Abstract Here we analyse the influence of assumptions made on boundary conditions (BCs) extracted from phase-contrast magnetic resonance imaging (PC-MRI) in vivo measured flow data, applied on ...hemodynamic models of human aorta. This study aims at investigating if the imposition of BCs based on defective information, even when measured and specific-to-the-subject, might lead to misleading numerical representations of the aortic hemodynamics. In detail, we focus on the influence of assumptions regarding velocity profiles at the inlet section of the ascending aorta, incorporating phase flow data within the computational model. The obtained results are compared in terms of disturbed shear and helical bulk flow structures, when the same measured flow rate is prescribed as inlet BC in terms of 3D or 1D (axial) measured or idealized velocity profiles. Our findings clearly indicate that: (1) the imposition of PC-MRI measured axial velocity profiles as inflow BC may capture disturbed shear with sufficient accuracy, without the need to prescribe (and measure) realistic fully 3D velocity profiles; (2) attention should be put in setting idealized or PC-MRI measured axial velocity profiles at the inlet boundaries of aortic computational models when bulk flow features are investigated, because helical flow structures are markedly affected by the BC prescribed at the inflow. We conclude that the plausibility of the assumption of idealized velocity profiles as inlet BCs in personalized computational models can lead to misleading representations of the aortic hemodynamics both in terms of disturbed shear and bulk flow structures.
Purpose
Despite its increasing application, radiomics has not yet demonstrated a solid reliability, due to the difficulty in replicating analyses. The extraction of radiomic features from clinical ...MRI (T1w/T2w) presents even more challenges because of the absence of well‐defined units (e.g. HU). Some preprocessing steps are required before the estimation of radiomic features and one of this is the intensity normalization, that can be performed using different methods. The aim of this work was to evaluate the effect of three different normalization techniques, applied on T2w‐MRI images of the pelvic region, on radiomic features reproducibility.
Methods
T2w‐MRI acquired before (MRI1) and 12 months after radiotherapy (MRI2) from 14 patients treated for prostate cancer were considered. Four different conditions were analyzed: (a) the original MRI (No_Norm); (b) MRI normalized by the mean image value (Norm_Mean); (c) MRI normalized by the mean value of the urine in the bladder (Norm_ROI); (d) MRI normalized by the histogram‐matching method (Norm_HM). Ninety‐one radiomic features were extracted from three organs of interest (prostate, internal obturator muscles and bulb) at both time‐points and on each image discretized using a fixed bin‐width approach and the difference between the two time‐points was calculated (Δfeature). To estimate the effect of normalization methods on the reproducibility of radiomic features, ICC was calculated in three analyses: (a) considering the features extracted on MRI2 in the four conditions together and considering the influence of each method separately, with respect to No_Norm; (b) considering the features extracted on MRI2 in the four conditions with respect to the inter‐observer variability in region of interest (ROI) contouring, considering also the effect of the discretization approach; (c) considering Δfeature to evaluate if some indices can recover some consistency when differences are calculated.
Results
Nearly 60% of the features have shown poor reproducibility (ICC < 0.5) on MRI2 and the method that most affected features reliability was Norm_ROI (average ICC of 0.45). The other two methods were similar, except for first‐order features, where Norm_HM outperformed Norm_Mean (average ICC = 0.33 and 0.76 for Norm_Mean and Norm_HM, respectively). In the inter‐observer setting, the number of reproducible features varied in the three structures, being higher in the prostate than in the penile bulb and in the obturators. The analysis on Δfeature highlighted that more than 60% of the features were not consistent with respect to the normalization method and confirmed the high reproducibility of the features between Norm_Mean and Norm_HM, whereas Norm_ROI was the less reproducible method.
Conclusions
The normalization process impacts the reproducibility of radiomic features, both in terms of changes in the image information content and in the inter‐observer setting. Among the considered methods, Norm_Mean and Norm_HM seem to provide the most reproducible features with respect to the original image and also between themselves, whereas Norm_ROI generates less reproducible features. Only a very small subset of feature remained reproducible and independent in any tested condition, regardless the ROI and the adopted algorithm: skewness or kurtosis, correlation and one among Imc2, Idmn and Idn from GLCM group.
Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the ...reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs).
Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis.
MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively).
The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.
Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. ...Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.
Spherical deconvolution methods have been applied to diffusion MRI to improve diffusion tensor tractography results in brain regions with multiple fibre crossing. Recent developments, such as the ...introduction of non-negative constraints on the solution, allow a more accurate estimation of fibre orientations by reducing instability effects due to noise robustness. Standard convolution methods do not, however, adequately model the effects of partial volume from isotropic tissue, such as gray matter, or cerebrospinal fluid, which may degrade spherical deconvolution results. Here we use a newly developed spherical deconvolution algorithm based on an adaptive regularization (damped version of the Richardson–Lucy algorithm) to reduce isotropic partial volume effects. Results from both simulated and in vivo datasets show that, compared to a standard non-negative constrained algorithm, the damped Richardson–Lucy algorithm reduces spurious fibre orientations and preserves angular resolution of the main fibre orientations. These findings suggest that, in some brain regions, non-negative constraints alone may not be sufficient to reduce spurious fibre orientations. Considering both the speed of processing and the scan time required, this new method has the potential for better characterizing white matter anatomy and the integrity of pathological tissue.
Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the ...directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.
Abstract Background and purpose During radiotherapy (RT) for head-and-neck cancer, parotid glands undergo significant anatomic, functional and structural changes which could characterize pre-clinical ...signs of an increased risk of xerostomia. Texture analysis is proposed to assess structural changes of parotids induced by RT, and to investigate whether early variations of textural parameters (such as mean intensity and fractal dimension) can predict parotid shrinkage at the end of treatment. Material and methods Textural parameters and volumes of 42 parotids from 21 patients treated with intensity-modulated RT for nasopharyngeal cancer were extracted from CT images. To individuate which parameters changed during RT, a Wilcoxon signed-rank test between textural indices (first and second RT week; first and last RT week) was performed. Discriminant analysis was applied to variations of these parameters in the first two weeks of RT to assess their power in predicting parotid shrinkage at the end of RT. Results A significant decrease in mean intensity (1.7 HU and 3.8 HU after the second and last weeks, respectively) and fractal dimension (0.016 and 0.021) was found. Discriminant analysis, based on volume and fractal dimension, was able to predict the final parotid shrinkage (accuracy of 71.4%). Conclusion Textural features could be used in combination with volume to characterize structural modifications on parotid glands and to predict parotid shrinkage at the end of RT.
The growing elderly population and the increased incidence of mild cognitive impairment (MCI) and Alzheimer's disease (AD) call for the improvement of the quality and the efficacy of the healthcare ...and social support services. Exercise and cognitive stimulation have been demonstrated to mitigate cognitive impairment and oxidative stress (OxS) has been recognized as a factor that contributes to the advancement of neurodegenerative diseases. Taking these aspects into account, the impact of a novel virtual reality (VR)-based program combining aerobic exercise and cognitive training has been evaluated in the pilot study proposed here. Ten patients (aged 73.3 ± 5.7 years) with MCI (Mini-Mental State Examination, MMSE: 23.0 ± 3.4) were randomly assigned to either 6 weeks physical and cognitive training (EXP) or control (CTR) group. Evaluations of cognitive profile, by a neuropsychological tests battery, and OxS, by collection of blood and urine samples, were performed before and at the end of the experimental period. The assessment of the patients' opinions toward the intervention was investigated through questionnaires. EXP group showed a tendency towards improvements in the MMSE, in visual-constructive test and visuo-spatial tests of attention, while CTR worsened. EXP group showed a greater improvement than CTR in the executive test, memory functions and verbal fluency. No statistical significance was obtained when comparing within and between both the groups, probably due to small number of subjects examined, which amplifies the effect of the slight heterogeneity in scores recorded. Despite a greater worsening of Daily Living Activities tests, all participants reported a better performance in real life, thanks to the elicited self-perceived improvement. After training intervention OxS (i.e., reactive oxygen species (ROS) production, oxidative damage of lipids and DNA) decreased resulting in significantly (range
< 0.05-0.001) lower in EXP vs. CTR group. Although not conclusive, the recorded effects in the present study are promising and suggest that this proposal would be a useful tool in support of cognitive training reducing OxS too. However, further studies on larger scale samples of patients are needed.
Brain tumors are particularly aggressive and represent a significant cause of morbidity and mortality in adults and children, affecting the global population and being responsible for 2.6% of all ...cancer deaths (as well as 30% of those in children and 20% in young adults). The blood-brain barrier (BBB) excludes almost 100% of the drugs targeting brain neoplasms, representing one of the most significant challenges to current brain cancer therapy. In the last decades, carbon dots have increasingly played the role of drug delivery systems with theranostic applications against cancer, thanks to their bright photoluminescence, solubility in bodily fluids, chemical stability, and biocompatibility. After a summary outlining brain tumors and the current drug delivery strategies devised in their therapeutic management, this review explores the most recent literature about the advances and open challenges in the employment of carbon dots as both diagnostic and therapeutic agents in the treatment of brain cancers, together with the strategies devised to allow them to cross the BBB effectively.
Purpose
To introduce and validate an automatic segmentation method for the discrimination of skeletal muscle (SM), and adipose tissue (AT) components (subcutaneous adipose tissue SAT and ...intermuscular adipose tissue IMAT) from T1‐weighted (T1‐W) magnetic resonance imaging (MRI) images of the thigh.
Materials and Methods
Eighteen subjects underwent an MRI examination on a 1.5T Philips Achieva scanner. Acquisition was performed using a T1‐W sequence (TR = 550 msec, TE = 15 msec), pixel size between 0.81–1.28 mm, slice thickness of 6 mm. Bone, AT, and SM were discriminated using a fuzzy c‐mean algorithm and morphologic operators. The muscle fascia that separates SAT from IMAT was detected by integrating a morphological‐based segmentation with an active contour Snake. The method was validated on five young normal weight, five older normal weight, and five older obese females, comparing automatic with manual segmentations.
Results
We reported good performance in the extraction of SM, AT, and bone in each subject typology (mean sensitivity above 96%, mean relative area difference of 1.8%, 2.7%, and 2.5%, respectively). A mean distance between contours pairs of 0.81 mm and a mean percentage of contour points with distance smaller than 2 pixels of 86.2% were obtained in the muscle fascia identification. Significant correlation was also found between manual and automatic IMAT and SAT cross‐sectional areas in all subject typologies (p < 0.001).
Conclusion
The proposed automatic segmentation approach provides adequate thigh tissue segmentation and may be helpful in studies of regional composition. J. MAGN. RESON. IMAGING 2016;43:601–610.
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