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In common, prostate cancer is regarded as the type of cancer, which occurs over the small walnut-shaped gland in men termed as the prostate. In addition to that, the prostate is considered as the most generally identified type of cancer among men. Here, the gland has been aided in the production of seminal fluid that has been utilized for transporting and nourishing the sperm. In order to exclude the existence of cancer in the tissues, prostate biopsy techniques are utilized. Moreover, the mortality rate due to this disease may be low in the last few years, but it is regarded as the leading cause of cancer. In this case, the automated intelligent techniques are helpful for aiding the pathologists in minimizing fatigue and enhancing the routing process. Moreover, there are some limitations in the traditional model, and it is tackled with the help of a new prostate cancer segmentation and classification approach. Firstly, images related to prostate cancer are attained from standard resources and offered as input to lesion segmentation. Here, lesion segmentation is performed with the help of Adaptive Dilated TransUNet + + to get the segmented image features. The parameters of Dilated TransUNet + + are tuned by utilizing a hybrid approach named Position-aided Pelican-Sea Lion Optimization (PPSLO). Then, the segmented images are offered as the input to Region-of-Interest (ROI) cropping, and the ROI cropped image is attained as the output. Further, the ROI cropped image is fed as the input to the prostate cancer classification phase. In this phase, prostate cancer is classified using Multiscale Adaptive DenseNet with Bi-directional Long Short Term Memory (MAD-Bi-LSTM) Layer, in which the parameters in the network are tuned by the developed approach PPSLO. Hence, the developed prostate cancer segmentation and classification model helps for securing an enhanced disease classification rate than other experimental observations. [ABSTRACT FROM AUTHOR]

Abstract Background Current guidelines of the radioiodine uptake (RAIU) test allow the use of different equipment, isotopes, activity and region-of-interest (ROI). We evaluated presence and extent of these differences in clinical practice and evaluated the effect of some of these variations on RAIU outcomes. Also, gamma camera-specific reference standards were calculated and retrospectively compared with measurements obtained during clinical RAIU tests. Materials and methods First, questionnaires were sent to Dutch nuclear medicine departments requesting information about equipment usage, isotope, isotope formulation, activity and measurement techniques. Secondly, a neck phantom containing a range of activities in capsule or water-dissolved formulation was scanned. Counts were measured using automatic ROI, square box ROI or all counts in the image. Thirdly, clinical RAIU data were collected during 2015–2018 using three different gamma cameras. Reference standards for each scanner were calculated using regression analysis between reference activity and measured counts. Uptake measurements using this gamma camera-specific reference standard were compared with original measurements. Results The survey demonstrated significant differences in isotope, isotope formulation, activity, use of neck phantoms, frequency and duration of reference measurements, distance to collimator, use of background measurements and ROI delineation. The phantom study demonstrated higher counts for the water-dissolved formulation than capsules using both automatic and square box ROI. Also, higher counts were found using a square box ROI than an automatic ROI. The retrospective study showed feasibility of RAIU calculations using camera-specific reference standards and good correlation with the original RAIU measurements. Conclusions This study demonstrated considerable technical variation in RAIU measurement in clinical practice. The phantom study demonstrated that these differences could result in differences in count measurements, potentially resulting in different dose calculations for radioactive iodine therapy. Retrospective data suggest that camera-specific reference standards may be used instead of individual reference measurements using separate activity sources, which may thus eliminate some sources of variation.

Due to the variety of fruit disorders, food production is highly impacted, resulting in economic degradation. So, proper disease recognition and classification system are vital to reduce agricultural losses and encourage economic growth. For this purpose, a multi-scale region-of-interest (ROI)-based deep learning (MRDL) model was developed, which localizes and classifies multiple fruit disorders based on the new attention principle and feature representation. But, the shadow regions in the fruit pictures influence the model efficiency and the picture dataset was highly dynamic while adding new classes for different disorders; because collecting diseased class labels for a single fruit picture was not simple. Hence in this article, a novel cycle-generative adversarial network (CycleGAN) model is integrated with the MRDL to generate shadow-free fruit pictures and increase the accuracy of classifying multiple classes of fruit infections. Initially, an attribute registration and shadow removal adversarial network (ArSAN) is proposed to generate various fruit pictures without shadow areas. In this ArSAN, the attributes of objects and shadows in fruit pictures are initially obtained. Then, those are labeled by the attribute enrolment scheme with the CycleGAN. Based on the labeled attributes, the filtering weights are set to all pictures in the real picture dataset. Also, a similarity finding scheme is used to discover the best-matched pictures for the generator network. As a result, multiple generators are trained to produce the shadow-free fruit picture dataset with diverse cleanness characteristics. Moreover, the newly generated pictures are passed to the MRDL model for localizing the infected fruit areas and classifying the multiple disease classes. Finally, the experimental outcomes reveal that the ArSAN-MRDL model attains a 93.8%, 93.78% and 93.76% accuracy on apple, citrus and tomato disorder picture sets, respectively contrasted to the MRDL, artificial neural network (ANN), 13-layer convolutional neural network (CNN), L2MXception, CycleGAN-DenseNet, and conditional GAN (cGAN)-VGG16 models. [ABSTRACT FROM AUTHOR]

PurposeTo evaluate repeatability of ROI-sampling strategies for quantifying hepatic proton density fat fraction (PDFF) and to assess error relative to the 9-ROI PDFF.MethodsThis was a secondary analysis in subjects with known or suspected nonalcoholic fatty liver disease who underwent MRI for magnitude-based hepatic PDFF quantification. Each subject underwent three exams, each including three acquisitions (nine acquisitions total). An ROI was placed in each hepatic segment on the first acquisition of the first exam and propagated to other acquisitions. PDFF was calculated for each of 511 sampling strategies using every combination of 1, 2, …, all 9 ROIs. Intra- and inter-exam intra-class correlation coefficients (ICCs) and repeatability coefficients (RCs) were estimated for each sampling strategy. Mean absolute error (MAE) was estimated relative to the 9-ROI PDFF. Strategies that sampled both lobes evenly ("balanced") were compared with those that did not ("unbalanced") using two-sample t tests.ResultsThe 29 enrolled subjects (23 male, mean age 24 years) had mean 9-ROI PDFF 11.8% (1.1-36.3%). With more ROIs, ICCs increased, RCs decreased, and MAE decreased. Of the 60 balanced strategies with 4 ROIs, all (100%) achieved inter- and intra-exam ICCs > 0.998, 55 (92%) achieved intra-exam RC

Background: Alzheimer’s disease (AD) is characterized by progressive deterioration of cognitive functions. Some individuals with subjective cognitive decline (SCD) are in the early phase of the disease and subsequently progress through the AD continuum. Although neuroimaging biomarkers could be used for the accurate and early diagnosis of preclinical AD, the findings in SCD samples have been heterogeneous. This study established the morphological differences in brain magnetic resonance imaging (MRI) findings between individuals with SCD and those without cognitive impairment based on a clinical sample of patients defined according to SCD-Initiative recommendations. Moreover, we investigated baseline structural changes in the brains of participants who remained stable or progressed to mild cognitive impairment or dementia. Methods: This study included 309 participants with SCD and 43 healthy controls (HCs) with high-quality brain MRI at baseline. Among the 99 subjects in the SCD group who were followed clinically, 32 progressed (SCDp) and 67 remained stable (SCDnp). A voxel-wise statistical comparison of gray and white matter (WM) volume was performed between the HC and SCD groups and between the HC, SCDp, and SCDnp groups. XTRACT ATLAS was used to define the anatomical location of WM tract damage. Region-of-interest (ROI) analyses were performed to determine brain volumetric differences. White matter lesion (WML) burden was established in each group. Results: Voxel-based morphometry (VBM) analysis revealed that the SCD group exhibited gray matter atrophy in the middle frontal gyri, superior orbital gyri, superior frontal gyri, right rectal gyrus, whole occipital lobule, and both thalami and precunei. Meanwhile, ROI analysis revealed decreased volume in the left rectal gyrus, bilateral medial orbital gyri, middle frontal gyri, superior frontal gyri, calcarine fissure, and left thalamus. The SCDp group exhibited greater hippocampal atrophy (p

Background: Current guidelines of the radioiodine uptake (RAIU) test allow the use of different equipment, isotopes, activity and region-of-interest (ROI). We evaluated presence and extent of these differences in clinical practice and evaluated the effect of some of these variations on RAIU outcomes. Also, gamma camera-specific reference standards were calculated and retrospectively compared with measurements obtained during clinical RAIU tests. Materials and methods: First, questionnaires were sent to Dutch nuclear medicine departments requesting information about equipment usage, isotope, isotope formulation, activity and measurement techniques. Secondly, a neck phantom containing a range of activities in capsule or water-dissolved formulation was scanned. Counts were measured using automatic ROI, square box ROI or all counts in the image. Thirdly, clinical RAIU data were collected during 2015–2018 using three different gamma cameras. Reference standards for each scanner were calculated using regression analysis between reference activity and measured counts. Uptake measurements using this gamma camera-specific reference standard were compared with original measurements. Results: The survey demonstrated significant differences in isotope, isotope formulation, activity, use of neck phantoms, frequency and duration of reference measurements, distance to collimator, use of background measurements and ROI delineation. The phantom study demonstrated higher counts for the water-dissolved formulation than capsules using both automatic and square box ROI. Also, higher counts were found using a square box ROI than an automatic ROI. The retrospective study showed feasibility of RAIU calculations using camera-specific reference standards and good correlation with the original RAIU measurements. Conclusions: This study demonstrated considerable technical variation in RAIU measurement in clinical practice. The phantom study demonstrated that these differences could result in differences in count measurements, potentially resulting in different dose calculations for radioactive iodine therapy. Retrospective data suggest that camera-specific reference standards may be used instead of individual reference measurements using separate activity sources, which may thus eliminate some sources of variation. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively.

Moving object detection (MOD) has become a popular topic in video analysis due to its use in several applications, including video coding in wireless surveillance. However, implementing MOD in constrained sensors is challenging due to their high complexity and energy consumption. Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems. In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed. The algorithm ensures a trade-off between detection accuracy and computational complexity. To this end, we propose constructing an activity map by measuring each block activity between successive frames. The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-the-art techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.

Abstract Background The magnetic resonance imaging (MRI)-based proton density fat fraction (PDFF) has become popular for quantifying liver fat content. However, the variability of the region-of-interest (ROI) sampling strategy may result in a lack of standardisation of this technology. In an effort to establish an accurate and effective PDFF measurement scheme, this study assessed the pathological correlation, the reader agreement, and time-burden of different sampling strategies with variable ROI size, location, and number. Methods Six-echo spoiled gradient-recalled-echo magnitude-based fat quantification was performed for 50 patients with obesity, using a 3.0-T MRI scanner. Two readers used different ROI sampling strategies to measure liver PDFF, three times. Intra-reader and inter-reader agreement was evaluated using intra-class correlation coefficients and Bland‒Altman analysis. Pearson correlations were used to assess the correlation between PDFFs and liver biopsy. Time-burden was recorded. Results For pathological correlations, the correlations for the strategy of using three large ROIs in Couinaud segment 3 (S3 3L-ROI) were significantly greater than those for all sampling strategies at the whole-liver level (P 0.995 and LOAs

BackgroundClinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment.PurposeTo identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy.Study typeRetrospective secondary analysis of prospectively acquired clinical research data.PopulationA total of 391 adults (173 men, 218 women) with known or suspected NAFLD.Field strength/sequenceConfounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique.AssessmentAn ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average.Statistical testingMean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses.ResultsThe study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) 0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA

The Region of interest (ROI) analysis is widely used in image analytics, video coding, computer graphics, computer vision, medical imaging, nuclear medicine, computer tomography and many other areas in medical applications. This ROI determination process using subjective method (e.g. using human vision) often differ from the objective ones (e.g. using mathematical modelling). However, there is no existing method in the literature that could provide a single decision when both methods’ ROI data is available. To address this limitation, a robust algorithm is developed by combining the human eye tracking (subjective) and the graph-based visual saliency modelling (objective) information to determine a more realistic ROI for a scene. To carry out this process, in one hand, several different independent human visual saliency factors such as pupil size, pupil dilation, central tendency, fixation pattern, and gaze plot for a group of twenty-two participants are collected by applying on a set of publicly available eighteen video sequences. On the other hand, the features of Graph based visual saliency (GBVS) highlights conspicuity in the scene. Gleaned from these two pieces of information, the proposed algorithm determines the final ROI based on some heuristics. Experimental results show that for a wide range of video sequences and compared to the existing deep learning based (MxSalNet) and depth pixel (DP) based ROI, the proposed ROI is more consistent to the benchmark ROI, which was previously decided by a group of video coding experts. As the subjective and objective options frequently create an ambiguity to reach a single decision on ROI, the proposed algorithm could determine an ultimate decision, which is eventually validated by experts’ opinion.

In recent years, the performance of people-counting models has been dramatically increased that they can be implemented in practical cases. However, the current models can only count all of the people captured in the inputted closed circuit television (CCTV) footage. Oftentimes, we only want to count people in a specific Region-of-Interest (RoI) in the footage. Unfortunately, simple approaches such as covering the area outside of the RoI are not applicable without degrading the performance of the models. Therefore, we developed a novel learning strategy that enables a deep-learning-based people counting model to count people only in a certain RoI. In the proposed method, the people counting model has two heads that are attached on top of a crowd counting backbone network. These two heads respectively learn to count people inside the RoI and negate the people count outside the RoI. We named this proposed method Gap Regularizer and tested it on ResNet-50, ResNet-101, CSRNet, and SFCN. The experiment results showed that Gap Regularizer can reduce the mean absolute error (MAE), root mean square error (RMSE), and grid average mean error (GAME) of ResNet-50, which is the smallest CNN model, with the highest reduction of 45.2%, 41.25%, and 46.43%, respectively. On shallow models such as the CSRNet, the regularizer can also drastically increase the SSIM by up to 248.65% in addition to reducing the MAE, RMSE, and GAME. The Gap Regularizer can also improve the performance of SFCN which is a deep CNN model with back-end features by up to 17.22% and 10.54% compared to its standard version. Moreover, the impacts of the Gap Regularizer on these two models are also generally statistically significant (P-value < 0.05) on the MOT17-09, MOT20-02, and RHC datasets. However, it has a limitation in which it is unable to make significant impacts on deep models without back-end features such as the ResNet-101.

Background: The magnetic resonance imaging (MRI)-based proton density fat fraction (PDFF) has become popular for quantifying liver fat content. However, the variability of the region-of-interest (ROI) sampling strategy may result in a lack of standardisation of this technology. In an effort to establish an accurate and effective PDFF measurement scheme, this study assessed the pathological correlation, the reader agreement, and time-burden of different sampling strategies with variable ROI size, location, and number. Methods: Six-echo spoiled gradient-recalled-echo magnitude-based fat quantification was performed for 50 patients with obesity, using a 3.0-T MRI scanner. Two readers used different ROI sampling strategies to measure liver PDFF, three times. Intra-reader and inter-reader agreement was evaluated using intra-class correlation coefficients and Bland‒Altman analysis. Pearson correlations were used to assess the correlation between PDFFs and liver biopsy. Time-burden was recorded. Results: For pathological correlations, the correlations for the strategy of using three large ROIs in Couinaud segment 3 (S3 3L-ROI) were significantly greater than those for all sampling strategies at the whole-liver level (P < 0.05). For inter-reader agreement, the sampling strategies at the segmental level for S3 3L-ROI and using three large ROIs in Couinaud segment 6 (S6 3L-ROI) and the sampling strategies at the whole-liver level for three small ROIs per Couinaud segment (27S-ROI), one large ROI per Couinaud segment (9L-ROI), and three large ROIs per Couinaud segment (27S-ROI) had limits of agreement (LOA) < 1.5%. For intra-reader agreement, the sampling strategies at the whole-liver level for 27S-ROI, 9L-ROI, and 27L-ROI had both intraclass coefficients > 0.995 and LOAs < 1.5%. The change in the time-burden was the largest (100.80 s) when 9L-ROI was changed to 27L-ROI. Conclusions: For hepatic PDFF measurement without liver puncture biopsy as the gold standard, and for general hepatic PDFF assessment, 9L-ROI sampling strategy at the whole-liver level should be used preferentially. For hepatic PDFF with liver puncture biopsy as the gold standard, 3L-ROI sampling strategy at the puncture site segment is recommended. [ABSTRACT FROM AUTHOR]

BackgroundGlaucoma is hypothesized to originate in the brain but manifests as an eye disease as it possesses the common features of neurodegeneration diseases. But there is no evidence to demonstrate the primary brain changes in glaucoma patients. In the present study, we have used Mendelian randomization (MR) to understand the causal effect of brain alterations on glaucoma.MethodsOur MR study was carried out using summary statistics from genome-wide associations for 110 diffusion tensor imaging (DTI) measurements of white matter (WM) tracts (17,706 individuals), 101 brain region-of-interest (ROI) volumes (19,629 individuals), and glaucoma (8,591 cases, 210,201 control subjects). The causal relationship was evaluated by multiplicative random effects inverse variance weighted (IVW) method and verified by two other MR methods, including MR Egger, weighted median, and extensive sensitivity analyses.ResultsGenetic liability to fornix fractional anisotropy (FX.FA) (OR = 0.71, 95%CI = 0.56–0.88, P = 2.44 × 10–3), and uncinate fasciculus UNC.FA (OR = 0.65, 95%CI = 0.48–0.88, P = 5.57 × 10–3) was associated with a low risk of glaucoma. Besides, the right ventral diencephalon (OR = 1.72, 95%CI = 1.17–2.52, P = 5.64 × 10–3) and brain stem (OR = 1.35, 95%CI = 1.08–1.69, P = 8.94 × 10–3) were associated with the increased risk of glaucoma. No heterogeneity and pleiotropy were detected.ConclusionOur study suggests that the fornix and uncinate fasciculus degenerations and injures of the right ventral diencephalon and brain stem potentially increase the occurrence of glaucoma and reveal the existence of the brain-eye axis.

Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n × n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes. [ABSTRACT FROM AUTHOR]

In two dimensions, we consider the problem of inversion of the attenuated X X -ray transform of a compactly supported function from data restricted to lines leaning on a given arc. We provide a method to reconstruct the function on the convex hull of this arc. The attenuation is assumed known. The method of proof uses the Hilbert transform associated with A A -analytic functions in the sense of Bukhgeim. [ABSTRACT FROM AUTHOR]

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.

Background: Evidences suggesting the association between behavioral anomalies in autism and white matter (WM) microstructural alterations are increasing. Diffusion tensor imaging (DTI) is widely used to infer tissue microstructure. However, due to its lack of specificity, the underlying pathology of reported differences in DTI measures in autism remains poorly understood. Herein, we applied neurite orientation dispersion and density imaging (NODDI) to quantify and define more specific causes of WM microstructural changes associated with autism in adults. Methods: NODDI (neurite density index [NDI], orientation dispersion index, and isotropic volume fraction [ISOVF]) and DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity, and radial diffusivity [RD]) measures were compared between autism (N = 26; 19 males and 7 females; 32.93 ± 9.24 years old) and age- and sex-matched typically developing (TD; N = 25; 17 males and 8 females; 34.43 ± 9.02 years old) groups using tract-based spatial statistics and region-of-interest analyses. Linear discriminant analysis using leave-one-out cross-validation (LDA-LOOCV) was also performed to assess the discriminative power of diffusion measures in autism and TD. Results: Significantly lower NDI and higher ISOVF, suggestive of decreased neurite density and increased extracellular free-water, respectively, were demonstrated in the autism group compared with the TD group, mainly in commissural and long-range association tracts, but with distinct predominant sides. Consistent with previous reports, the autism group showed lower FA and higher MD and RD when compared with TD group. Notably, LDA-LOOCV suggests that NDI and ISOVF have relatively higher accuracy (82%) and specificity (NDI, 84%; ISOVF, 88%) compared with that of FA, MD, and RD (accuracy, 67–73%; specificity, 68–80%). Limitations: The absence of histopathological confirmation limit the interpretation of our findings. Conclusions: Our results suggest that NODDI measures might be useful as imaging biomarkers to diagnose autism in adults and assess its behavioral characteristics. Furthermore, NODDI allows interpretation of previous findings on changes in WM diffusion tensor metrics in individuals with autism. [ABSTRACT FROM AUTHOR]

Malus ×arnoldiana accession MAL0004 has been found to be resistant to moderately and highly virulent strains of the fire blight causal pathogen – the Gram-negative bacterium, Erwinia amylovora. Genetic analyses with an F1 segregating population derived from crossing the highly susceptible apple cultivar ‘Idared’ and MAL0004 led to the detection and mapping of the fire blight resistance locus of M. ×arnoldiana to linkage group (LG)12 (FB_Mar12). FB_Mar12 mapped at the distal end of LG12 below the apple SSR Hi07f01 in an interval of approximately 6 cM (Centimorgan), where both the fire blight resistance loci of M. floribunda 821 and ‘Evereste’ were located. We fine mapped the region containing FB_Mar12 using 892 progenies. Mining of the region of interest (ROI) on the ‘Golden Delicious’ doubled haploid genome (GDDH13) identified the presence of 2.3 Mb (megabases) in the homologous region. Of 40 primer pairs designed within this region, 20 were polymorphic and nine were mapped, leading to the identification of 24 significant recombinant individuals whose phenotypes were informative in determining the precise position of the locus within a 0.57 cM interval. Analyses of tightly linked marker sequences on the M. baccata draft genome revealed scaffolds of interest putatively harboring the resistance loci of M. ×arnoldiana, a hybrid between M. baccata and M. floribunda. Open reading frame (ORF) analyses led to the prediction of first fire blight resistance candidate genes with serine/threonine kinase and leucine-rich repeat domains, including homologs of previously identified ‘Evereste’ candidate genes. We discuss the implications of these results on breeding for resistance to fire blight.

Malus × arnoldiana accession MAL0004 has been found to be resistant to moderately and highly virulent strains of the fire blight causal pathogen – the Gram-negative bacterium, Erwinia amylovora. Genetic analyses with an F1 segregating population derived from crossing the highly susceptible apple cultivar 'Idared' and MAL0004 led to the detection and mapping of the fire blight resistance locus of M. ×arnoldiana to linkage group (LG)12 (FB_Mar12). FB_Mar12 mapped at the distal end of LG12 below the apple SSR Hi07f01 in an interval of approximately 6 cM (Centimorgan), where both the fire blight resistance loci of M. floribunda 821 and 'Evereste' were located. We fine mapped the region containing FB_Mar12 using 892 progenies. Mining of the region of interest (ROI) on the 'Golden Delicious' doubled haploid genome (GDDH13) identified the presence of 2.3 Mb (megabases) in the homologous region. Of 40 primer pairs designed within this region, 20 were polymorphic and nine were mapped, leading to the identification of 24 significant recombinant individuals whose phenotypes were informative in determining the precise position of the locus within a 0.57 cM interval. Analyses of tightly linked marker sequences on the M. baccata draft genome revealed scaffolds of interest putatively harboring the resistance loci of M. ×arnoldiana , a hybrid between M. baccata and M. floribunda. Open reading frame (ORF) analyses led to the prediction of first fire blight resistance candidate genes with serine/threonine kinase and leucine-rich repeat domains, including homologs of previously identified 'Evereste' candidate genes. We discuss the implications of these results on breeding for resistance to fire blight. [ABSTRACT FROM AUTHOR]

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks. [ABSTRACT FROM AUTHOR]

Light field videos provide a rich representation of real-world, thus the research of this technology is of urgency and interest for both the scientific community and industries. Light field applications such as virtual reality and post-production in the movie industry require a large number of viewpoints of the captured scene to achieve an immersive experience, and this creates a significant burden on light field compression and streaming. In this paper, we first present a light field video dataset captured with a plenoptic camera. Then a new region-of-interest (ROI)-based video compression method is designed for light field videos. In order to further improve the compression performance, a novel view synthesis algorithm is presented to generate arbitrary viewpoints at the receiver. The experimental evaluation of four light field video sequences demonstrates that the proposed ROI-based compression method can save 5%-7% in bitrates in comparison to conventional light field video compression methods. Furthermore, the proposed view synthesis-based compression method not only can achieve a reduction of about 50% in bitrates against conventional compression methods, but the synthesized views can exhibit identical visual quality as their ground truth.

Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n × n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes.

In some forms of gait analysis, it is important to be able to capture when the heel strikes occur. In addition, in terms of video analysis of gait, it is important to be able to localise the heel where it strikes on the floor. In this study, a new motion descriptor, acceleration flow, is introduced for detecting heel strikes. The key frame of heel strike can be determined by the quantity of acceleration flow within the region of interest, and positions of the strike can be found from the centre of rotation caused by radial acceleration. Our approach has been tested on a number of databases which were recorded indoors and outdoors with multiple views and walking directions for evaluating the detection rate under various environments. Experiments show the ability of our approach for both temporal detection and spatial positioning. The immunity of this new approach to three anticipated types of noises in real CCTV footage is also evaluated in our experiments. The authors acceleration flow detector is shown to be less sensitive to Gaussian white noise, whilst being effective with images of low‐resolution and with incomplete body position information when compared with other techniques.

Remote photoplethysmography (rPPG) is a video-based non-contact heart rate measurement technology. It is a fact that most existing rPPG methods fail to deal with the spatiotemporal features of the video, which is significant for the extraction of the rPPG signal. In this paper, we propose a 3D central difference convolutional network (CDCA-rPPGNet) to measure heart rate, with an attention mechanism to combine spatial and temporal features. First, we crop and stitch the region of interest together through facial landmarks. Next, the high-quality regions of interest are fed to CDCA-rPPGNet based on a central difference convolution, which can enhance the spatiotemporal representation and capture rich relevant time contexts by collecting time difference information. In addition, we integrate the attention module into the neural network, aiming to strengthen the ability of the neural network to extract video channels and spatial features, so as to obtain more accurate rPPG signals. In summary, the three main contributions of this paper are as follows: (1) the proposed network base on central difference convolution could better capture the subtle color changes to recover the rPPG signals; (2) the proposed ROI extraction method provides high-quality input to the network; (3) the attention module is used to strengthen the ability of the network to extract features. Extensive experiments are conducted on two public datasets—the PURE dataset and the UBFC-rPPG dataset. In terms of the experiment results, our proposed method achieves 0.46 MAE (bpm), 0.90 RMSE (bpm) and 0.99 R value of Pearson’s correlation coefficient on the PURE dataset, and 0.60 MAE (bpm), 1.38 RMSE (bpm) and 0.99 R value of Pearson’s correlation coefficient on the UBFC dataset, which proves the effectiveness of our proposed approach.

Purpose: To compare three region-of-interest (ROI) settings in the assessment of ADC in a clinical trial, and to evaluate the effectiveness of ADC in assessing therapy-induced changes and predicting clinical outcomes. Methods: In a 52-week clinical trial involving patients with axial spondyloarthritis, mean sacroiliac joint (SIJ) ADC measurements using structured, lesion-based, and index-lesion ROI-settings were assessed at baseline and weeks 4, 16, and 52. Variation among the three ROI-settings, correlations with Spondyloarthritis Research Consortium of Canada (SPARCC)-bone marrow edema (BME) SIJ inflammation indices, standardized response means (SRMs), and effectiveness in predicting clinical outcomes were analyzed. Results: Forty of the 53 patients had at least one assessable SIJ lesion on ADC at baseline. The mean of the structured ROI ADC (ADCstruc) was 230 μmm2/s (standard deviation [SD] = 120). This was significantly lower (p 0.92) and ADCindex (SRM > 0.87) while moderate for ADCstruc (SRM:0.54-0.67). Baseline ADC and changes in ADC did not predict clinical outcomes. Conclusions: Lesion-based and index-lesion ROI ADC could both be used to evaluate the effectiveness of tumor necrosis factor inhibitor therapy. None of the methods could predict clinical outcomes.

Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality. [ABSTRACT FROM AUTHOR]

To solve the shortcomings that current robot vision system accuracy is instable, unit operation time is overlong and automatic compensation function is lacked, this paper proposes robot vision system based on trinocular vision from the perspective of system hardware structure improvement and software algorithm optimization. Firstly, logic communication channel among PLC sensor, robot and vision PC shall be established, to realize real-time acquisition and processing of operation condition of material. Then design nine-point calibration method according to scaling, rotating and translation parameters between robot and vision coordinate system, and calibrate three industrial cameras to realize accurate binding of image pixel coordinate and robot world coordinate. Finally, choose region-of-interest, optimize the minimum mean square error matching algorithm and realize balance of system accuracy and speed according to project standard of product. Experimental test result shows that compared with current robot vision system, system in this paper has higher vision accuracy and operation efficiency. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to quickly design fixation plates for fractured proximal humerus and distal radius according to the requirements of surgical treatment. Therefore, a new method to quickly design cloverleaf plate appropriate for proximal humerus and volar plate appropriate for distal radius is put forward. First, three-dimensional (3D) reconstruction models of fractured proximal humerus and distal radius were generated based on deforming mean parametric models of proximal humerus and distal radius, respectively. Second, based on region-of-interest marked on the 3D reconstruction model of proximal humerus and distal radius, abutted surfaces of cloverleaf plate and volar plate were established, respectively. Then, parametric abutted surface was established after setting rational parameters for the surface of the cloverleaf plate. Parametric abutted surface of volar plate was established using the same method. Finally, parametric cloverleaf plate and volar plate are generated through thickening their respective parametric abutted surfaces. The parametric plates, acting as templates, accelerate and simplify the design process and therefore allow users to construct plate with editing valid parameters easily. Group of cloverleaf plates and volar plates with different sizes were generated quickly, showing that the proposed method is feasible and effective.

ObjectivesWith the increased use of computed tomography (CT) in clinics, dose reduction is the most important feature people seek when considering new CT techniques or applications. We developed an intensity-weighted region-of-interest (IWROI) imaging method in an exact half-fan geometry to reduce the imaging radiation dose to patients in cone-beam CT (CBCT) for image-guided radiation therapy (IGRT). While dose reduction is highly desirable, preserving the high-quality images of the ROI is also important for target localization in IGRT.MethodsAn intensity-weighting (IW) filter made of copper was mounted in place of a bowtie filter on the X-ray tube unit of an on-board imager (OBI) system such that the filter can substantially reduce radiation exposure to the outer ROI. In addition to mounting the IW filter, the lead-blade collimation of the OBI was adjusted to produce an exact half-fan scanning geometry for a further reduction of the radiation dose. The chord-based rebinned backprojection-filtration (BPF) algorithm in circular CBCT was implemented for image reconstruction, and a humanoid pelvis phantom was used for the IWROI imaging experiment.ResultsThe IWROI image of the phantom was successfully reconstructed after beam-quality correction, and it was registered to the reference image within an acceptable level of tolerance. Dosimetric measurements revealed that the dose is reduced by approximately 61% in the inner ROI and by 73% in the outer ROI compared to the conventional bowtie filter-based half-fan scan.ConclusionsThe IWROI method substantially reduces the imaging radiation dose and provides reconstructed images with an acceptable level of quality for patient setup and target localization. The proposed half-fan-based IWROI imaging technique can add a valuable option to CBCT in IGRT applications.

Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods are operator-dependent, organ-specific, and/or need advanced equipment. Therefore, we developed a robust, minimally operator-dependent, and tissue-transposable digital method for fibrosis quantification. The proposed method involves a novel algorithm for more specific and more sensitive detection of collagen fibers stained by picrosirius red (PSR), a computer-assisted segmentation of histological structures, and a new automated morphological classification of fibers according to their compactness. The new algorithm proved more accurate than classical filtering using principal color component (red-green-blue; RGB) for PSR detection. We applied this new method on established mouse models of liver, lung, and kidney fibrosis and demonstrated its validity by evidencing topological collagen accumulation in relevant histological compartments. Our data also showed an overall accumulation of compact fibers concomitant with worsening fibrosis and evidenced topological changes in fiber compactness proper to each model. In conclusion, we describe here a robust digital method for fibrosis analysis allowing accurate quantification, pattern recognition, and multi-organ comparisons useful to understand fibrosis dynamics.

Background: Alzheimer's disease (AD) is characterized by progressive deterioration of cognitive functions. Some individuals with subjective cognitive decline (SCD) are in the early phase of the disease and subsequently progress through the AD continuum. Although neuroimaging biomarkers could be used for the accurate and early diagnosis of preclinical AD, the findings in SCD samples have been heterogeneous. This study established the morphological differences in brain magnetic resonance imaging (MRI) findings between individuals with SCD and those without cognitive impairment based on a clinical sample of patients defined according to SCD-Initiative recommendations. Moreover, we investigated baseline structural changes in the brains of participants who remained stable or progressed to mild cognitive impairment or dementia., Methods: This study included 309 participants with SCD and 43 healthy controls (HCs) with high-quality brain MRI at baseline. Among the 99 subjects in the SCD group who were followed clinically, 32 progressed (SCDp) and 67 remained stable (SCDnp). A voxel-wise statistical comparison of gray and white matter (WM) volume was performed between the HC and SCD groups and between the HC, SCDp, and SCDnp groups. XTRACT ATLAS was used to define the anatomical location of WM tract damage. Region-of-interest (ROI) analyses were performed to determine brain volumetric differences. White matter lesion (WML) burden was established in each group., Results: Voxel-based morphometry (VBM) analysis revealed that the SCD group exhibited gray matter atrophy in the middle frontal gyri, superior orbital gyri, superior frontal gyri, right rectal gyrus, whole occipital lobule, and both thalami and precunei. Meanwhile, ROI analysis revealed decreased volume in the left rectal gyrus, bilateral medial orbital gyri, middle frontal gyri, superior frontal gyri, calcarine fissure, and left thalamus. The SCDp group exhibited greater hippocampal atrophy (p < 0.001) than the SCDnp and HC groups on ROI analyses. On VBM analysis, however, the SCDp group exhibited increased hippocampal atrophy only when compared to the SCDnp group (p < 0.001). The SCD group demonstrated lower WM volume in the uncinate fasciculus, cingulum, inferior fronto-occipital fasciculus, anterior thalamic radiation, and callosum forceps than the HC group. However, no significant differences in WML number (p = 0.345) or volume (p = 0.156) were observed between the SCD and HC groups., Conclusions: The SCD group showed brain atrophy mainly in the frontal and occipital lobes. However, only the SCDp group demonstrated atrophy in the medial temporal lobe at baseline. Structural damage in the brain regions was anatomically connected, which may contribute to early memory decline., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

We propose a saturation-aware human attention region-of-interest (SA-HAROI) video compression method that performs a perceptual adaptive quantization algorithm on video frames as a function of the distribution of their luminance, motion vector, and color saturation. Our work is an application of a psycho-visual study that demonstrated that human attention automatically enhanced perceived saturation. Consequently, the adaptive quantization phase of our compression algorithm is characterized by a luminance and saturation-aware just noticeable distortion (JND) function. After running multiple experiments on 18 videos with various resolutions ranging from QCIF to 4 K, results showed that our method achieves higher compression than that of both the H.264/AVC JM and the HEVC HM while maintaining subjective quality. We observed that in comparison to both implementation of the standards (JM and HM), for an IPPP coding structure, the performance of our algorithm culminated with HD and 4 K videos yielding a bit rate reduction averaging 15% and an encoding time reduction of about 20% in certain cases. Finally, after comparing our method to other similar techniques, we concluded that saturation is a significant parameter in the improvement of video compression. [ABSTRACT FROM AUTHOR]

Population aging has prompted considerable interest in identifying modifiable factors that may help protect the brain and its functions. Collectively, epidemiological studies show that leisure activities with high mental and social demands are linked with better cognition in old age. The extent to which socio-intellectual activities relate to the brain’s structure is, however, not yet fully understood. This systematic review and meta-analysis summarizes magnetic resonance imaging studies that have investigated whether cognitive and social activities correlate with measures of gray and white matter volume, white matter microstructure and white matter lesions. Across eighteen included studies (total n = 8429), activity levels were associated with whole-brain white matter volume, white matter lesions and regional gray matter volume, although effect sizes were small. No associations were found for global gray matter volume and the evidence concerning white matter microstructure was inconclusive. While the causality of the reviewed associations needs to be established, our findings implicate socio-intellectual activity levels as promising targets for interventions aimed at promoting healthy brain aging. [ABSTRACT FROM AUTHOR]

Abstract Purpose Measures of white matter (WM) microstructure inferred from diffusion magnetic resonance imaging (dMRI) are useful for studying brain development. There is uncertainty about agreement between FA and MD values obtained from region-of-interest (ROI) versus whole tract approaches. We investigated agreement between dMRI measures using ROI and Probabilistic Neighbourhood Tractography (PNT) in genu of corpus callosum (gCC) and corticospinal tracts (CST). Materials and Methods 81 neonates underwent 64 direction DTI at term equivalent age. FA and MD values were extracted from a 8 mm3 ROI placed within the gCC, right and left posterior limbs of internal capsule. PNT was used to segment gCC and CSTs to calculate whole tract-averaged FA and MD. Agreement between values obtained by each method was compared using Bland–Altman statistics and Pearson's correlation. Results Across the 3 tracts the mean difference in FA measured by PNT and ROI ranged between 0.13 and 0.17, and the 95% limits of agreement did not include the possibility of no difference. For MD, the mean difference in values obtained from PNT and ROI ranged between 0.101 and 0.184 mm2/s × 10−3 mm2/s: the mean difference in gCC was 0.101 × 10−3 mm2/s with 95% limits of agreement that included the possibility of no difference, but there was significant disagreement in MD values measured in the CSTs. Conclusion Agreement between dMRI measures of neonatal WM microstructure calculated from ROI and whole tract averaged methods is weak. ROI approaches may not provide sufficient representation of tract microstructure at the level of neural systems in newborns. Highlights • Agreement is poor between dMRI parameters measured in regions of interest within tracts versus whole tract averaged values. • ROI approaches may not provide sufficient representation of whole tract microstructure in the developing brain. • These observations have implications for the development of neonatal MR image biomarkers. [ABSTRACT FROM AUTHOR]

One of the main challenges in the smart-phone world is that they are battery constrained and the development of battery technologies have not kept pace with the required energy demand. In particular, there are still significant technological gaps on developing energy-aware solutions that would prolong the battery life of devices without affecting the quality of the distributed video/multimedia content. In this aspect, this paper proposes DE-BAR—a process based innovation that will provide a seamless battery saving mechanism, based on backlight and adaptive region of interest of the streamed multimedia content. This work intends to look at the nature of the video/multimedia content that is received in the device and adapts the energy consumption dynamically at three levels: Screen Colour, backlight and Intensity; and adaptive Region-of-Interest (RoI) based variation in the multimedia content. Notably, the work provides the mechanism for real-time adaptation. The colour intensity, number of RoI for the video sequence and the frame rate is decided by the spatial and temporal complexity of the video. The energy consumption is measured using an Arduino board while video quality is analyzed using extensive subjective tests. The results indicate that more than 50% energy could be saved in the device while retaining above average perceptual video quality. [ABSTRACT FROM AUTHOR]

Background and objective Digital tomosynthesis (DTS) based on filtered-backprojection (FBP) reconstruction requires a full field-of-view (FOV) scan and relatively dense projections, which results in high doses for medical imaging purposes. To overcome these difficulties, we investigated region-of-interest (ROI) or interior DTS reconstruction where the x-ray beam span covers only a small ROI containing a target area. Methods An iterative method based on compressed-sensing (CS) scheme was compared with the FBP-based algorithm for ROI-DTS reconstruction. We implemented both algorithms and performed a systematic simulation and experiments on body and skull phantoms. The image characteristics were evaluated and compared. Results The CS-based algorithm yielded much better reconstruction quality in ROI-DTS compared to the FBP-based algorithm, preserving superior image homogeneity, edge sharpening, and in-plane resolution. The image characteristics of the CS-reconstructed images in ROI-DTS were not significantly different from those in full-FOV DTS. The measured CNR value of the CS-reconstructed ROI-DTS image was about 12.3, about 1.9 times larger than that of the FBP-reconstructed ROI-DTS image. Conclusions ROI-DTS images of substantially high accuracy were obtained using the CS-based algorithm and at reduced imaging doses and less computational cost, compared to typical full-FOV DTS images. We expect that the proposed method will be useful for the development of new DTS systems. [ABSTRACT FROM AUTHOR]

Objectives Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree ( GFT) design called the LITHium Intelligent Agent ( LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1H- MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. Methods We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1H- MRS scans at baseline pre-treatment. We trained LITHIA using 18 1H- MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. Results LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. Conclusions The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. [ABSTRACT FROM AUTHOR]

In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA's CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target's related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area. [ABSTRACT FROM AUTHOR]

In a population-based genome-wide association (GWA) study of n -back working memory task-related brain activation, we extracted the average percent BOLD signal change (2-back minus 0-back) from 46 regions-of-interest (ROIs) in functional MRI scans from 863 healthy twins and siblings. ROIs were obtained by creating spheres around group random effects analysis local maxima, and by thresholding a voxel-based heritability map of working memory brain activation at 50%. Quality control for test-retest reliability and heritability of ROI measures yielded 20 reliable ( r > 0.7) and heritable ( h 2 > 20%) ROIs. For GWA analysis, the cohort was divided into a discovery ( n = 679) and replication ( n = 97) sample. No variants survived the stringent multiple-testing-corrected genome-wide significance threshold ( p < 4.5 × 10 −9 ), or were replicated ( p < 0.0016), but several genes were identified that are worthy of further investigation. A search of 529,379 genomic markers resulted in discovery of 31 independent single nucleotide polymorphisms (SNPs) associated with BOLD signal change at a discovery level of p < 1 × 10 −5 . Two SNPs (rs7917410 and rs7672408) were associated at a significance level of p < 1 × 10 −7 . Only one, most strongly affecting BOLD signal change in the left supramarginal gyrus ( R 2 = 5.5%), had multiple SNPs associated at p < 1 × 10 −5 in linkage disequilibrium with it, all located in and around the BANK1 gene. BANK1 encodes a B-cell-specific scaffold protein and has been shown to negatively regulate CD40-mediated AKT activation. AKT is part of the dopamine-signaling pathway, suggesting a mechanism for the involvement of BANK1 in the BOLD response to working memory. Variants identified here may be relevant to (the susceptibility to) common disorders affecting brain function. [ABSTRACT FROM AUTHOR]

The objective of this study is to present and test a new ultra-low-cost linear scan based tomography architecture. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomographic architecture was named parallel translational computed tomography (PTCT). In previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artefact. In order to overcome this limitation, we in this study proposed two backprojection filtering (BPF)- type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms cannot only more accurately reconstruct ROI images from truncated projections but also generate high-quality images for the entire image support in some circumstances. [ABSTRACT FROM AUTHOR]