Radiological markers of CSF α-synuclein aggregation in Parkinson's disease patients.

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.

Automatic segmentation of femoral tumors by nnU-net.

Background: Metastatic femoral tumors may lead to pathological fractures during daily activities. A CT-based finite element analysis of a patient's femurs was shown to assist orthopedic surgeons in making informed decisions about the risk of fracture and the need for a prophylactic fixation. Improving the accuracy of such analyses ruqires an automatic and accurate segmentation of the tumors and their automatic inclusion in the finite element model.

Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression.

Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression.

Handling Missing MRI Data in Brain Tumors Classification Tasks: Usage of Synthetic Images vs. Duplicate Images and Empty Images.

Background: Deep-learning is widely used for lesion classification. However, in the clinic patient data often has missing images.

Purpose: To evaluate the use of generated, duplicate and empty(black) images for replacing missing MRI data in AI brain tumor classification tasks.

Differentiating spinal pathologies by deep learning approach.

Background Context: Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clinical judgment incorporating lab findings and radiological studies. When the diagnosis is uncertain, a biopsy is almost always mandatory since the treatment is dictated by the type of pathology.

Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology.

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.

Lesions to both somatic and affective pain pathways lead to decreased salience network connectivity.

Human pain is a salient stimulus composed of two main components: a sensory/somatic component, carrying peripheral nociceptive sensation via the spinothalamic tract and brainstem nuclei to the thalamus and then to sensory cortical regions, and an affective (suffering) component, where information from central thalamic nuclei is carried to the anterior insula, dorsal anterior cingulate cortex and other regions. While the sensory component processes information about stimulus location and intensity, the affective component processes information regarding pain-related expectations, motivation to reduce pain and pain unpleasantness. Unlike investigations of acute pain that are based on the introduction of real-time stimulus during brain recordings, chronic pain investigations are usually based on longitudinal and case-control studies, which are limited in their ability to infer the functional network topology of chronic pain.

Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma.

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.

Neuromelanin and T*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease.

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.

BV-GAN: 3D time-of-flight magnetic resonance angiography cerebrovascular vessel segmentation using adversarial CNNs.

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.

Aberrant dopamine transporter and functional connectivity patterns in LRRK2 and GBA mutation carriers.

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.

Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.

Purpose: Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient's EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure.

Whole brain and deep gray matter structure segmentation: Quantitative comparison between MPRAGE and MP2RAGE sequences.

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.

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning approach based on conventional magnetic resonance imaging (MRI) for non-invasive classification of SCLC vs. NSCLC brain metastases.

Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

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.

Virtual biopsy using MRI radiomics for prediction of BRAF status in melanoma brain metastasis.

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.

Ruminative Tendency Relates to Ventral Striatum Functionality: Evidence From Task and Resting-State fMRI.

Background: Ruminative responding involves repetitive and passive thinking about one's negative affect. This tendency interferes with initiation of goal-directed rewarding strategies, which could alleviate depressive states. Such reward-directed response selection has been shown to be mediated by ventral striatum/nucleus accumbens (VS/NAcc) function.

Neuroanatomical Risk Factors for Posttraumatic Stress Disorder in Recent Trauma Survivors.

Background: Low hippocampal volume could serve as an early risk factor for posttraumatic stress disorder (PTSD) in interaction with other brain anomalies of developmental origin. One such anomaly may well be the presence of a large cavum septum pellucidum (CSP), which has been loosely associated with PTSD. We performed a longitudinal prospective study of recent trauma survivors.

Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.

Background: Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI.

Purpose: To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post-contrast T -weighted (T W) MRI.

Repeatability of dynamic contrast enhanced v parameter in healthy subjects and patients with brain tumors.

Purpose: To study the repeatability of plasma volume (v) extracted from dynamic-contrast-enhanced (DCE) MRI in order to define threshold values for significant longitudinal changes, and to assess changes in patients with high-grade-glioma (HGG).

Methods: Twenty eight healthy subjects, of which eleven scanned twice, were used to assess the repeatability of v within the normal-appearing brain tissue and to define threshold values for significant changes based on least-detected-differences (LDD) of mean v values and histogram comparisons using earth-mover's-distance (EMD). Sixteen patients with HGG were scanned longitudinally with eight patients scanned before and following bevacizumab therapy.

Differentiation between vasogenic edema and infiltrative tumor in patients with high-grade gliomas using texture patch-based analysis.

Background: High-grade gliomas (HGGs) induce both vasogenic edema and extensive infiltration of tumor cells, both of which present with similar appearance on conventional MRI. Using current radiological criteria, differentiation between these tumoral and nontumoral areas within the nonenhancing lesion area remains challenging.

Purpose: To use radiomics patch-based analysis, based on conventional MRI, for the classification of the nonenhancing lesion area in patients with HGG into tumoral and nontumoral components.

MRI radiomics analysis of molecular alterations in low-grade gliomas.

Purpose: Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts.

Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

Background: Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort.

Subjects And Methods: A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation.

DaT-SPECT assessment depicts dopamine depletion among asymptomatic G2019S LRRK2 mutation carriers.

Identification of early changes in Dopamine-Transporter (DaT) SPECT imaging expected in the prodromal phase of Parkinson's disease (PD), are usually overlooked. Carriers of the G2019S LRRK2 mutation are known to be at high risk for developing PD, compared to non-carriers. In this work we aimed to study early changes in Dopamine uptake in non-manifesting PD carriers (NMC) of the G2019S LRRK2 mutation using quantitative DaT-SPECT analysis and to examine the potential for early prediction of PD.

Vascular territorial segmentation and volumetric blood flow measurement using dynamic contrast enhanced magnetic resonance angiography of the brain.

This study proposes a method for territorial segmentation and volumetric flow rate (VFR) distribution measurement of cerebral territories based on time-resolved contrast enhanced magnetic-resonance-angiography (MRA). The method uses an iterative region-growing algorithm based on bolus-arrival-time with increased temporal resolution. Eight territories were segmented: (1) right and (2) left internal carotid arteries, including the middle cerebral artery (ICA+MCA), excluding the anterior cerebral arteries (ACA); (3) right and left ACA (R+L-ACA); (4) right and (5) left external carotid arteries (ECA); (6) right and (7) left posterior cerebral arteries (PCA); and (8) vertebrobasilar territory.