New brain atlas—Mapping the human brain in vivo with 7.0 T MRI and comparison with postmortem histology: Will these images change modern medicine?

Cho, Zang-Hee; Kim, Young-Bo; Han, Juhee; Min, Hoon Ki; Kim, Kyoung Nam; Choi, Sang-Han; Veklerov, Eugene; Shepp, Larry

doi:10.1002/ima.20143

Cited by 37 publications

(45 citation statements)

References 15 publications

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“…23 T2*-weighted MRI provides excellent contrast in the midbrain due to its high iron content. [24][25][26] Previous magnetic resonance work has focused mostly on localizing and measuring characteristics of the whole SN [26][27][28] or distinguishing the SNpc and SNpr, 28,29 although there has been some inconsistency in the definition of boundaries and subareas of the SN in Figure 2 Nigrosome 1 in vivo in iron-(T2*-weighted) and neuromelanin-(MT-T1-weighted) sensitive 7 T MRI Example of 2 coregistered, high-resolution, in vivo images of a healthy control (HC) (49 years old) shows the substantia nigra and nigrosome 1 (white arrows). In the T2*-weighted image (left, 0.3 3 0.3 3 0.3 mm 3 voxels), the nigrosome is hyperintense due to its low iron content; in the MT-T1-weighted sequence (right, 0.6 3 0.6 3 0.6 mm 3 voxels), the nigrosome is hyperintense due to its high neuromelanin content.…”

mentioning

confidence: 99%

Visualization of nigrosome 1 and its loss in PD

Blazejewska

Schwarz²,

Pitiot³

et al. 2013

Neurology

223

255

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Objective: This study assessed whether high-resolution 7 T MRI allowed direct in vivo visualization of nigrosomes, substructures of the substantia nigra pars compacta (SNpc) undergoing the greatest and earliest dopaminergic cell loss in Parkinson disease (PD), and whether any disease-specific changes could be detected in patients with PD.Methods: Postmortem (PM) midbrains, 2 from healthy controls (HCs) and 1 from a patient with PD, were scanned with high-resolution T2*-weighted MRI scans, sectioned, and stained for iron and neuromelanin (Perl), TH, and calbindin. To confirm the identification of nigrosomes in vivo on 7 T T2*-weighted scans, we assessed colocalization with neuromelanin-sensitive T1-weighted scans. We then assessed the ability to depict PD pathology on in vivo T2*-weighted scans by comparing data from 10 patients with PD and 8 age-and sex-matched HCs.Results: A hyperintense, ovoid area within the dorsolateral border of the otherwise hypointense SNpc was identified in the HC brains on in vivo and PM T2*-weighted MRI. Location, size, shape, and staining characteristics conform to nigrosome 1. Blinded assessment by 2 neuroradiologists showed consistent bilateral absence of this nigrosome feature in all 10 patients with PD, and bilateral presence in 7/8 HC. Many MRI studies have reported Parkinson disease (PD)-associated changes in the substantia nigra (SN). 1 However, delineating the boundaries and assessing neurodegeneration in the SN remains challenging. 2 Recently developed ultra-high-field MRI systems produce very high spatial resolution T2*-weighted images providing detailed SN morphologic information. Correlation of high-resolution MRI data and histology 3 may enable a more precise definition of the boundaries and substructures of the SN in vivo, and hence a more accurate demonstration of PD pathology.The SN is divided into the pars compacta (SNpc), which is densely packed with neuromelanin-containing dopaminergic cells, and the pars reticulata (SNpr), which is formed by loose aggregations of GABAergic medium and large neurons. 4 The majority of neurons in the SNpc project to the neostriatum (putamen and caudate nucleus). Five distinct subgroups of dopaminecontaining neurons in calbindin-negative zones within the SNpc, nigrosomes, have been identified using immunostaining for calbindin D 28K . 5 The largest, nigrosome 1, is lens-shaped and situated along the rostral/caudal axis of the SN in its dorsal part, at caudal and intermediate levels ( figure 8 of Damier et al. 5 ; figure e-1 on the Neurology ® Web site at www.neurology.org). Postmortem (PM) studies have shown dopaminergic neuronal loss in PD to be higher in the

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confidence: 99%

Visualization of nigrosome 1 and its loss in PD

Blazejewska

Schwarz²,

Pitiot³

et al. 2013

Neurology

223

255

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“…7(a) for comparison. As demonstrated in [69], 7.0-tesla MR image can reveal the brains’ architecture with resolution equivalent to that obtained from thin slices in vitro . Thus, researchers are able to observe clearly the fine brain structures in µm unit, which was only possible with in vitro imaging in the past.…”

Section: Methodsmentioning

confidence: 99%

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Kim

Wang

et al. 2016

IEEE Trans. Biomed. Eng.

245

149

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Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art.

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“…The 7.0 T MR scanner (Cho et al, 2010) enables achieving the high signal-to-noise ratio (SNR) as well as the dramatically increased contrast and resolution compared to 3.0 T images. As demonstrated in Cho et al (2008), 7.0 T image can clearly reveal the fine in vivo brain structures, with equivalent resolution to that obtained from the sectional slices by in vitro histological imaging. Thus, 7.0 T imaging technique can trigger the innovative development in hippocampus analysis for clinical studies due to its high capability of discovering the μm-level morphological patterns of human brain.…”

Section: Introductionmentioning

confidence: 82%

“…The main reasons include 1) more severe intensity inhomogeneity in the 7.0 T images compared to 1.5 T or 3.0 T images; 2) high signal-to-noise ratio (SNR) which brings forth plenty of anatomical details at the expense of troublesome image noise; and 3) incomplete brain volume (i.e., only a segment of the brain is scanned) due to practical issues such as the trade-off between acquisition time and SNR. Accordingly, no automatic hippocampus segmentation methods have been developed for 7.0 T images, except some manual or semi-automatic methods (Cho et al, 2008, 2010; Yushkevich et al, 2009). Although automatic segmentation method for hippocampal subfields (e.g., cornu ammonis fields 1–3, dentate gyrus, and subiculum) for 4.0 T MR image has been developed in Yushkevich et al (2010), it can only deal with the subfields and additionally requires the manually labeled hippocampus.…”

Section: Introductionmentioning

confidence: 99%

Automatic hippocampus segmentation of 7.0Tesla MR images by combining multiple atlases and auto-context models

Kim

et al. 2013

NeuroImage

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In many neuroscience and clinical studies, accurate measurement of hippocampus is very important to reveal the inter-subject anatomical differences or the subtle intra-subject longitudinal changes due to aging or dementia. Although many automatic segmentation methods have been developed, their performances are still challenged by the poor image contrast of hippocampus in the MR images acquired especially from 1.5 or 3.0 Tesla (T) scanners. With the recent advance of imaging technology, 7.0 T scanner provides much higher image contrast and resolution for hippocampus study. However, the previous methods developed for segmentation of hippocampus from 1.5 T or 3.0 T images do not work for the 7.0 T images, due to different levels of imaging contrast and texture information. In this paper, we present a learning-based algorithm for automatic segmentation of hippocampi from 7.0 T images, by taking advantages of the state-of-the-art multi-atlas framework and also the auto-context model (ACM). Specifically, ACM is performed in each atlas domain to iteratively construct sequences of location-adaptive classifiers by integrating both image appearance and local context features. Due to the plenty texture information in 7.0 T images, more advanced texture features are also extracted and incorporated into the ACM during the training stage. Then, under the multi-atlas segmentation framework, multiple sequences of ACM-based classifiers are trained for all atlases to incorporate the anatomical variability. In the application stage, for a new image, its hippocampus segmentation can be achieved by fusing the labeling results from all atlases, each of which is obtained by applying the atlas-specific ACM-based classifiers. Experimental results on twenty 7.0 T images with the voxel size of 0.35 × 0.35 × 0.35 mm3 show very promising hippocampus segmentations (in terms of Dice overlap ratio 89.1 ± 0.020), indicating high applicability for the future clinical and neuroscience studies.

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New brain atlas—Mapping the human brain in vivo with 7.0 T MRI and comparison with postmortem histology: Will these images change modern medicine?

Cited by 37 publications

References 15 publications

Visualization of nigrosome 1 and its loss in PD

Visualization of nigrosome 1 and its loss in PD

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Automatic hippocampus segmentation of 7.0Tesla MR images by combining multiple atlases and auto-context models

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