computed tomography
PulseAugur coverage of computed tomography — every cluster mentioning computed tomography across labs, papers, and developer communities, ranked by signal.
- used by Polyethylene Terephthalate 70%
- used by deep learning 70%
- used by X-ray 70%
- competes with magnetic resonance imaging 60%
- used by Mauritius 60%
- used by magnetic resonance imaging 50%
- instance of Ultrasound : journal of the British Medical Ultrasound Society 50%
- competes with Ultrasound 50%
- instance of deep learning 50%
10 天有情绪数据
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Ancient Chinese texts describe 'immortal mirror' akin to CT scan
Ancient Chinese texts from the Tang dynasty describe an "immortal mirror" capable of displaying internal organs, functioning as an early conceptualization of modern CT scans. These legends also included portable version…
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FlexiCT foundation models advance CT imaging analysis
Researchers have developed FlexiCT, a new family of foundation models for computed tomography (CT) imaging. These models were trained using an agglomerative continual pretraining strategy on a massive dataset of 266,227…
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New diffusion model enhances CT image synthesis with physics consistency
Researchers have developed EPC-3D-Diff, a new conditional 3D latent diffusion model designed to improve the synthesis of CT images from CBCT data. This model incorporates a physics-derived equivariance loss that ensures…
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Deep Learning Models Achieve 98% Accuracy in COVID-19 Image Classification
Researchers have conducted a comprehensive comparison of various deep learning architectures for classifying COVID-19 from CT and X-ray lung imagery. The study utilized pre-trained models including VGG, Densenet, Resnet…
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Diffusion model generates realistic PET images from uniform activity maps
Researchers have developed a novel diffusion model, termed PAD, capable of generating realistic heterogeneous PET images from uniform organ activity maps. This model adapts a natural image text-to-image decoder for medi…
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New U-Net model offers efficient spine CT segmentation for edge devices
Researchers have developed SpineContextResUNet, a new 3D Residual U-Net architecture designed for efficient segmentation of spinal CT scans. This model addresses the high computational demands of existing methods by usi…
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Interpretable AI detects aerospace composite defects with traceable explanations
Researchers have developed a new interpretable deep learning model, p-ResNet-50, for detecting defects in aerospace composites using X-ray tomography. This model not only achieves high accuracy comparable to traditional…
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Deep learning model accurately segments cardiac fat deposits
Researchers have developed a new deep learning method for segmenting cardiac fat deposits using computed tomography scans. The approach utilizes the pix2pix generative adversarial network, adapted for image-to-image tra…
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Episodic sampling improves medical image segmentation in low-data scenarios
Researchers have developed an episodic sampling method to improve class-balanced batch construction for medical image segmentation, particularly in scenarios with imbalanced datasets. This technique, adapted from few-sh…
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SMIT method leads in transferability for medical image segmentation
Researchers have benchmarked nine self-supervised learning (SSL) methods for their transferability in medical image segmentation tasks. The study found that the Self-Distilled Masked Image Transformer (SMIT) method, whi…
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New framework unifies CT image analysis with language-guided reasoning
Researchers have developed a unified framework that integrates language-guided visual reasoning for CT image interpretation. This autoregressive model uses task-routing tokens to trigger detection and segmentation heads…
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Deep learning predicts ovarian cancer chemo response from CT scans
Researchers have developed a deep learning framework to predict patient response to neoadjuvant chemotherapy for ovarian cancer using CT scans. The model analyzes 3D lesion masks derived from pre-treatment CT images, en…
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SynthRAD2025 challenge shows AI improves synthetic CT for radiotherapy
The SynthRAD2025 challenge report details advancements in generating synthetic computed tomography (sCT) images for radiotherapy planning. This year's challenge focused on converting MRI or cone-beam CT (CBCT) into CT-e…
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New H3D-MarNet framework enhances CT image quality for radiotherapy
Researchers have developed H3D-MarNet, a novel two-stage framework designed to improve CT image quality for radiotherapy. The system first suppresses metal artifacts using wavelet-based denoising and then transforms kil…
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Insurance companies deny coverage for common neck surgery implants, overriding medical consensus.
Insurance companies are increasingly refusing to cover interbody device spacers used in anterior cervical discectomy and fusion (ACDF) surgeries, labeling them as experimental. This practice overrides current medical ev…
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Medical foundation models lag behind radiomics for renal lesion CT analysis
A new benchmark study evaluated the effectiveness of three medical foundation models (FMs) for stratifying renal lesions in CT scans. While FMs showed promise by matching the performance of a 3D ResNet trained from scra…
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LLMs filter clinical scans to create whole-body CT reference charts
Researchers have developed an LLM-based system to filter pathological findings from clinical CT scan reports. This method allows for the creation of healthier reference cohorts from over 350,000 CT examinations. The sys…
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MedGemma 1.5 model enhances medical imaging and EHR understanding
Researchers have introduced MedGemma 1.5 4B, an advanced medical AI model designed to handle diverse medical data modalities. This new version integrates capabilities for high-dimensional medical imaging like CT and MRI…
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MedSR-Vision framework benchmarks deep learning for medical image super-resolution
Researchers have developed MedSR-Vision, a new deep learning framework designed to enhance the quality of medical images across various modalities like MRI, CT, and X-ray. This framework allows for the evaluation and co…
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AI lung nodule screening sensitivity varies with CT reconstruction and nodule phase
A new paper explores how the position of a lung nodule within a CT scan's reconstruction cycle, known as z-phase, can significantly impact the sensitivity of AI-based detection systems. The study found that when the rat…