Novel Mixture of Materials Produces Bone- and Soft Tissue-Like Imaging Density

The objective of the present study was to access to imaging material density close to or identical density imaging of bone and soft tissue, from raw materials of nature to be used in different model applications and to provide comprehensive evaluation of the imaging system and techniques under realistic conditions in radiology departments for educational purposes. The palm tree of abundance in Saudi Arabia was chosen to study the date’s seeds and palm leaves in terms of photographic density. The results achieved were referring to the lack of imaging density of dates seeds and palm leaves compared to bone density. Thus, it was necessary to use two additional materials: Salt and eggshells in order to find the highest density and graphic approach to bone density. The present preliminary study indicated that the permanent and stable model can be achieved by palm leaves, salt & eggshell powder with imaging material density close to the imaging density of the bone and soft tissue for achieving more clinical skills and medical education.

Neurite Orientation Dispersion and Density Imaging of Rat Brain Microstructural Changes due to Middle Cerebral Artery Occlusion at a 3T MRI

The purpose of this work was to demonstrate the feasibility of neurite orientation dispersion and density imaging(NODDI)in characterizing the brain tissue microstructural changes of middle cerebral artery occlusion(MCAO)in rats at 3T MRI,and to validate NODDI metrics with histology.A multi-shell diffusion MRI protocol was performed on 11 MCAO rats and 10 control rats at different post-operation time points of 0.5,2,6,12,24 and 72 h.NODDI orientation dispersion index(ODI)and intracellular volume fraction(V_(ic))metrics were compared between MCAO group and control group.The evolution of NODDI metrics was characterized and validated by histology.Infarction was consistent with significantly increased ODI and V_(ic)in comparison to control tissues at all time points(P<0.001).Lesion ODI increased gradually from 0.5 to 72 h,while its V_(ic)showed a more complicated and fluctuated evolution.ODI and V_(ic)were significantly different between hyperacute and acute stroke periods(P<0.001).The NODDI metrics were found to be consistent with the histological findings.In conclusion,NODDI can reflect microstructural changes of brain tissues in MCAO rats at 3T MRI and the metrics are consistent with histology.This study helps to prepare NODDI for the diagnosis and management of ischemic stroke in translational research and clinical practice.

Tissue Microstructure Estimation of SANDI Based on Deep Network

Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size and density to biophysical model for the first time.In addition to neurite density,it can achieve their joint estimation non-invasively using dMRI.In the traditional method,parameters of the SANDI are estimated in a maximum likelihood frame-work,where the nonlinear model fitting is computationally intensive.Also,the present methods require a large number of diffusion gradients.Efficient and accurate algorithms for tissue microstructure estimation of SANDI is still a challenge currently.Consequently,we introduce deep learning method for tissue microstructure estimation of the SANDI model.The model comprises two functional components.The first component produces the sparse representation of diffusion sig-nals of input patches.The second component computes tissue microstructure from the sparse repre-sentation given by the first component.The deep network can produce not only tissue microstruc-ture estimates but also the uncertainty of the estimates with a reduced number of diffusion gradi-ents.Then,multiple deep networks are trained and their results are fused for the final prediction of tissue microstructure and uncertainty quantification.The deep network was evaluated on the MGH Connectome Diffusion Microstructure Dataset.Results indicate that our approach outperforms the traditional methods in terms of estimation accuracy.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer’s and Parkinson’s diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings;however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer's and Parkinson's diseases, the first and second most common neurodegenerative diseases, respectively.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

Use of 3-D magnetic resonance electrical impedance tomography in detecting human cerebral stroke: a simulation study

We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electrical Impedance Tomography (MREIT) approach is based on the J-Substitution algorithm and is expanded to imaging 3-D subject conductivity distribution changes. Computer simulation studies have been conducted to evaluate the present MREIT imaging approach. Simulations of both types of cerebral stroke, hemorrhagic stroke and ischemic stroke, were performed on a four-sphere head model. Simulation results showed that the correlation coefficient (CC) and relative error (RE) between target and estimated conductivity distributions were 0.9245±0.0068 and 8.9997%±0.0084%, for hemorrhagic stroke, and 0.6748±0.0197 and 8.8986%±0.0089%, for ischemic stroke, when the SNR (signal-to-noise radio) of added GWN (Gaussian White Noise) was 40. The convergence characteristic was also evaluated according to the changes of CC and RE with different iteration numbers. The CC increases and RE decreases monotonously with the increasing number of iterations. The present simulation results show the feasibility of the proposed 3-D MREIT approach in hemorrhagic and ischemic stroke detection and suggest that the method may become a useful alternative in clinical diagnosis of acute cerebral stroke in humans.

Preliminary examination of early neuroconnectivity features in the R6/1 mouse model of Huntington's disease by ultra-high field diffusion MRI

During the last decades,advances in the understanding of genetic,cellular,and microstructural alterations associated to Huntington's disease(HD)have improved the understanding of this progressive and fatal illness.However,events related to early neuropathological events,neuroinflammation,deterioration of neuronal connectivity and compensatory mechanisms still remain vastly unknown.Ultra-high field diffusion MRI(UHFD-MRI)techniques can contribute to a more comprehensive analysis of the early microstructural changes observed in HD.In addition,it is possible to evaluate if early imaging microstructural parameters might be linked to histological biomarkers.Moreover,qualitative studies analyzing histological complexity in brain areas susceptible to neurodegeneration could provide information on inflammatory events,compensatory increase of neuroconnectivity and mechanisms of brain repair and regeneration.The application of ultra-high field diffusion-MRI technology in animal models,particularly the R6/1 mice(a common preclinical mammalian model of HD),provide the opportunity to analyze alterations in a physiologically intact model of the disease.Although some disparities in volumetric changes across different brain structures between preclinical and clinical models has been documented,further application of different diffusion MRI techniques used in combination like diffusion tensor imaging,and neurite orientation dispersion and density imaging have proved effective in characterizing early parameters associated to alteration in water diffusion exchange within intracellular and extracellular compartments in brain white and grey matter.Thus,the combination of diffusion MRI imaging techniques and more complex neuropathological analysis could accelerate the discovery of new imaging biomarkers and the early diagnosis and neuromonitoring of patients affected with HD.

Neural circuits and temporal plasticity in hindlimb representation of rat primary somatosensory cortex:revisited by multi-electrode array on brain slices

Objective The well-established planar multi-electrode array recording technique was used to investigate neural circuits and temporal plasticity in the hindlimb representation of the rat primary somatosensory cortex （S1 area） . Methods Freshly dissociated acute brain slices of rats were subject to constant perfusion with oxygenated artificial cerebrospinal fluid （95% O2 and 5% CO2） , and were mounted on a Med64 probe （64 electrodes, 8×8 array） for simultaneous multi-site electrophysiological recordings. Current sources and sinks across all the 64 electrodes were transformed into two-dimensional current source density images by bilinear interpolation at each point of the 64 electrodes. Results The local intracortical connection, which is involved in mediation of downward information flow across layers II-VI, was identified by electrical stimulation （ES） at layers II-III. The thalamocortical connection, which is mainly involved in mediation of upward information flow across layers II-IV, was also characterized by ES at layer IV. The thalamocortical afferent projections were likely to make more synaptic contacts with S1 neurons than the intracortical connections did. Moreover, the S1 area was shown to be more easily activated and more intensively innervated by the thalamocortical afferent projections than by the intracortical connections. Finally, bursting conditioning stimulus （CS） applied within layer IV of the S1 area could success-fully induce long-term potentiation （LTP） in 5 of the 6 slices （83.3%） , while the same CS application at layers II-III induced no LTP in any of the 6 tested slices. Conclusion The rat hindlimb representation of S1 area is likely to have at least 2 patterns of neural circuits on brain slices： one is the intracortical circuit （ICC） formed by interlaminar connections from layers II-III, and the other is the thalamocortical circuit （TCC） mediated by afferent connections from layer IV. Besides, ICC of the S1 area is spatially limited, with less plasticity, while TCC is spatially extensive and exhibits a better plasticity in response to somatosensory afferent stimulation. The present data provide a useful experimental model for further studying microcircuit properties in S1 cortex at the network level in vitro.

Simultaneous quantification of hepatic MRI-PDFF and R2* in a rabbit model with nonalcoholic fatty liver disease

Quantification of hepatic fat and iron content is important for early detection and monitoring of nonalcoholic fatty liver disease(NAFLD) patients. This study evaluated quantification efficiency of hepatic proton density fat fraction(PDFF) by MRI using NAFLD rabbits. R2* was also measured to investigate whether it correlates with fat levels in NAFLD. NAFLD rabbit model was successfully established by high fat and cholesterol diet. Rabbits underwent MRI examination for fat and iron analyses,compared with liver histological findings. MR examinations were performed on a 3.0 T MR system using multi-echo 3 D gradient recalled echo(GRE) sequence. MRI-PDFF showed significant differences between different steatosis grades with medians of3.72%(normal), 5.43%(mild), 9.11%(moderate) and 11.17%(severe), whereas this was not observed in R2*. Close correlation between MRI-PDFF and histological steatosis was observed(r=0.78, P=0.000). Hepatic iron deposit was not found in any rabbits. There was no correlation between R2* and either liver MRI-PDFF or histological steatosis. MR measuring MRI-PDFF and R2* simultaneously provides promising quantification of steatosis and iron. Rabbit NAFLD model confirmed accuracy of MRI-PDFF for liver fat quantification. R2* measurement and relationship between fat and iron of NAFLD liver need further experimental investigation.

Real-time Follow-up Head Tracking in Dynamic Complex Environments

In the modes of both object motion and camera motion,an enhanced Camshift algorithm,which is based on suppressing similar color features of background and on joint color probability density distribution image,is proposed to real-time track head in dynamic complex environment.The system consists of face detection module,head tracking module and camera control module.When tracking fails,a self-recovery mechanism is introduced.At first the Adaboost face detector based on Haar-like features is implemented to find frontal faces,the false positive is filtered according to the skin color criterion,and the true face is used to initialize the tracking module.In hue saturation value(HSV) colorspace,the hue-saturation(H-S) histogram of face skin and the saturation-value(S-V) histogram of hair are built to produce the joint color probability density distribution image,and this is intended to realize the head tracking with arbitrary pose.During tracking,region of interest(ROI) is introduced,and the color probability density distribution of a specified background area outside the ROI is learned,similar color features in the head are suppressed according to the learning result.The background suppression step is intended to resolve the problem that the tracker maybe fails when the head is distracted by backgrounds having similar colors with the head.A closed loop control model based on speed regulation is applied to drive an active camera to center the head.Once tracking drift or failure is detected,the system stops tracking and returns to the face detection module.Our experimental results show that the presented system is well suitable for tracking head with arbitrary pose in dynamic complex environments,also the active camera can track moving head smoothly and stably.The system is computationally efficient and can run in real-time completely.

Optimization of magnetic resonance sequences in lymph node staging of nasopharyngeal carcinoma

Background Detection rate of retropharyngeal lymph node metastasis in patients with nasopharyngeal carcinoma （NPC） needs to be improved. The purpose of this study was to compare three magnetic resonance （MR） sequences for detecting lymph nodes in patients with NPC. Methods Between July 2007 and March 2008, MR staging of pre-treated tumor was conducted on 120 patients with pathologically confirmed NPC. The outcome of three different sequences for MR NPC staging were compared： coronal short T1 inversion recovery （STIR）, axial proton density fat-suppressed （PDWI fs）, and coronal contrast enhanced fast spin echo T1 weighted fat-suppressed （CE FSE TlWl fs）. Nodal classification method （1999） was applied to count the number of retropharyngeal and cervical lymph nodes discovered by each MR sequence. Paired t tests were used for statistical analysis. Results A total of 2575 lymph nodes were found using coronal STIR sequence; 1816 lymph nodes for coronal CE FSE TIWI fs sequence and 2638 lymph nodes for axial PDWl fs sequence. Significant differences existed in the number of lymph nodes detected by axial PDWI fs and coronal CE FSE T1WI fs sequence （paired t test, P 〈0.05）, with the former sequence getting higher numbers. Statistical differences also existed between coronal STIR and coronal CE FSE TlWl fs sequence （paired ttest, P 〈0.05）, with the former sequence getting higher numbers. No significant difference was found between coronal STIR sequence and axial PDWI fs sequence （paired ttest, P 〉0.05）. Conclusions For the detection of retropharyngeal and cervical lymph nodes, coronal STIR sequence and axial PDWI fs sequence have similar performance and both sequences showed better detection than CE FSE TIWI fs sequence. Furthermore, by combining coronal STIR sequence and axial PDWI fs sequence, we can improve the detection of lymph nodes in NPC N-staging before treatment, especially for lymph nodes located in the thoracic entrance.

Genome-wide Studies Reveal Genetic Risk Factors for Hepatic Fat Content

Genetic susceptibility to metabolic associated fatty liver disease(MAFLD)is complex and poorly characterized.Accurate characterization of the genetic background of hepatic fat content would provide insights into disease etiology and causality of risk factors.We performed genome-wide association study(GWAS)on two noninvasive definitions of hepatic fat content:magnetic resonance imaging proton density fat fraction(MRI-PDFF)in 16,050 participants and fatty liver index(FLI)in 388,701 participants from the United Kingdom(UK)Biobank(UKBB).Heritability,genetic overlap,and similarity between hepatic fat content phenotypes were analyzed,and replicated in 10,398 participants from the University Medical Center Groningen(UMCG)Genetics Lifelines Initiative(UGLI).Meta-analysis of GWASs of MRI-PDFF in UKBB revealed five statistically significant loci,including two novel genomic loci harboring CREB3L1(rs72910057-T,P=5.40E−09)and GCM1(rs1491489378-T,P=3.16E−09),respectively,as well as three previously reported loci:PNPLA3,TM6SF2,and APOE.GWAS of FLI in UKBB identified 196 genome-wide significant loci,of which 49 were replicated in UGLI,with top signals in ZPR1(P=3.35E−13)and FTO(P=2.11E−09).Statistically significant genetic correlation(rg)between MRI-PDFF(UKBB)and FLI(UGLI)GWAS results was found(rg=0.5276,P=1.45E−03).Novel MRI-PDFF genetic signals(CREB3L1 and GCM1)were replicated in the FLI GWAS.We identified two novel genes for MRI-PDFF and 49 replicable loci for FLI.Despite a difference in hepatic fat content assessment between MRI-PDFF and FLI,a substantial similar genetic architecture was found.FLI is identified as an easy and reliable approach to study hepatic fat content at the population level.