Is small fiber neuropathy induced by gadolinium-based contrast agents?

OBJECTIVES: In recent years, complaints of patients about burning pain in arms and legs after the injection of gadolinium-based contrast agents (GBCAs) have been reported. In the current study, we investigated changes of small fibers in the epidermis as a potential cause of the patient complaints in a mouse model. METHODS: Six groups of 8 mice were intravenously injected with either a macrocyclic GBCA (gadoteridol, gadoterate meglumine, gadobutrol), a linear GBCA (gadodiamide or gadobenate dimeglumine) (1 mmol/kg body weight), or saline (NaCl 0.9%). Four weeks after injection, animals were euthanized, and footpads were assessed using immunofluorescence staining. Intraepidermal nerve fiber density (IENFD) was calculated, and the median number of terminal axonal swellings (TASs) per IENFD was determined. RESULTS: Nonparametric Wilcoxon signed-rank test revealed significantly lower IENFDs for all GBCAs compared with the control group (P < 0.0001) with the linear GBCAs showing significantly lower IENFDs than the macrocyclic GBCAs (P < 0.0001). The linear GBCAs presented significantly more TAS per IENFD than the control group (P < 0.0001), whereas no significant increase of TAS per IENFD compared with the control group was found for macrocyclic GBCAs (P < 0.237). INTERPRETATION: It is unclear whether or at what dosage the decrease of IENFDs and the increase of TAS per IENFD found in the current animal model will appear in humans and if it translates into clinical symptoms. However, given the highly significant findings of the current study, more research in this field is required

Sensitivity of different MRI sequences in the early detection of melanoma brain metastases.

After the emergence of new MRI techniques such as susceptibility- and diffusion-weighted imaging (SWI and DWI) and because of specific imaging characteristics of melanoma brain metastases (MBM), it is unclear which MRI sequences are most beneficial for detection of MBM. This study was performed to investigate the sensitivity of six clinical MRI sequences in the early detection of MBM.Medical records of all melanoma patients referred to our center between November 2005 and December 2016 were reviewed for presence of MBM. Analysis encompassed six MRI sequences at the time of initial diagnosis of first or new MBM, including non-enhanced T1-weighted (T1w), contrast-enhanced T1w (ceT1w), T2-weighted (T2w), T2w-FLAIR, susceptibility-weighted (SWI) and diffusion-weighted (DWI) MRI. Each lesion was rated with respect to its conspicuity (score from 0-not detectable to 3-clearly visible).Of 1210 patients, 217 with MBM were included in the analysis and up to 5 lesions per patient were evaluated. A total of 720 metastases were assessed and all six sequences were available for 425 MBM. Sensitivity (conspicuity ≥2) was 99.7% for ceT1w, 77.0% for FLAIR, 64.7% for SWI, 61.0% for T2w, 56.7% for T1w, and 48.4% for DWI. Thirty-one (7.3%) of 425 lesions were only detectable by ceT1w but no other sequence.Contrast-enhanced T1-weighting is more sensitive than all other sequences for detection of MBM. Disruption of the blood-brain-barrier is consistently an earlier sign in MBM than perifocal edema, signal loss on SWI or diffusion restriction

Example of applied image analysis.

<p>Axial cMRI scans of a 70-year-old woman with newly diagnosed brain metastases from malignant melanoma (MBM). Conspicuity scores of each MBM are indicated in brackets below the respective lesion. <b>(a)</b> Non-enhanced T1-weighted image, right MBM: conspicuity score (CS) 3, left MBM: CS 1. <b>(b)</b> Contrast-enhanced T1-weighted image, right MBM: CS 3, left MBM: CS 3. <b>(c)</b> T2-weighted image, right MBM: CS 3, left MBM: CS 2. <b>(d)</b> Fast low angle shot 2D susceptibility-weighted imaging (SWI), right MBM: CS 3, left MBM: CS 2. <b>(e)</b> Fluid-attenuated inversion recovery (FLAIR) image, right MBM: CS 2, left MBM: CS 3. <b>(f)</b> Diffusion-weighted image (DWI TRACE), right MBM: CS 3, left MBM: CS 1.</p

Mean Conspicuity scores and standard deviations of the six different MRI sequences at first diagnosis MRI.

<p>Abbr.: DWI, Diffusion-weighted image; FLAIR, Fluid-attenuated inversion recovery; SWI, Susceptibility-weighted image; ceT1w, contrast-enhanced T1-weighted image; T2w, T2-weighted image; T1w, native T1-weighted image.</p

MRI systems and contrast media.

<p>MRI systems and contrast media.</p

Conspicuity scores.

<p>Conspicuity scores.</p

CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation

Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer)

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Abstract Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25–45% for sensitivity and 4–11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms