The glymphatic system has in previous studies been shown as fundamental to clearance of waste metabolites from the brain interstitial space, and is proposed to be instrumental in normal ageing and ...brain pathology such as Alzheimer's disease and brain trauma. Assessment of glymphatic function using magnetic resonance imaging with intrathecal contrast agent as a cerebrospinal fluid tracer has so far been limited to rodents. We aimed to image cerebrospinal fluid flow characteristics and glymphatic function in humans, and applied the methodology in a prospective study of 15 idiopathic normal pressure hydrocephalus patients (mean age 71.3 ± 8.1 years, three female and 12 male) and eight reference subjects (mean age 41.1 + 13.0 years, six female and two male) with suspected cerebrospinal fluid leakage (seven) and intracranial cyst (one). The imaging protocol included T1-weighted magnetic resonance imaging with equal sequence parameters before and at multiple time points through 24 h after intrathecal injection of the contrast agent gadobutrol at the lumbar level. All study subjects were kept in the supine position between examinations during the first day. Gadobutrol enhancement was measured at all imaging time points from regions of interest placed at predefined locations in brain parenchyma, the subarachnoid and intraventricular space, and inside the sagittal sinus. Parameters demonstrating gadobutrol enhancement and clearance in different locations were compared between idiopathic normal pressure hydrocephalus and reference subjects. A characteristic flow pattern in idiopathic normal hydrocephalus was ventricular reflux of gadobutrol from the subarachnoid space followed by transependymal gadobutrol migration. At the brain surfaces, gadobutrol propagated antegradely along large leptomeningeal arteries in all study subjects, and preceded glymphatic enhancement in adjacent brain tissue, indicating a pivotal role of intracranial pulsations for glymphatic function. In idiopathic normal pressure hydrocephalus, we found delayed enhancement (P < 0.05) and decreased clearance of gadobutrol (P < 0.05) at the Sylvian fissure. Parenchymal (glymphatic) enhancement peaked overnight in both study groups, possibly indicating a crucial role of sleep, and was larger in normal pressure hydrocephalus patients (P < 0.05 at inferior frontal gyrus). We interpret decreased gadobutrol clearance from the subarachnoid space, along with persisting enhancement in brain parenchyma, as signs of reduced glymphatic clearance in idiopathic normal hydrocephalus, and hypothesize that reduced glymphatic function is instrumental for dementia in this disease. The study shows promise for glymphatic magnetic resonance imaging as a method to assess human brain metabolic function and renders a potential for contrast enhanced brain extravascular space imaging.
The complementation of arterial and venous phases visual information of CTs can help better distinguish the pancreas from its surrounding structures. However, the exploration of cross-phase ...contextual information is still under research in computer-aided pancreas segmentation. This paper presents M
Net, a framework that integrates multi-scale multi-view information for multi-phase pancreas segmentation. The core of M
Net is built upon a dual-path network in which individual branches are set up for two phases. Cross-phase interactive connections bridging the two branches are introduced to interleave and integrate dual-phase complementary visual information. Besides, we further devise two types of non-local attention modules to enhance the high-level feature representation across phases. First, we design a location attention module to generate cross-phase reliable feature correlations to suppress the misalignment regions. Second, the depth-wise attention module is used to capture the channel dependencies and then strengthen feature representations. The experiment data consists of 224 internal CTs (106 normal and 118 abnormal) with 1 mm slice thickness, and 66 external CTs (29 normal and 37 abnormal) with 5 mm slice thickness. We achieve new state-of-the-art performance with average DSC of 91.19% on internal data, and promising result with average DSC of 86.34% on external data.
Currently, the gold standard for dental imaging is projection radiography or cone-beam computed tomography (CBCT). These methods are fast and cost-efficient, but exhibit poor soft tissue contrast and ...expose the patient to ionizing radiation (X-rays). The need for an alternative imaging modality e.g. for soft tissue management has stimulated a rising interest in dental magnetic resonance imaging (MRI) which provides superior soft tissue contrast. Compared to X-ray imaging, however, so far the spatial resolution of MRI is lower and the scan time is longer. In this contribution, we describe wireless, inductively-coupled intraoral coils whose local sensitivity enables high resolution MRI of dental soft tissue. In comparison to CBCT, a similar image quality with complementary contrast was obtained ex vivo. In-vivo, a voxel size of the order of 250 ∙ 250 ∙ 500 μm(3) was achieved in 4 min only. Compared to dental MRI acquired with clinical equipment, the quality of the images was superior in the sensitive volume of the coils and is expected to improve the planning of interventions and monitoring thereafter. This method may enable a more accurate dental diagnosis and avoid unnecessary interventions, improving patient welfare and bringing MRI a step closer to becoming a radiation-free alternative for dental imaging.
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological ...diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence-based approaches to BM cytomorphology.
Advances in time-of-flight PET Surti, Suleman; Karp, Joel S
Physica medica,
01/2016, Letnik:
32, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Highlights • TOF PET currently has improved performance without any design trade-offs. • Improvement is due to better scintillators, photo-sensors, and image reconstruction. • TOF PET is now a ...standard technique in PET imaging.
Understanding how brain development normally proceeds is a premise of understanding neurodevelopmental disorders. This has sparked a wealth of magnetic resonance imaging (MRI) studies. Unfortunately, ...they are in marked disagreement on how the cerebral cortex matures. While cortical thickness increases for the first 8-9 years of life have repeatedly been reported, others find continuous cortical thinning from early childhood, at least from age 3 or 4 years. We review these inconsistencies, and discuss possible reasons, including the use of different scanners, recording parameters and analysis tools, and possible effects of variables such as head motion. When tested on the same subsample, 2 popular thickness estimation methods (CIVET and FreeSurfer) both yielded a continuous thickness decrease from 3 years. Importantly, MRI-derived measures of cortical development are merely our best current approximations, hence the term "apparent cortical thickness" may be preferable. We recommend strategies for reaching consensus in the field, including multimodal neuroimaging to measure phenomena using different techniques, for example, the use of T1/T2 ratio, and data sharing to allow replication across analysis methods. As neurodevelopmental origins of early- and late-onset disease are increasingly recognized, resolving inconsistencies in brain maturation trajectories is important.
The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is widely documented. This scanner effect is detrimental to the design and validation ...of predictive or prognostic models and limits the use of large multicenter cohorts. To reduce the impact of this scanner effect, the ComBat method has been proposed and is now used in various contexts. The purpose of this article is to explain and illustrate the use of ComBat based on practical examples. We also give examples in which the ComBat assumptions are not met and, thus, in which ComBat should not be used.
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of ...convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.
Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All ...existing deep learning models attempt to segment both types of vessels simultaneously by using a unified pixel-wise loss that treats all vessel pixels with equal importance. Due to the highly imbalanced ratio between thick vessels and thin vessels (namely the majority of vessel pixels belong to thick vessels), the pixelwise loss would be dominantly guided by thick vessels and relatively little influence comes from thin vessels, often leading to low segmentation accuracy for thin vessels. To address the imbalance problem, in this paper, we explore to segment thick vessels and thin vessels separately by proposing a three-stage deep learning model. The vessel segmentation task is divided into three stages, namely thick vessel segmentation, thin vessel segmentation, and vessel fusion. As better discriminative features could be learned for separate segmentation of thick vessels and thin vessels, this process minimizes the negative influence caused by their highly imbalanced ratio. The final vessel fusion stage refines the results by further identifying nonvessel pixels and improving the overall vessel thickness consistency. The experiments on public datasets DRIVE, STARE, and CHASE_DB1 clearly demonstrate that the proposed three-stage deep learning model outperforms the current state-of-the-art vessel segmentation methods.
How cryo-EM is revolutionizing structural biology Bai, Xiao-chen; McMullan, Greg; Scheres, Sjors H.W
Trends in biochemical sciences (Amsterdam. Regular ed.),
01/2015, Letnik:
40, Številka:
1
Journal Article
Recenzirano
•Cryo-EM single-particle analysis reaches near-atomic resolution for a wide variety of specimens.•Direct electron detectors yield images of unprecedented quality.•New movie-processing methods correct ...for beam-induced sample movements.•New classification methods separate images of different structures.
For many years, structure determination of biological macromolecules by cryo-electron microscopy (cryo-EM) was limited to large complexes or low-resolution models. With recent advances in electron detection and image processing, the resolution by cryo-EM is now beginning to rival X-ray crystallography. A new generation of electron detectors record images with unprecedented quality, while new image-processing tools correct for sample movements and classify images according to different structural states. Combined, these advances yield density maps with sufficient detail to deduce the atomic structure for a range of specimens. Here, we review the recent advances and illustrate the exciting new opportunities that they offer to structural biology research.
PDF
