The purpose of the current study was to develop deep learning‐regularized, single‐step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A ...deep learning‐regularized, single‐step QSM quantification model, named SS‐POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS‐POCSnet, a data fidelity term based on a single‐step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS‐POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS‐POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS‐POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility‐tuned structured similarity index measure of 0.823 ± 0.02, high‐frequency error norm of 37.0 ± 5.7, and peak signal‐to‐noise ratio of 42.8 ± 1.1. SS‐POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS‐POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning‐regularized, single‐step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.
This study developed a deep learning‐regularized, single‐step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning‐regularized, single‐step QSM quantification model, named SS‐POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS‐POCSnet, a data fidelity term based on a single‐step model was iteratively applied, which combined the spherical mean value kernel and dipole model. Meanwhile, SS‐POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values.
This study proposed a semisupervised loss function named level‐set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid‐attenuated inversion recovery images. The ...training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V‐Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU‐SVD, n = 360) and the multiple sclerosis cohort (HKU‐MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer‐assisted Intervention (MICCAI) WMH challenge database (MICCAI‐WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI‐CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU‐SVD testing set (n = 20), DSC = 0.77 on the HKU‐MS testing set (n = 5), and DSC = 0.78 on MICCAI‐WMH testing set (n = 30). The segmentation results obtained by our semisupervised V‐Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature.
This article presented a study of training a white matter hyperintensity segmentation network on T2‐weighted fluid‐attenuated inversion recovery images without using manual labeled data. The segmentation performance outperform than other semisupervised and unsupervised methods.
Background
Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are central nervous system (CNS) inflammatory demyelinating disorders. It is clinically important to distinguish ...MS from NMOSD, as treatment and prognosis differ. Brainstem involvement is common in both disorders.
Purpose
To investigate whether the patterns of brainstem atrophy on volumetric analysis in MS and NMOSD were different and correlated with clinical disability.
Study Type
Case–control cross‐sectional study.
Subjects
In all, 17 MS, 13 NMOSD, and 18 healthy control (HC) subjects were studied.
Field Strength/Sequence
T1‐weighted and T2w spin‐echo images were acquired with a 3T scanner.
Assessment
Semiautomated segmentation and volumetric measurement of brainstem regions were performed. Anatomical information was obtained from whole brain T1w images using a 3D magnetization‐prepared rapid gradient‐echo (MPRAGE) imaging sequence (TR/TE/T: 7.0/3.2/800 msec, voxel size: 1 × 1 × 1 mm3, scan time: 10 min 41 sec).
Statistical Tests
Independent samples t‐test, Mann–Whitney U‐test, partial correlation, and multiple regression analysis.
Results
Baseline characteristics were similar across the three groups, without significant difference in disease duration (P = 0.354) and EDSS score (P = 0.159) between MS and NMOSD subjects. Compared to HC, MS subjects had significantly smaller normalized whole brainstem (−5.2%, P = 0.027), midbrain (−8.3%, P = 0.0001), and pons volumes (−5.9%, P = 0.048), while only the normalized medulla volume was significantly smaller in NMOSD subjects compared to HC (−8.5% vs. HC, P = 0.024). Normalized midbrain volume was significantly smaller in MS compared to NMOSD subjects (−5.0%, P = 0.014), whereas normalized medulla volume was significantly smaller in NMOSD compared to MS subjects (−8.1%, P = 0.032). Partial correlations and multiple regression analysis revealed that smaller normalized whole brainstem, pons, and medulla oblongata volumes were associated with greater disability on the Expanded Disability Status Scale (EDSS), Functional System Score (FSS)‐brainstem and FSS‐cerebellar in NMOSD subjects.
Data Conclusion
Differential patterns of brainstem atrophy were observed, with the midbrain being most severely affected followed by pons in MS, whereas only the medulla oblongata was affected in NMOSD.
Level of Evidence: 2
Technical Efficacy: Stage 3
J. Magn. Reson. Imaging 2018;47:1601–1609.
Aim
Mild behavioural impairment (MBI) is a neurobehavioural syndrome characterized by emergent neuropsychiatric symptoms in later life. There has been no systematic review or meta‐analysis on the ...prevalence of MBI. The main aim of the study is to calculate the pooled prevalence of MBI.
Methods
A search of the literature on MBI in mild cognitive impairment (MCI), cognitively normal (CN), and subjective cognitive impairment (SCI) and CN but at risk (CN‐AR) subjects published between 1 January 2003 and 28 September 2020 was conducted. Meta‐analysis using a random effects model was performed to determine the pooled estimate of the prevalence of MBI. Meta‐regression was performed to identify factors contributing to the variance of prevalence rate. A systematic review was also performed to study the impact of MBI in cognitive outcomes and its correlation to the pathology and genetics of Alzheimer's disease.
Results
Eleven studies conducted among 15 689 subjects underwent meta‐analysis, revealing the pooled prevalence of MBI to be 33.5% (95% confidence interval (CI): 22.6%–46.6%). Seven studies conducted among 1358 MCI subjects underwent meta‐analysis, revealing the pooled prevalence to be 45.5% (95%CI: 36.1%–55.3%). Four studies conducted among 13 153 CN subjects underwent meta‐analysis, revealing the pooled prevalence to be 17.0% (95%CI: 7.2%–34.9%). Five studies conducted among 1158 SCI or CN‐AR subjects underwent meta‐analysis, revealing the pooled prevalence to be 35.8% (95%CI: 21.4%–53.2%). A systematic review of 13 studies showed that MBI has a significant impact on cognitive deterioration and is associated with the pathology and genetics of Alzheimer's disease.
Conclusions
In MCI, CN, and SCI and CN‐AR subjects, MBI is common. Our finding is potentially useful in planning future clinical trials.
Background
Mild behavioural impairment (MBI) is a neurobehavioural syndrome characterised by later life emergence of persistent neuropsychiatric symptoms. Our previous meta‐analysis showed that MBI ...is prevalent among cognitively normal (CN), subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) subjects. This study is to calculate the pooled prevalence of MBI domains among CN, SCI, and MCI subjects.
Methods
A search of relevant literature published between 1 January 2003 and 6 August 2021 was conducted. Meta‐analysis using a random effects model and meta‐regression was performed.
Results
Ten studies conducted among 12 067 subjects (9758 CN, 1057 SCI and 1252 MCI) with retrievable MBI domains data underwent meta‐analysis, revealing pooled prevalence of affective dysregulation (AFD), impulse dyscontrol (IDS), decreased motivation (DMT), social inappropriateness (SIP) and abnormal perception/thought (APT) of 32.84% (95% CI 24.44–42.5%), 26.67% (95% CI 18.24–37.23%), 12.58% (95% CI 6.93–21.75%), 6.05% (95% CI 3.44–10.42%), and 2.81% (95% CI 1.67–4.69%) respectively. AFD and APT domains demonstrated ordinal increase in pooled prevalence from CN, SCI and MCI subgroups, but meta‐regression demonstrated no significant difference in MBI domains prevalence among cognitive subgroups (in contrast to the significant increase in MBI prevalence from CN to SCI to MCI). The pooled prevalence of AFD and IDS are greater than that of DMT, SIP and APT among all cognitive subgroups. Several variables were found to explain the high heterogeneity.
Conclusions
AFD and IDS are the two most prevalent MBI domains and remain the same with cognitive deterioration. This finding is potentially relevant to clinical practice.
Alzheimer's disease (AD) is the commonest cause of dementia, characterized by the clinical presentation of progressive anterograde episodic memory impairment. However, atypical presentation of ...patients is increasingly recognized. These atypical AD include logopenic aphasia, behavioural variant AD, posterior cortical atrophy, and corticobasal syndrome. These atypical AD are more common in patients with young onset AD before the age of 65 years old. Since medical needs (including the behavioural and psychological symptoms of dementia) of atypical AD patients could be different from typical AD patients, it is important for clinicians to be aware of these atypical forms of AD. In addition, disease modifying treatment may be available in the future. This review aims at providing an update on various important subtypes of atypical AD including behavioural and psychological symptoms.
Multiple sclerosis (MS) and neuromyelitis optica (NMO) are two common types of inflammatory demyelinating disease of the central nervous system. Early distinction of NMO from MS is crucial but quite ...challenging. In this study, 13 NMO spectrum disorder patients (Expanded Disability Status Scale (EDSS) of 3.0 ± 1.7, ranging from 2 to 6.5; disease duration of 5.3 ± 4.7 years), 17 relapsing–remitting MS patients (EDSS of 2.6 ± 1.4, ranging from 1 to 5.5; disease duration of 7.9 ± 7.8 years) and 18 healthy volunteers were recruited. Diffusional kurtosis imaging was employed to discriminate NMO and MS patients at the early or stable stage from each other, and from healthy volunteers. The presence of alterations in diffusion and diffusional kurtosis metrics in normal‐appearing white matter (NAWM) and diffusely increased mean diffusivity (MD) in the cortical normal‐appearing gray matter (NAGM) favors the diagnosis of MS rather than NMO. Meanwhile, normal diffusivities and kurtosis metrics in all NAWM as well as increases in MD in the frontal and temporal NAGM suggest NMO. Our results suggest that diffusion and diffusional kurtosis metrics may well aid in discriminating the two diseases.
Diffusional kurtosis imaging was employed for discriminating neuromyelitis optica (NMO) and multiple sclerosis (MS) patients from each other and from healthy volunteers. Alterations in conventional diffusion and kurtosis metrics in normal‐appearing WM (NAWM) and diffusely increased mean diffusivity in cortical normal‐appearing GM (NAGM) would favor the diagnosis of MS rather than NMO. Meanwhile, normal diffusivities and kurtosis metrics in all NAWM with increases in mean diffusivity in frontal and temporal NAGM suggest NMO.
Abstract
To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants ...was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET). At each time of evaluation, every participant was categorized into SCD, MCI or dementia syndromal group and further into AD-related, non-AD related or non-specific type etiological subgroup. Post 18F-Flutemetamol PET, the significant changes were seen in the syndromal MCI group (57%,
p
< 0.001) involving the following etiological subgroups: AD-related MCI (57%,
p
< 0.01) and non-specific MCI (100%,
p
< 0.0001); and syndromal dementia group (61%,
p
< 0.0001) consisting of non-specific dementia subgroup (100%,
p
< 0.0001). In the binary regression model, amyloid status significantly influenced the diagnostic results of interim analysis (
p
< 0.01). 18F-Flutemetamol PET can have incremental value following MRI measurements, particularly reflected in the change of diagnosis of individuals with unclear etiology and AD-related-suspected patients due to the role in complementing AD-related pathological information.
There have been few studies performed to examine the pathophysiological differences between different types of psychosis, such as between delusional disorder (DD) and schizophrenia (SZ). Notably, ...despite the different clinical characteristics of DD and schizophrenia (SZ), antipsychotics are deemed equally effective pharmaceutical treatments for both conditions. In this context, dopamine dysregulation may be transdiagnostic of the pathophysiology of psychotic disorders such as DD and SZ. In this study, an examination is made of the dopamine synthesis capacity (DSC) of patients with SZ, DD, other psychotic disorders, and the DSC of healthy subjects. Fifty-four subjects were recruited to the study, comprising 35 subjects with first-episode psychosis (11 DD, 12 SZ, 12 other psychotic disorders) and 19 healthy controls. All received an
F-DOPA positron emission tomography (PET)/magnetic resonance (MR) scan to measure DSC (K
value) within 1 month of starting antipsychotic treatment. Clinical assessments were also made, which included Positive and Negative Syndrome Scale (PANSS) measurements. The mean K
was significantly greater in the caudate region of subjects in the DD group (ES = 0.83, corrected p = 0.048), the SZ group (ES = 1.40, corrected p = 0.003) and the other psychotic disorder group (ES = 1.34, corrected p = 0.0045), compared to that of the control group. These data indicate that DD, SZ, and other psychotic disorders have similar dysregulated mechanisms of dopamine synthesis, which supports the utility of abnormal dopamine synthesis in transdiagnoses of these psychotic conditions.
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