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To differentiate hypo-/hypertelorism (abnormal) from normal fetuses using automatic biometric measurements and machine learning (ML) classification based on MRI.MRI data of normal (n = 244) and abnormal (n = 52) fetuses of 22-40 weeks' gestational age (GA), scanned between March 2008 and June 2020 on 1.5/3T systems with various TThe automatic method yielded a mean difference of BOD = 0.70 mm, IOD = 0.81 mm, OD = 1.00 mm, and a 3D-Dice score of OV = 93.7%. In normal fetuses, all four measurements increased with GA. Constant values were detected for BOD-ratio = 1.56 ± 0.05 and IOD-ratio = 0.60 ± 0.05 across all GA and when calculated from previously published reference data of both MRI and ultrasound. A random forest classifier yielded the best results on an independent test set (n = 58): AUC-ROC = 0.941 and FThe developed fully automatic method demonstrates high performance on varied clinical imaging data. The new BOD and IOD ratios and ML multi-parametric classifier are suggested to improve the differentiation of hypo-/hypertelorism from normal fetuses.• A fully automatic method for computing fetal ocular biometry from MRI is proposed, achieving high performance, comparable to that of an expert fetal neuro-radiologist. • Two new parameters, IOD-ratio and BOD-ratio, are proposed for routine clinical use in ultrasound and MRI. These two ratios are constant across gestational age in normal fetuses, consistent across studies, and differentiate between fetuses with and without hypo/hypertelorism. • Multi-parametric machine learning classification based on automatic measurements and the two new ratios improves the identification of fetal ocular anomalies beyond the accepted criteria (5

Supplementary Material 2

Supplementary Material 1

Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI.The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome.Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases.The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.

Purpose Characterizing vessel territories can provide crucial information for evaluation of cerebrovascular disorders. In this study, we present a novel postprocessing pipeline for vascular territorial imaging of cerebral arteries based on a noncontrast enhanced time-resolved 4D magnetic resonance angiography (MRA). Methods Eight healthy participants, 1 Moyamoya patient, and 1 arteriovenous malformations patient were recruited. Territorial segmentation and relative blood flow rate calculations of cerebral arteries including left and right middle cerebral arteries and left and right posterior cerebral arteries were carried out based on the 4D MRA-derived arterial arrival time maps of intracranial vessels. Results Among healthy young subjects, the average relative blood flow rate values corresponding to left and right middle cerebral arteries and left and right posterior cerebral arteries were 35.9 ± 5.9%, 32.9 ± 7.5%, 15.4 ± 3.8%, and 15.9 ± 2.5%, respectively. Excellent agreement was observed between relative blood flow rate values obtained from the proposed 4D MRA-based method and reference 2D phase contrast MRI. Abnormal cerebral circulations were visualized and quantified on both patients using the developed technique. Conclusion The vascular territorial imaging technique developed in this study allowed for the generation of territorial maps with user-defined level of details within a clinically feasible scan time, and as such may provide useful information to assess cerebral circulation balance in different pathologies.

Purpose Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI. Methods A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A fivefold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel-based classification of the tumor into enhanced, non-enhanced, and cystic components was performed using fuzzy c-means clustering. Results The results of the automatic tumor segmentation were: mean dice score = 0.736 ± 0.025, precision = 0.918 ± 0.014, and recall = 0.635 ± 0.039 for the validation data, and dice score = 0.761 ± 0.011, precision = 0.794 ± 0.028, and recall = 0.742 ± 0.012 for the test data. The accuracy of the voxel-based classification of tumor components was 0.94, with precision = 0.89, 0.97, and 0.85, and recall = 1.00, 0.79, and 0.94 for the non-enhanced, enhanced, and cystic components, respectively. Conclusion This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.

MRI was suggested as a promising method for the diagnosis and assessment of Parkinson's Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T

In recent years, several automatic methods have been proposed to differentiate between brain tumor recurrence and treatment-related changes following radiotherapy, based on conventional and advanced MRI methods. Vascular parameters extracted from dynamic contrast-enhanced (DCE) were suggested as potential markers for disease progression. Yet, most studies that proposed machine-learning methods or relayed on threshold values of DCE for classification, did not analyze separately Glioblastoma (GBM) and brain metastasis, and often offer the same method/threshold values, neglecting the different vascularity of these two tumor types. Understanding and quantifying the differences between tumors type can improve early diagnosis of recurrence and may improve the reliability of identifying treatment-related changes. The aim of this study was to quantify vascular parameters of tumor recurrence and to assess differences between GBM and brain metastasis, specifically metastasis of breast cancer. METHOD: 41 MRI scans were included: 24 from patients with GBM: 20 with recurrence and 4 with treatment-related changes, and 17 scans from patients with brain metastasis of breast cancer: 10 with recurrence and 7 with treatment-related changes (diagnosis were based on radiological follow-up/histopathology, when available). MRIs were performed on a 3.0 Tesla MRI scanner (Siemens MAGNETOM Prisma) and included T1, T1+contrast, and DCE images. DCE analysis was performed using DUSTER and Ktrans, Vp and Kep pharmacokinetic parameters were compared between groups. RESULTS: Patients with GBM often demonstrated both tumor recurrence and areas with treatment-related changes. Significantly differences were detected between tumor recurrence and treatment-related changes, and higher Vp and Ktrans were detected in recurrent GBM as compared to recurrent breast cancer metastasis. Cutoff for differentiation between tumor recurrence and treatment-related changes will be presented specifically for each tumor type. To conclude, when defining quantitative threshold values for clinical decision-making, the inherent differences between primary and metastatic brain tumors should be taken into account.

Background: Current evidence suggest that 25%–33% of stroke-survivors develop post-stroke cognitive impairment (PSCI). The licensed drug Maraviroc, a CCR5-antagonist, is postulated to act via a neuroprotective mechanism that may offer the potential of preventing progression to vascular dementia. Our hypothesis: Maraviroc may have the potential to augment learning skills and cognitive performance by affecting synaptic plasticity, along with neuro-inflammatory modulation in patients with cerebral small vessel disease (SVD) and PSCI. Design: MARCH is a multi-center, double-blind randomized-control Phase-II trial of Maraviroc 150 or 600 mg/day versus placebo for 12-months in five stroke centers in Israel. Included are patients diagnosed with recent (1–24 months) subcortical stroke who experience mild PSCI and have evidence of white matter lesions and SVD on neuroimaging. Outcomes: Primary outcomes: 1. Change in cognitive scores. 2. Drug related adverse events. Secondary outcomes: change in functional and affective scores, MRI-derived measures, inflammatory markers, carotid atherosclerosis, cerebrospinal-fluid biomarkers in a sub-study. A sample size of 60 in each treatment group and 30 in the placebo group (total – 150 participants) provides 80% power between the treatment and the placebo groups. Conclusions: The results of this work could lead to a novel, readily available, therapeutic avenue to reduce PSCI, and possibly other pathologies. This study will test safety and effectiveness of Maraviroc in limiting cognitive deterioration and/or post stroke cognitive impairment in patients with cerebral small vessel disease. Schedule: First-patient first-visit was May 2021. Recruitment to complete in 2023, follow-up to complete in 2024.