AIMC Topic: Tomography, X-Ray Computed

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Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.

Medical physics
BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemorrhages, using only healthy training data. Recent advancements in generative models have achieved suc...

Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols.

NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.

Sensors (Basel, Switzerland)
Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial pain, headaches, thick nasal discharge, and a reduced sense of smell. However, accurately diagnosing ...

Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.

Biomedical physics & engineering express
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that ...

Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.

International journal of legal medicine
Bone age assessment (BAA) means challenging tasks in forensic science especially in some extreme situations like only skulls found. This study aimed to develop an accurate three-dimensional deep learning (DL) framework at skull CT metadata for BAA an...

Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses.

Japanese journal of radiology
PURPOSE: To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols.

Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.

Scientific reports
Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessi...

Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.

Abdominal radiology (New York)
One of the most prevalent cancers in the world is kidney cancer (KC). A precise diagnosis, which is influenced by a number of variables, such as the size or volume of the tumor, the types and stages of the cancer, etc., is essential for the treatment...

Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection.

Medical image analysis
Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-f...