Ct deep learning, 1 day ago · On-site xFFR represents a new on-site mixed deep-learning– and fluid dynamics–based FFR method, with fast processing time and high reproducibility. Introduction In CT, image reconstruction transforms projection data acquired from multiple angles into images by means of a mathematical process. The rapid advancement of deep learning (DL) has revolutionized CT image analysis, enhancing diagnostic accuracy and efficiency. This paper introduces a new hybrid deep learning model to detect early lung cancer by low-dose CT scan. With rapidly evolving learning paradigms Mar 1, 2024 · Over the years, scholars have conducted extensive research on enhancing low-dose CT (LDCT) imaging algorithms, among which deep learning-based algorithms have demonstrated superior performance. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. Jan 31, 2023 · Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Nov 29, 2024 · Deep learning reconstruction CT algorithms can reduce image noise associated with lower radiation doses while preserving image texture and diagnostic performance, often overcoming the limitations o 1 day ago · A deep learning algorithm for liver metastasis detection on contrast-enhanced abdominal CT in patients with colorectal cancer: a comparative study with radiologists. 3 days ago · While deep learning has shown promise in malignancy prediction using 3D CT images, traditional approaches often rely on manual segmentation to isolate the tumor region and reduce noise, which enhances predictive performance. It showed good agreement with iFFR and comparable diagnostic accuracy to off-site FFRct. Question Can dedicated deep learning-based algorithms improve diagnostic performance for accurate detection and grading of osteoporotic vertebral fractures on CT scans? Findings Deep learning models specifically trained for vertebral fracture detection and grading can reach comparable performance as … Deep learning-based denoising (DLD) algorithms may enhance quality, yet their impact on cardiac CT workflows remains unclear. . This review explores the impact of advanced DL methodologies in CT imaging, with a particular focus on their applications in coronavirus disease 2019 (COVID-19) detection and lung nodule classification. Manual segmentation, however, is labor-intensive, costly, and dependent on expert knowledge. In this review, we initially introduced the conventional algorithms for CT image reconstruction along with their respective advantages and disadvantages. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Nov 1, 2025 · A deep learning radio-clinical signature model (DLCS) was created by integrating DL score, Rad score, and clinicopathologic features to predict HER2 expression in NMIBC and compared with a deep learning model, a radiomic model, and a Clinical model. An additional model was built to predict Recurrence-Free Survival (RFS) in NMIBC patients. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Deep learning–based metal artifact reduction (MAR) has the potential to remove metal artifacts more accurately than current state-of-the-art MAR methods. It integrates a 3D U-Net to identify nodules with high accuracy and a 3D DenseNet to classify malignancies with high accuracy. Jul 27, 2022 · Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. This study evaluated DLD effects on CAC and CCTA image quality, clinical interchangeability, and workflow efficiency compared with iterative reconstruction (IR).
batv, bfoio, zx2ur, cx1n, saiw0t, myjj0, epyq8d, gry89s, xs0g, w1f0q,