 0：主磁场强度，B 0图中通常仅显示磁场分布相对于主磁场的差异。
TE：回波时间(echo time)，指信号激发中心到回波中心之间的时间间隔。
TR：重复时间(repetition time)，指序列两次相邻激发之间的时间间隔。
表1
Figure PCTCN2022134310-appb-000006
上述对实施例的描述是为便于本技术领域的普通技术人员能理解和应用本发明。熟悉本领域技术的人员显然可以容易地对上述实施例做出各种修改，并把在此说明的一般原理应用到其他实施例中而不必经过创造性的劳动。因此，本发明不限于上述实施例，本领域技术人员根据本发明的揭示，对于本发明做出的改进和修改都应该在本发明的保护范围之内。

Claims (7)

  1. 一种肝脏多参数定量成像方法，包括如下步骤：
    (1)针对所需定量的组织参数设计并生成基于平面回波的多参数磁共振数据采集序列，所述组织参数包括横向驰豫时间T 2和有效横向驰豫时间
    Figure PCTCN2022134310-appb-100001
    (2)将上述多参数磁共振数据采集序列导入磁共振扫描仪，在一次屏气过程中利用扫描仪对被试者肝脏部位进行扫描，获得每一回波时间下的多横切面原始肝脏k空间数据；
    (3)利用经典并行成像算法对多横切面原始肝脏k空间数据进行重建，得到每一回波时间下对应的多横切面肝脏图像；
    (4)利用具有相反相位编码方向的回波对应的所有图像对估计出引起图像畸变的非均匀的主磁场B 0，将主磁场B 0合并到编码矩阵中并加入低秩矩阵约束以构建图像重建模型，通过对模型优化求解重建出各回波时间下对应的多横切面肝脏图像；
    (5)给定所需定量的组织参数的动态变化范围以及离散化步长，基于Bloch方程建立反映回波信号时序变化的字典；
    (6)将步骤(4)重建得到的多横切面肝脏图像的每一像素点的时序变化信号与字典中的回波信号逐一进行匹配，从而为每一像素点索引得到具体的组织参数值，进而得到所需组织参数的定量图像。
  2. 根据权利要求1所述的肝脏多参数定量成像方法，其特征在于：所述步骤(1)设计的多参数磁共振数据采集序列由两种激发角度的脉冲P1和P2重复交错组成，相邻两个脉冲P1的间隔为一个重复时间(TR)，一个TR中包含有三个回波，依次为梯度回波、混合回波以及自旋回波，脉冲P2位于梯度回波与混合回波之间；对于相邻两个TR，其中一个TR中的回波采用自上而下的相位编码方向，另一个TR中的回波采用自下而上的相位编码方向。
  3. 根据权利要求1所述的肝脏多参数定量成像方法，其特征在于：所述步骤(1)生成多参数磁共振数据采集序列采用msBUDA和SAGE联合采集的方式，其中msBUDA方法将多次激发中的奇数次和偶数次激发用相反的相位编码方向来采集数据，从而得到相反的几何畸变图像，进而估计出引起其发生畸变的非均 匀的主磁场B 0强度分布；SAGE采集方法则是在平面回波序列中设计增加多个不同加权的回波数据读出，包括自旋回波、梯度回波以及自旋-梯度混合回波，由此在一个TR中获得三个不同回波时间的回波信号。
  4. 根据权利要求1所述的肝脏多参数定量成像方法，其特征在于：所述步骤(4)中将具有相反相位编码方向的回波对应的所有图像对输入到核磁数据处理软件FSL的topup软件中，即可估计出引起图像畸变的非均匀的主磁场B 0。
  5. 根据权利要求1所述的肝脏多参数定量成像方法，其特征在于：所述图像重建模型的表达式如下：
    Figure PCTCN2022134310-appb-100002
    其中：F s为第s次激发的欠采样傅里叶变换操作，W s为基于主磁场B 0的第s次激发的畸变操作，N s为自定义的总激发次数，C为从无畸变的梯度回波数据中估计得到的线圈磁敏感图，x s为对应第s次激发的重建图像，d s为k空间数据中对应第s次激发所扫描采集得到的数据，‖ ‖ 2表示2范数，‖ ‖ *表示核范数，λ为权重系数，x为所有x s组成的集合，H(x)为对x进行低秩先验约束得到的结果。
  6. 根据权利要求1所述的肝脏多参数定量成像方法，其特征在于：所述步骤(4)中采用凸集投影方法对图像重建模型进行迭代优化求解，逐一重建出每一回波时间下每一横切面的肝脏图像，迭代在数据一致项和低秩约束项之间交替，停止条件为两次连续迭代之间的均方误差达到0.01％。
  7. 根据权利要求1所述的肝脏多参数定量成像方法，其特征在于：所述Bloch方程的表达式如下：
    Figure PCTCN2022134310-appb-100003
    其中：S(t)为在回波时间t下采集到的MRI信号，T为自旋回波的回波时间，S 0 I为90°脉冲激发后的初始信号，S 0 II为90°脉冲和180°脉冲激发后的叠加初始信号，R 2＝1/T 2，R 2 *＝1/T 2 *；建立字典过程中，在给定组织参数动态变化范围的同时，需给定δ＝S 0 I/S 0 II的数字范围为1～1.82。
PCT/CN2022/134310 2021-11-29 2022-11-25 一种肝脏多参数定量成像方法 WO2023093842A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US18/120,988 US20230210446A1 (en) 2021-11-29 2023-03-13 Method for simultaneous multiple magnetic resonance parameter mapping of liver

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111447712.4 2021-11-29
CN202111447712.4A CN114217255B (zh) 2021-11-29 2021-11-29 一种快速的肝脏多参数定量成像方法

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/120,988 Continuation-In-Part US20230210446A1 (en) 2021-11-29 2023-03-13 Method for simultaneous multiple magnetic resonance parameter mapping of liver

Publications (1)

Publication Number Publication Date
WO2023093842A1 true WO2023093842A1 (zh) 2023-06-01

Family

ID=80699179

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/134310 WO2023093842A1 (zh) 2021-11-29 2022-11-25 一种肝脏多参数定量成像方法

Country Status (3)

Country Link
US (1) US20230210446A1 (zh)
CN (1) CN114217255B (zh)
WO (1) WO2023093842A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114217255B (zh) * 2021-11-29 2022-09-20 浙江大学 一种快速的肝脏多参数定量成像方法
CN115137347B (zh) * 2022-05-10 2023-05-26 浙江大学 三维超短回波时间磁共振指纹成像的髓鞘定量成像方法
CN116299108A (zh) * 2023-03-27 2023-06-23 广东省人民医院 基于3d-buda采集和联合低秩约束重建的mr成像方法及系统

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160291107A1 (en) * 2015-04-02 2016-10-06 The General Hospital Corporation 3D Balanced EPI Magnetic Resonance Fingerprinting
US20180031667A1 (en) * 2016-07-26 2018-02-01 The General Hospital Corporation System and method for magnetic resonance fingerprinting in the presence of inhomogeneous magnetic fields
CN110133554A (zh) * 2018-02-08 2019-08-16 深圳先进技术研究院 一种基于分数阶模型的磁共振指纹成像方法、装置及介质
CN110133553A (zh) * 2019-05-10 2019-08-16 浙江大学 一种超短回波时间磁共振指纹弛豫时间测量方法
CN111090069A (zh) * 2019-11-21 2020-05-01 深圳先进技术研究院 定量磁共振成像参数确定方法、装置、设备及存储介质
CN111537931A (zh) * 2020-04-28 2020-08-14 深圳先进技术研究院 快速磁共振多参数成像方法和装置
CN111685764A (zh) * 2020-05-13 2020-09-22 复旦大学 一种基于磁共振指纹成像的快速定量T1ρ的方法
CN112617796A (zh) * 2020-12-30 2021-04-09 深圳市联影高端医疗装备创新研究院 磁共振功能成像方法、装置、设备、存储介质及系统
CN112819949A (zh) * 2021-02-07 2021-05-18 哈尔滨工业大学 一种基于结构化低秩矩阵的磁共振指纹图像重建方法
CN113129401A (zh) * 2021-03-22 2021-07-16 厦门大学 一种参数化磁共振成像的图像重建方法
CN113391250A (zh) * 2021-07-09 2021-09-14 清华大学 组织属性的多参数定量测试系统及其方法
CN114217255A (zh) * 2021-11-29 2022-03-22 浙江大学 一种快速的肝脏多参数定量成像方法

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105232046B (zh) * 2015-11-26 2017-12-29 厦门大学 一种基于重叠回波的单扫描定量磁共振t2成像方法
CN105785298B (zh) * 2016-03-10 2018-08-17 大连锐谱科技有限责任公司 一种高精度三维化学位移成像方法
EP3531154A1 (en) * 2018-02-22 2019-08-28 Koninklijke Philips N.V. Dixon mr imaging using a multi-gradient-echo sequence
CN108663644B (zh) * 2018-03-14 2020-02-14 厦门大学 基于残差网络重建的单扫描定量磁共振t2*成像方法
US10799183B2 (en) * 2018-11-07 2020-10-13 General Electric Company Methods and systems for whole body imaging
CN110780247B (zh) * 2019-11-12 2021-02-12 无锡鸣石峻致医疗科技有限公司 一种基于磁共振原理的器官脂肪无创定量检测方法
CN111881887A (zh) * 2020-08-21 2020-11-03 董秀园 基于多摄像头的运动姿态监测和指导方法及装置
CN112150568A (zh) * 2020-09-16 2020-12-29 浙江大学 基于Transformer模型的磁共振指纹成像重建方法
CN113406544B (zh) * 2021-06-18 2023-04-25 中国科学院电工研究所 一种人体生物组织的磁共振电磁特性参数成像方法和装置

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160291107A1 (en) * 2015-04-02 2016-10-06 The General Hospital Corporation 3D Balanced EPI Magnetic Resonance Fingerprinting
US20180031667A1 (en) * 2016-07-26 2018-02-01 The General Hospital Corporation System and method for magnetic resonance fingerprinting in the presence of inhomogeneous magnetic fields
CN110133554A (zh) * 2018-02-08 2019-08-16 深圳先进技术研究院 一种基于分数阶模型的磁共振指纹成像方法、装置及介质
CN110133553A (zh) * 2019-05-10 2019-08-16 浙江大学 一种超短回波时间磁共振指纹弛豫时间测量方法
CN111090069A (zh) * 2019-11-21 2020-05-01 深圳先进技术研究院 定量磁共振成像参数确定方法、装置、设备及存储介质
CN111537931A (zh) * 2020-04-28 2020-08-14 深圳先进技术研究院 快速磁共振多参数成像方法和装置
CN111685764A (zh) * 2020-05-13 2020-09-22 复旦大学 一种基于磁共振指纹成像的快速定量T1ρ的方法
CN112617796A (zh) * 2020-12-30 2021-04-09 深圳市联影高端医疗装备创新研究院 磁共振功能成像方法、装置、设备、存储介质及系统
CN112819949A (zh) * 2021-02-07 2021-05-18 哈尔滨工业大学 一种基于结构化低秩矩阵的磁共振指纹图像重建方法
CN113129401A (zh) * 2021-03-22 2021-07-16 厦门大学 一种参数化磁共振成像的图像重建方法
CN113391250A (zh) * 2021-07-09 2021-09-14 清华大学 组织属性的多参数定量测试系统及其方法
CN114217255A (zh) * 2021-11-29 2022-03-22 浙江大学 一种快速的肝脏多参数定量成像方法

Also Published As

Publication number Publication date
CN114217255A (zh) 2022-03-22
CN114217255B (zh) 2022-09-20
US20230210446A1 (en) 2023-07-06

Similar Documents

Publication Publication Date Title
WO2023093842A1 (zh) 一种肝脏多参数定量成像方法
Ueda et al. Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice
Jaubert et al. Multi‐parametric liver tissue characterization using MR fingerprinting: simultaneous T1, T2, T2*, and fat fraction mapping
Ben‐Eliezer et al. Rapid and accurate T2 mapping from multi–spin‐echo data using Bloch‐simulation‐based reconstruction
US8842896B2 (en) Imaging of moving objects
Küstner et al. MR image reconstruction using a combination of compressed sensing and partial Fourier acquisition: ESPReSSo
CN106574954A (zh) 针对epi的具有奈奎斯特伪影校正的并行mr成像
CN105814449B (zh) 具有水/脂肪分离的零回波时间mr成像
Sotiropoulos et al. RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI
CN105143906A (zh) 金属抗mr成像
CN116774121A (zh) 磁共振成像系统和方法
CN106646300B (zh) 一种超极化气体磁共振影像处理方法
Cheng et al. Compressed sensing: From research to clinical practice with data-driven learning
Wang et al. Parcel: physics-based unsupervised contrastive representation learning for multi-coil mr imaging
Chao et al. Fast diffusion imaging with high angular resolution
Wang et al. High‐fidelity direct contrast synthesis from magnetic resonance fingerprinting
Smith et al. Potential of compressed sensing in quantitative MR imaging of cancer
Kathiravan et al. A review of magnetic resonance imaging techniques
Murata et al. Effect of hybrid of compressed sensing and parallel imaging on the quantitative values measured by 3D quantitative synthetic MRI: A phantom study
CN113391250B (zh) 组织属性的多参数定量测试系统及其方法
JP7230149B2 (ja) 磁気共鳴イメージングで得た画像の処理方法、画像処理プロブラム、及び、計算機
Wang et al. High-fidelity direct contrast synthesis from magnetic resonance fingerprinting
He et al. Compressed sensing-based simultaneous recovery of magnitude and phase MR images via dual trigonometric sparsity
Vaish et al. TL-HARDI: transform learning based accelerated reconstruction of HARDI data
Zhu et al. Sparse precontrast T1 mapping for high‐resolution whole‐brain DCE‐MRI

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22897924

Country of ref document: EP

Kind code of ref document: A1