Wilson disease (WD) primarily presents with hepatic and neurological symptoms. While hepatic symptoms typically precede the neurological manifestations, copper accumulates in the brain already in this patient group and leads to subclinical brain MRI abnormalities including T2 hyperintensities and atrophy. This study aimed to assess brain morphological changes in mild hepatic WD. WD patients without a history of neurologic symptoms and decompensated cirrhosis and control participants underwent brain MRI at 3T scanner including high-resolution T1-weighted images. A volumetric evaluation was conducted on the following brain regions: nucleus accumbens, caudate, pallidum, putamen, thalamus, amygdala, hippocampus, midbrain, pons, cerebellar gray matter, white matter (WM), and superior peduncle, using Freesurfer v7 software. Whole-brain analyses using voxel- and surface-based morphometry were performed using SPM12. Statistical comparisons utilized a general linear model adjusted for total intracranial volume, age, and sex. Twenty-six WD patients with mild hepatic form (30 ± 9 years [mean age ± SD]); 11 women; mean treatment duration 13 ± 12 (range 0-42) years and 28 healthy controls (33 ± 9 years; 15 women) were evaluated. Volumetric analysis revealed a significantly smaller pons volume and a trend for smaller midbrain and cerebellar WM in WD patients compared to controls. Whole-brain analysis revealed regions of reduced volume in the pons, cerebellar, and lobar WM in the WD group. No significant differences in gray matter density or cortical thickness were found. Myelin or WM in general seems vulnerable to low-level copper toxicity, with WM volume loss showing promise as a marker for assessing brain involvement in early WD stages.

• MeSH
• bílá hmota patologie   diagnostické zobrazování   MeSH
• dospělí MeSH
• hepatolentikulární degenerace * patologie   diagnostické zobrazování   MeSH
• játra patologie   diagnostické zobrazování   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• mladý dospělý MeSH
• mozek * patologie   diagnostické zobrazování   MeSH
• šedá hmota patologie   diagnostické zobrazování   MeSH
• studie případů a kontrol MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

PURPOSE: The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts. METHODS: Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects. RESULTS: The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitative T2*$$ {\mathrm{T}}_2^{\ast } $$ relaxometry analyses. CONCLUSION: Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.

• MeSH
• algoritmy * MeSH
• artefakty * MeSH
• fantomy radiodiagnostické * MeSH
• kalibrace MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mozek diagnostické zobrazování   MeSH
• počítačové zpracování obrazu * metody   MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0.82±0.07$$ 0.82\pm 0.07 $$ is reduced to: 0.66±0.04$$ 0.66\pm 0.04 $$ (Sign Correction); 0.34±0.04$$ 0.34\pm 0.04 $$ and 0.32±0.04$$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value <0.05$$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.

• MeSH
• algoritmy * MeSH
• aorta * diagnostické zobrazování   MeSH
• artefakty * MeSH
• fantomy radiodiagnostické MeSH
• interpretace obrazu počítačem metody   MeSH
• intrakraniální aneurysma diagnostické zobrazování   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mozek diagnostické zobrazování   MeSH
• počítačová simulace MeSH
• počítačové zpracování obrazu * metody   MeSH
• poměr signál - šum * MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy. METHODS: A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis. RESULTS: Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%). CONCLUSIONS: For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

• MeSH
• hodnocení rizik metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• multiparametrická magnetická rezonance * metody   MeSH
• nádory prostaty * patologie   diagnostické zobrazování   diagnóza   krev   MeSH
• prostata patologie   diagnostické zobrazování   MeSH
• prostatický specifický antigen * krev   MeSH
• retrospektivní studie MeSH
• ROC křivka MeSH
• senioři MeSH
• stupeň nádoru MeSH
• ultrazvukem navigovaná biopsie metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. METHODS: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. RESULTS: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. CONCLUSIONS: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.

• MeSH
• dospělí MeSH
• internet MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• neuromuskulární nemoci * diagnóza   diagnostické zobrazování   MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

The study evaluates the efficacy of RETROICOR (Retrospective Image Correction) in mitigating physiological artifacts within multi-echo (ME) fMRI data. Two RETROICOR implementations were compared: applying corrections to individual echoes (RTC_ind) versus composite multi-echo data (RTC_comp). Data from 50 healthy participants were collected using diverse acquisition parameters, including multiband acceleration factors and varying flip angles, on a Siemens Prisma 3T scanner. Key metrics such as temporal signal-to-noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals demonstrated improved data quality in both RETROICOR models, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°). Differences between RTC_ind and RTC_comp were minimal, suggesting both methods are viable for practical applications. While the highest acceleration (multiband factor 8) degraded data quality, RETROICOR's compatibility with faster acquisition sequences was confirmed. These findings underscore the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations.

• MeSH
• artefakty * MeSH
• dospělí MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mapování mozku * metody   MeSH
• mladý dospělý MeSH
• mozek * diagnostické zobrazování   fyziologie   MeSH
• počítačové zpracování obrazu * metody   MeSH
• poměr signál - šum MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. METHODS: We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). RESULTS: The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. CONCLUSIONS: Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.

• MeSH
• dospělí MeSH
• glioblastom * patologie   klasifikace   mortalita   diagnostické zobrazování   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• míra přežití MeSH
• mladý dospělý MeSH
• nádory mozku * patologie   klasifikace   mortalita   diagnostické zobrazování   MeSH
• následné studie MeSH
• prognóza MeSH
• senioři MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH

BACKGROUND: Cerebellar Mutism Syndrome (CMS) is a neurological complication of posterior fossa (PF) tumour surgery in children, and postoperative speech impairment (POSI) is the cardinal symptom of CMS. The role of tumour volume on the risk of POSI remains unexplored. This study investigates the association between tumour volume and the risk of POSI. METHODS: We included 360 patients from the European CMS study with available preoperative T1-weighted contrast-enhanced brain MRI. Speech status was assessed within two weeks postoperatively and categorised into three levels: habitual speech, severely reduced speech, and mutism. Tumour volumes were calculated using the BrainLab Elements SmartBrushTM, a semi-automated segmentation tool. We used proportional odds models to estimate the odds ratio (OR) with adjustments for tumour location, pathology, and age. Based on the primary analysis, a risk stratification model for medulloblastoma patients was constructed, and the optimal volume cut-off was determined with Youden's Index. RESULTS: We found no effect of the overall tumour volume on the risk of POSI. This result did not change when adjusted for tumour location, pathology, and age. We found an association between tumour volume of medulloblastoma and the risk of POSI (unadjusted OR of 1.04 per increase in cm3 (95% CI 1.01;1.07, p = 0.01)), which did not change when adjusting for tumour location and age. The risk stratification cut-off for the tumour volume of medulloblastoma was calculated to be 16,5 cm3. Patients with medulloblastoma and preoperative tumour volumes below 16,5 cm3 had an absolute risk of 13% for POSI (low-risk group), whereas patients with preoperative tumour volumes above 16,5 cm3 had an absolute risk of 50% for POSI (high-risk group). CONCLUSION: Our data showed an association between preoperative tumour volume and the risk of POSI in children with medulloblastoma, while no association was found for the volume of other tumour types. We suggest a straightforward cut-off risk model for assessing the risk of POSI in children with medulloblastoma based on preoperative tumour volume. This approach can aid clinicians in informing patients and parents about the complications related to CMS following PF tumour surgery in children. CLINICAL TRIALS: ID NCT02300766 (October 2014).

• MeSH
• dítě MeSH
• infratentoriální nádory * chirurgie   patologie   MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• meduloblastom * chirurgie   patologie   MeSH
• mladiství MeSH
• mutismus * etiologie   MeSH
• nádory mozečku * chirurgie   patologie   MeSH
• pooperační komplikace * etiologie   MeSH
• poruchy řeči * etiologie   MeSH
• předškolní dítě MeSH
• prospektivní studie MeSH
• tumor burden * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH

BACKGROUND: The hypothalamus (HT) plays a crucial role in regulating eating behaviors. Disruptions in its function have been linked to the development of weight-related disorders. Nevertheless, its characterization remains a challenge. OBJECTIVES: We assessed the structural alterations of individual HT nuclei related to eating behaviors in patients with weight-related disorders, and their association with body mass index (BMI) and severity of eating disorders. METHODS: Forty-four young females with normal weight (HC, n = 21), restrictive anorexia nervosa (AN, n = 13), and living with obesity (OB, n = 10) were explored in vivo using 7-T high-resolution (0.6 mm isotropic voxel) T1 quantitative magnetic resonance imaging (MRI). Volumes and quantitative T1 values of individual HT nuclei were compared after whole-brain normalization using nonparametric tests (corrected for multiple comparisons for groups and regions). We investigated the parameters associated with BMI and eating disorders, such as MRI parameters of HT nuclei, ghrelin and leptin levels, depression, and anxiety using multivariate nonlinear partial least square (NIPALS). RESULTS: Both AN and OB showed higher volumes of HT relative to HC (Zscores: 0.78 ± 1.06; 1.43 ± 1.51). AN showed significantly higher volumes and T1 values of the right paraventricular nucleus (PaVN) (volume Zscore: 1.82 ± 1.45; T1 Zscore: 3.76 ± 4.67), and higher T1 values of the left PaVN (Zscore: 2.25 ± 2.37) and right periventricular nuclei (Zscore: 3.73 ± 4.81). NIPALS models showed that lower BMI in AN was associated with structural alterations of the bilateral PaVN, right anterior commissure, and left fornix (FX). Higher BMI in OB was associated with structural alterations within the right PaVN, bilateral FX, left posterior hypothalamic nucleus, right lateral HT, and right anterior hypothalamic area. Finally, the severity of eating disorders was associated with larger structural alterations within the bilateral PaVN, bilateral arcuate hypothalamic nuclei, right bed nucleus of stria terminalis, left medial preoptic nucleus, and right tubero-mammillary hypothalamic nucleus. CONCLUSIONS: Weight-related disorders are associated with significant micro and macrostructural alterations in HT nuclei involved in eating behaviors.

• MeSH
• dospělí MeSH
• hypothalamus * diagnostické zobrazování   patologie   MeSH
• index tělesné hmotnosti MeSH
• leptin krev   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• mentální anorexie * diagnostické zobrazování   patologie   MeSH
• mladiství MeSH
• mladý dospělý MeSH
• obezita * diagnostické zobrazování   patologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age. METHODS: We performed a systematic search of PubMed and Scopus. We used the brain age gap, which represents the difference between one's predicted and chronological age, as an index of brain structural integrity. We calculated the Cohen d statistic for mean differences in the brain age gap of people with and without diabetes, hypertension, or obesity and performed random effects meta-analyses. RESULTS: We identified 185 studies, of which 14 met inclusion criteria. Among the 3 cardiometabolic risk factors, diabetes had the highest effect size (12 study samples; d = 0.275, 95% confidence interval [CI] 0.198-0.352; n = 47 436), followed by hypertension (10 study samples; d = 0.113, 95% CI 0.063-0.162; n = 45 102) and obesity (5 study samples; d = 0.112, 95% CI 0.037-0.187; n = 15 678). These effects remained significant in sensitivity analyses that included only studies that controlled for confounding effects of the other cardiometabolic risk factors. LIMITATIONS: Our study tested effect sizes of only categorically defined cardiometabolic risk factors and is limited by inconsistencies in diabetes classification, a smaller pooled sample in the obesity analysis, and limited age range reporting. CONCLUSION: Our findings show that each of the cardiometabolic risk factors uniquely contributes to brain structure, as captured by brain age. The effect size for diabetes was more than 2 times greater than the independent effects of hypertension and obesity. We therefore highlight diabetes as a primary target for the prevention of brain structural changes that may lead to cognitive decline and dementia.

• MeSH
• diabetes mellitus * epidemiologie   patologie   MeSH
• hypertenze * epidemiologie   patologie   MeSH
• kardiometabolické riziko * MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mozek * diagnostické zobrazování   patologie   MeSH
• obezita * epidemiologie   patologie   MeSH
• stárnutí patologie   MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• systematický přehled MeSH