定量磁化率成像用于帕金森病研究进展

帕金森病(PD)是以黑质多巴胺能神经元损伤及铁沉积为病理特征的神经退行性疾病。定量磁化率成像(QSM)是根据相位信息定量评估组织中铁含量的MR技术，对铁沉积较传统梯度回波成像更为敏感。本文就QSM用于PD的研究进展进行综述。     

联合酰胺质子转移成像和定量磁化率成像在帕金森病诊断中的应用价值

目的 探讨酰胺质子转移成像(APT)和定量磁化率成像(QSM)对帕金森病（PD）诊断的临床应用价值。方法 纳入2022年6月～2023年6月新疆医科大学第二附属医院明确诊断为PD的患者38例作为PD组,另招募同时期相匹配的健康志愿者22例作为对照（HC）组。对所有受试者进行QSM和APT序列扫描,利用后处理软件获取所有受试者黑质区域的磁化率值（MSV）及非对称性磁化转移率（MTRasym）,利用Logistic回归建模两种参数联合诊断时的预测概率,采用ROC曲线比较分析单一成像技术及两种成像技术联合的诊断效能。结果 对比HC组,PD组双侧黑质的平均MSV值升高,平均MTRasym值减低,差异有统计学意义(P<0.001)。运动症状受影响较重侧黑质的MSV值高于受影响较轻侧（P<0.001）,运动症状受影响较重侧黑质的MTRasym值低于受影响较轻侧(P<0.05)。使用双侧黑质MSV和MTRasym的平均值,APT、QSM以及QSM联合APT的ROC曲线下面积分别为0.812、0.873、0.897,使用运动症状受影响较重侧黑质的MSV和MTRasym值,QSM联合A...     

定量磁敏感加权成像在急性缺血性脑卒中中的应用

磁敏感加权成像(susceptibility weighted imaging,SWI)是一种利用组织磁敏感性不同而成像的技术,对缺氧缺血及颅内矿物质沉积非常敏感,已被广泛应用于急性缺血性脑卒中的诊断,但SWI不能对磁化率进行定量测定,随着定量磁敏感图(quantitative susceptibility mapping,QSM)的发展,这一缺陷逐步被弥补。QSM是基于梯度回波磁共振相位数据的一种新型的可以非侵入性地评估体内磁性组织之间磁敏感性差异的技术,它通过测量磁性物质的磁化率值来实现对体内磁性物质的定量,目前它在量化体内铁含量、钙化及静脉氧饱和度变化等方面已有了多种多样的应用。本文将就定量磁敏感加权成像的基本原理及其在急性缺血性脑卒中中的应用进行概述。     

回波平面成像序列用于儿童中枢神经系统定量磁化率成像

目的 分析回波平面成像(EPI)序列在儿童中枢神经系统定量磁化率成像(QSM)中的应用价值。方法 回顾性分析43例接受常规头部MR检查儿童,分别以传统梯度回波序列及EPI序列采集中枢神经系统QSM,获得传统标准QSM(Std-QSM)及EPI-QSM定量图;测量全脑及19个脑区QSM定量值,对比以2种方法所获结果的差异,分析其相关性。结果 Std-QSM采集时间4 min 53 s,EPI-QSM为24 s。以2种方法所获左侧苍白球、左侧尾状核、双侧壳核、双侧额中回及左侧颞中回的QSM定量值差异均有统计学意义(P均＜0.05),其余脑区QSM定量值差异均无统计学意义(P均＞0.05)。以2种方法所测各脑区QSM定量值均呈正相关(r/r_s:0.519~0.919,P均＜0.001)。以Std-QSM与EPI-QSM所测全脑QSM定量值分别为0.388(-0.303,1.416)、0.452(-0.171,1.465),差异无统计学意义(Z=-0.488,P=0.625),且二者呈线性正相关(R=0.766,P＜0.001),回归方程为S_Std-QSM=1.35×S_EPI-QSM-0.25。结论 以EPI序列采集儿童中枢神经系统QSM可明显缩短采集时间。     

定量磁化率成像与CT评估帕金森病患者脑铁含量

目的 采用定量磁化率成像（QSM）与CT评估帕金森病（PD）患者脑铁含量,评价磁化率（MSV）与CT值的相关性。方法 纳入30例PD患者（PD组）和30名健康受试者（对照组）,采集其颅脑QSM及CT图像,手动勾画脑内各部位ROI,测量并比较组间各部位MSV和CT值的差异。采用Pearson相关系数评估MSV与CT值的相关性。结果 PD组双侧苍白球（GP）、红核（RN）、黑质（SN）及丘脑（THA）的MSV及CT值均大于对照组（P均<0.05）。2组双侧GP的MSV均与CT值呈正相关（r：0.376~0.546,P均<0.05）。结论 QSM与CT评估PD患者脑铁含量具有一致性。GP的MSV与CT值显著相关。     

多模态磁共振成像技术在深部脑刺激治疗帕金森病中的研究进展

帕金森病(Parkinson’s disease,PD)是全球中老年常见的第二大神经退行性疾病,以静止性震颤、肌肉强直、动作迟缓和姿势平衡障碍为显著特征,其患病率预计将在未来三十年内增加一倍。随着药物治疗的弊端逐渐显现,深部脑刺激(deep brain stimulation,DBS)作为一种新型辅助疗法受到广泛关注。MRI可在活体状态下无创地提供脑组织的结构、功能和代谢等信息,对评估DBS治疗PD后的效果及指导治疗具有较大的临床意义。因此,本文就结构MRI(structural MRI,sMRI)、扩散张量成像(diffusion tensor imaging,DTI)、血氧水平依赖功能MRI(blood oxygenation level dependent-functional MRI,BOLD-fMRI)、磁敏感加权成像(susceptibility-weighted imaging,SWI)、定量磁化率成像(quantitative susceptibility mapping,QSM)、磁共振波谱(magnetic resonance spectroscopy,MRS)等多...     

定量磁化率成像对比复发缓解型多发性硬化与视神经脊髓炎谱系疾病患者深部灰质铁含量

目的 以定量磁化率成像(QSM)观察复发缓解型多发性硬化(RRMS)及视神经脊髓炎谱系疾病(NMOSD)患者深部灰质铁含量差异及其与运动及认知障碍的关系.方法 针对35例RRMS(RRMS组)、30例NMOSD(NMOSD组)患者及30名健康人(对照组)基于颅脑QSM获取各灰质核团定量磁化率值(QSV),比较组间各QS...     

定量磁化率成像在阿尔茨海默病诊断及病程进展跟踪中的应用研究

阿尔茨海默病（Aizheimer’s disease, AD）是一种中枢神经系统的退行性疾病,脑内铁稳态失调被认为是AD重要的病理学特征之一。定量磁化率成像（quantitative susceptibility mapping, QSM）作为一种无创MRI技术,对铁的存在非常敏感,能够以高空间分辨率来量化局部组织磁化率。近年来,QSM技术已经对不同脑区的磁化率以及与其他病理生物标志物之间的关系进行了研究,本文旨在分析铁稳态失调对AD病理的影响以及QSM技术在AD早期诊断及病程跟踪方面的潜在应用价值,为AD的早期诊断及治疗提供客观的神经影像学依据。     

定量磁化率成像评估高原人脑氧代谢的可行性北大核心CSCD

目的观察定量磁化率成像(QSM)测量高原人脑氧代谢率(CMRO_(2))的可行性。方法纳入高原(高原组)及平原地区(平原组)汉族健康志愿者各34名,测量其血红蛋白(Hb)水平、红细胞计数(RBC)及动脉血氧饱和度(SaO_(2));利用QSM联合3D动脉自旋标记技术,获取静息态下大脑定量磁敏感值,利用后处理软件录入高原地区健康志愿者Hb和SaO_(2),计算全脑白质和灰质的氧摄取分数(OEF)、脑血流量(CBF)、动脉血氧含量(CaO_(2))及CMRO_(2),并与QSM常规方法所测平原组OEF及CMRO_(2)进行比较。结果高原组Hb及RBC均大于(P均<0.05)而SaO_(2)小于平原组(P<0.05);组间CaO_(2)差异无统计学意义(P>0.05)。高原组脑白质及脑灰质OEF高于平原组(P均<0.05);组间CBF及CMRO_(2)差异均无统计学意义(P均>0.05)。结论经校准Hb和SaO_(2)后,QSM技术能更准确地反映高原人在低压、缺氧环境下的脑氧代谢情况。     

磁敏感加权成像对帕金森病患者的诊断价值

目的:探讨磁敏感加权成像(SWI)在帕金森病(Parkinson disaese,PD)的诊断及其病情严重程度评估中的临床应用。方法:选取PD患者30例和健康志愿者30例,应用SWI对所有受试者锥体外系多个核团计算相位值,进行统计学分析。结果:PD组黑质致密带、苍白球、壳核相位值均低于健康志愿者组,差异有统计学意义(P0.01)。PD组黑质致密带、苍白球、壳核相位值早期即降低,并随着病情加重,相位值进一步降低,差异有统计学意义(P0.05)。结论:SWI技术对PD的诊断及评估具有重要的临床价值。     

Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization

Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique that provides the spatial distribution of magnetic susceptibility values of tissues. QSMs can be obtained by deconvolving the dipole kernel from phase images, but the spectral nulls in the dipole kernel make the inversion ill-posed. In recent years, deep learning approaches have shown a comparable QSM reconstruction performance to the classic approaches, in addition to the fast reconstruction time. Most of the existing deep learning methods are, however, based on supervised learning, so matched pairs of input phase images and ground-truth maps are needed. Moreover, it was reported that the deep learning-based methods fail to reconstruct QSM when the resolution of test data is different from the trained resolution. To address this, here we propose an unsupervised resolution-agnostic QSM deep learning method. The proposed method does not require QSM labels for training and reconstructs QSM with various resolutions by using adaptive instance normalization. Experimental results and clinical validation confirm that the proposed method provides accurate QSM with various resolutions compared to other deep learning approaches, and shows competitive performance to the best classical approaches in addition to the ultra-fast reconstruction.     

定量磁化率成像的基本原理及方法概述

定量磁化率成像(quantitative susceptibility mapping,QSM)是磁共振成像(magnetic resonance imaging,MRI)中一项新兴的用于定量测量组织磁化特性的技术。利用定量磁化率成像,可以对组织的铁含量、钙化、血氧饱和度等进行有效的定量分析,对脑出血、多发性硬化症及帕金森综合症等脑神经疾病的研究和诊断也具有重要意义。定量磁化率图像的重建是一个复杂的过程,包括几个不同的步骤,因此其准确性受到很多因素的影响。本文主要概述定量磁化率成像的基本原理和重建流程,并对重建过程中每个步骤的主要方法进行介绍。同时,也将对当前定量磁化率成像的几种主要临床应用进行介绍。     

MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping

Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ?₁ and ?₂ norm regularized QSM algorithms. These regularized approaches solve for the underlying magnetic susceptibility distribution, a sensitive measure of the tissue iron concentration, that gives rise to the observed signal phase. Regularized QSM methodology also involves a pre-processing step that removes, by dipole fitting, unwanted background phase effects due to bulk susceptibility variations between air and tissue and requires data acquisition only at a single field strength. For validation, performances of the two QSM methods were measured against published estimates of regional brain iron from postmortem and in vivo data. The in vivo comparison was based on data previously acquired using Field-Dependent Relaxation Rate Increase (FDRI), an estimate of MRI relaxivity enhancement due to increased main magnetic field strength, requiring data acquired at two different field strengths. The QSM analysis was based on susceptibility-weighted images acquired at 1.5 T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5 T and 3.0 T. Both datasets were collected in the same healthy young and elderly adults. The in vivo estimates of regional iron concentration comported well with published postmortem measurements; both QSM approaches yielded the same rank ordering of iron concentration by brain structure, with the lowest in white matter and the highest in globus pallidus. Further validation was provided by comparison of the in vivo measurements, ?₁-regularized QSM versus FDRI and ?₂-regularized QSM versus FDRI, which again yielded perfect rank ordering of iron by brain structure. The final means of validation was to assess how well each in vivo method detected known age-related differences in regional iron concentrations measured in the same young and elderly healthy adults. Both QSM methods and FDRI were consistent in identifying higher iron concentrations in striatal and brain stem ROIs (i.e., caudate nucleus, putamen, globus pallidus, red nucleus, and substantia nigra) in the older than in the young group. The two QSM methods appeared more sensitive in detecting age differences in brain stem structures as they revealed differences of much higher statistical significance between the young and elderly groups than did FDRI. However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.     

Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study

Quantitative susceptibility mapping (QSM) is a novel technique which allows determining the bulk magnetic susceptibility distribution of tissue in vivo from gradient echo magnetic resonance phase images. It is commonly assumed that paramagnetic iron is the predominant source of susceptibility variations in gray matter as many studies have reported a reasonable correlation of magnetic susceptibility with brain iron concentrations in vivo. Instead of performing direct comparisons, however, all these studies used the putative iron concentrations reported in the hallmark study by Hallgren and Sourander (1958) for their analysis. Consequently, the extent to which QSM can serve to reliably assess brain iron levels is not yet fully clear. To provide such information we investigated the relation between bulk tissue magnetic susceptibility and brain iron concentration in unfixed (in situ) post mortem brains of 13 subjects using MRI and inductively coupled plasma mass spectrometry. A strong linear correlation between chemically determined iron concentration and bulk magnetic susceptibility was found in gray matter structures (r=0.84, p<0.001), whereas the correlation coefficient was much lower in white matter (r=0.27, p<0.001). The slope of the overall linear correlation was consistent with theoretical considerations of the magnetism of ferritin supporting that most of the iron in the brain is bound to ferritin proteins. In conclusion, iron is the dominant source of magnetic susceptibility in deep gray matter and can be assessed with QSM. In white matter regions the estimation of iron concentrations by QSM is less accurate and more complex because the counteracting contribution from diamagnetic myelinated neuronal fibers confounds the interpretation.     

基于定量磁化率成像人脑深层灰质核团概率性图谱的构建

目的基于定量磁化率成像(quantitative susceptibility mapping,QSM)技术制作可用于自动分割大脑深层灰质核团的概率图谱。材料与方法 15名健康受试者参与研究,所有受试者扫描均在3.0 T磁共振成像设备系统上完成。在随机选取的10名受试者得到的标准空间QSM图上,手动勾画出六个双侧脑深部灰质核团,之后采用相应的图谱评价方法选择最优概率阈值的图谱作为最终的概率图谱。在其余5名受试者得到的标准空间QSM图上,分别使用三种图谱(概率图谱、AAL图谱和Johns Hopkins图谱)自动分割和由2名研究者手动勾画出六个双侧脑深部灰质核团感兴趣区,并分别计算自动分割与手动勾画得到的区域的相似度Dice系数和磁化率值,以评价概率图谱的准确性。结果在基底节区域,概率图谱分割结果的Dice系数明显高于AAL图谱,但和Johns Hopkins图谱区别不大;在颅底和小脑区域,概率图谱分割结果的Dice系数明显高于Johns Hopkins图谱。与其他两种图谱相比,概率图谱自动分割深部核团后测量得到的磁化率值,更接近于手动勾画核团测量得到的磁化率值,其差别更小。结论基于多名受试者QSM图像构建的脑深部灰质核团概率图谱,对大脑灰质核团分割效果更加可靠,可有效提高图像分析工作的效率。     

NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data

Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.