A strategy for using tissue water as a concentration standard in 1 H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with The unsuppressed "internal" water signal was introduced as a concentration reference for single-voxel proton magnetic resonance spectroscopy ( 1 H-MRS) of the brain over a decade ago (1-4). However, to our knowledge, a detailed description of how this method could be applied to spectroscopic imaging (SI), or an examination of its potential sources of error has yet to be reported. In the majority of SI studies that reported "absolute" metabolite concentrations, the metabolite signals were converted to moles per liter or kilograms of tissue using either external metabolite solutions (5-7) or ventricle water (8,9), and relatively few groups have reported using internal water (10,11). The principal advantage of using internal water in SI studies is that certain factors and potential sources of error that need to be considered when using external concentration references (e.g., RF homogeneity, coil loading, or the SI point spread function (PSF)) are obviated, since the water and metabolite signals come from the same voxel and are acquired in essentially the same way.The major assumptions when using internal water, on the other hand, are that the water densities and signal relaxation times of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the region of interest (ROI) can be reliably estimated and, furthermore, do not change significantly among the studied groups. Moreover, it is essential that the volume fractions of these tissues and CSF in each SI voxel are accurately measured. Measuring partial volume effects is also a requirement when using external referencing methods, but the demand on accuracy is greater when using internal water. This is because only the signal from the combined GM-WM fraction of the total water, in which the detectable metabolites are exclusively located, is used as the concentration reference. The observed water signal, however, arises from a combination of the GM, WM, and CS...

The right SPL is a cortical area that appears ideally placed to unify disparate sensory inputs to create a coherent sense of having a body. The authors propose that inadequate activation of the right SPL leads to the unnatural situation in which the sufferers can feel the limb in question being touched without it actually incorporating into their body image, with a resulting desire for amputation. The authors introduce the term 'xenomelia' as a more appropriate name than apotemnophilia or body integrity identity disorder, for what appears to be an unrecognised right parietal lobe syndrome.

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLabgit/CA-Net

Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild (and some moderate) TBI can be difficult to diagnose due to lack of obvious external injuries and because the injuries are often not visible on conventional acute MRI or CT. Injured brain tissues in TBI patients generate pathological low-frequency neuronal magnetic signal (delta waves 1-4 Hz) that can be measured and localized by magnetoencephalography (MEG). We hypothesize that abnormal MEG delta waves originate from gray matter neurons that experience de-afferentation due to axonal injury to the underlying white matter fiber tracts, which is manifested on diffusion tensor imaging (DTI) as reduced fractional anisotropy. The present study used a neuroimaging approach integrating findings of magnetoencephalography (MEG) and diffusion tensor imaging (DTI), evaluating their utility in diagnosing mild TBI in 10 subjects in whom conventional CT and MRI showed no visible lesions in 9. The results show: (1) the integrated approach with MEG and DTI is more sensitive than conventional CT and MRI in detecting subtle neuronal injury in mild TBI; (2) MEG slow waves in mild TBI patients originate from cortical gray matter areas that experience de-afferentation due to axonal injuries in the white matter fibers with reduced fractional anisotropy; (3) findings from the integrated imaging approach are consistent with post-concussive symptoms; (4) in some cases, abnormal MEG delta waves were observed in subjects without obvious DTI abnormality, indicating that MEG may be more sensitive than DTI in diagnosing mild TBI.

No abstract

Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1–4 Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1–4 Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI.

The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEG's insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.

A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and temporal feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects significantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.