Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.

Background: Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis.

Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data.

Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable.

Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse.

A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set.

Persistent perfusion abnormalities at day 1 correspond to different clinical trajectories after stroke.

Background: Perfusion abnormalities after thrombolysis are frequent within and surrounding ischemic lesions, but their relative frequency is not well known.

Objective: To describe the different patterns of perfusion abnormalities observed at 24 hours and compare the characteristics of the patients according to their perfusion pattern.

Methods: From our thrombolysis registry, we included 226 consecutive patients with an available arterial spin labeling (ASL) perfusion sequence at day 1.

Erdheim-Chester disease: look it in the eye. An orbital magnetic resonance imaging study.

Erdheim-Chester disease (ECD) is a rare L-group histiocytosis. Orbital involvement is found in a third of cases, but few data are available concerning the radiological features of ECD-related orbital disease (ECD-ROD). Our aim was to characterize the initial radiological phenotype and outcome of patients with ECD-ROD.

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

Background And Objective: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to help them avoid common pitfalls that will bias and discredit their results. Several tools have been proposed to help deep learning users design their framework for neuroimaging data sets. Software overview: We present here ClinicaDL, one of these software tools.

Benefit of mechanical thrombectomy in acute ischemic stroke related to calcified cerebral embolus.

Purpose: Mechanical thrombectomies (MT) in patients with large vessel occlusion (LVO) related to calcified cerebral embolus (CCE) have been reported, through small case series, being associated with low reperfusion rate and worse outcome, compared to regular MT. The purpose of the MASC (Mechanical Thrombectomy in Acute Ischemic Stroke Related to Calcified Cerebral Embolus) study was to evaluate the incidence of CCEs treated by MT and the effectiveness of MT in this indication.

Methods: The MASC study is a retrospective multicentric (n = 37) national study gathering the cases of adult patients who underwent MT for acute ischemic stroke with LVO related to a CCE in France from January 2015 to November 2019.

Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects.

Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types.

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse.

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools.

Pathomechanisms behind cognitive disorders following ruptured anterior communicating aneurysms: A diffusion tensor imaging study.

Introduction: After the rupture of anterior communicating aneurysms, most patients experience debilitating cognitive disorders; and sometimes even without showing morphological anomaly on MRI examinations. Diffusion Tensor Imaging (DTI) may help understanding the pathomechanisms leading to such disorders in this subset of patients.

Methods: After independent assessment, we constituted a population of patients with normal morphological imaging (ACOM group).

Simultaneously acquired PET and ASL imaging biomarkers may be helpful in differentiating progression from pseudo-progression in treated gliomas.

Objectives: The aim of this work was investigating the methods based on coupling cerebral perfusion (ASL) and amino acid metabolism ([F]DOPA-PET) measurements to evaluate the diagnostic performance of PET/MRI in glioma follow-up.

Methods: Images were acquired using a 3-T PET/MR system, on a prospective cohort of patients addressed for possible glioma progression. Data were preprocessed with statistical parametric mapping (SPM), including registration on T1-weighted images, spatial and intensity normalization, and tumor segmentation.

Reliability and accuracy of time-resolved contrast-enhanced magnetic resonance angiography in hypervascular spinal metastases prior embolization.

Objectives: Preoperative embolization of hypervascular spinal metastases (HSM) is efficient to reduce perioperative bleeding. However, intra-arterial digital subtraction angiography (IA-DSA) must confirm the hypervascular nature and rule out spinal cord arterial feeders. This study aimed to evaluate the reliability and accuracy of time-resolved contrast-enhanced magnetic resonance angiography (TR-CE-MRA) in assessing HSM prior to embolization.

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge.

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1.

Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review.

We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues.

MRI features of intra-axial histiocytic brain mass lesions.

Aim: To describe MRI features, including diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and perfusion-weighted imaging (PWI), of intra-axial tumour-like presentations of four different subtypes of histiocytosis.

Material And Methods: The brain MRI findings of 23 patients with histologically proven histiocytosis were reviewed retrospectively (11 Langerhans cell histiocytosis [LCH], eight Erdheim-Chester disease [ECD], one overlap form LCH/ECD, two Rosai-Dorfman disease [RDD], and one haemophagocytic lymphohistiocytosis [HLH]) with single or multiple enhancing intraparenchymal brain lesions.

Results: Histiocytic brain mass lesions show some similar MRI features including Supra and/or infratentorial and/or paraventricular subcortical well-delineated masses, linear ependymal enhancement along the ventricles and brain stem lesions.

Are Gadolinium-Enhanced MR Sequences Needed in Simultaneous F-FDG-PET/MRI for Tumor Delineation in Head and Neck Cancer?

Background And Purpose: PET/MRI with F-FDG has demonstrated the advantages of simultaneous PET and MR imaging in head and neck cancer imaging, MRI allowing excellent soft-tissue contrast, while PET provides metabolic information. The aim of this study was to evaluate the added value of gadolinium contrast-enhanced sequences in the tumor delineation of head and neck cancers on F-FDG-PET/MR imaging.

Materials And Methods: Consecutive patients who underwent simultaneous head and neck F-FDG-PET/MR imaging staging or restaging followed by surgery were retrospectively included.

Retrospective Observational Study of Brain MRI Findings in Patients with Acute SARS-CoV-2 Infection and Neurologic Manifestations.

Background This study provides a detailed imaging assessment in a large series of patients infected with coronavirus disease 2019 (COVID-19) and presenting with neurologic manifestations. Purpose To review the MRI findings associated with acute neurologic manifestations in patients with COVID-19. Materials and Methods This was a cross-sectional study conducted between March 23 and May 7, 2020, at the Pitié-Salpêtrière Hospital, a reference center for COVID-19 in the Paris area.

Spinal cord infarction during venoarterial-extracorporeal membrane oxygenation support.

Spinal cord infarction (SCI) is a rare disease among central nervous system vascular diseases. Only a little is known about venoarterial extracorporeal membrane oxygenation (VA-ECMO)-related SCI. Retrospective observational study conducted, from 2006 to 2019, in a tertiary referral center on patients who developed VA-ECMO-related neurovascular complications, focusing on SCI.