Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT.

Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations.

Methods: The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.

Purpose: The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.

Clinical Interpretation of Intravoxel Incoherent Motion Perfusion Imaging in the Brain.

Intravoxel incoherent motion (IVIM) perfusion imaging extracts information on blood motion in biological tissue from diffusion-weighted MR images. The method is attractive from a clinical stand point, because it measures in essence local quantitative perfusion, without intravenous contrast injection. Currently, the clinical interpretation of IVIM perfusion maps focuses on the IVIM perfusion fraction maps, but improvements in image quality of the IVIM pseudo-diffusion maps, using advanced postprocessing tools involving artificial intelligence, could lead to an increased interest in this parameters, as it could provide additional local perfusion information in the clinical setting, not otherwise available with other perfusion techniques.

Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects.

Commercial software based on artificial intelligence (AI) is entering clinical practice in neuroradiology. Consequently, medico-legal aspects of using Software as a Medical Device (SaMD) become increasingly important. These medico-legal issues warrant an interdisciplinary approach and may affect the way we work in daily practice.

Correlating volumetric and linear measurements of brain metastases on MRI scans using intelligent automation software: a preliminary study.

Purpose: The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) working group proposed a guide for treatment responses for BMs by utilizing the longest diameter; however, despite recognizing that many patients with BMs have sub-centimeter lesions, the group referred to these lesions as unmeasurable due to issues with repeatability and interpretation. In light of RANO-BM recommendations, we aimed to correlate linear and volumetric measurements in sub-centimeter BMs on contrast-enhanced MRI using intelligent automation software.

Methods: In this retrospective study, patients with BMs scanned with MRI between January 1, 2018, and December 31, 2021, were screened.

Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies.

Purpose: The development of advanced estimators for intravoxel incoherent motion (IVIM) modeling is often motivated by a desire to produce smoother parameter maps than least squares (LSQ). Deep neural networks show promise to this end, yet performance may be conditional on a myriad of choices regarding the learning strategy. In this work, we have explored potential impacts of key training features in unsupervised and supervised learning for IVIM model fitting.

Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis.

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022.

The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology.

Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity of such AI tools, it is not always obvious for the clinical neuroradiologist to capture the technical specifications of these applications, notably as commercial tools very rarely provide full details. The clinical neuroradiologist is thus confronted with the increasing dilemma to base clinical decisions on the output of AI tools without knowing in detail what is happening inside the "black box" of those AI applications.

Signal to noise and b-value analysis for optimal intra-voxel incoherent motion imaging in the brain.

Intravoxel incoherent motion (IVIM) is a method that can provide quantitative information about perfusion in the human body, in vivo, and without contrast agent. Unfortunately, the IVIM perfusion parameter maps are known to be relatively noisy in the brain, in particular for the pseudo-diffusion coefficient, which might hinder its potential broader use in clinical applications. Therefore, we studied the conditions to produce optimal IVIM perfusion images in the brain.

Optimizing Deep Learning Algorithms for Segmentation of Acute Infarcts on Non-Contrast Material-enhanced CT Scans of the Brain Using Simulated Lesions.

Purpose: To test the efficacy of lesion segmentation using a deep learning algorithm on non-contrast material-enhanced CT (NCCT) images with synthetic lesions resembling acute infarcts.

Materials And Methods: In this retrospective study, 40 diffusion-weighted imaging (DWI) lesions in patients with acute stroke (median age, 69 years; range, 62-76 years; 17 women; screened between 2011 and 2017) were coregistered to 40 normal NCCT scans (median age, 70 years; range, 55-76 years; 25 women; screened between 2008 and 2011), which produced 640 combinations of DWI-NCCT with and without lesions for training ( = 420), validation ( = 110), and testing ( = 110). The signal intensity on the NCCT scans was depressed by 4 HU (a 13% drop) in the region of the diffusion-weighted lesion.

Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks.

Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, segmentation is performed on -weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report on a comparative study of automated, learning-based brain segmentation on various other contrasts of MRI and also computed tomography (CT) scans and investigate the anatomical soft-tissue information contained in these imaging modalities.

Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain: Analysis of cancer and acute stroke.

The intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion.

Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models.

We study two state of the art deep generative networks, the Introspective Variational Autoencoder and the Style-Based Generative Adversarial Network, for the generation of new diffusion-weighted magnetic resonance images. We show that high quality, diverse and realistic-looking images, as evaluated by external neuroradiologists blinded to the whole study, can be synthesized using these deep generative models. We evaluate diverse metrics with respect to quality and diversity of the generated synthetic brain images.

Infarct Growth despite Successful Endovascular Reperfusion in Acute Ischemic Stroke: A Meta-analysis.

Background: Infarct volume inversely correlates with good recovery in stroke. The magnitude and predictors of infarct growth despite successful reperfusion via endovascular treatment are not known.

Purpose: We aimed to summarize the extent of infarct growth in patients with acute stroke who achieved successful reperfusion (TICI 2b-3) after endovascular treatment.

Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning.

Purpose: To compare the segmentation and detection performance of a deep learning model trained on a database of human-labeled clinical stroke lesions on diffusion-weighted (DW) images to a model trained on the same database enhanced with synthetic stroke lesions.

Materials And Methods: In this institutional review board-approved study, a stroke database of 962 cases (mean patient age ± standard deviation, 65 years ± 17; 255 male patients; 449 scans with DW positive stroke lesions) and a normal database of 2027 patients (mean age, 38 years ± 24; 1088 female patients) were used. Brain volumes with synthetic stroke lesions on DW images were produced by warping the relative signal increase of real strokes to normal brain volumes.

Simulation of intravoxel incoherent perfusion signal using a realistic capillary network of a mouse brain.

Purpose: To simulate the intravoxel incoherent perfusion magnetic resonance magnitude signal from the motion of blood particles in three realistic vascular network graphs from a mouse brain.

Methods: In three networks generated from the cortex of a mouse scanned by two-photon laser microscopy, blood flow in each vessel was simulated using Poiseuille's law. The trajectories, flow speeds and phases acquired by a fixed number of simulated blood particles during a Stejskal-Tanner bipolar pulse gradient scheme were computed.

Measuring Perfusion: Intravoxel Incoherent Motion MR Imaging.

The signal acquired in vivo using a diffusion-weighted MR imaging (DWI) sequence is influenced by blood motion in the tissue. This means that perfusion information from a DWI sequence can be obtained in addition to thermal diffusion, if the appropriate sequence parameters and postprocessing methods are applied. This is commonly regrouped under the denomination intravoxel incoherent motion (IVIM) perfusion MR imaging.

Effect of Sex on Clinical Outcome and Imaging after Endovascular Treatment of Large-Vessel Ischemic Stroke.

Background And Purpose: It is unclear if sex differences explain some of the variability in the outcomes of stroke patients who undergo endovascular treatment (EVT). In this study we assess the effect of sex on radiological and functional outcomes in EVT-treated acute stroke patients and determine if differences in baseline perfusion status between men and women might account for differences in outcomes.

Methods: We included patients from the CRISP (Computed tomographic perfusion to Predict Response to Recanalization in ischemic stroke) study, a prospective cohort study of acute stroke patients who underwent EVT up to 18 hours after last seen well.

Evaluation of the clinical utility of maximum intensity projections of 3D contrast-enhanced, T1-weighted imaging for the detection of brain metastases.

Background: To visualize and assess brain metastases on magnetic resonance imaging, radiologists face an ever-increasing pressure to perform faster and more efficiently. The usage of maximum intensity projections (MIPs) of contrast-enhanced T1-weighed (T1ce) magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) images proposes to increase reading efficiency by increasing lesion conspicuity while reducing in the number of images to be reviewed.

Aim: To assess if MIPs save reading time and achieve the same level of diagnostic accuracy as standard 1 mm T1ce images for the detection of brain metastases.

Spatial Resolution and the Magnitude of Infarct Volume Measurement Error in DWI in Acute Ischemic Stroke.

Background And Purpose: Infarct volume in acute ischemic stroke is an important prognostic marker and determines endovascular treatment decisions. This study evaluates the magnitude and potential clinical impact of the error related to partial volume effects in infarct volume measurement on diffusion-weighted MR imaging in acute stroke and explores how increasing spatial resolution could reduce this error.

Materials And Methods: Diffusion-weighted imaging of 393 patients with acute stroke, of whom 56 had anterior circulation large-vessel occlusion, was coregistered to standard space.

Tilt-Corrected Region Boundaries May Enhance the Alberta Stroke Program Early Computed Tomography Score for Less Experienced Raters.

Background: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is widely used to quantify early ischemic changes in the anterior circulation but has limited inter-rater reliability.

Aims: We investigated whether application of 3-dimensional boundaries outlining the ASPECTS regions improves inter-rater reliability and accuracy.

Methods: We included all patients from our DEFUSE 2 database who had a pretreatment noncontrast computed tomography scan (NCCT) of acceptable quality.

MRgFUS Pallidothalamic Tractotomy for Chronic Therapy-Resistant Parkinson's Disease in 51 Consecutive Patients: Single Center Experience.

There is a long history, beginning in the 1940s, of ablative neurosurgery on the pallidal efferent fibers to treat patients suffering from Parkinson's disease (PD). Since the early 1990s, we undertook a re-actualization of the approach to the subthalamic region, and proposed, on a histological basis, to target specifically the pallidothalamic tract at the level of Forel's field H1. This intervention, the pallidothalamic tractotomy (PTT), has been performed since 2011 using the MR-guided focused ultrasound (MRgFUS) technique.

Demonstration of asymmetric muscle perfusion of the back after exercise in patients with adolescent idiopathic scoliosis using intravoxel incoherent motion (IVIM) MRI.

The purpose of this work was to quantify muscular perfusion patterns of back muscles after exercise in patients with adolescent idiopathic scoliosis (AIS) using intravoxel incoherent motion (IVIM) MR perfusion imaging. The paraspinal muscles of eight patients with AIS (Cobb angle 35 ± 10°, range [25-47°]) and nine healthy volunteers were scanned with a 1.5 T MRI, at rest and after performing a symmetric back muscle exercise on a Roman chair.