Nuclei Scanner Setup Unveiled: A 35-step Quick Start Guide – Abnormal iron deposits in the deep nuclei of the gray matter are associated with many neurological diseases. With the Quantitative Susceptibility Mapping (QSM) technique, brain iron content can be quantified in vivo. To estimate the magnetic susceptibility of deep gray matter nuclei in QSM, it is mandatory to first segment the nuclei of interest, and several automated methods have been proposed in the literature. This study proposes a contrast focus for U-Net core segmentation and evaluates the performance of different MRI tools on two datasets obtained from different sequences with different parameters. Experimental results show that our proposed method outperforms common network structures in both databases. The impact of training and infrastructure strategies was also discussed, showing that extending the duration of the trial could lead to clear improvements. In the training phase, our results showed that data enrichment, deep supervision, and nonuniform patch selection contributed significantly to improving segmentation accuracy, indicating that the selection of training strategies and infrastructure is as important as the design of advanced networks. structures.
With the advent of quantitative susceptibility mapping (QSM) techniques in the last decade, quantitative measurements of brain iron content can be obtained in vivo (Langkamer et al., 2010; Liu et al., 2015, 2017). QSM uses tissue magnetic susceptibility as a physical magnetic resonance imaging (MRI) parameter, which shows how the local magnetic field in tissue changes when an external magnetic field is applied (Li et al., 2019). Tissue magnetic susceptibility can provide unique information about tissue iron composition (Li et al., 2019). Compared to other iron sensing techniques, the transverse relaxation rate (R.
Nuclei Scanner Setup Unveiled: A 35-step Quick Start Guide
‘), field-dependent velocity enhancements, phase information from susceptibility-weighted images and magnetic field correlation imaging, QSM can overcome the limitations of this technique, such as the relatively low accuracy of R .
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* Other confounding factors (water and calcium content), geometry and orientation of phase images, and low sensitivity to small changes in brain iron (Stankiewicz et al., 2007; Bilgic et al., 2012; Distung et al., 2013); Chai et al., 2019). QSM is more accurate in measuring iron content and strongly correlates with iron concentration in postmortem brain tissue (Langkammer et al., 2010).
Quantitative measurement of brain iron content using QSM has been implicated in the role of iron in brain development, modulation of physiological function, and aging (Salami et al., 2018; Peterson et al., 2019), as well as in various neurosciences . Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, metabolic diseases (hepatic encephalopathy and renal encephalopathy), sleep disorders, diseases of the hematological system, and cerebrovascular diseases (Chai et al., 2015a; Xia et al., 2015; Miao et al., 2015). ; ., 2018; Chai et al., 2019; Valdes Hernandez et al., 2019; Pudlak et al., 2020; Cogswell et al., 2021; Thomas et al., 2021; Zhang et al. ., 2022). Iron deposits in gray matter nuclei in normal individuals and abnormal accumulation of iron in gray matter nuclei in these neurological diseases, gray matter nuclei are critical target structures for the study of abnormal iron deposition. Previous studies have found that common structural MR images such as T
High-resolution images do not clearly show iron-rich gray matter nuclei, such as the substantia nigra (SN), red nucleus (RN), and dentate nucleus (DN; Beliveau et al., 2021). Consequently, most popular brain atlases, including FreeSurfer, FMRIB Software Library (FSL), and Statistical Parametric Map (SPM), do not detect these cores. Most segmentation tools fail to extract these nuclei (Beliveau et al., 2021). However, all gray matter nuclei, including SN, RN, DN, show a clear contrast (higher signal) than the surrounding brain tissue in QSM images, because QSM is highly sensitive to iron, although its amount is low and QSM is also rich in iron. . can increase relative contrast (Beliveau et al., 2021). Visible contrast can help to clearly and accurately identify gray matter nuclei. Measurement of iron content requires manual specification of gray matter nuclei of interest (VOI), which is highly dependent on operator experience and introduces some bias (Chai et al., 2022). Creating sounds by hand was also laborious and time-consuming, limiting widespread use outside of research interests. To date, one study has used SWI as a targeted method because SWI can provide enhanced contrast for visualization of gray matter nuclei, which has a wider range of clinical applications compared to iron-sensitive methods other than QSM and SWI ( Beliveau et al., 2021). However, the visualization and segmentation of nuclei using SWI was insufficient, and quantitative measurement of iron content became an important step in the clinical evaluation of abnormal iron deposition for the diagnosis of neurological diseases. Therefore, QSM as a target format can provide improved contrast similar to SWI and provide direct and quantitative information on iron content (Liu et al., 2015).
Deep learning has recently been successfully applied to biomedical image segmentation tasks (Mina et al., 2021). It has been shown that in many medical image segmentation tasks, such as tumor segmentation (Minze et al., 2015; Chang et al., 2018), stroke lesion segmentation (Meyer et al., 2017; Liu et al. , 2018) and organ segmentation (Gibson et al., 2018), deep learning methods are able to significantly outperform traditional atlas-based methods. Many deep learning-based medical image segmentation tasks use U-Net (Ronneberger et al., 2015) or its variants (Cicek et al., 2016; Chang et al., 2018; Liu et al., 2018; Meng et al. al. , 2018) are accepted. ., 2018). , 2018; Wang et al., 2020). By incorporating tight coupling between the encoder and decoder layers, U-Net architectures are able to efficiently combine spatial and semantic information even with small training sets. To further improve the segmentation accuracy of U-Net, some changes in the coding part or transition link have been proposed in the literature. Encoder modifications have mainly focused on encoder extension (Chen et al., 2019; Wang et al., 2019; Ibtehaj and Rahman, 2020) to enrich the feature maps in different domains. In the context of jumping, decoders have been modified to exploit salient features, including different attentional mechanisms (Oktai et al., 2018; Gu et al., 2021).
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Deep learning methods are also more powerful and accurate than atlas-based methods when applied to the task of segmenting brain gray matter nuclei (Guan et al., 2021; Chai et al., 2022). For example, Chai et al. . On kernel segmentation with lightweight neural networks. Guan et al. . Pure architecture.
Many deep learning methods are mainly focused on network architecture representation, and most of them are developed based on U-Net. However, teaching strategies were not emphasized. In this study, we tried to emphasize not only the network structures but also the fine-tuning of the network with appropriate training and strategies. In particular, we adopted a small modification of the U-Net network by introducing contrast attention (CA) modules and tried to improve the segmentation accuracy without introducing additional network parameters. This study was performed on two different datasets (Dataset I and II) using different QSM-acquired NMR sequences with different imaging parameters on different NMR equipment. Dataset I was randomly divided into a training set of 42 subjects and a test set of 20 subjects. The network was trained on the training set and evaluated on the test set and data set II. Experimental results showed that on both datasets, the proposed method was able to outperform other U-Net structures, including 3D U-Net (Cicek et al., 2016), U-Net Attention (Oktay et al., 2018)) . DeepQSMSeg (Guan et al., 2021), which demonstrates the generalizability of the proposed method. The results of different training strategies are also discussed, suggesting that data augmentation, deep tracking, and informal patch selection are useful for improving segmentation accuracy.
This prospective study was approved by the review board and ethics committee of Tianjin First Central Hospital. Informed consent was obtained from all subjects before MRI examination. In our study, two datasets were obtained using different MRI sequences from different MRI devices, dataset I with sixty-two healthy subjects (age range 22–60 years, mean 37.34 ± 11.32 years; male 24 and women 38) and data set II with 20 . Six healthy subjects (age range 54–72 years, mean 62.44 ± 4.35 years; males 18 and females 9). All are shipped from Tianjin First Center
