A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson’s disease at 3T: An efficient susceptibility map‐weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet)

Jo, Minju; Oh, Se‐Hong

doi:10.1002/mp.13999

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…This structure has also been observed on SWI at 3T with reduced contrast ( 14 , 16 ). The phase information, used here as a weighting mask, improves the visualization of the nigrosome's ( 16 – 18 ). This improvement stems from the difference in susceptibility between Nigrosome-1 and the surrounding nigral matrix in healthy individuals.…”

Section: Nigrosome Imagingmentioning

confidence: 99%

Modern Brainstem MRI Techniques for the Diagnosis of Parkinson's Disease and Parkinsonisms

2020

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The brainstem is the earliest vulnerable structure in many neurodegenerative diseases like in Multiple System Atrophy (MSA) or Parkinson's disease (PD). Up-to-now, MRI studies have mainly focused on whole-brain data acquisition. Due to its spatial localization, size, and tissue characteristics, brainstem poses particular challenges for MRI. We provide a brief overview on recent advances in brainstem-related MRI markers in Parkinson's disease and Parkinsonism's. Several MRI techniques investigating brainstem, mainly the midbrain, showed to be able to discriminate PD patients from controls or to discriminate PD patients from atypical parkinsonism patients: iron-sensitive MRI, nigrosome imaging, neuromelanin-sensitive MRI, diffusion tensor imaging and advanced diffusion imaging. A standardized multimodal brainstem-dedicated MRI approach at high resolution able to quantify microstructural modification in brainstem nuclei would be a promising tool to detect early changes in parkinsonian syndromes.

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Section: Nigrosome Imagingmentioning

confidence: 99%

Modern Brainstem MRI Techniques for the Diagnosis of Parkinson's Disease and Parkinsonisms

2020

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“…Computer‐aided methods have been a growing interest in the assessment and treatment of serious brain diseases, such as brain tumors, 15 autism, 16 and Parkinson's disease 17 . AD as one of the serious brain diseases also receives much attention.…”

Section: Introductionmentioning

confidence: 99%

“…Computer-aided methods have been a growing interest in the assessment and treatment of serious brain diseases, such as brain tumors, 15 autism, 16 and Parkinson's disease. 17 AD as one of the serious brain diseases also receives much attention. To achieve the reliable diagnosis of AD and MCI, machine learning-(ML) or deep learning-(DL) based methods have been developed in many studies based on structural magnetic resonance imaging (sMRI).…”

Section: Introductionmentioning

confidence: 99%

Ensemble of convolutional neural networks and multilayer perceptron for the diagnosis of mild cognitive impairment and Alzheimer's disease

Jiang

et al. 2022

Medical Physics

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Background: Structural magnetic resonance imaging (sMRI) can provide morphological information about the structure and function of the brain in the same scanning process. It has been widely used in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Purpose: To capture the anatomical changes in the brain caused by AD/MCI, deep learning-based MRI image analysis methods have been proposed in recent years. However, it is observed that the performance of most existing methods is limited as they only construct a single type of deep network and ignore the significance of other clinical information. Methods: To make up for these defects, an ensemble framework that incorporates three types of dedicatedly-designed convolutional neural networks (CNNs) and a multilayer perceptron (MLP) network is proposed, where three CNNs with entropy-based multi-instance learning pooling layers have more reliable feature selection abilities. The dedicatedly-designed base classifiers can make use of the heterogeneous data, and empower the framework with enhanced diversity and robustness. In particular, to consider the interactions among the base classifiers, a novel multi-head self -attention voting scheme is designed. Moreover, considering the chance that MCI can be transformed to AD, the proposed framework is designed to diagnose AD and predict MCI conversion simultaneously, with the aid of the transfer learning technique. Results: For performance evaluation and comparison, extensive experiments are conducted on the public dataset of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results show that the proposed ensemble framework provides superior performance under most of the evaluation metrics. Especially, the proposed framework achieves state-of -the-art diagnostic accuracy (98.61% for the AD diagnosis task, and 84.49% for the MCI conversion prediction task). Conclusions: These promising results demonstrate the proposed ensemble framework can accurately diagnose AD patients and predict the conversion of MCI patients, which has the potential of clinical practice for diagnosing AD and MCI.

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A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson’s disease at 3T: An efficient susceptibility map‐weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet)

Cited by 2 publications

References 33 publications

Modern Brainstem MRI Techniques for the Diagnosis of Parkinson's Disease and Parkinsonisms

Modern Brainstem MRI Techniques for the Diagnosis of Parkinson's Disease and Parkinsonisms

Ensemble of convolutional neural networks and multilayer perceptron for the diagnosis of mild cognitive impairment and Alzheimer's disease

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