PreVISE: an efficient virtual reality system for SEEG surgical planning.

Epilepsy is a neurological disorder characterized by recurring seizures that can cause a wide range of symptoms. Stereo-electroencephalography (SEEG) is a diagnostic procedure where multiple electrodes are stereotactically implanted within predefined brain regions to identify the seizure onset zone, which needs to be surgically removed or disconnected to achieve remission of focal epilepsy. This procedure is complex and challenging due to two main reasons.

Virtual reality-based preoperative planning for optimized trocar placement in thoracic surgery: A preliminary study.

Video-assisted thoracic surgery (VATS) is a minimally invasive approach for treating early-stage non-small-cell lung cancer. Optimal trocar placement during VATS ensures comprehensive access to the thoracic cavity, provides a panoramic endoscopic view, and prevents instrument crowding. While established principles such as the Baseball Diamond Principle (BDP) and Triangle Target Principle (TTP) exist, surgeons mainly rely on experience and patient-specific anatomy for trocar placement, potentially leading to sub-optimal surgical plans that increase operative time and fatigue.

Optical tuning of the terahertz response of black phosphorus quantum dots: effects of weak carrier confinement.

In contrast to few-layer black phosphorus (BP) with a relatively larger area, BP quantum dots (BP-QDs) are expected to have distinctive electromagnetic response and carrier behaviors, especially in low-frequency range such as in the THz regime. Herein, we experimentally investigate the THz properties of BP-QDs as well as the optical control of these properties. It is demonstrated that the effects of weak carrier confinement, which is associated with diffusive restoring current in each BP-QD, contribute significantly to the effective THz conductivity of BP-QDs.

Design, manufacturing, and multi-modal imaging of stereolithography 3D printed flexible intracranial aneurysm phantoms.

Background: Physical vascular phantoms are instrumental in studying intracranial aneurysms and testing relevant imaging tools and training systems to provide improved clinical care. Current vascular phantom production methods have major limitations in capturing the biophysical and morphological characteristics of intracranial aneurysms with good fidelity and multi-modal imaging capacity. With stereolithography (SLA) 3D printing technology becoming more accessible, newer flexible and transparent printing materials with higher precision controls open the door for improving the efficiency and quality of producing anthropomorphic vascular phantoms but have rarely been explored for the application.

Sequencing-Dependent Impact of Carbon Coating on Microstructure Evolution and Electrochemical Performance of Pre-lithiated SiO Anodes: Enhanced Efficiency and Stability via Pre-Coating Strategy.

Silicon monoxide (SiO) has attracted considerable interest as anode material for lithium-ion batteries (LIBs). However, their poor initial Coulombic efficiency (ICE) and conductivity limit large-scale applications. Prelithiation and carbon-coating are common and effective strategies in industry for enhancing the electrochemical performance of SiO.

Virtual and Augmented Reality in Ventriculostomy: A Systematic Review.

Background: Ventriculostomy, one of the most common neurosurgical procedures, involves inserting a draining catheter into the brain's ventricular system to alleviate excessive cerebrospinal fluid accumulation. Traditionally, this procedure has relied on freehand techniques guided by anatomical landmarks, which have shown a high rate of misplacement. Recent advancements in virtual reality (VR) and augmented reality (AR) technologies have opened up new possibilities in the field.

SCANED: Siamese collateral assessment network for evaluation of collaterals from ischemic damage.

This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy.

circCPA4 induces malignant behaviors of prostate cancer via miR-491-5p/SHOC2 feedback loop.

Objective: circCPA4 has been defined to be an oncogenic gene. This study examined whether circCPA4 regulates Prostate Cancer (PC) development and revealed its molecular mechanism.

Methods: PC tissues and PC cell lines were collected, in which circCPA4/miR-491-5p/SHOC2 levels were evaluated by RT-qPCR and immunoblot.

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from magnetic resonance imaging.

Background: There is an increasing interest in assessing paraspinal morphology and composition in relation to low back pain (LBP). However, variations in methods and segmentation protocols contribute to the inconsistent findings in the literature. We present an on-line resource, the ParaspInaL muscLe segmentAtion pRoject (PILLAR, https://projectpillar.

Hollow-core pear-shaped conjoined-tube fiber with low loss in the ultraviolet band.

PCTF (pear-shaped conjoined-tube fiber) is presented as a new ultraviolet (UV) guiding fiber with low loss. Results indicate that two PCTFs have better properties than that of previous studies in the UV band. The total loss of two PCTFs is less than 1 dB/km, and its bandwidth exceeds 150 nm between 0.

A population-averaged structural connectomic brain atlas dataset from 422 HCP-aging subjects.

Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration.

Poly(ionic liquid)/OPBI Composite Membrane with Excellent Chemical Stability for High-Temperature Proton Exchange Membrane.

Despite the outstanding proton conductivity of phosphoric acid (PA)-doped polybenzimidazole (PBI) membranes as high-temperature proton exchange membranes (HT-PEMs), chemical stability is a critical issue for the operation life of PEM fuel cells (PEMFCs). Herein, we introduced polymerized [HVIM]HPO ionic liquids (PIL) into an OPBI membrane to accelerate proton transfer and enhance the chemical stability of the membrane. Based on the regulation of the intrinsic viscosity of PIL, the entanglement between PIL chains and OPBI chains is enhanced to prevent the loss of PIL and the oxidative degradation of membrane materials.

A dataset of multi-contrast unbiased average MRI templates of a Parkinson's disease population.

Parkinson's disease (PD) is a complex neurodegenerative disorder affecting regions such as the substantia nigra (SN), red nucleus (RN) and locus coeruleus (LC). Processing MRI data from patients with PD requires anatomical structural references for spatial normalization and structural segmentation. Extending our previous work, we present multi-contrast unbiased MRI templates using nine 3T MRI modalities: T1w, T2*w, T1-T2* fusion, R2*, T2w, PDw, fluid-attenuated inversion recovery (FLAIR), susceptibility-weighted imaging, and neuromelanin-sensitive MRI (NM).

The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review.

Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and difficult to interpret. Appropriate image processing can greatly enhance the applicability of the imaging modality in clinical practice.

LGALS1 was related to the prognosis of clear cell renal cell carcinoma identified by weighted correlation gene network analysis combined with differential gene expression analysis.

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis.

Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

Purpose: Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods in treatment decision-making.

Methods: We adapted a pre-trained EfficientNet B0 network through transfer learning to improve collateral evaluation using slice-based and subject-level classification.

Fluorescent property of carbon dots extracted from cigarette smoke and the application in bio-imaging.

Cigarette smoke is one of the six major pollution sources in the room air. It contains large number of particles with size less than 10 nm. There exist carbon dots (CDs) in cigarette smoke which have strong fluorescence and with good bio-compatibility and low toxicity.

Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection.

Purpose: In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety.

Methods: We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches.