暂无摘要。

BACKGROUND: Quantitative susceptibility mapping (QSM) is a post-processing technique that creates brain susceptibility maps reflecting metal burden through tissue magnetic susceptibility. We assessed topographic differences in magnetic susceptibility between participants with and without Wilson\'s disease (WD), correlating these findings with clinical severity, brain volume, and biofluid copper and iron indices.
METHODS: A total of 43 patients with WD and 20 unaffected controls, were recruited. QSM images were derived from a 3T MRI scanner. Clinical severity was defined using the minimal Unified Wilson\'s Disease Rating Scale (M-UWDRS) and Montreal Cognitive Assessment scoring. Differences in magnetic susceptibilities between groups were evaluated using general linear regression models, adjusting for age and sex. Correlations between the susceptibilities and clinical scores were analyzed using Spearman\'s method.
RESULTS: In age- and sex-adjusted analyses, magnetic susceptibility values were increased in WD patients compared with controls, including caudate nucleus, putamen, globus pallidus, and substantia nigra (all p < 0.01). Putaminal susceptibility was greater with an initial neuropsychiatric presentation (n = 25) than with initial hepatic dysfunction (n = 18; p = 0.04). Susceptibility changes correlated negatively with regional brain volume in almost all topographic regions. Serum ferritin, but not serum copper or ceruloplasmin, correlated positively with magnetic susceptibility level in the caudate nucleus (p = 0.04), putamen (p = 0.04) and the hippocampus (p = 0.03). The dominance of magnetic susceptibility in cortical over subcortical regions correlated with M-UWDRS scores (p < 0.01).
CONCLUSIONS: The magnetic susceptibility changes could serve as a surrogate marker for patients with WD.

Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks, particularly in image generation. However, their notable performance heavily relies on labelled datasets, which limits their application in medical images due to the associated high-cost annotations. Current DPM-related methods for lesion detection in medical imaging, which can be categorized into two distinct approaches, primarily rely on image-level annotations. The first approach, based on anomaly detection, involves learning reference healthy brain representations and identifying anomalies based on the difference in inference results. In contrast, the second approach, resembling a segmentation task, employs only the original brain multi-modalities as prior information for generating pixel-level annotations. In this paper, our proposed model - discrepancy distribution medical diffusion (DDMD) - for lesion detection in brain MRI introduces a novel framework by incorporating distinctive discrepancy features, deviating from the conventional direct reliance on image-level annotations or the original brain modalities. In our method, the inconsistency in image-level annotations is translated into distribution discrepancies among heterogeneous samples while preserving information within homogeneous samples. This property retains pixel-wise uncertainty and facilitates an implicit ensemble of segmentation, ultimately enhancing the overall detection performance. Thorough experiments conducted on the BRATS2020 benchmark dataset containing multimodal MRI scans for brain tumour detection demonstrate the great performance of our approach in comparison to state-of-the-art methods.

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

OBJECTIVE: The image quality of synthetized FLAIR (fluid attenuated inversion recovery) images is generally inferior to its conventional counterpart, especially regarding the lesion contrast mismatch. This work aimed to improve the lesion appearance through a hybrid methodology.
METHODS: We combined a full brain 5-min MR-STAT acquisition followed by FLAIR synthetization step with an ultra-under sampled conventional FLAIR sequence and performed the retrospective and prospective analysis of the proposed method on the patient datasets and a healthy volunteer.
RESULTS: All performance metrics of the proposed hybrid FLAIR images on patient datasets were significantly higher than those of the physics-based FLAIR images (p < 0.005), and comparable to those of conventional FLAIR images. The small difference between prospective and retrospective analysis on a healthy volunteer demonstrated the validity of the retrospective analysis of the hybrid method as presented for the patient datasets.
CONCLUSIONS: The proposed hybrid FLAIR achieved an improved lesion appearance in the clinical cases with neurological diseases compared to the physics-based FLAIR images, Future prospective work on patient data will address the validation of the method from a diagnostic perspective by radiological inspection of the new images over a larger patient cohort.

Solitary fibrous tumors (SFTs) are rare mesenchymal neoplasms that can occur intraventricularly, presenting diagnostic and management challenges. We describe a case of a 21-year-old male with no significant medical history who presented with intermittent headaches and vomiting, progressing to continuous symptoms. Neurological examination was unremarkable. Brain MRI revealed an isointense lesion in the occipital horn of the left lateral ventricle, diagnosed as an SFT. Surgical excision via a transcortical approach was successful, followed by postoperative radiotherapy. This case highlights the complexities in diagnosing and treating intraventricular SFTs, emphasizing the need for comprehensive evaluation and multimodal management strategies.

X-linked adrenoleukodystrophy (X-ALD) caused by ATP-binding cassette subfamily D member 1 (ABCD1) gene defects is the most common inherited peroxisomal disorder.The female cerebello-brainstem dominant type in which cerebellum and brainstem are mainly involved is very rare. We report a 40-year-old female who was diagnosed as the rare disorder with magnetic resonance imaging (MRI) and genetic analysis mainly. Her initial symptoms were progressive slurred speech and writing disturbance. Her brain MRI showed obvious atrophy of brainstem and cerebellum. She did not have adrenal insufficiency. Genetic analysis showed a heterozygous missense mutation in exon 4 of the coding region of ABCD1 (c.1252C > T, p.Arg418Trp).This is the first report of this particular mutation being associated with the cerebello-brainstem dominant phenotype of X-ALD, as well as the first description of this X-ALD variant in a (heterozygous) female patient.X-ALD should be considered in young and middle-aged patients with slow-progressing ataxia and dysarthria.

暂无摘要。

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or \'g\')? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate. There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9-59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1-88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas.