AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 1871 to 1880 of 5128 articles

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

European radiology
OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the Un...

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

Medical physics
BACKGROUND: The main application of [18F] FDG-PET ( FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FL...

Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning.

Journal of endourology
Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. This retrospective study included 259 scans from 113 symptomatic patients being treated for urolithiasis at a single medical center between...

HCformer: Hybrid CNN-Transformer for LDCT Image Denoising.

Journal of digital imaging
Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learnin...

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19.

Scientific reports
Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model...

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Investigative radiology
Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial...

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

Abdominal radiology (New York)
BACKGROUND: CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to ...

Deep learning approach for differentiating indeterminate adrenal masses using CT imaging.

Abdominal radiology (New York)
PURPOSE: Distinguishing stage 1-2 adrenocortical carcinoma (ACC) and large, lipid poor adrenal adenoma (LPAA) via imaging is challenging due to overlapping imaging characteristics. This study investigated the ability of deep learning to distinguish A...

Comparison of computed tomography and dual-energy X-ray absorptiometry in the evaluation of body composition in patients with obesity.

Frontiers in endocrinology
OBJECTIVE: a) To evaluate the accuracy of the pre-existing equations (based on cm2 provided by CT images), to estimate in kilograms (Kg) the body composition (BC) in patients with obesity (PwO), by comparison with Dual-energy X-ray absorptiometry (DX...