Specific Regions Of Interest Research Articles

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

SAGAN: Skip attention generative adversarial networks for few-shot image generation

The task of producing high-quality, realistic, and diverse images based on a few instances of newly emerging or long-tail categories is known as few-shot image generation. Despite prior works showing outstanding results, the quality and diversity of the outputs are still limited. In this paper, we tackle this problem by presenting a range of innovative fusion techniques based on attention mechanisms with generative adversarial networks. Our proposed framework introduces global skip attention that links matching residual blocks of symmetric encoder-decoder pairs to generate new instance objects. Additionally, we incorporate an alignment algorithm based on spatial transformer networks into our pipeline encoder to address feature misalignment. In the decoding phase of our attention-based decoder, we propose a novel attention mechanism within each fusion residual block, which leads to capturing long-range dependencies in feature maps. An attention reconstruction loss function has been proposed to balance adversarial training between the generator and discriminator, mitigate mode collapse, and guide the generator to focus on specific regions of interest within images. Finally, we apply a back summation to the decoding outputs, resulting in unified features through a weighted combination of similar characteristics. Extensive experiments conducted on five few-shot image datasets demonstrate the effectiveness of our proposed model. The source code of the proposed model can be found on GitHub https://github.com/Aldhubri/SAGAN.

A novel deep learning-based method for automatic stereology of microglia cells from low magnification images

Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.

Towards a Canary Islands barcode database for soil biodiversity: revealing cryptic and unrecorded mite species diversity within insular soils.

Soil arthropod diversity contributes to a high proportion of the total biodiversity on Earth. However, most soil arthropods are still undescribed, hindering our understanding of soil functioning and global biodiversity estimations. Inventorying soil arthropods using conventional taxonomical approaches is particularly difficult and costly due to the great species richness, abundance and local-scale heterogeneity of mesofauna communities and the poor taxonomic background knowledge of most lineages. To alleviate this situation, we have designed and implemented a molecular barcoding framework adapted to soil fauna. This pipeline includes different steps, starting with a morphology-based selection of specimens which are imaged. Then, DNA is extracted non-destructively. Both images and voucher specimens are used to assign a taxonomic identification, based on morphology that is further checked for consistency with molecular information. Using this procedure, we studied 239 specimens of mites from the Canary Islands including representatives of Mesostigmata, Sarcoptiformes and Trombidiformes, of which we recovered barcode sequences for 168 specimens that were morphologically identified to 49 species, with nine specimens that could only be identified at the genus or family levels. Multiple species delimitation analyses were run to compare molecular delimitations with morphological identifications, including ASAP, mlPTP, BINs and 3% and 8% genetic distance thresholds. Additionally, a species-level search was carried out at the Biodiversity Databank of the Canary Islands (BIOTA) to evaluate the number of species in our dataset that were not previously recorded in the archipelago. In parallel, a sequence-level search of our sequences was performed against BOLD Systems. Our results reveal that multiple morphologically identified species correspond to different molecular lineages, which points to significant levels of unknown cryptic diversity within the archipelago. In addition, we evidenced that multiple species in our dataset constituted new records for the Canary Islands fauna and that the information for these lineages within online genetic repositories is very incomplete. Our study represents the first systematic effort to catalogue the soil arthropod mesofauna of the Canary Islands and establishes the basis for the Canary Islands Soil Biodiversity barcode database. This resource will constitute a step forward in the knowledge of these arthropods in a region of special interest.

Offline Handwritten Text Extraction and Recognition Using CNN-BLSTM-CTC Network

Offline handwriting recognition is a significant research area that aims at tackling problems encountered with handwritten forms in college application and registration processes. The objective of this study is to address the problems of English language offline handwriting recognition via CNN-BLSTM-CTC neural network applied for an NCE Admission form. The system uses OpenCV for image processing, TensorFlow for neural network training and handwritten text recognition, and trains and tests it on the IAM database using image segmentation-based handwriting recognition. With the help of proper image verification, the system allows the users to upload images of the NCE Admission form provided that they strictly comply with the specified format; it denies access to images not conforming to the set standards. Following the successful delivery of a valid image, the form goes through extensive processing that includes text extraction from specific regions of interest (ROIs). The extracted texts are then passed to text recognition block. The recognized texts are then recorded in a CSV file under respective fields. The text recognition model has a CER of approximately 9.33%. The study performed with 15 NCE Admission forms found that the average Character Error Rate (CER) was approximately 12.2% for scanned images and 19.3% for camera-captured images. The results show that accuracy depends on aspects such as the quality and orientation of the image; thus, scanned images are preferred for better performance.

Application of geometric shape-based CT field-of-view extension algorithms in an all-digital positron emission tomography/computed tomography system.

Computed tomography (CT)-based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field-of-view (FOV) leads to errors in PET AC. In order to enhance the quantitative accuracy of PET imaging in the all-digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape-based FOV extension algorithms in this system. We implemented two geometric shape-based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms. Truncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension. The two geometric shape-based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all-digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all-digital PET/CT systems.

Comparing feature selection methods to improve the classification of Mild Cognitive Impairment patients based on Magnetoencephalography data

AbstractBackgroundMachine learning (ML) algorithms are generating great interest for early diagnose of Alzheimer’s disease (AD). Despite several approaches have been developed, limited research has been conducted on the impact of feature selection in classification performance. In this study we compare two feature selection methods, LASSO and Elastic Nets (EN), applied to ML algorithms based on magnetoencephalography (MEG) data from mild cognitive impairment (MCI) patients.MethodAn eyes‐closed resting‐state MEG recording was performed by 262 participants (117 healthy controls and 145 MCI). Functional connectivity measures were estimated for all classical frequency bands using the Phase‐Locking Value. To develop the ML algorithms, specific regions of interest from the default mode network, based on the Automatic Anatomical Labelling atlas, were selected, resulting in a total of 325 features. After dividing the dataset into training (80%) and test (20%) sets, feature selection with 10000 iterations and five‐fold cross validation was performed independently for each band using LASSO and EN algorithms. Afterwards, several classification models were trained with a five‐fold cross validation and then validated on the test set, over 500 iterations.ResultThe classifier showing the best performance was the linear Support Vector Machine for the beta frequency band (12‐30 Hz). Given the similarity of the training and test performance values, the results reveal that overfitting was avoided when using the 45 features selected by LASSO instead of the 47 features selected by EN, reaching a validation accuracy of 77%. Additionally, LASSO consistently identified three functional connectivity links as relevant in all frequency bands but alpha: right superior frontal gyrus &amp; right superior frontal gyrus orbital, left posterior cingulate cortex &amp; left hippocampus, right posterior cingulate cortex &amp; right precuneus.ConclusionML algorithms applied to MEG data can successfully discriminate MCI patients from healthy controls. In addition, LASSO algorithm applied for feature selection leads to better classification performance. Moreover, the resulting relevant functional connectivity links involve areas affected by AD pathology even in early stages of its development (López‐Sanz et al., 2019). Further exploration of this methodology is needed, combining this data with structural neuroimaging or complementary biomarkers.

The role of transcranial direct current stimulation in improving learning and memory performances of patients with mild cognitive impairment and mild Alzheimer’s disease: a functional magnetic resonance imaging study

AbstractBackgroundTranscranial direct current stimulation (tDCS) is a safe non‐invasive brain stimulation (NIBS) procedure which helps to stimulate a particular region of interest by modulating the neuronal firing rate, thereby modulating the cognitive functioning. The tDCS intervention is known to show improvement on memory recall and recognition in patients with mild cognitive impairment (MCI) and mild Alzheimer’s Disease (mild AD)MethodWe investigated the effect of anodal stimulation at the left dorsolateral prefrontal cortex (DLPFC) and cathodal stimulation at the right supra‐orbital area in patients with MCI (n = 18) and mild AD (n = 20) (Refer Table 1), after due approval from Institute Ethics Committee NIMHANS. Both baseline and post‐intervention assessments for a single patient were administered by the different assessors allocated randomly using NIMHANS neuropsychological battery for elderly (NNB‐E). Two resting state functional magnetic resonance imaging (rsfMRI) acquisitions, before and after the ten sessions of tDCS, each session lasting 20 minutes. A spherical mask of 5 mm diameter was chosen a priori on the left middle frontal gyrus (LMFG) as a seed for performing seed based functional connectivity (sbFC) analysis using default preprocessing pipeline and pre‐post analysis setup in CONN 18b toolbox.ResultThe combined sample of patients with MCI and mild AD showed significant improvement in word list learning trial 3 (pre tDCS 5.85 (1.59); post tDCS 6.63 (1.67); Z = 3.586, p&lt;0.0001) and delayed recall (pre tDCS 1.50 (1.88); post tDCS 2.45 (2.33); Z = 3.656, p&lt;0.0001) scores using NNB‐E. The sbFC analysis showed significantly reduced functional connectivity between left MFG (the region of anodal stimulation) and posterior cingulate cortex (PCC) as well as precuneus cortex (cluster‐level extent threshold p‐FDR &lt;0.05) after tDCS. Post‐hoc tests showed that these reductions after tDCS intervention were also found to be significantly associated with attention and auditory verbal learning memory scores (Refer Figure 1 and Table 2).ConclusionThe tDCS intervention helped reducing the functional connectivity at rest, which was responsible for compensatory neuronal processes found in early AD. These findings unravel the therapeutic role of tDCS by enhancing auditory verbal memory performance and altering the functional connectivity at PCC and precuneus cortex in earlier stages of AD.

A Volume‐Based Alternative for classifying ATN: Data from the Tau Propagation over Time (T‐POT) cohort

AbstractBackgroundAlzheimer’s disease (AD) is characterized by the cerebral accumulation of amyloid‐beta (A), tau (T), and progressive neurodegeneration (N). The widely used ATN system, with regard to positron emission tomography (PET) biomarkers, categorizes AD based on the mean signal in specific regions of interest (ROI). However, this procedure disregards the spatial extent of pathology and neurodegeneration. Here, we propose an alternative quantification of the volume, i.e., fill states, of A, T and N in (pre)‐clinical AD.MethodWe analyzed data from the Tau Propagation over Time (T‐POT) study, including cognitively unimpaired individuals (CU, n = 58), and patients with mild cognitive impairment (MCI, n = 20) or AD dementia (n = 4). C11‐PIB‐PET (A), 18F‐AV1451 (T) and perfusion‐phase 18F‐AV1451 scans (N) were spatially and intensity‐normalized (reference: cerebellum). To quantify the volume of A, T and N, we z‐standardized and subsequently binarized all scans within–modality using a z‐score threshold. Fill states were then computed as the sum of abnormal voxels relative to a whole‐brain mask. Finally, mean fill states were compared across groups of clinical status (CU, MCI, AD) and partial correlations of either fill states or mean PET signal in established, tracer‐specific ROIs with cognitive performance (MMSE) were computed, adjusting for age, sex and education.ResultMean fill states reflected clinical status, as they increased with disease progression (CU: A = 4%, T = 4%, N = 3%; MCI: A = 15%, T = 11%, N = 4%; AD dementia: A = 20%, T = 23%, N = 5%). Moreover, A and T fill states were negatively associated with MMSE (rhoA = ‐.299, p &lt; .001; rhoT = ‐.318, p &lt; .01; rhoN = ‐.147, p = .20), while associations of mean PET signal and MMSE tended to be weaker (rhoA(global) = ‐.255, p = .03; rhoT(temporalmetaROI) = ‐.275, p = .01; rhoN(metaROI) = .179, p = .12).ConclusionWe present a competitive quantification scheme for ATN that is associated with both, clinical status and cognitive performance. These results, while currently validated in a larger sample, suggest that the spatiotemporal dynamics of pathology and neurodegeneration in the AD continuum are well captured by our multi‐parametric approach, which is possibly superior compared to classification from mean PET signal intensity.

Associations between microstructural tissue changes, white matter hyperintensity severity, and cognitive impairment: an intravoxel incoherent motion imaging study

IntroductionWhite matter hyperintensities (WMHs) are a common age- and vascular risk factor-related disease and have been recognized to play an important role in cognitive impairment. However, it is still unclear what the mechanism of this effect is. In this study, intravoxel incoherent motion (IVIM) was employed to assess the microvasculature and parenchymal microstructure changes of WMHs and explore their relationship with cognitive function.MethodsForty-nine WMH patients and thirty-one healthy controls underwent IVIM imaging, a diffusion technique that provides parenchymal diffusivity D, intravascular diffusivity D*, and perfusion fraction f . The IVIM dual exponential model parameters were obtained in specific regions of interest, including deep white matter hyperintensities (DWMHs), periventricular white matter hyperintensities (PWMHs), and normal-appearing white matter (NAWM). The independent-sample t-test or Mann–Whitney U-test was utilized to compare IVIM parameters between patients and controls. The Kruskal–Wallis test or one-way analysis of variance was used to compare IVIM parameters among DWMH, PWMH, and NAWM for patients. The Wilcoxon two-sample test or independent-sample t-test was used to assess the differences in IVIM parameters based on the severity of WMH. The multivariate linear regression analysis was conducted to explore the factors influencing cognitive scores.ResultsWMH patients exhibited significantly higher parenchymal diffusivity D than controls in DWMH, PWMH, and NAWM (all p &amp;lt; 0.05). IVIM parameters in the three groups (DWMH, PWMH, and NAWM) were significantly different for patients (all p &amp;lt; 0.001). The severe WMH group had a significantly higher parenchymal diffusivity D (DWMH and PWMH) than mild WMH (both p &amp;lt; 0.05). The multiple linear regression analysis identified D in DWMH and PWMH as influencing cognitive function scores (all p &amp;lt; 0.05).ConclusionIVIM has the potential to provide a quantitative marker of parenchymal diffusivity for assessing the severity of WMH and may serve as a quantitative marker of cognitive dysfunction in WMH patients.

66 Omega-3 Fatty Acids, Cognition, and Brain Volume in Healthy Elderly Adults

Objective:One common concern amongst the aging population is that of worsening memory. Speed of processing and executive functions are also areas of age-related decline that affect daily living. Lifestyle modifications such as diet, exercise, and sleep have garnered intense interest as potential methods to prevent or delay cognitive decline. Among dietary factors, omega-3 fatty acids (FAs) have been documented as containing a myriad of health benefits, including neuroprotective effects. The aim of this study is to examine the associations between omega-3 FAs, cognitive function, and neuroanatomical regions of interest in a healthy aging population.Participants and Methods:Adults aged 65 and older (n=40, 48.9% Female) were recruited for the Loma Linda University Adventist Health Study-2 Cognitive and Neuroimaging Substudy. Participants had a mean age of 76.25 years (SD=8.29), 16.78 years of education (SD=2.53), and were predominantly White (85.0%). Participants received a two-hour neurocognitive battery, including measures of immediate and delayed memory (Rey Auditory Verbal Learning Test, RAVLT; WMS-IV Logical Memory, LM), processing speed (Stroop), and executive functions (Stroop Color/Word). Participants underwent brain imaging on a 3T Siemens MRI, including a 3D T1-weighted MPRAGE sequence. Cortical reconstruction and volumetric segmentation were performed using FreeSurfer software. Blood samples were collected for fatty acid analysis. Individual FAs were expressed as a percent of total FAs. An omega-3 index was constructed as the sum of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) FAs. Correlational analyses, controlling for age, sex, and education, investigated relationships between omega-3 levels (individual and index) and (a) cognitive function (immediate and delayed memory, processing speed, executive functions), and (b) brain volumes in specific regions of interest (hippocampus, entorhinal cortex, frontal pole, white matter).Results:EPA was significantly positively correlated with Stroop Color (r=.34, p=.048). Although not statistically significant, trends were observed between the omega-3 index and Stroop Color (r=.30, p=.08), and between both DHA and the omega-3 index with RAVLT – delayed recall (r=.29, p=.095; r=.30, p=.08, respectively). With regards to regional brain volumes, EPA and the omega-3 index were both significantly positively correlated with the entorhinal cortex (r=.34, p=.041; r=.41, p=.01, respectively) and white matter volume (r=.36, p=.028; r=.34, p=.038, respectively). DHA was significantly positively correlated with white matter volume (r=.34, p=.044).Conclusions:Blood levels of EPA were positively correlated with a measure of processing speed, and trends were observed between DHA, the omega-3 index and[GN1] verbal memory, and between the omega-3 index and processing speed. We also found that omega-3 FA values were associated with greater brain volume in the entorhinal cortex and white matter in our sample of healthy older adults. Atrophy of the entorhinal cortex has been associated with pathological processes. Additionally, white matter is known to effect processing speed. These findings may offer support for the idea that omega-3 FAs exert their neuroprotective effects by fortifying areas of the brain, specifically the entorhinal cortex and white matter, that promote maintenance of cognitive function in late life.

A new global oceanic multi-model net primary productivity data product

Abstract. Net primary production of the oceans contributes approximately half of the total global net primary production, and long-term observational records are required to assess any climate-driven changes. The Ocean Colour Climate Change Initiative (OC-CCI) has proven to be robust whilst also being one of the longest records of ocean colour. However, to date, only one primary production algorithm has been applied to this data product, with other algorithms typically applied to single-sensor missions. The data product presented here addresses this issue by applying five algorithms to the OC-CCI data product, which allows the user to interrogate the range of distributions across multiple models and to identify consensus or outliers for their specific region of interest. Outputs are compared to single-sensor data missions, highlighting good overall global agreement, with some small regional discrepancies. Inter-model assessments address the source of these discrepancies, highlighting the choice of the mixed-layer data product as a vital component for accurate primary production estimates. The datasets are published in the Zenodo repository at https://doi.org/10.5281/zenodo.7849935, https://doi.org/10.5281/zenodo.7858590, https://doi.org/10.5281/zenodo.7860491 and https://doi.org/10.5281/zenodo.7861158 (Ryan-Keogh et al., 2023a, b, c, d).

ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia

We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.

End-to-end memory-efficient reconstruction for cone beamCT.

Cone beam computed tomography (CBCT) plays an important role in many medical fields nowadays. Unfortunately, the potential of this imaging modality is hampered by lower image quality compared to the conventional CT, and producing accurate reconstructions remains challenging. A lot of recent research has been directed towards reconstruction methods relying on deep learning, which have shown great promise for various imaging modalities. However, practical application of deep learning to CBCT reconstruction is complicated by several issues, such as exceedingly high memory costs of deep learning methods when working with fully 3D data. Additionally, deep learning methods proposed in the literature are often trained and evaluated only on data from a specific region of interest, thus raising concerns about possible lack of generalization to other regions. In this work, we aim to address these limitations and propose LIRE: a learned invertible primal-dual iterative scheme for CBCTreconstruction. LIRE is a learned invertible primal-dual iterative scheme for CBCT reconstruction, wherein we employ a U-Net architecture in each primal block and a residual convolutional neural network (CNN) architecture in each dual block. Memory requirements of the network are substantially reduced while preserving its expressive power through a combination of invertible residual primal-dual blocks and patch-wise computations inside each of the blocks during both forward and backward pass. These techniques enable us to train on data with isotropic 2 mm voxel spacing, clinically-relevant projection count and detector panel resolution on current hardware with 24 GBvideo random access memory (VRAM). Two LIRE models for small and for large field-of-view (FoV)setting were trained and validated on a set of 260 + 22 thorax CT scans and tested using a set of 142 thorax CT scans plus an out-of-distribution dataset of 79 head and neck CT scans. For both settings, our method surpasses the classical methods and the deep learning baselines on both test sets. On the thorax CT set, our method achieves peak signal-to-noise ratio (PSNR) of 33.84 ± 2.28 for the small FoV setting and 35.14 ± 2.69 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. On the head and neck CT set, our method achieves PSNR of 39.35 ± 1.75 for the small FoV setting and 41.21 ± 1.41 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. Additionally, we demonstrate that LIRE can be finetuned to reconstruct high-resolution CBCT data with the same geometry but 1 mm voxel spacing and higher detector panel resolution, where it outperforms the U-Net baseline aswell. Learned invertible primal-dual schemes with additional memory optimizations can be trained to reconstruct CBCT volumes directly from the projection data with clinically-relevant geometry and resolution. Such methods can offer better reconstruction quality and generalization compared to classical deep learning baselines.

The sacroiliac joint: An original and highly sensitive tool to highlight altered bone phenotype in murine models of skeletal disorders

Bone disorders may affect the skeleton in different ways, some bones being very impaired and others less severely. In translational studies using murine models of human skeletal diseases, the bone phenotype is mainly evaluated at the distal femur or proximal tibia. The sacroiliac joint (SIJ), which connects the spine to the pelvis, is involved in the balanced transfer of mechanical energy from the lumbar spine to the lower extremities. Because of its role in biomechanical stress, the SIJ is a region of particular interest in various bone diseases. Here we aimed to characterize the SIJ in several murine models to develop a highly reliable tool for studying skeletal disorders.We performed a 12-month in vivo micro-computed tomography (micro-CT) follow-up to characterize the SIJ in wild-type (WT) C57BL/J6 mice and compared the bone microarchitecture of the SIJ and the distal femur at 3 months by micro-CT and histology. To test the sensitivity of our methodology, the SIJ and distal femur were evaluated at 3 and 6 months, in 2 murine models of skeletal disorder, X-linked hypophosphatemia (Hyp mice) and HLA-B27 transgenic mice and compared to WT mice. A multimodal analysis was performed, using a combination of microCT and histological analysis.With the Hyp model, the SIJ displayed more bone microarchitecture alterations than the distal femur. Hyp mice showed a significant reduction in trabecular bone at both the distal femur and sacral slope as compared with WT mice, with a significant positive correlation between trabecular bone parameters of the distal femur and sacral side of the SIJ. Furthermore, trabecular bone parameters (Bone Volume/Total Volume (BV/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N), trabecular pattern factor (Tb.Pf)) were significantly increased compared to femoral parameters at the SIJ. The sacral articular cortical bone, which is indicative of osteoarticular lesions, was altered in Hyp mice. Interestingly, in accordance to previous studies, HLA-B27 transgenic mice did not show any osteoarticular lesions as compared with WT mice. Cortical bone parameters (thickness, porosity), as well as scoring performed with double blinding, did not show difference between the 2 genotypes.The characterization and evaluation of the SIJ surface appears very sensitive to emphasize alterations of bone and joint. The SIJ may represent a valuable tool to investigate both bone and local osteoarticular alterations in murine models of skeletal disorders and might be a relevant site for assessing the response to treatment of chronic bone diseases.

Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T.

Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric "T2* contrast efficiency" for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.

The OSCAR-MP Consensus Criteria for Quality Assessment of Retinal Optical Coherence Tomography Angiography.

Optical coherence tomography angiography (OCTA) is a noninvasive high-resolution imaging technique for assessing the retinal vasculature and is increasingly used in various ophthalmologic, neuro-ophthalmologic, and neurologic diseases. To date, there are no validated consensus criteria for quality control (QC) of OCTA. Our study aimed to develop criteria for OCTA quality assessment. To establish criteria through (1) extensive literature review on OCTA artifacts and image quality to generate standardized and easy-to-apply OCTA QC criteria, (2) application of OCTA QC criteria to evaluate interrater agreement, (3) identification of reasons for interrater disagreement, revision of OCTA QC criteria, development of OCTA QC scoring guide and training set, and (4) validation of QC criteria in an international, interdisciplinary multicenter study. We identified 7 major aspects that affect OCTA quality: (O) obvious problems, (S) signal strength, (C) centration, (A) algorithm failure, (R) retinal pathology, (M) motion artifacts, and (P) projection artifacts. Seven independent raters applied the OSCAR-MP criteria to a set of 40 OCTA scans from people with MS, Sjogren syndrome, and uveitis and healthy individuals. The interrater kappa was substantial (κ 0.67). Projection artifacts were the main reason for interrater disagreement. Because artifacts can affect only parts of OCTA images, we agreed that prior definition of a specific region of interest (ROI) is crucial for subsequent OCTA quality assessment. To enhance artifact recognition and interrater agreement on reduced image quality, we designed a scoring guide and OCTA training set. Using these educational tools, 23 raters from 14 different centers reached an almost perfect agreement (κ 0.92) for the rejection of poor-quality OCTA images using the OSCAR-MP criteria. We propose a 3-step approach for standardized quality control: (1) To define a specific ROI, (2) to assess the occurrence of OCTA artifacts according to the OSCAR-MP criteria, and (3) to evaluate OCTA quality based on the occurrence of different artifacts within the ROI. OSCAR-MP OCTA QC criteria achieved high interrater agreement in an international multicenter study and is a promising QC protocol for application in the context of future clinical trials and studies.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline.

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal-tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 μm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2 ) compared to other, light-based microscope technologies. Capturing higher-resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high-dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open-source cell segmentation and IMC analysis pipeline with previously published and freely available software.

Bias field correction-based active contour model for region of interest extraction in digital image

The region-based Active Contour Model (ACM) is a widely known variational segmentation model for extracting or segmenting a digital image into numerous sections for further analysis. Distinguishing between global and specific segmentation models within this paradigm is possible. The global segmentation model is incapable of selectively segmenting the region of interest (ROI) from the input image, which leads to an over-segmented problem. A variety of models have been devised to address the task of selective segmentation, which involves the extraction of the boundary of a particular region of interest (ROI) inside a digital image. The Primal Dual Selective Segmentation (PDSS) model has been recently introduced and exhibits significant potential in terms of accuracy. Nevertheless, the presence of intensity inhomogeneity in digital images disrupts the precision and localisation of target regions of segmentation. Therefore, it is important to take into account bias field adjustment, also known as correction for intensity inhomogeneity. So, this study came up with a new selective segmentation model called the Selective Segmentation with Bias Field Correction (SSBF) model by combining the idea of the existing PDSS model with the level set-based bias field correction technique. To solve the proposed SSBF model, we first derived the Euler-Lagrange (EL) equation and solved it in MATLAB software. The Intersection over Union (IOU) coefficient, also known as the Dice (DSC) and Jaccard (JSC) similarity metrics, evaluated the proposed model's accuracy. Experimental results demonstrate that the JSC and DSC values of the proposed model were 13.4% and 7.2% higher, respectively, than the competing model.

Effects of climate change on gilthead seabream aquaculture in the Mediterranean

Aquaculture of gilthead seabream, arguably the most important finfish aquaculture species in the Mediterranean, faces changing environmental conditions due to faster-than-average climate change in the region. We utilize physiological modelling to estimate effects of moderate and severe climate change on key indices of aquaculture production for all coastal regions of the Mediterranean. Two publicly available global climate change scenarios with daily sea temperature projections serve as forcing for the physiological model during two-year farming cycles representing: (i) reference period starting in 2021, (ii) mid-term effects starting in 2051, and (iii) long-term effects starting in 2091.We investigate effects of climate change by analyzing changes in time for fish to reach a market size, feed conversion ratio at the market size, and the weight of the fish and the associated feed conversion ratio after two years of farming. Additionally, we track the number of days with sea water temperatures equal to or greater than 28 °C during the two-year period, when gilthead seabream starts experiencing temperature stress. Time to market size generally decreases with climate change from the initial average of 450 days for the reference period by up to 36%. Feed conversion ratio at market size does not appreciably change with climate change, but it does change for the two-year culturing period for up to 10 %, primarily due to faster growth in warmer sea water, and the correspondingly greater weight achieved over the two-year growth cycle.While the outlook for aquaculture is positive in the mid-term, some indicators show a negative trend in the long-term. The long-term effects of climate change will be greatest in the currently most productive farming regions of the Mediterranean: Levantine, Aegean, and Adriatic seas, and coastal waters of Tunisia. Our analysis focuses on basin-level features, but we provide geospatially referenced simulation results that can be used to analyze effects of climate change in a particular region of interest.