Publications by authors named "Dafna Ben Bashat"

Alpha-synuclein (αS) aggregation is a widely regarded hallmark of Parkinson's disease (PD) and can be detected through synuclein amplification assays (SAA). This study investigated the association between cerebrospinal fluid (CSF) radiological measures in 41 PD patients (14 iPD, 14 GBA1-PD, 13 LRRK2-PD) and 14 age-and-sex-matched healthy controls. Quantitative measures including striatal binding ratios (SBR), whole-brain and deep gray matter volumes, neuromelanin-MRI (NM-MRI), functional connectivity (FC), and white matter (WM) diffusion-tensor imaging (DTI) were calculated.

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Background: The American Academy of Pediatrics advises that the nutrition of preterm infants should target a body composition similar to that of a fetus in utero. Still, reference charts for intrauterine body composition are missing. Moreover, data on sexual differences in intrauterine body composition during pregnancy are limited.

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Background: Cytomegalovirus (CMV) is the most common intrauterine infection and may be associated with unfavorable outcomes. While some CMV-infected fetuses may show gross or subtle brain abnormalities on MRI, their clinical significance may be unclear. Conversely, normal development cannot be guaranteed in CMV-infected fetuses with normal MRI.

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Placental-related fetal growth restriction, resulting from placental dysfunction, impacts 3-5% of pregnancies and is linked to elevated risk of adverse neurodevelopmental outcomes. In response, the fetus employs a mechanism known as brain-sparing, redirecting blood flow to the cerebral circuit, for adequate supply to the brain. In this study we aimed to quantitatively evaluate disparities in gyrification and brain volumes among fetal growth restriction, small for gestational age and appropriate-for gestational-age fetuses.

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Article Synopsis
  • The study aims to develop an automated method to quantitatively assess fetal brain gyrification using standard 2D MR imaging, instead of relying on subjective visual assessments.
  • It involves analyzing imaging data of 162 fetuses—134 controls and 28 with lissencephaly or polymicrogyria—to calculate various gyrification parameters and differentiate between normal and abnormal conditions.
  • Results indicate significant changes in gyrification with gestational age for normal fetuses, as well as reductions in lissencephaly and polymicrogyria cases, with machine learning algorithms effectively classifying these conditions.
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Background: Small for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification.

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Objectives: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR).

Methods: Retrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases.

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Objectives: Fat-water MRI can be used to quantify tissues' lipid content. We aimed to quantify fetal third trimester normal whole-body subcutaneous lipid deposition and explore differences between appropriate for gestational age (AGA), fetal growth restriction (FGR), and small for gestational age fetuses (SGAs).

Methods: We prospectively recruited women with FGR and SGA-complicated pregnancies and retrospectively recruited the AGA cohort (sonographic estimated fetal weight [EFW] ≥ 10th centile).

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Article Synopsis
  • In-utero fetal MRI is becoming a crucial method for diagnosing and analyzing the developing brain, but manually segmenting cerebral structures is slow and error-prone.
  • The Fetal Tissue Annotation (FeTA) Challenge was established in 2021 to promote the creation of automatic segmentation algorithms, utilizing a dataset with seven segmented fetal brain tissue types.
  • The challenge saw 20 international teams submit algorithms, primarily based on deep learning techniques like U-Nets, with one team's asymmetrical U-Net architecture significantly outperforming others, establishing a benchmark for future segmentation efforts.
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Background: Creutzfeldt-Jakob Disease (CJD) is the most common prion disease in humans causing a rapidly progressive neurological decline and dementia and is invariably fatal. The familial forms (genetic CJD, gCJD) are caused by mutations in the PRNP gene encoding for the prion protein (PrP). In Israel, there is a large cluster of gCJD cases, carriers of an E200K mutation in the PRNP gene, and therefore the largest population of at-risk individuals in the world.

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White-matter tract segmentation in patients with brain pathology can guide surgical planning and can be used for tissue integrity assessment. Recently, TractSeg was proposed for automatic tract segmentation in healthy subjects. The aim of this study was to assess the use of TractSeg for corticospinal-tract (CST) segmentation in a large cohort of patients with brain pathology and to evaluate its consistency in repeated measurements.

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Objective: 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.

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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* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT.

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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.

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Cerebrovascular vessel segmentation is a key step in the detection of vessel pathology. Brain time-of-flight magnetic resonance angiography (TOF-MRA) is a main method used clinically for imaging of blood vessels using magnetic resonance imaging. This method is primarily used to detect narrowing, blockage of the arteries, and aneurysms.

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Objectives: To differentiate hypo-/hypertelorism (abnormal) from normal fetuses using automatic biometric measurements and machine learning (ML) classification based on MRI.

Methods: 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 T-weighted sequences and image resolutions, were included.

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Non-manifesting carriers (NMCs) of Parkinson's disease (PD)-related mutations such as LRRK2 and GBA are at an increased risk for developing PD. Dopamine transporter (DaT)-spectral positron emission computed tomography is widely used for capturing functional nigrostriatal dopaminergic activity. However, it does not reflect other ongoing neuronal processes; especially in the prodromal stages of the disease.

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Article Synopsis
  • Fetal ventriculomegaly is a common prenatal abnormality, and identifying other subtle issues can help predict outcomes, from normal development to severe neurodevelopmental problems.
  • The paper reviews how to understand the causes and imaging patterns of ventriculomegaly and hydrocephalus in fetuses through pattern recognition using fetal MRI.
  • This radiological method can improve predictions about the clinical progression and treatment effectiveness for fetal hydrocephalus.
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Background: Advanced magnetic resonance imaging (MRI) methods are increasingly being used to assess the human placenta. Yet, the structure-function interplay in normal placentas and their associations with pregnancy risks are not fully understood.

Purpose: To characterize the normal human placental structure (volume and umbilical cord centricity index (CI)) and function (perfusion) ex-vivo using MRI, to assess their association with birth weight (BW), and identify imaging-markers for placentas at risk for dysfunction.

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Objective: T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence.

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Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are cerebral biparietal diameter (CBD), bone biparietal diameter (BBD), and trans-cerebellum diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error.

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A novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus causing coronavirus disease 2019 (COVID-19) disease, which emerged as a global pandemic. Data regarding the implications of COVID-19 disease at early gestation on fetal and obstetric outcomes is scarce. Thus, our aim was to investigate the effect of first and second trimester maternal COVID-19 disease on fetal and perinatal outcomes.

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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.

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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.

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