AIMC Topic: Tomography, X-Ray Computed

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A comparative study of two automated solutions for cross-sectional skeletal muscle measurement from abdominal computed tomography images.

Medical physics
BACKGROUND: Measurement of cross-sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) level from computed tomography (CT) images is becoming one of the reference methods for sarcopenia diagnosis. However, manual skeletal muscle segmenta...

Segmentation of multiple Organs-at-Risk associated with brain tumors based on coarse-to-fine stratified networks.

Medical physics
BACKGROUND: Delineation of Organs-at-Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time-consuming, labor-intensive and affected by inter- and intra-observer variability, a robust and efficient automati...

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RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Journal of biomedical optics
SIGNIFICANCE: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP...

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy.

International journal of computer assisted radiology and surgery
PURPOSE: Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increas...

Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser.

Medical physics
BACKGROUND: The purpose of a convolutional neural network (CNN)-based denoiser is to increase the diagnostic accuracy of low-dose computed tomography (LDCT) imaging. To increase diagnostic accuracy, there is a need for a method that reflects the feat...

Deep Learning-based Approach to Predict Pulmonary Function at Chest CT.

Radiology
Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function w...

A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain.

Journal of digital imaging
Advanced visualization techniques such as maximum intensity projection (MIP) and volume rendering (VR) are useful for evaluating neurovascular anatomy on CT angiography (CTA) of the brain; however, interference from surrounding osseous anatomy is com...

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.

Scientific reports
Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (...