Visualization of coronary computed tomography angiography (CCTA) in standard cardiac planes requires reorientation of the three-dimensional image volume because the heart is double-oblique relative to the axial scan plane. To standardize this process across multi-phase cine CCTA, we developed an automated framework that segments cardiac structures and detects anatomic landmarks to define a patient-specific cardiac coordinate system. Landmark-derived left ventricular (LV) long-axis and LV-to-right ventricular transverse vectors were used to compute a rotation matrix for reorienting all 20 cine CCTA volumes. Six fixed slice planes were then defined to extract three long-axis (LAX) and three short-axis (SAX) slices at each phase. Matched CCTA and cardiac magnetic resonance (MR) slices were compared in 25 patients at diastasis, end-diastole (ED), and end-systole (ES), using automated segmentation-derived measurements of the combined LV cavity and LV myocardium area (LV + LVM). Agreement was evaluated with intraclass correlation coefficients (ICCs), Bland–Altman analysis, and repeated-measures mixed-effects models. ICCs were 0.927 (95% CI, 0.895 to 0.950) at diastasis, 0.950 (95% CI, 0.928 to 0.966) at ED, and 0.919 (95% CI, 0.887 to 0.943) at ES. Bland–Altman analysis showed small CT-positive biases of 1.05, 0.47, and 1.86 cm2 at diastasis, ED, and ES, corresponding to relative biases of 2.8%, 1.2%, and 5.8% of the phase-specific mean MR LV + LVM area. Mixed-effects models showed no statistically significant phase-level bias at diastasis or ED, whereas ES showed a small but significant CT-positive bias. These findings support automated CCTA reorientation as an interpretable framework for reproducible LAX/SAX slice extraction and future quantitative functional cine CCTA analysis.

For comprehensive anatomical analysis of a mouse brain, accurate and efficient registration of the experimental brain samples to a reference atlas is necessary. Here, we introduce Bell Jar, a semiautomated solution that can align and annotate tissue sections with anatomical structures from a reference atlas as well as detect fluorescent signals with cellular resolution (e.g., cell bodies or nuclei). Bell Jar utilizes Mattes mutual information-directed B-spline transformations to achieve precise alignments, even with damaged sample tissues. While user input remains a requirement for fine-tuning section matches, the platform streamlines the process, aiding rapid analyses in high-throughput neuroanatomy studies. As a standalone desktop application with a user-friendly interface, Bell Jar's performance, which surpasses traditional manual and existing automated methods, can improve the reproducibility and throughput of histological analyses.

Score matching based diffusion has shown to achieve the state of art results in generation modeling. In the original score matching based diffusion algorithm, the forward equation is a differential equation for which the probability density equation evolves according to a linear partial differential equation, the Fokker-Planck equation. A drawback of this approach is that one needs the data distribution to have a Lipschitz logarithmic gradient. This excludes a large class of data distributions that have a compact support. We present a deterministic diffusion process for which the vector fields are always Lipschitz and hence the score does not explode for probability measures with compact support. This deterministic diffusion process can be seen as a regularization of the porous media equation equation, which enables one to guarantee long term convergence of the forward process to the noise distribution. Though the porous media equation is itself not always guaranteed to have a Lipschitz vector field, it can be used to understand the closeness of the output of the algorithm to the data distribution as a function of the the time horizon and score matching error. This analysis enables us to show that the algorithm has better dependence on the score matching error than approaches based on stochastic diffusions. Using numerical experiments we verify our theoretical results on example one and two dimensional data distributions which are compactly supported. Additionally, we validate the approach on a modified MNIST data set for which the distribution is concentrated on a compact set. In each of the experiments, the approach using deterministic diffusion performs better that the diffusion algorithm with stochastic forward process, when considering the FID scores of the generated samples.

Clinical application of cardiac magnetic resonance (CMR) is expanding but CMR assessment of LV diastolic function is still being validated. The purpose of this study was to validate assessments of left ventricular (LV) diastolic dysfunction (DD) using CMR by comparing with transthoracic echocardiography (TTE) performed on the same day. Patients with suspected or diagnosed cardiomyopathy (n = 63) and healthy volunteers (n = 24) were prospectively recruited and included in the study. CMR diastolic parameters were measured on cine images and velocity-encoded phase contrast cine images and compared with corresponding parameters measured on TTE. A contextual correlation feature tracking method was developed to calculate the mitral annular velocity curve. LV DD was classified by CMR and TTE following 2016 guidelines. Overall DD classification was 78.1% concordant between CMR and TTE (p < 0.0001). The trans-mitral inflow parameters correlated well between the two modalities (E, r = 0.78; A, r = 0.90; E/A, r = 0.82; all p < 0.0001) while the remaining diastolic parameters showed moderate correlation (e’, r = 0.64; E/e’, r = 0.54; left atrial volume index (LAVi), r = 0.61; all p < 0.0001). Classification of LV diastolic function by CMR showed good concordance with standardized grades established for TTE. CMR-based LV diastolic function may be integrated in routine clinical practice. Name of the registry: Technical Development of Cardiovascular Magnetic Resonance Imaging. Trial registration number: NCT00027170. Date of registration: November 26, 2001. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT00027170

Single-cell analyses parse the brain's billions of neurons into thousands of 'cell-type' clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.

Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority class and diminishing accuracy for the minority class. This study introduces the Reinforcement Learning (RL) Framework with Oversampling and Undersampling Algorithm (RLFOUA) to address imbalanced datasets. RLFOUA combines RL with diverse resampling algorithms, creating an adaptive learning environment. It integrates the novel True False Rate Synthetic Minority Oversampling Technique (TFRSMOTE) algorithm, emphasizing data-level approaches. Additionally, RLFOUA employs a cost-sensitive approach based on classification metrics. Using the CSE-CIC-IDS2018 and NSL-KDD datasets, RLFOUA demonstrates substantial improvement over existing resampling techniques. Achieving an accuracy of 0.9981 for NSL-KDD and 0.9846 for CSE-CIC-IDS2018, the framework's performance is evaluated using F1 score, accuracy, precision, recall, and a proposed Index Metric (IM). RLFOUA presents a significant advancement in addressing class imbalance challenges in IDS. It shows an average accuracy improvement of 21.5% compared to the recent resampling technique AESMOTE on the NSL-KDD dataset.

Malicious cyber-attacks can hide themselves in large imbalanced datasets which makes it difficult for Intrusion Detection Systems (IDS) to detect them. Much research has been done in using machine learning and deep learning algorithms for Intrusion Detection Systems, but only a small number of them address imbalanced datasets. In this paper, we apply Markov Chain Monte Carlo (MCMC) Algorithm, Generative Adversarial Networks (GANs) algorithm, and oversampling to the CSE-CIC-IDS2018 dataset to balance the dataset. Then, we compare different Machine Learning algorithms with original dataset, balanced dataset with oversampling, balanced dataset with GAN, and balanced dataset with MCMC. The results show that neither MCMC Algorithm nor GANs is a good algorithm for balancing imbalance binary IDS datasets. The results show that Logistic Regression with original imbalanced dataset provided the best performance in predicting the attacks in imbalance binary IDS datasets with accuracy of 0.88, precision of 0.88, recall of 0.99, Mean Error of 0.11, Root Mean Square Error of 0.33, and Mean Absolute Error of 0.11. Neural Network and GANs performance were close to Logistic Regression specifically regarding accuracy, but Logistic Regression was faster.

What are the cellular-level structural and functional changes underlying newly adaptive behaviors in the mammalian brain? In this issue of Neuron, Inada et al. (2022) identify the brain-wide connectivity and synaptic plasticity changes of hypothalamic oxytocin+ neurons in male mice contributing to their parental behaviors.

To investigate the anatomical organization of neural circuits across the whole brain, it is essential to register the experimental brain tissues to a reference atlas accurately. This procedure is also a prerequisite to quantify the locations and numbers of cells of interest in specific regions. However, it remains challenging to do registration on experimental tissue due to the intrinsic variation among the specimens, tissue deformation introduced by histological processing, and the potential inconsistency in the judgment of the experimenter during manual annotation. Here, we introduce Bell Jar, a multi-platform analysis tool with semi-automated affine warping of atlas maps onto microscopic images of brain slices and machine learning-based cell detection. Bell Jar’s intuitive GUI and internal dependency management enable users of all skill levels to obtain accurate results without programming expertise. To compare the performance of Bell Jar with previously published methods 1–4 , we labeled neurons in the mouse visual cortex with either an engineered rabies virus or an adeno-associated virus (AAV) for neural circuit tracing 5 , and quantified Bell Jar’s performance at each step of the pipeline for image alignment, segmentation, and cell counting. We demonstrated that Bell Jar’s output is as reliable as manual counting by an expert; it is more accurate than currently available techniques even with noisy data and takes less time with fewer user interventions. Bell Jar is an easy-to-navigate application that provides a reproducible, automated analysis workflow to facilitate the precise mapping of histological images of the mouse brain to the reference atlas and the quantification of cellular signals it is trained to recognize.### Competing Interest StatementThe authors have declared no competing interest.

Neuronal cell types are classically defined by their molecular properties, anatomy and functions. Although recent advances in single-cell genomics have led to high-resolution molecular characterization of cell type diversity in the brain 1 , neuronal cell types are often studied out of the context of their anatomical properties. To improve our understanding of the relationship between molecular and anatomical features that define cortical neurons, here we combined retrograde labelling with single-nucleus DNA methylation sequencing to link neural epigenomic properties to projections. We examined 11,827 single neocortical neurons from 63 cortico-cortical and cortico-subcortical long-distance projections. Our results showed unique epigenetic signatures of projection neurons that correspond to their laminar and regional location and projection patterns. On the basis of their epigenomes, intra-telencephalic cells that project to different cortical targets could be further distinguished, and some layer 5 neurons that project to extra-telencephalic targets (L5 ET) formed separate clusters that aligned with their axonal projections. Such separation varied between cortical areas, which suggests that there are area-specific differences in L5 ET subtypes, which were further validated by anatomical studies. Notably, a population of cortico-cortical projection neurons clustered with L5 ET rather than intra-telencephalic neurons, which suggests that a population of L5 ET cortical neurons projects to both targets. We verified the existence of these neurons by dual retrograde labelling and anterograde tracing of cortico-cortical projection neurons, which revealed axon terminals in extra-telencephalic targets including the thalamus, superior colliculus and pons. These findings highlight the power of single-cell epigenomic approaches to connect the molecular properties of neurons with their anatomical and projection properties.

First pass gadolinium-enhanced cardiovascular magnetic resonance (CMR) perfusion imaging allows fully quantitative pixel-wise myocardial blood flow (MBF) assessment, with proven diagnostic value for coronary artery disease. Segmental analysis requires manual segmentation of the myocardium. This work presents a fully automatic method of segmenting the left ventricular myocardium from MBF pixel maps, validated on a retrospective dataset of 247 clinical CMR perfusion studies, each including rest and stress images of three slice locations, performed on a 1.5T scanner. Pixel-wise MBF maps were segmented using an automated pipeline including region growing, edge detection, principal component analysis, and active contours to segment the myocardium, detect key landmarks, and divide the myocardium into sectors appropriate for analysis. Automated segmentation results were compared against a manually defined reference standard using three quantitative metrics: Dice coefficient, Cohen Kappa and myocardial border distance. Sector-wise average MBF and myocardial perfusion reserve (MPR) were compared using Pearson's correlation coefficient and Bland-Altman Plots. The proposed method segmented stress and rest MBF maps of 243 studies automatically. Automated and manual myocardial segmentation had an average (± standard deviation) Dice coefficient of 0.86 ± 0.06, Cohen Kappa of 0.86 ± 0.06, and Euclidian distances of 1.47 ± 0.73 mm and 1.02 ± 0.51 mm for the epicardial and endocardial border, respectively. Automated and manual sector-wise MBF and MPR values correlated with Pearson's coefficient of 0.97 and 0.92, respectively, while Bland-Altman analysis showed bias of 0.01 and 0.07 ml/g/min. The validated method has been integrated with our fully automated MBF pixel mapping pipeline to aid quantitative assessment of myocardial perfusion CMR.

Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application.

Single-cell transcriptomics of neocortical neurons have revealed more than 100 clusters corresponding to putative cell types. For inhibitory and subcortical projection neurons (SCPNs), there is a strong concordance between clusters and anatomical descriptions of cell types. In contrast, cortico-cortical projection neurons (CCPNs) separate into surprisingly few transcriptomic clusters, despite their diverse anatomical projection types. We used projection-dependent single-cell transcriptomic analyses and monosynaptic rabies tracing to compare mouse primary visual cortex CCPNs projecting to different higher visual areas. We find that layer 2/3 CCPNs with different anatomical projections differ systematically in their gene expressions, despite forming only a single genetic cluster. Furthermore, these neurons receive feedback selectively from the same areas to which they project. These findings demonstrate that gene-expression analysis in isolation is insufficient to identify neuron types and have important implications for understanding the functional role of cortical feedback circuits.

OBJECTIVES The authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients. BACKGROUND Fully quantitative CMR perfusion pixel maps were previously validated with microsphere MBF measurements and showed potential in clinical applications, but the methods required laborious manual processes and were excessively time-consuming. METHODS CMR perfusion imaging was performed on 80 patients with known or suspected coronary artery disease (CAD) and 17 healthy volunteers. Significant CAD was defined by quantitative coronary angiography (QCA) as >= 70% stenosis. Nonsignificant CAD was defined by: 1) QCA as < 70% stenosis; or 2) coronary computed tomography angiography as < 30% stenosis and a calcium score of 0 in all vessels. Automatically generated MBF maps were compared with manual quantification on healthy volunteers. Diagnostic performance of the automated MBF pixel maps was analyzed on patients using absolute MBF, myocardial perfusion reserve (MPR), and relative measurements of MBF and MPR. RESULTS The correlation between automated and manual quantification was excellent (r = 0.96). Stress MBF and MPR in the ischemic zone were lower than those in the remote myocardium in patients with significant CAD (both p < 0.001). Stress MBF and MPR in the remote zone of the patients were lower than those in the normal volunteers (both p < 0.001). All quantitative metrics had good area under the curve (0.864 to 0.926), sensitivity (82.9% to 91.4%), and specificity (75.6% to 91.1%) on per-patient analysis. On a per-vessel analysis of the quantitative metrics, area under the curve (0.837 to 0.864), sensitivity (75.0% to 82.7%), and specificity (71.8% to 80.9%) were good. CONCLUSIONS Fully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. Published by Elsevier on behalf of the American College of Cardiology Foundation.

PurposeTo present and assess an automatic nonrigid image registration framework that compensates motion in cardiac magnetic resonance imaging (MRI) perfusion series and auxiliary images acquired under a wide range of conditions to facilitate myocardial perfusion quantification.Materials and MethodsOur framework combines discrete feature matching for large displacement estimation with a dense variational optical flow formulation in a multithreaded architecture. This framework was evaluated on 291 clinical subjects to register 1.5T and 3.0T steady‐state free‐precession (FISP) and fast low‐angle shot (FLASH) dynamic contrast myocardial perfusion images, arterial input function (AIF) images, and proton density (PD)‐weighted images acquired under breath‐hold (BH) and free‐breath (FB) settings.ResultsOur method significantly improved frame‐to‐frame appearance consistency compared to raw series, expressed in correlation coefficient (R2 = 0.996 ± 3.735E‐3 vs. 0.978 ± 2.024E‐2, P < 0.0001) and mutual information (3.823 ± 4.098E‐1 vs. 2.967 ± 4.697E‐1, P < 0.0001). It is applicable to both BH (R2 = 0.998 ± 3.217E‐3 vs. 0.990 ± 7.527E‐3) and FB (R2 = 0.995 ± 3.410E‐3 vs. 0.968 ± 2.257E‐3) paradigms as well as FISP and FLASH sequences. The method registers PD images to perfusion T1 series (9.70% max increase in R2 vs. no registration, P < 0.001) and also corrects motion in low‐resolution AIF series (R2 = 0.987 ± 1.180E‐2 vs. 0.964 ± 3.860E‐2, P < 0.001). Finally, we showed the myocardial perfusion contrast dynamic was preserved in the motion‐corrected images compared to the raw series (R2 = 0.995 ± 6.420E‐3).ConclusionThe critical step of motion correction prior to pixel‐wise cardiac MR perfusion quantification can be performed with the proposed universal system. It is applicable to a wide range of perfusion series and auxiliary images with different acquisition settings.Level of Evidence: 3Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2017;46:1060–1072.

Methods Rest and adenosine stress perfusion imaging was performed on 17 normal volunteers. A saturation recovery SSFP dual-sequence technique was used to acquire three myocardial slices and an arterial input function (AIF) image series. A proton-density weighted image was acquired at the beginning of each series. Fully quantitative perfusion pixel maps were generated by an automated processing method, including non-rigid motion correction, surface-coil intensity correction, AIF and myocardial signal and contrast timing detection, and model constrained deconvolution. The results of the automatically generated MBF pixel maps were compared with manual quantification using an 18-segment model.

Background Quantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification. Methods Gadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95 % confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements. Results Two clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63 % of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95 % CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs ( p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation ( p = NS to p = 0.03). Conclusion The proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available.