Brain Cancer

Deep learning based two-way feature depiction model for brain tumor detection.

Brain tumors are one of the most fatal disorders that cause one of the highest mortalities in the world. Gliomas are the most common primary brain tumors originating from glial cells in the central nervous system. Traditionally, a tissue sample is extracted and examined for its genetic and characteristic properties. This method is invasive, painful, and takes a longer period to produce results. Va...

Optimisation of radiation dose and image quality in paediatric chest and abdominal CT examinations: A systematic review.

INTRODUCTION: Computed tomography (CT) is indispensable for the rapid evaluation of paediatric chest and abdominal pathology, yet it delivers relatively high ionizing radiation doses compared with other imaging modalities. Wide inter-institutional variability in dose metrics underscores the need for systematic optimisation that balances radiation safety with diagnostic image quality. The purpose o...

BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations.

We present a retrospective dataset of contrast-enhanced T1-weighted magnetic resonance imaging scans from 140 patients with brain metastases who under...

The Big Data Science Center at the Shanghai Synchrotron Radiation Facility: the architecture of the superfacility.

In the context of the global big data deluge, concerted efforts are being made to address the challenges faced by large scientific facilities. These e...

Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting.

Accurate localization and counting of tiny electronic components in high-resolution X-ray images is a critical yet challenging task in nuclear science...

Lung ultrasound in interstitial lung disease: from bedside screening to prognostic integration and AI-assisted standardization.

Interstitial lung diseases (ILDs) require early recognition and longitudinal assessment, yet repeated high-resolution computed tomography (HRCT) is of...

Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.

OBJECTIVE: To compare the radiomics features of pseudocontinuous arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) perfusion-weig...

Magnetic Resonance Spectroscopy Deep Learning with Magnetic Resonance Background Generator Enables In Vivo Metabolite Quantification of Hepatic Encephalopathy.

OBJECTIVE: Short-TE Proton Magnetic Resonance Spectroscopy (SPMRS) allows non-invasive, radiation-free detection of biomolecules including key brain m...

Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.

Glioblastoma (GBM) is one of the most aggressive and lethal primary brain tumors in adults, characterized by dynamic clonal evolution and extensive ge...

Neural Representation Learning for Compact and Efficient Modeling of Monte Carlo Phase Space Data.

PURPOSE: Monte Carlo (MC) simulations provide gold standard dose calculations in radiation therapy but generate large phase space (PHSP) files that li...

Comparative analysis of CNN-LSTM and CNN-BiLSTM hybrid deep learning models for solar radiation prediction.

Solar radiation forecasting is a complex task since the radiation signal is nonlinear, intermittent and is significantly influenced by meteorological ...

Radiation Resilient Synthetic Antiferromagnets-Based Neuromorphic Device for Sea Surface Temperature Reconstruction.

Reconstruction of sea surface temperature is critical for marine monitoring, yet conventional edge devices based on complementary metal-oxide-semicond...

Phase-only synthesis of cosecant-squared patterns with reduced sidelobes in large planar arrays via physics-informed deep neural networks.

This paper introduces a deep learning-based framework for phase-only synthesis of cosecant-squared (csc²) radiation patterns in planar antenna arrays ...

Radiation scatter at fluoroscopy-guided endoscopic procedures: Building towards further optimization.

INTRODUCTION: Exposure to ionizing radiation by endoscopy personnel during fluoroscopy-guided procedures remains a health hazard. We aimed to evaluate...

Tracer-agnostic diffusion model-based CT-free attenuation correction for brain PET: Comprehensive evaluation across 14 tracers.

OBJECTIVE: Accurate attenuation correction (AC) is critical in quantitative brain PET imaging. Conventional CT-based AC methods increase radiation exp...

A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.

OBJECTIVE: To construct and validate a multi-task deep learning model based on ConvNeXt-Tiny for synchronous prediction of isocitrate dehydrogenase (I...

Integrated single-cell RNA sequencing and Bulk-RNA technologies reveal the immunological characteristics of lactylation related-genes in glioblastoma.

OBJECTIVE: Glioblastoma (GBM) is the most aggressive type of intracranial malignant tumor, known for its extremely poor prognosis. Lactylation, a newl...

Optimising chest computed tomography (CT) scan ranges in paediatric patients to reduce radiation exposure.

AIM: The aim of this study was to accurately position the scan range of unenhanced chest computed tomography (CT) scans for paediatric patients by cla...

Delta radiomics for predicting early radiation-induced lung injury after thoracic radiotherapy: a retrospective paired-CT study.

PURPOSE: Early radiation-induced lung injury remains a clinically relevant complication after thoracic radiotherapy. We compared pretreatment, posttre...

CT in adult patients with Polytrauma: current practices, protocol optimization, structured reporting and future directions.

Computed tomography [CT] is the frontline imaging modality for the assessment of polytrauma patients because of its speed, diagnostic accuracy and inf...