AIMC Topic: Tomography, X-Ray Computed

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Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patients.

Biomedical physics & engineering express Jan 22, 2025
. This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided breast cancer patients during the initial computed tomography (CT) appointment.. Anatomic distances wer...
Humans Machine Learning Algorithms Lung Logistic Models Radiotherapy Planning, Computer-Assisted Unilateral Breast Neoplasms Inhalation Breath Holding Aged Adult Breast Neoplasms Middle Aged Retrospective Studies Tomography, X-Ray Computed Female
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PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention.

Medical & biological engineering & computing Jan 21, 2025
In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies a...
Humans Tomography, X-Ray Computed Colorectal Neoplasms Diagnosis, Computer-Assisted Neural Networks, Computer Deep Learning Algorithms Prognosis
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Robot-Assisted CT-Guided Biopsy with an Artificial Intelligence-Based Needle-Path Generator: An Experimental Evaluation Using a Phantom Model.

Journal of vascular and interventional radiology : JVIR Jan 21, 2025
PURPOSE: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for computed tomography (CT)-guided biopsy compared with the conventional freehand technique.
Robotic Surgical Procedures Radiographic Image Interpretation, Computer-Assisted Artificial Intelligence Humans Feasibility Studies Tomography, X-Ray Computed Predictive Value of Tests Radiography, Interventional Needles Robotics Image-Guided Biopsy Equipment Design Phantoms, Imaging
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Unpaired Dual-Modal Image Complementation Learning for Single-Modal Medical Image Segmentation.

IEEE transactions on bio-medical engineering Jan 21, 2025
OBJECTIVE: Multi-modal MR/CT image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to acquire aligned multi-modal images of a patient in clinical practice due to the high cost and specific allergic re...
Magnetic Resonance Imaging Multimodal Imaging Image Interpretation, Computer-Assisted Machine Learning Algorithms Tomography, X-Ray Computed Humans
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

BMC gastroenterology Jan 21, 2025
OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarteri...
Liver Neoplasms Humans Carcinoma, Hepatocellular Retrospective Studies Chemoembolization, Therapeutic Antineoplastic Combined Chemotherapy Protocols Radiomics Immune Checkpoint Inhibitors Tyrosine Kinase Inhibitors Tomography, X-Ray Computed Combined Modality Therapy Female Male Prognosis Middle Aged Aged Adult Deep Learning Protein Kinase Inhibitors
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Perfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture.

International journal of computer assisted radiology and surgery Jan 20, 2025
PURPOSE: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times...
Four-Dimensional Computed Tomography Lung Deep Learning Supervised Machine Learning Perfusion Imaging Female Humans Male Tomography, X-Ray Computed Middle Aged
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Deep learning-based MVIT-MLKA model for accurate classification of pancreatic lesions: a multicenter retrospective cohort study.

La Radiologia medica Jan 20, 2025
BACKGROUND: Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images t...
Diagnosis, Differential Pancreatic Neoplasms Male Middle Aged Deep Learning Humans Retrospective Studies Aged Adult Tomography, X-Ray Computed Female
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Opportunistic AI for enhanced cardiovascular disease risk stratification using abdominal CT scans.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 20, 2025
This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging,...
Middle Aged Aged Radiography, Abdominal Humans Risk Assessment Male Deep Learning Tomography, X-Ray Computed Cardiovascular Diseases Female
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Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.

Scientific reports Jan 20, 2025
Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with...
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Punctures Animals Electromagnetic Phenomena Humans Deep Learning Male Phantoms, Imaging Lung Neoplasms Tomography, X-Ray Computed
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Dual-domain Wasserstein Generative Adversarial Network with Hybrid Loss for Low-dose CT Imaging.

Physics in medicine and biology Jan 20, 2025
Low-dose computed tomography (LDCT) has gained significant attention in hospitals and clinics as a popular imaging modality for reducing the risk of x-ray radiation. However, reconstructed LDCT images often suffer from undesired noise and artifacts, ...
Humans Neural Networks, Computer Generative Adversarial Networks Tomography, X-Ray Computed Image Processing, Computer-Assisted Radiation Dosage
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