Branded Chart Kernel for Habits Investigation

Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 ​min) and surface-based thickness analysis (within only around 1 ​h runtime). For sustainability of this approach we perform extensive validation we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.Proton magnetic resonance spectroscopy (1H-MRS) of the fetal brain can be used to study emerging metabolite profiles in the developing brain. Identifying early deviations in brain metabolic profiles in high-risk fetuses may offer important adjunct clinical information to improve surveillance and management during pregnancy. find more Objective To investigate the normative trajectory of the fetal brain metabolites during the second half of gestation, and to determine the impact of using different Cramer-Rao Lower Bounds (CRLB) threshold on metabolite measurements using magnetic resonance spectroscopy. Study design We prospectively enrolled 219 pregnant women with normal fetal ultrasound and biometric measures. We performed a total of 331 fetal 1H-MRS studies with gestational age in the rage of 18-39 weeks with 112 of the enrolled participants scanned twice. All the spectra in this study were acquired on a GE 1.5 T scanner using long echo-time of 144 ​ms and analyzed in LCModel. Results We successfully acquired and analyzed fetal 1H-MRS with a success rate of 93%. We observed increases in total NAA, total creatine, total choline, scyllo inositol and total NAA-to-total choline ratio with advancing GA. Our results also showed faster increases in total NAA and total NAA-to-total choline ratio during the third trimester compared to the second trimester. We also observed faster increases in total choline and total NAA in female fetuses. Increasing the Cramer-Rao lower bounds threshold progressively from 100% to 40%-20% increased the mean metabolite concentrations and decreased the number of observations available for analysis. Conclusion We report serial fetal brain biochemical profiles in a large cohort of health fetuses studied twice in gestation with a high success rate in the second and third trimester of pregnancy. We present normative in-vivo fetal brain metabolite trajectories over a 21-week gestational period which can be used to non-invasively measure and monitor brain biochemistry in the healthy and high-risk fetus.Objective To show technical highlights of a nerve-sparing laparoscopic eradication of deep endometriosis (DE) with posterior compartment peritonectomy. Design Demonstration of the technique with narrated video footage. Setting An urban general hospital. A systematic review and meta-analysis suggested significant advantages of the nerve-sparing technique when considering the relative risk of persistent urinary retention in the treatment of DE [1]. In addition, a recent paper suggested that complete excision of DE with the posterior compartment peritonectomy could be surgical treatment of choice to decrease postoperative pain, improve fertility rate, and prevent future recurrence [2]. However, in DE, nerve-sparing procedures are even more challenging than oncologic radical procedures, because the pathology resembles both ovarian/rectal cancer in terms of visceral involvement, and advanced cervical cancer in terms of wide parametrial infiltration through the pelvic wall. Interventions The video highlights the anatomic and technical aspects of a fertility- and nerve-sparing surgery in DE with the posterior compartment peritonectomy.