AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 2671 to 2680 of 5128 articles

TBNet: a context-aware graph network for tuberculosis diagnosis.

Computer methods and programs in biomedicine
Tuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, ...

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

Computers in biology and medicine
BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order ...

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies.

Abdominal radiology (New York)
PURPOSE: In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiati...

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis.

European radiology
OBJECTIVE: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR).

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.

The British journal of radiology
OBJECTIVES: The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric a...

Noise reduction profile: A new method for evaluation of noise reduction techniques in CT.

Medical physics
PURPOSE: Noise power spectrum (NPS) is a commonly used performance metric to evaluate noise-reduction techniques (NRT) in imaging systems. The images reconstructed with and without an NRT can be compared via their NPS to better understand the NRT's e...

Progressive attention module for segmentation of volumetric medical images.

Medical physics
PURPOSE: Medical image segmentation is critical for many medical image analysis applications. 3D convolutional neural networks (CNNs) have been widely adopted in the segmentation of volumetric medical images. The recent development of channelwise and...

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans.

Computers in biology and medicine
Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests f...

A deep learning method for eliminating head motion artifacts in computed tomography.

Medical physics
PURPOSE: Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a ...

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy.

Medical physics
PURPOSE: Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an inte...