The Combination of MR Elastography and Proton Density Fat Fraction Improves Diagnosis of Nonalcoholic Steatohepatitis.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is rapidly increasing worldwide. It is subdivided into nonalcoholic fatty liver (NAFL) and the more aggressive form, nonalcoholic steatohepatitis (NASH), which carries a higher risk of developing fibrosis and cirrhosis. There is currently no reliable non-invasive method for differentiating NASH from NAFL. PURPOSE: To investigate the ability of magnetic resonance imaging (MRI)-based imaging biomarkers to diagnose NASH and moderate fibrosis as well as assess their repeatability. STUDY TYPE: Prospective. SUBJECTS: Sixty-eight participants (41% women) with biopsy-proven NAFLD (53 NASH and 15 NAFL). Thirty participants underwent a second MRI in order to assess repeatability. FIELD STRENGTH/SEQUENCE: 3.0 T; MR elastography (MRE) (a spin-echo echo-planar imaging [SE-EPI] sequence with motion-encoding gradients), MR proton density fat fraction (PDFF) and R2* mapping (a multi-echo three-dimensional gradient-echo sequence), T1 mapping (a single-point saturation-recovery technique), and diffusion-weighted imaging (SE-EPI sequence). ASSESSMENT: Quantitative MRI measurements were obtained and assessed alone and in combination with biochemical markers (cytokeratin-18 [CK18] M30, alanine transaminase [ALT], and aspartate transaminase [AST]) using logistic regression models. Models that could differentiate between NASH and NAFL and between moderate to advanced fibrosis (F2-4) and no or mild fibrosis (F0-1), based on the histopathological results, were identified. STATISTICAL TESTS: Independent samples t-test, Pearson's chi-squared test, area under the receiver operating characteristic curve (AUROC), Spearman's correlation, intra-individual coefficient of variation, and intraclass correlation coefficient (ICC). Statistical significance was set at P < 0.05. RESULTS: There was a significant difference between the NASH and NAFL groups with liver stiffness assessed with MRE, CK18 M30, and ALT, with an AUROC of 0.74, 0.76, and 0.70, respectively. Both MRE and PDFF contributed significantly to a bivariate model for diagnosing NASH (AUROC = 0.84). MRE could significantly differentiate between F2-4 and F0-1 (AUROC = 0.74). A model combining MRE with AST improved the diagnosis of F2-4 (AUROC = 0.83). The ICC for repeatability was 0.94 and 0.99 for MRE and PDFF, respectively. DATA CONCLUSION: MRE can potentially diagnose NASH and differentiate between fibrosis stages. Combining MRE with PDFF improves the diagnosis of NASH. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Actigraphy measurement of physical activity and energy expenditure in narcolepsy type 1, narcolepsy type 2 and idiopathic hypersomnia: A Sensewear Armband study.

The cause of obesity in narcolepsy type 1 (NT1) patients is not fully understood. The present study investigated if a reduced physical activity could explain weight gain in NT1. Seventy-nine patients were included in this retrospective study and divided into an NT1 group (n = 56) and a non-NT1 group (n = 23), including NT2 and idiopathic hypersomnia (IH). Accelerometry-derived measures of physical activity, total energy expenditure and skin temperature were collected from patients during seven consecutive days without medication. In addition, results from multiple sleep latency tests and the Epworth Sleepiness Scale questionnaire, body weight, height and CSF orexin/hypocretin were acquired. Three measurements of physical activity, including metabolic equivalent of task (MET), the average time of physical activity and step count, were compared without differences between groups. Neither could we find a significant difference in total energy expenditure or skin temperature. Thus, by analysing accelerometric data, we could not find any differences in the amount of physical activity or total energy expenditure explaining overweight in NT1.

Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI.

PURPOSE: An approach for the automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water-fat MRI scans of the abdomen was investigated, using 2 different neural network architectures. METHODS: The 2 fully convolutional network architectures U-Net and V-Net were trained, evaluated, and compared using the water-fat MRI data. Data of the study Tellus with 90 scans from a single center was used for a 10-fold cross-validation in which the most successful configuration for both networks was determined. These configurations were then tested on 20 scans of the multicenter study beta-cell function in JUvenile Diabetes and Obesity (BetaJudo), which involved a different study population and scanning device. RESULTS: The U-Net outperformed the used implementation of the V-Net in both cross-validation and testing. In cross-validation, the U-Net reached average dice scores of 0.988 (VAT) and 0.992 (SAT). The average of the absolute quantification errors amount to 0.67% (VAT) and 0.39% (SAT). On the multicenter test data, the U-Net performs only slightly worse, with average dice scores of 0.970 (VAT) and 0.987 (SAT) and quantification errors of 2.80% (VAT) and 1.65% (SAT). CONCLUSION: The segmentations generated by the U-Net allow for reliable quantification and could therefore be viable for high-quality automated measurements of VAT and SAT in large-scale studies with minimal need for human intervention. The high performance on the multicenter test data furthermore shows the robustness of this approach for data of different patient demographics and imaging centers, as long as a consistent imaging protocol is used.

Successful epilepsy surgery in a patient with neurosarcoidosis.

This case concerns a patient with generalized neurosarcoidosis and pharmacoresistant focal epilepsy. Although immunosuppressive therapy resulted in remission of the neurosarcoidosis, seizures continued and were shown to originate from the right temporal lobe (TL). The patient underwent a right anterior temporal lobe resection (TLR) and obtained >90% reduction of seizure frequency.

Speech, language, and cognitive dysfunction in children with focal epileptiform activity: A follow-up study.

We reviewed the medical history, EEG recordings, and developmental milestones of 19 children with speech and language dysfunction and focal epileptiform activity. Speech, language, and neuropsychological assessments and EEG recordings were performed at follow-up, and prognostic indicators were analyzed. Three patterns of language development were observed: late start and slow development, late start and deterioration/regression, and normal start and later regression/deterioration. No differences in test results among these groups were seen, indicating a spectrum of related conditions including Landau-Kleffner syndrome and epileptic language disorder. More than half of the participants had speech and language dysfunction at follow-up. IQ levels, working memory, and processing speed were also affected. Dysfunction of auditory perception in noise was found in more than half of the participants, and dysfunction of auditory attention in all. Dysfunction of communication, oral motor ability, and stuttering were noted in a few. Family history of seizures and abundant epileptiform activity indicated a worse prognosis.

Excellent outcome after prolonged status epilepticus due to non-paraneoplastic limbic encephalitis.

Limbic encephalitis (LE) is frequently associated with malignancy. Non-paraneoplastic LE is less common and in this form, voltage-gated potassium channel (VGKC) antibodies are usually found. However in 2007 the spectrum was further extended by a report on four patients with presumed non-paraneoplastic LE in whom neither VGKC-antibodies nor other antibodies could be found (Samarasekera et al. 2007). Despite immunmodulatory treatment all these patients had severe neurological residual symptoms. Here we describe a further patient in whom extensive diagnostic procedures suggested non-paraneoplastic antibody-negative limbic encephalitis. Although this woman had prolonged status epilepticus during seven weeks, her outcome was excellent.

Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

Modification of the average reference montage: dynamic average reference.

The common average reference (CAR) is influenced strongly by high-amplitude artifacts at the recording electrodes. The authors suggest an algorithm for average reference modification that allows application of the average reference montage in the presence of high-amplitude artifacts at one or several EEG data channels. This dynamic average reference (DAR) method is based on analysis of kurtosis (DARkurt) or kurtosis and standard deviation (DARSD) of amplitude distribution at a single time point and elimination of outliers before calculating the average reference. This procedure can be performed on-line. The DAR improves visual EEG representation considerably in the presence of high-amplitude artifacts. The DAR also influences artifact-free EEG, increasing slightly the amplitude of high-amplitude waveforms. The modeling of the dipole source in the spherical conductor has shown that the DAR compared with the CAR can both decrease and increase the average reference bias depending on position and orientation of the source. In practice, the influence of the DAR with tested parameters on the artifact-free EEG was low and never exceeded 12% of the amplitude of the CAR-referenced signal. The DAR method can be useful for routine analysis of EEG contaminated by high-amplitude artifacts and for long-term EEG monitoring.

Remote sessions and frequency analysis for improved insight into cerebral function during pediatric and neonatal intensive care.

A project involving recording and analysing EEG together with cardiovascular signals and temperature has been initiated. The aim of this project is to establish difficulties and possibilities involved with implementing a system for remote sessions and analysing EEG in correlation with other physiological signals. One objective is to find indicators of cerebral function during postasphyxia neonatal intensive care and pediatric cardiopulmonary bypass surgery with hypothermia. Remote sessions for joint interpretation have been carried out between pediatricians and clinical neurophysiologists, and EEG has been analyzed using frequency analyzing tools. One result is the discovery of reversible spectral changes coinciding with blood pressure falls, which may indicate loss of autoregulation function. This finding is one outcome from initial use of a system, developed during the project to facilitate communication about, and analysis of the recorded signals. Thus, already from a limited number of remote sessions and the use of basic signal processing techniques, important results have been achieved and better insight has been gained of how cerebral function is affected by cardiopulmonary bypass surgery. In this paper, we present our experiences from introducing a system for remote consultations, and evaluate the use for such a system in the current applications.

Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials.

One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert independently identified the expected source location, further corroborating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations.

Epilepsy surgery in children with accompanying impairments.

The aim of this study was to assess seizure outcome 2 years after epilepsy surgery in a consecutive series of paediatric patients, with special focus on children with learning disabilities and other neuroimpairments in addition to the epilepsy. Outcome 2 years after surgery was assessed in 110 of 125 children operated upon for drug resistant epilepsy in Gothenburg 1987-2006. More than half of the children had learning disabilities, 43% motor impairments and 30% a neuropsychiatric diagnosis. Fifty-six per cent of those with an IQ < 70 became seizure-free or had a >75% reduction in seizure frequency, and two thirds if the operation was a resection. The corresponding figure in those with more than 100 seizures per month was 15 out of 31, and another seven had a 50-75% reduction in seizure frequency. The message is that learning disability, motor impairment and psychiatric morbidity should not be contraindications for paediatric epilepsy surgery. More than half of the children with learning disabilities had a worthwhile seizure outcome, with even better results after resective surgery. Children with drug resistant epilepsy and additional severe neurological impairments should have the benefit of referral to a tertiary centre for evaluation for epilepsy surgery.

On the fully automatic construction of a realistic head model for EEG source localization.

Accurate multi-tissue segmentation of magnetic resonance (MR) images is an essential first step in the construction of a realistic finite element head conductivity model (FEHCM) for electroencephalography (EEG) source localization. All of the segmentation approaches proposed to date for this purpose require manual intervention or correction and are thus laborious, time-consuming, and subjective. In this paper we propose and evaluate a fully automatic method based on a hierarchical segmentation approach (HSA) incorporating Bayesian-based adaptive mean-shift segmentation (BAMS). An evaluation of HSA-BAMS, as well as two reference methods, in terms of both segmentation accuracy and the source localization accuracy of the resulting FEHCM is also presented. The evaluation was performed using (i) synthetic 2D multi-modal MRI head data and synthetic EEG (generated for a prescribed source), and (ii) real 3D T1-weighted MRI head data and real EEG data (with expert determined source localization). Expert manual segmentation served as segmentation ground truth. The results show that HSA-BAMS outperforms the two reference methods and that it can be used as a surrogate for manual segmentation for the construction of a realistic FEHCM for EEG source localization.

Non-invasive EEG source localization using particle swarm optimization: a clinical experiment.

One of the most important steps of pre-surgical diagnosis in patients with medically intractable epilepsy is to find the precise location of the epileptogenic foci. An Electroencephalography (EEG) is a non-invasive standard tool used at epilepsy surgery center for pre-surgical diagnosis. In this paper a modified particle swarm optimization (MPSO) method is applied to a real EEG data, i.e., a somatosensory evoked potentials (SEPs) measured from a healthy subject, to solve the EEG source localization problem. A high resolution 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEPs data. The non-invasive EEG source analysis methods localized the somatosensory cortex area where our clinical expert expected the received SEPs. The proposed inverse problem solver found the global minima with acceptable accuracy and reasonable number of iterations.