AIMC Topic: Tomography, X-Ray Computed

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Deep multi-scale resemblance network for the sub-class differentiation of adrenal masses on computed tomography images.

Artificial intelligence in medicine
OBJECTIVE: The accurate classification of mass lesions in the adrenal glands ('adrenal masses'), detected with computed tomography (CT), is important for diagnosis and patient management. Adrenal masses can be benign or malignant and benign masses ha...

Learning low-dose CT degradation from unpaired data with flow-based model.

Medical physics
BACKGROUND: There has been growing interest in low-dose computed tomography (LDCT) for reducing the X-ray radiation to patients. However, LDCT always suffers from complex noise in reconstructed images. Although deep learning-based methods have shown ...

A novel fast kilovoltage switching dual-energy computed tomography technique with deep learning: Utility for non-invasive assessments of liver fibrosis.

European journal of radiology
PURPOSE: To investigate whether the iodine density of liver parenchyma in the equilibrium phase and extracellular volume fraction (ECV) measured by deep learning-based spectral computed tomography (CT) can enable noninvasive liver fibrosis staging.

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

International journal of radiation oncology, biology, physics
PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images.

Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.

JAMA network open
IMPORTANCE: Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires...

Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks.

IEEE transactions on medical imaging
Encoding-decoding (ED) CNNs have demonstrated state-of-the-art performance for noise reduction over the past years. This has triggered the pursuit of better understanding the inner workings of such architectures, which has led to the theory of deep c...

Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer.

Digestive diseases and sciences
BACKGROUND: Computed tomography is the most commonly used imaging modality for preoperative assessment of lymph node status, but the reported accuracy is unsatisfactory.

Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.

Ultrasound in medicine & biology
Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non-linear mapping from the projection data to the sound-speed image (SSI). The traditional reconstruct...

A multitask deep learning approach for pulmonary embolism detection and identification.

Scientific reports
Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold stan...