Set Of ROIs Research Articles

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.

The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

Alterations in the integrity of cortical and subcortical circuits in AD and MCI: Connectivity study in rs‐fMRI.

AbstractBackgroundrs‐FC has been altered in AD continuum with hyper‐hypoconnectivity patterns attributed not only to regional changes, but to disruption of functional integration or aberrant connectivity between cortical and subcortical brain regions . This study seeks to find connectivity patterns in MCI and AD with a different network extraction approach taking each network as a whole and not separated into ROIs.Methodsrs‐fMRI data from ADNI3 and OASIS3 of 64/85 AD/Control subjects, 57/59 MCI/Control subjects from both repositories were preprocessed with the CONN toolbox. Resting state networks (RSN) were extracted by cGICA using GIFT toolbox (semi‐blind INFOMAX) with 300 ROI Set template with 14 RSN. A non parametric 2‐sample t‐test was applied using the SnPM tool, all analyses in SnPM were performed using 10000 random permutations and the anatomical association of each reported cluster is extracted from the AAL atlas using the xjView tool . Regions that had statistically significant clusters with p FWE<0.001 and with k> = 100 voxels were analyzed.ResultsThere were alterations in regions of every network for each groups comparison (p < 0.001). Several ROI were repeatedly altered in analyzed RSN, temporal pole, MTG, STG, cerebellar crus, OFC, PCC, MCC, precuneus, AG, SMG, SPL, IPL, SFG, MFG, IFG, thalamus, FG and posterior cerebellar lobe. Decreased/increased activation area, changes in activation intensity and reorganization of activation areas, specially reduced activation areas in AD and greater activation areas in MCI. The RSN with more changes in connectivity patterns were DMN, DAN/VAN, MTL, SAL and FPN.ConclusionChanges in connectivity in MCI and AD show reorganization of RSN related to enlargement, reduction or displacement of activation areas, the greatest amount of change was observed in MCI with increases in activation areas and in AD a general decrease in activation areas was observed, these alterations are consistent with the inverted u‐pattern that has been described in other studies . The common ROI we found altered had been related with accumulation and aggregation of p‐Tau and βA and this could be an explanation of connectivity changes duet to the neurodegeneration and synaptic dysfunction caused by this proteins

Automatic ROI Setting Method Based on LSC for a Traffic Congestion Area

Congested regions in videos put forward higher requirements for target detection algorithms, and the key detection of congested regions provides optimization directions for improving the accuracy of detection algorithms. In order to make the target detection algorithm pay more attention to the congested area, an automatic selection method of a traffic congestion area based on surveillance videos is proposed. Firstly, the image is segmented with superpixels, and a superpixel boundary map is extracted. Then, the mean filtering method is used to process the superpixel boundary map, and a fixed threshold is used to filter pixels with high texture complexity. Finally, a maximin method is used to extract the traffic congestion area. Monitoring data of night and rainy days were collected to expand the UA-DETRAC data set, and experiments were carried out on the extended data set. The results show that the proposed method can realize automatic setting of the congestion area under various weather conditions, such as full light, night and rainy days.

Optimizing early detection of beta‐amyloid accumulation with PET using spatial extent

AbstractBackgroundAs clinical trials shift towards prevention, there has been increasing interest in improving early detection of beta‐amyloid (Aβ), below standard global PET positivity thresholds. While many studies have demonstrated that some regions appear more vulnerable to the earliest deposits, those regions have varied widely across studies. We sought to develop more flexible metrics for identifying subthreshold individuals at risk for progression to global Aβ positivity by evaluating a wide range of early regional targets and expanding our measurement beyond tracer retention (DVR/SUVR) to include spatial extent.Method269 clinically‐normal older adults were included from the Harvard Aging Brain Study, focusing on 150 initially globally PIB‐ adults with longitudinal PET. Region‐specific positivity thresholds were computed using iterative longitudinal two‐level linear mixed effect models that identified the cutoff beyond which local accumulation significantly increases over time. Next, we identified all regions significantly predictive of future global accumulation in initially PIB‐ adults and generated 3 aggregate masks at liberal (p<.05), moderate (p<.001) and conservative a‐levels (Bonferroni p<.0007). DVR and spatial extent (number of elevated ROIs) were computed in each mask. Receiver operator characteristic (ROC) curve analysis determined sensitivity/specificity of each metric to predict future global positivity. External validation was performed using globally florbetapir‐negative (FBP‐) healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n=211).ResultRegional variations in measurement reliability were detected (Fig1) and accounted for by employing accumulation‐based regional positivity thresholds. A broad set of ROIs were identified as potential early targets based on prediction of global accumulation, encompassing bilateral cingulate, medial frontal/parietal cortices and left‐lateralized lateral frontal/parietal/temporal cortices (Fig2). ROC analyses demonstrated that spatial extent outperformed DVR in predicting progression to global PIB+ within 3 years (Fig3), with 82% sensitivity and 97% and 98.5% specificity, respectively, for the two larger aggregate masks. External validation in ADNI (Fig4) again demonstrated the superior performance of spatial extent (SE=100%, SP=91.6%) in predicting progression to global positivity within 3 years.ConclusionOur findings demonstrate that due to a high level of heterogeneity in early Aβ deposition, measures of spatial extent across a broad set of neocortical regions are more sensitive to detect early Aβ than traditional DVR/SUVR.

Diffusion-weighted MR imaging in chronic non-bacterial osteitis: Proof-of-concept of the apparent diffusion coefficient as an outcome measure.

BackgroundThe apparent diffusion coefficient (ADC), as determined by whole-body diffusion-weighted MRI, may be useful as an outcome measure for monitoring response to treatment in chronic non-bacterial osteitis.PurposeTo test and demonstrate the feasibility of ADC-measurement methods for use as outcome measure in chronic non-bacterial osteitis.Materials and MethodsUsing data from a randomized pilot study, feasibility of change-score ADC between baseline and second MRI (ΔADC12) and third MRI (ΔADC13) as outcome measure was assessed in three settings: “whole-lesion,” “single-slice per lesion,” and “index-lesion per patient”. Bone marrow edema lesions were depicted on short tau inversion recovery sequence at baseline and copied to ADC maps at the three time-points. Correlations between the three settings were measured as were analysis of variances. Discriminant validity was assessed as inter- and intra-observer reproducibility and smallest detectable change.Results12 subjects were enrolled, and MRI was performed at baseline and weeks 12 and 36. Pearson correlation was high (r > 0.86; p ≤ 0.01) for ΔADC between single-slice—whole-lesion and whole-lesion—index-lesion and tended to be significant for single-slice—index-lesion settings (p = 0.06). For ΔADC12 and ΔADC13, Bland–Altman plots showed small differences (0.02, 0.03) and narrow 95% limits-of-agreement (−0.13–0.09, −0.07–0.05 μm2/s) between whole-lesion and single-slice ROI settings. Inter-observer reproducibility measured by intra-class correlation coefficient was poor-to-fair (range: 0.09–0.31), whereas intra-observer reproducibility was good-to-excellent (range: 0.67–0.90). Smallest detectable changes were between 0.21–0.28 μm2/s.ConclusionADC change-score as outcome measure was feasible, and the single-slice per lesion ROI setting performed almost equally to whole-lesion setting resulting in reduced assessment time.

Lower cortical thickness is associated with reduced gray matter cerebral blood flow across the AD continuum

AbstractBackgroundCortical thinning is an early marker of Alzheimer’s disease (AD) neurodegenerative changes. We examined relationships of cortical thickness and cerebral blood flow (CBF) to assess whether structural brain changes in AD‐related regions are associated with functional changes in blood flow dynamics. Previous reports of CBF indicate reduced blood flow progressing along the AD continuum from normal cognition (NC) to mild cognitive impairment (MCI) to AD. We hypothesized that reduced cortical thickness in AD‐specific brain regions would relate to reduced global gray matter (GM) CBF.MethodParticipants enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort (N=373, mean age=70.5 years) underwent neuropsychological assessment, and T1 and pcASL MRI to assess brain structure and CBF. Participants were adjudicated as cognitively unimpaired (NC, N=212) or impaired (MCI or AD, N=161) using NIA‐Alzheimer’s Association criteria. T1 MRI were processed using FreeSurfer v5.3; an “AD‐signature” meta‐ROI cortical thickness value was calculated, which was thresholded to classify participants as neurodegeneration‐negative (N‐, above threshold) or ‐positive (N+, below threshold). PcASL MRI were processed to yield CBF images that were normalized to MNI space (Fig.1). A 2‐tissue partial volume correction was applied to yield voxelwise GM CBF. Mean GM CBF across a set of cortical AAL ROIs was calculated for each participant. A MANCOVA model adjusted for age, sex and education measured differences in GM CBF between N‐ and N+ groups. We additionally assessed cognitive status as a moderator of these associations.ResultWe found a significant difference in GM CBF between N‐ and N+ groups (p=0.010). Participants with lower meta‐ROI cortical thickness had reduced GM CBF compared to participants above the threshold (Fig.2). The interaction of cognitive status and N+/‐ group revealed lower CBF for impaired participants (Fig.3, unadjusted p<0.001), which remained significant after adjustment (p=0.014).ConclusionIn our ADRC cohort, neurodegenerative changes within brain regions vulnerable to AD pathology were associated with global GM CBF reductions. Cognitively unimpaired N+ participants exhibited similar levels of GM CBF as impaired N‐ participants. Future studies will assess whether unimpaired N+ participants with reduced CBF have a higher risk of conversion to MCI or AD.

Quantitative bone scan imaging using BSI and BUV: an approach to evaluate ARONJ early.

The usefulness of bone scan index (BSI), a quantitative metric of the area of uptake in computer-aided diagnosis in bone scintigraphy, has been reported for the diagnosis of anti-resorptive-agent-related osteonecrosis of the jaw (ARONJ). The aim of this study is to validate the diagnostic ability of BSI for the early detection of ARONJ. In addition, the Bone uptake value (BUV), another quantitative index obtained from bone scintigraphy that indicates the degree of radioisotope (RI) accumulation, was used to improve the diagnostic ability for early detection of ARONJ. A total of 34 patients (11 with ARONJ, 23 without ARONJ) who were administered anti-resorptive-agents for bone metastasis and had incidentally consulted a dental surgeon within 3months after regular whole-body bone scintigraphy were retrospectively included in the study. The bone scintigraphy data were subjected to semiquantitative analysis of uptake in the jaw using BONENAVI (FUJIFILM Toyama Chemical, Co. Ltd., Tokyo, Japan; EXINI Diagnostics AB, Lund, Sweden) and BUV software (Technical Society for Quantitative Bone Scintigraphy and Fujifilm Toyama Chemical Co., Ltd. Tokyo, Japan). The ROI was set semi-automatically on mandibular hotspots, and the regional BSI was termed BSIJ. Planar anterior and posterior images were then sent to BUV software, with the ROI set manually as for BSI, and the regional BUV was termed BUVJ. Mean BSIJ values for the ARONJ positive and ARONJ negative groups were 0.17 ± 0.83 and 0.03 ± 0.50%, respectively. Mean BUVJ values for the ARONJ positive and ARONJ negative groups were 0.47 ± 0.17 and 0.19 ± 0.11, respectively. BSIJ × BUVJ values for the ARONJ positive versus ARONJ negative groups were 0.088 ± 0.067 vs. 0.007 ± 0.010. The AUC for BSIJ, BUVJ and BSIJ × BUVJ was 0.949, 0.951 and 0.988, respectively. The BSI metric of a CAD system for bone scintigraphy was useful for the early detection of ARONJ. Accuracy was improved with the additional use of BUVJ data. We recommend that SPECT imaging be performed when bone scintigraphy reveals focal or diffuse uptake in the mandible with high BSIJ and BUVJ.

Functional networks, language impairment and recovery after treatment in aphasia

Functional networks, language impairment and recovery after treatment in aphasia

Detection of Dominant Intraprostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multimodal Radiomics Analysis

Detection and classification of Dominant Intraprostatic Lesions (DILs) play an important role in diagnosis and radiation treatment response assessment of patients with prostate cancer (PCa). The aim of this pilot study was to construct an adaptive model for characterization and detection of DILs from normal tissue using radiomics features extracted from T2-weighted images and Apparent Diffusion Coefficient (ADC) maps. Twenty-one patients with evidence of PCa with no prior treatment underwent MRI study. An ultrasound guided needle biopsy was performed to confirm the diagnosis. Two image modalities were acquired from the pelvis of all patients using a 3.0 T MR scanner: Axial T2 Weighted (T2W) fast spin echo (TE/TR = 4389/110 ms, FA = 90º, voxel size = 0.42 X 0.42 X 2.4 mm3) and axial diffusion weighted (DW) imaging (TE/TR = 4000/85 ms, FA = 90º, voxel size = 1.79 X 1.79 X 0.56 mm3, with b-values = 0 and 1000 [sec-mm-2] for constructing ADC maps). Using the diagnostic report of each PCa patient, a set of ROIs delineating DILs and normal tissues were drawn on each image modality. One hundred sixty-five radiomics features were extracted from tumor and normal volumes. A Partial Least Square Correlation (PLSC) along with one-way ANOVA were recruited to identify the most discriminant radiomics features (DILs versus normal) from multimodal information (T1WI and ADC). An artificial neural networks (ANN) was constructed based on the optimal feature set to classify the DILs and normal tissue. Using Leave-One-Out Cross Validation (LOOCV) techniques, the ANN was trained, optimized, and finally evaluated. Among 165 radiomics features, 8 features (2 features from Two-Dimensional Wavelet Transform, 4 features from Two-Dimensional Gabor Filter, 2 features from Gray Level Co-occurrence Matrix) were found to be significantly different between the DILs and normal tissue in presence of multimodal information. Using the 8 radiomics features as input, after training and optimization (with 8, 5, and 1 neurons in its input, hidden and output layers, termination error = 0.022) of the ANN with LOOCV, it was able to differentiate the DILs and normal groups with predictive power (Az Test) of 84%. When the training vector was randomly permuted 1000 times, the permutation-invariant efficiency of the ANN was 4.5%. This pilot study demonstrates the feasibility of combining radiomics analysis using multimodal MR information and adaptive model to detect DILs in patients with prostate cancer. The study is limited by the number of patients, which can impact the optimal features selected, and also might render a predictive model susceptible to Type II errors. Additionally, the radiomics features selected from multimodal MRI might be impacted by the intensities and contrast of the T2WI and ADC maps. These factors, and the incorporation of additional MR modalities along with pathological-based information into the adaptive model, are being investigated.

Strain Elastography System with Quality Control - Auto Strain Ratio System

Strain elastography (SE) and shear wave speed imaging provide us with tissue stiffness information that reflects the pathological information, important for breast diagnosis and treatment. And Strain Ratio is a popular diagnosis method of strain elastography evaluation. However, it can be associated with operator bias in image formation, scanning technique, the selection of the appropriate elastography frame, and ROI setting for strain ratio measurement. To overcome these problems, we have developed an “Auto Strain Ratio System (ASRS)”, which requires no manual compression whilst scanning, with full auto frame selection on freeze and fully automatic ROI target setting. And a preliminary prospective clinical study was performed in 2015. 232 breast masses assessed as BI-RADS Category 3 or above by B-mode images, were scanned using SE. The ASRS values was compared to conventional manual strain ratio measurement (MSR) values by experienced doctors and the diagnostic performance and quality evaluated. There was a significant correlation between the MSR and ASRS with R=0.79 (p<0.001). The MSR (cut-off=3.8) had a sensitivity of 89%, a specificity of 75%, an accuracy of 78%, a positive predictive value (PPV) of 52%, and a negative predictive value (NPV) of 96%. The ASRS (cut-off=3.9) had a sensitivity of 82%, specificity of 89 %, an accuracy of 87%, a PPV of 69%, and a NPV of 94%. The AUCs were 0.88 on MSR and 0.89 on ASRS. We have demonstrated that it is possible to quantify SE and control its accuracy. The ASRS is expected to contribute to the standardization of breast elastography.

Concept of an upright wearable positron emission tomography imager in humans.

BackgroundPositron Emission Tomography (PET) is traditionally used to image patients in restrictive positions, with few devices allowing for upright, brain‐dedicated imaging. Our team has explored the concept of wearable PET imagers which could provide functional brain imaging of freely moving subjects. To test feasibility and determine future considerations for development, we built a rudimentary proof‐of‐concept prototype (Helmet_PET) and conducted tests in phantoms and four human volunteers.MethodsTwelve Silicon Photomultiplier‐based detectors were assembled in a ring with exterior weight support and an interior mechanism that could be adjustably fitted to the head. We conducted brain phantom tests as well as scanned four patients scheduled for diagnostic F18‐ FDG PET/CT imaging. For human subjects the imager was angled such that field of view included basal ganglia and visual cortex to test for typical resting‐state pattern. Imaging in two subjects was performed ~4 hr after PET/CT imaging to simulate lower injected F18‐ FDG dose by taking advantage of the natural radioactive decay of the tracer (F18 half‐life of 110 min), with an estimated imaging dosage of 25% of the standard.ResultsWe found that imaging with a simple lightweight ring of detectors was feasible using a fraction of the standard radioligand dose. Activity levels in the human participants were quantitatively similar to standard PET in a set of anatomical ROIs. Typical resting‐state brain pattern activation was demonstrated even in a 1 min scan of active head rotation.ConclusionTo our knowledge, this is the first demonstration of imaging a human subject with a novel wearable PET imager that moves with robust head movements. We discuss potential research and clinical applications that will drive the design of a fully functional device. Designs will need to consider trade‐offs between a low weight device with high mobility and a heavier device with greater sensitivity and larger field of view.

Analisis Pemilihan Region of Interest (RoI) pada Magnetic Resonance Spectroscopy (MRS)

Background: Magnetic Resonance Spectroscopy (MRS) is an advance examination of MRI which produces a spectra describes metabolic information of tissues. ROI setting was very important in producing the spectra with diagnostic value that called Full-Width at Half Maximum (FWHM). In clinical applications, Radiographers select several ROIs in obtaining FWHM values without any specific benchmark which in turn directly affects on the accuracy of staging brain tumor. This study aims to examine the FWHM profiles based on the ROI selections, and to determine the effect of selected ROIs to FWHM values.Methods: This research was a quantitative study with an experimental approach. Data were assessed by an expert Radiographer during a period of 30 days on June 2016 in Radiology Department of Siloam Kebon Jeruk Jakarta Hospital using a specific bottle phantom MRI 2000 ml. The phantom was scanned with 1.5 Tesla MRI scanner, wuth various selection of the ROIs sized 20 mm, 25 mm, 30 mm, 35 mm and 40 mm at the centre position, 12 o'clock position, 3 o'clock position, 6 o'clock position and 9 o'clock position. Data was analyzed using linier regression test of SPSS V.16 software. The confident interval of influence of the ROI size in producing FWHM values was 95%.Results: The results showed the optimum FWHM values, 12-20, produced from the ROI size of 20-30 mm at the centre position. Regression analysis showed significant result (p-value 0.05), which was null hypothesis was rejected. It can be concluded that the ROI size setting effected on the resulted FWHM values. The smaller ROI size, FWHM value will decrease with higher homogeneity and vise versa.Conclusion: Optimum FWHM value was produced by ROI size of 20 – 30 mm at the centre position. There was influence of ROI size setting to FWHM value.

A symmetry-based method to infer structural brain networks from probabilistic tractography data.

A symmetry-based method to infer structural brain networks from probabilistic tractography data.

Resting state functional connectivity changes induced by prior brain state are not network specific

Resting state functional connectivity (rFC) is used to identify functionally related brain areas without requiring subjects to perform specific tasks. Previous work suggests that prior brain state, as determined by the activity engaged in immediately prior to collection of resting state data, can influence the networks recovered by rFC analyses.We determined the prevalence and network specificity of rFC changes induced by manipulations of prior state (including an unstructured (unconstrained) state, and language and motor tasks). Three blocks of rest data (one after each of the specified prior states) were acquired on each of 25 subjects. We hypothesised that prior state induced changes in rFC would be greatest within the networks most actively recruited by that prior state.Changes in rFC were greatest following the motor task and, contrary to our hypothesis, were not network specific. This was demonstrated by comparing (1) the timecourses within a set of ROIs selected on the basis of task-related de/activation, and (2) seed-based whole brain voxel-wise connectivity maps, seeded from local maxima in the task-related de/activation maps. Changes in connectivity strength tended to manifest as increases in rFC relative to that in the unstructured rest state, with change maps resembling partially complete maps of the primary sensory cortices and the cognitive control network. The majority of rFC changes occurred in areas moderately (but not weakly) connected to the seeds. Constrained prior states were associated with lower across-participant variance in rFC.This systematic investigation of the effect of prior brain state on rFC indicates that the rFC changes induced by prior brain state occur both in brain networks related to that brain activity and in networks nominally unrelated to that brain activity.

Development of an automatic ROI setting program for input function determination in 99mTc-ECD non-invasive cerebral blood flow quantification

Non-invasive quantitative measurements are useful for clinical study as these are simple and pain-free procedures. A new non-invasive semi-automatic quantitative measurement method, the improved brain uptake ratio (IBUR) method using 99mTc-ECD SPECT, has recently been reported. If an automatic ROI setting algorithm could be developed to determine the input function for the IBUR method, analysis of regional cerebral blood flow (rCBF) can be completed within a few min without recourse to complex techniques, through a fully automatic rCBF analysis program. The purpose of this study was to develop an automatic input function determination program for 99mTc-ECD non-invasive cerebral blood flow quantification and to confirm the feasibility of use of this program.The images of 15 consecutive patients who underwent both 99mTc-ECD chest RI angiography and SPECT examinations were used for development of the automatic arterial input function program. The images of 69 consecutive patients were used for validation of the program.The coincidence ratio between the ROI automatic method and the manual setting method was 98%. The mean difference in the ROI location was ±6.4 mm in the X direction and ±8.6 mm in the Y direction. Individual rCBF values obtained using these independent techniques were also reasonably well correlated (r = 0.95). The total time for the IBUR analysis using the automatic method is 2–3 min as compared to 20–30 min for the current analysis method. This technique improves the throughput of nuclear medical examinations.

ROI impact on SUVR quantification in amyloid PET

Objectives The imaging of amyloid plaque PET is a promising early marker of Alzheimer's disease. The literature reports different positivity thresholds based on SUVr (Standard Uptake Value ratio) varying the method of quantitation. The choice of the reference atlas has not been studied. The purpose of this study is to compare the effect of two sets of ROI on of amyloid burden quantification, using the same methods of pretreatment and semi-automatic quantification in the same population. Materials and methods A multicenter study from the MAPT acquisitions from 10 to 15 min, 50 min after a bolus injection of 3.7 MBq/ kg of Florebetapir (Amyvid ® ) were performed. The images were visually assessed by three trained observers on a binary scale (+/−) blinded of clinical data. Then a semi-automatic quantification was performed on six cortical regions with two different atlases different set of VOIs (Avid ® and WFU) to obtain two SUVr taking the cerebellum as reference region. The SUVr in each region zone and the cortical average over six regions SUVr means were compared. The positivity thresholds for each method were calculated using visual analysis as a reference. Results 198 subjects (76 ± 4.3 years) were included. On visual analysis 36% of the subjects were amyloid positive (76.6 ± 4.5 years). The average SUVr is greater ROI for WFU ROIs (1.4 ± 0.17 against 1.16 ± 0.19). The SUVr are very different according to the atlas used ( t ± 0.0001), but strongly correlated (q = 0908 pearson, p = 0.001). The semiautomatic quantitative analysis gives 35% (WFU) and 37% (Avid ® ) of amyloid positive subjects with a respective threshold of 1.45 and 1.16. Conclusions SUVr depend on ROI set. However, determining the positivity threshold specific to each of them, we can obtain similar results in terms of diagnostic performance. This variability in ROI set should be taken into account in the analysis of results and especially in meta-analyzes

Seal

Location-based services (LBS) have become more and more ubiquitous recently. Existing methods focus on finding relevant points-of-interest (POIs) based on users' locations and query keywords. Nowadays, modern LBS applications generate a new kind of spatio-textual data, regions-of-interest (ROIs), containing region-based spatial information and textual description, e.g., mobile user profiles with active regions and interest tags. To satisfy search requirements on ROIs, we study a new research problem, called spatio-textual similarity search: Given a set of ROIs and a query ROI, we find the similar ROIs by considering spatial overlap and textual similarity. Spatio-textual similarity search has many important applications, e.g., social marketing in location-aware social networks. It calls for an efficient search method to support large scales of spatio-textual data in LBS systems. To this end, we introduce a filter-and-verification framework to compute the answers. In the filter step, we generate signatures for the ROIs and the query, and utilize the signatures to generate candidates whose signatures are similar to that of the query. In the verification step, we verify the candidates and identify the final answers. To achieve high performance, we generate effective high-quality signatures, and devise efficient filtering algorithms as well as pruning techniques. Experimental results on real and synthetic datasets show that our method achieves high performance.

Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution

The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach.Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI).Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected.Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.

Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively analyze the status of the white matter anatomy in a tract-specific manner in many types of diseases. This approach, however, involves subjective judgment in the tract-editing process to extract only the tracts of interest. This process, usually performed by manual delineation of regions of interest, is also time-consuming, and certain tracts, especially the short cortico-cortical association fibers, are difficult to reconstruct. In this paper, we propose an automated approach for reconstruction of a large number of white matter tracts. In this approach, existing anatomical knowledge about tract trajectories (called the Template ROI Set or TRS) were stored in our DTI-based brain atlas with 130 three-dimensional anatomical segmentations, which were warped non-linearly to individual DTI data. We examined the degree of matching with manual results for selected fibers. We established 30 TRSs to reconstruct 30 prominent and previously well-described fibers. In addition, TRSs were developed to delineate 29 short association fibers that were found in all normal subjects examined in this paper (N=20). Probabilistic maps of the 59 tract trajectories were created from the normal subjects and were incorporated into our image analysis tool for automated tract-specific quantification.

Mapping selective neuronal loss and microglial activation in the salvaged neocortical penumbra in the rat

Rescuing the ischemic penumbra from infarction is the mainstay of acute stroke therapy. However, the rescued penumbra may be affected by selective neuronal loss (SNL) and microglial activation (MA), which may hinder functional recovery and hence represent potential new therapeutic targets. Imaging them in vivo is currently attracting considerable interest, but relevant rat models are needed to underpin methods development and validation. Although striatal SNL/MA is well described following proximal MCA occlusion (MCAo), neocortical SNL/MA is still poorly characterized, yet has greater clinical relevance. This study aimed to assess the distribution and intensity of neocortical SNL and MA in a distal clip MCAo model known to cause severe neocortical ischemia. Spontaneously hypertensive rats were subjected to 45 min distal MCAo with ipsilateral common carotid artery occlusion. At day 14, post mortem SNL and MA were mapped using NeuN and OX42 immunohistochemistry, respectively. In a separate group, cerebral blood flow (CBF) was mapped during MCAo using 14C-iodoantipyrine autoradiography. Values for SNL, MA, and CBF were obtained in the same set of anatomical ROIs covering the cortical MCA territory. Extensive SNL and MA affected the non-infarcted MCA cortex, adopting a well-defined regional distribution and a striking patchy/pseudo-columnar pattern. Regional intensities of SNL and MA were strongly inter-correlated, and also strikingly related to occlusion CBF, showing sharp rises for CBF <40%, i.e. the penumbra threshold. This rat model may be useful in providing in vitro reference for studies aiming to validate novel imaging tracers of SNL and MA in vivo.