Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.
Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.
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DOI：
10.1002/hbm.25770
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发表时间：
2022-04-15
影响因子：
4.8
通讯作者：
Haacke EM
中科院分区：
文献类型：
--
作者：
Jin Z;Wang Y;Jokar M;Li Y;Cheng Z;Liu Y;Tang R;Shi X;Zhang Y;Min J;Liu F;He N;Yan F;Haacke EM
帕金森病(PD)是一种慢性进行性神经退行性疾病，病理特征为黑质致密部(SNPC)早期黑质黑色素(NM)丢失和黑质(SN)铁沉积增加。症状开始时，黑质变性表现为SNPC腹外侧层50%至70%的有色神经元丢失。此外，利用磁共振成像(MRI)，红核(RN)和丘脑底核(STN)的铁沉积和体积变化已被报道与疾病状态和进展速度有关。此外，STN也是晚期帕金森病患者进行脑深部刺激治疗的重要靶点。因此，准确地在体内描绘SN及其亚区和其他中脑结构，如RN和STN，可能有助于更好地研究PD中铁和NM的变化。我们的目标是使用MRI模板创建一种基于单个多ECHO磁化传递对比梯度回波(MTC-GRE)成像序列的铁和NM对比度的自动中脑深部灰质核团分割方法。使用来自3DMTC-GRE序列的短回声TE=2.5ms数据来寻找NM富集区，而使用第二回波TE=1.15 ms来计算87名健康受试者(平均年龄 ± SD：63.4 ± 6.2 ，范围：45-81 )的定量易感图谱。根据这些数据，我们创建了NM和Iron模板，并计算了模板空间中每个中脑核的边界，将这些边界映射回原始空间，然后使用动态规划算法对原始空间中的边界进行微调，以匹配每个人的NM和Iron特征的细节。使用双重映射方法来改进任意给定个体的中脑到模板空间的形态映射的性能。在NM富集区和磁化率图中使用阈值方法来优化芯片相似系数和体积比。对SN的NM以及含SN、STN和RN的铁的结果都表明与手动绘制的结构非常一致。在对数据应用任何阈值之前，这些结构的骰子相似系数和体积比分别为0.85、0.87、0.75和0.92和0.93、0.95、0.89、1.05。使用这种全自动的基于模板的深部灰质映射方法，可以准确地测量中脑核团的组织属性，如体积、铁含量和NM含量。我们的目标是使用磁共振成像(MRI)模板创建一种基于铁和神经黑素(NM)对比的自动中脑深部灰质核团分割方法，该对比来自单个、多ECHO磁化转移对比梯度回波(MTC-GRE)成像序列。
Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei. Our goal was to use a magnetic resonance imaging (MRI) template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and neuromelanin (NM) contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence.
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